Allosteric Modulator Discovery: From ... - American Chemical Society

Subscriber access provided by WEBSTER UNIV

Perspective

Allosteric Modulator Discovery: From Serendipity to Structure-Based Design Shaoyong Lu, Xinheng He, Duan Ni, and Jian Zhang J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.8b01749 • Publication Date (Web): 28 Feb 2019 Downloaded from http://pubs.acs.org on February 28, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Medicinal Chemistry

Allosteric

Modulator

Discovery:

From

Serendipity

to

Structure-Based Design

Shaoyong Lu†‡§, Xinheng He†§, Duan Ni†§, Jian Zhang*†‡

†Department

of Pathophysiology, Key Laboratory of Cell Differentiation and

Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China ‡Medicinal

Bioinformatics Center, Shanghai Jiao Tong University, School of

Medicine, Shanghai, 200025, China

Key Words: Allostery; Allosteric sites; Orthosteric sites; Drug discovery; Structure-based design.

*To whom correspondence should be addressed: Dr. Jian Zhang, Ph.D. Phone: +86-21-63846590-776922 Fax: +86-21-64154900 E-mail: [email protected]

1

ACS Paragon Plus Environment

Journal of Medicinal Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT Allosteric modulators bound to structurally diverse allosteric sites can achieve better pharmacological advantages than orthosteric ligands. The discovery of allosteric modulators, however, has been traditionally serendipitous, owing to the complex nature of allosteric modulation. Recent advances in the understanding of allosteric regulatory mechanisms and remarkable availability of structural data of allosteric proteins and modulators, as well as their complexes, have greatly contributed to the development of various computational approaches to guide the structure-based discovery of allosteric modulators. This perspective first outlines the evolution of the allosteric concept and describes the advantages and hurdles facing allosteric modulator discovery. The current available computational approaches, together with experimental approaches aiming to highlight allosteric studies, are then highlighted, emphasizing successful examples with their combined applications. We aimed to increase the awareness of the feasibility of the structure-based discovery of allosteric modulators using an integrated computational and experimental paradigm.

2


Page 2 of 72

Page 3 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table of Contents

3



1. INTRODUCTION Allostery is an inherent property of biomacromolecules in which topographically distinct binding sites within a protein are functionally coupled and can communicate over a distance.1–3 Typically, the allosteric signal propagation from allosteric to functional, orthosteric sites is elicited by perturbations at the allosteric site such as effector (ions, small molecules, proteins, DNA, and RNA) binding, point mutations, and posttranslational modifications.4,5 The existence of allosteric modulation in the control of biomacromolecule function allows for exquisite control of innumerable biological processes, ranging from enzyme catalysis, gene expression, and cellular differentiation to metabolism and homeostasis.6–10 Because of the omnipresence of allosteric control over normal cellular processes, allosteric modulation has thus been regarded as the second secret of life.11 This notion has been well established as a fundamental biological phenomenon for insights into cellular functions and diseases.5,12 From a pharmaceutical standpoint, protein allostery provides a promising, novel opportunity for the development of innovative therapeutics. Owing to the nonoverlap of allosteric and orthosteric sites, allosteric modulators, by attaching to allosteric sites, did not compete with endogenous or exogenous ligands bound to orthosteric sites. This feature guarantees the cooperative regulation of protein function by both allosteric modulators and orthosteric ligands in individual proteins.13–15 Moreover, the much more diversified allosteric sites relative to the highly conserved orthosteric sites 4


Page 4 of 72

Page 5 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


endow allosteric modulators with potentially enhanced selectivity and reduced toxicity than orthosteric ligands.16–18 Despite the potential advantages of allosteric therapeutics, allosteric modulator discovery has encountered a key challenge. Over the years, most allosteric modulators have been discovered serendipitously by high-throughput screening experiments.19 Such unexpected findings manifest a lack of a comprehensive understanding of allosteric interactions and their effects on the molecular mechanism of proteins. Fortunately, structural data pertaining to allosteric proteins and their allosteric sites and modulators have become more available in the past few years.20 Such data are beneficial to develop computational approaches to predict allosteric interactions and mechanisms and subsequently screen allosteric modulators for the intended allosteric sites. Mounting evidence suggests that allosteric modulators can be discovered in conjunction with computational and experimental methods, indicative of the advent of structure-based allosteric modulator discovery.21–27 In this perspective, we briefly overview the evolution of the allosteric concept and describe the advantages and hurdles facing allosteric modulator discovery. Importantly, we elucidate the currently available computational methods that complement experimental methods and provide powerful tools to facilitate allosteric modulator discovery. We mainly focus on the identification of allosteric modulators guided by computational methods, followed by experimental validations. We outline some guidelines to increase the awareness of the structure-based discovery of allosteric modulators using an integrated computational and experimental paradigm. 5



Page 6 of 72

Finally, we discuss the future directions that can contribute to allosteric modulator discovery.

2. PAST AND PRESENT OF THE ALLOSTERIC CONCEPT The pioneering work by Bohr in 1904 uncovered an allosteric phenomenon where the binding of carbon dioxide influences the binding affinity of oxygen to hemoglobin. This well-recognized phenomenon describing the cooperative binding of molecules to two distinct sites is known as the “Bohr effect”, which is currently named the “allosteric effect”.5 In 1961, Monod and Jacob first proposed the concept of allostery to account for the mechanism of feedback inhibition of enzyme activity in which “the inhibitor is not a steric analogue of the substrate”.28 Later, in the 1960s, two classic models of allosteric interactions were created to explain the cooperative binding of regulatory molecules; one is the ‘concerted’ or MWC (Monod-Wyman-Changeux) (1965)29 model, and the other is the ‘sequential’ or KNF (Koshland-Nemethy-Filmer) (1966)30 model. Both models underscore the role of conformational changes in the allosteric regulation of two end-state structures. In 1984, Cooper and Dryden proposed a theoretical model named “dynamics-driven allostery” by which allostery occurs without significant conformational changes in the average structure, revealing the importance of the entropic contribution to allostery.31 Advances in solution nuclear magnetic resonance (NMR)

spectroscopy32

have

substantially 6


contributed

to

probe

Page 7 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


“dynamics/entropy-driven allostery” through the characterization of protein internal motions such as in the catabolite activator protein (CAP),33,34 PDZ domains,35 calmodulin,36–38 and protein kinase A.39 Dramatically, Frederick et al. found a linear relationship between the protein conformational entropy and change in the overall binding entropy,37 revealing the important role of conformational entropy in the recognition of proteins.40 They further developed an empirical, quantitative method to describe the relationship between changes in conformational dynamics characterized by the motion of side chains and changes in protein conformational entropy.41,42 In 1999, Nussinov and coworkers proposed a “conformational selection and population shift” model that explains how allostery works from the viewpoint of free energy landscape theory.43–45 This model broadens the concept of allostery from two structural states to conformational ensembles of multiple states. In the same year, Ranganathan and coworkers proposed an “allosteric networks” model by which signal transmission between distinct sites of proteins is achieved by a network of physically interconnected residues.46 This structural view of allostery has been demonstrated by numerous studies using high-resolution structures.47–50 Recently, Hilser and coworkers have proposed an “ensemble allosteric” model to explain the origin of allostery.51–53 The Hilser’s model is similar to the Nussinov’s model in the explanation of the allosteric origin. Both models assume that all possible conformations of a protein ensemble are populated in terms of their respective energies, and the binding of allosteric effectors to the protein reshapes the free energy landscape. However, Hilser’s model further extends Nussinov’s model to account for 7



allostery in intrinsically disordered proteins; the former provides a framework that unifies the illustrations of allostery in structured, dynamic and disordered systems. As mentioned above, allosteric concepts and models have advanced over 50 years since the word “allostery” was coined by Monod and Jacob.28 It is obvious that the concept of allostery has evolved from the two-state model, the static structural model to the dynamic ensemble model.54–56 Importantly, some models have been applied to develop computational methods to predict allosteric sites and mutations, discover allosteric modulators, assess allosteric interactions, and investigate allosteric mechanisms, all of which will be elaborated in the following sections.

3. ADVANTAGES AND CHALLENGES FACING ALLOSTERIC MODULATOR DISCOVERY 3.1. Advantages Allosteric modulators harbor several distinct advantages over orthosteric ligands that occupy a protein’s active site. The advantageous characteristics of allosteric modulators can be witnessed by their high specificity and low adverse effects.57–59 G protein-coupled receptors (GPCRs) and protein kinases are the two most important drug targets in therapeutic pharmacology. The highly evolutionally conserved endogenous orthosteric binding sites represent a key challenge to develop selective orthosteric drugs for GPCRs and protein kinases. It is recognized that orthosteric ligands of an intended GPCR or protein kinase frequently suffer from cross-reactivities with its homologous proteins, leading to unwanted side effects and 8


Page 8 of 72

Page 9 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


off-target toxicity.16,60 As an alternative, targeting the structural diversity and topological difference of allosteric sites enables one to surmount two major obstacles encountered by orthosteric ligands.61,62 For instance, pyrazolopyridone-based compound 10 (1) bound to the allosteric site at the C-lobe of the kinase exhibits excellent selectivity for p38α (IC50: 1.2 μM) against three closely related structures, p38β, p38γ, and p38δ (IC50: >40 μM) (Figure 1), due to the residue differences within this site.63

Figure 1. Surface representation of the X-ray crystal structure of p38α complexed to an allosteric compound 10 (1) (PDB code 3NEW). The orthosteric ATP binding site and allosteric site at the C-lobe of the kinase are highlighted in blue and red, respectively. Multiple sequence alignments of allosteric site residues in p38α, p38β, p38γ, and p38δ are shown, with identical and similarity residues marked by black and gray, respectively. 9



Allosteric modulators have a spatiotemporal basis in specificity. They, together with endogenous ligands, exert their cooperative actions by the fine-tuned modulation of the protein function rather than by shutting off or turning on endogenous physiological signaling.13 This feature can result in increased safety in the event of an overdose of allosteric drugs.16 Importantly, allosteric modulators have the potential to combat drug-resistant mutations situated at orthosteric sites in patients. For example, the ‘gatekeeper mutation’ T315I of BCR-ABL1 causes resistance to a set of clinically approved orthosteric drugs such as imatinib, bosutinib, nilotinib, and dasatinib.64 Treatment of the T315I mutant with an allosteric inhibitor ABL001 (asciminib) bound to the myristoyl pocket at the C-lobe of the kinase can overcome drug resistance (Figure 2).65,66

10


Page 10 of 72

Page 11 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2. Surface representation of the X-ray crystal structure of BCR-ABL1 complexed to both orthosteric drug imatinib and the allosteric inhibitor ABL001 (asciminib) (PDB code 5MO4), with orthosteric and allosteric sites highlighted in blue and red, respectively.

A protein exists in an ensemble consisting of numerous, distinct conformational states that may be involved in multiple signaling pathways.9,67 An allosteric modulator binding to a unique conformation of the protein can achieve biased signaling.13 For example, G protein- or β-arrestin-mediated downstream signaling cascades of GPCRs can be activated by different conformational states of receptor-ligand complexes.68 Remarkably, allosteric regulation also provides a new avenue for drugging 11



‘undruggable’ targets that are difficult to target pharmacologically.69,70 This category of proteins is intractable to drugs because of strong substrate binding, the lack of deep binding sites, or large and flat protein-protein interaction (PPI) interfaces.71,72 Under these circumstances, allosteric modulators can bypass direct competition with orthosteric sites or PPI interfaces and bind alternative sites to fine-tune protein activities. Furthermore, cryptic (or hidden) allosteric sites can be captured from the minor or intermediate conformations of the protein and exploited for drug design, if the evident binding sites are invisible in the solved crystal structures.73–75 Currently, many previously considered ‘undruggable’ targets have proven to be treatable through allosteric regulatory modules, including K-Ras,76,77 transcription factors (MYC78 and nuclear factor-κB79), the SH2-kinase domain PPI interface of BCR-ABL,80 phosphatases (SHP2,81 PTP1B,82 and PP2A83), hypoxia inducible factor-2,84 and STAT3.85 The explosive growth in the number of allosteric modulators in recent years has demonstrated advantages to the exploration of allostery in drug discovery.86 However, allosteric modulator discovery also poses significant challenges, which may be relieved by the deeper understanding of the molecular details of allosteric mechanisms and structural and biochemical properties of allosteric proteins and modulators. 3.2. Challenges A key step for allosteric modulator discovery is the identification of bona fide allosteric sites in a protein. Unlike orthosteric ligands that occupy a conserved, known 12


Page 12 of 72

Page 13 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


orthosteric site, allosteric sites are less evolutionally conserved than orthosteric sites (vide infra).87,88 Theoretically, any binding pocket in a protein spatially distinct from the orthosteric site can be regarded as a latent allosteric site. Moreover, the regulatory effect of the latent allosteric site on the orthosteric site is difficult to determine. These disadvantages have severely impeded the allosteric modulator discovery process over a long period. Fortunately, the ample availability of structural complexes of allosteric protein-modulator interaction provided by X-ray crystallography and NMR spectroscopy in recent years has partially addressed this quandary.20 Many computational methods (see below) have been developed to predict allosteric sites based on the characteristics of known allosteric sites and the underlying mechanisms of allosteric regulation,89,90 facilitating the structure-based discovery of allosteric modulators. Challenges can also stem from the emergence of drug-resistant mutations at allosteric sites, the same situation faced by orthosteric drugs. For example, in the case of Ba/F3 cell lines expressing BCR-ABL1 variants, single-point mutations (A337V, P465S, V468F, and I502L) at the allosteric myristoyl site in the C-lobe of the kinase confer resistance to the allosteric inhibitor ABL001 (Figure 3).65 Furthermore, a combination of ABL001 and an orthosteric drug nilotinib can inhibit the mutant more effectively than each agent alone.66 This additive effect suggests the possibility of a synergistic effect of allosteric and orthosteric drugs in the treatment of human diseases. A large body of evidence suggests that allosteric sites have evolved under lower evolutionary pressure compared with orthosteric sites, implying that allosteric 13



mutations occur more frequently than orthosteric mutations.87,88 Indeed, more than 20 disease-associated mutations at the allosteric regulator binding domain of pyruvate kinase have been observed owing to the evolutionarily less conserved allosteric site.91 Additionally, mutations can occur at the allosteric communication pathways beyond allosteric sites, altering the protein dynamics and causing indirect resistance to allosteric modulators.47 Such mutations are very difficult to identify experimentally because of the complexity of allosteric networks in proteins.

Figure 3. Cartoon representation of the X-ray crystal structure of BCR-ABL1. The drug-resistant mutation residues at the orthosteric site (Thr315) and allosteric site 14


Page 14 of 72

Page 15 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(Ala337, Pro465, Val468, and Ile502) are depicted by sphere representations.

Additionally, allosteric modulators show significant species differences owing to the low evolutionary conservation of allosteric sites.5 For instance, the actions of allosteric candidates are effective in recombinant human receptors in vitro. However, when using rodent receptors or models to examine drug effects, it is possible to obtain the opposite result.92 In general, allosteric modulators have lower binding affinity than orthosteric ligands93 and often encounter “flat,” nontractable structure-activity relationships.16 Furthermore, allosteric modulators often have lower aqueous solubility than orthosteric ligands.16 These properties can not only lead to the difficulty of advancing allosteric modulators as drug candidates for clinical testing but also to the crystallization of allosteric protein-modulator complexes.

4. METHODS TO INVESTIGATE ALLOSTERY 4.1. Experimental Approaches X-ray crystallography, the most frequently used experimental approach in structural biology, has provided insights into structural and mechanistic investigations of biologically important macromolecules.94 Advances in X-ray crystallographic technology in the past decade have substantially contributed to determine the structures of challenging macromolecules such as GPCRs,95 the proteasome,96 the maltose transporter,97 and eukaryotic exosomes.98 Remarkably, recent breakthroughs 15



in the field of single-particle cryo-electron microscopy (cryo-EM) have enabled the characterization of the conformation of large macromolecular assemblies such as viruses,99 cytoskeletal proteins,100 and spliceosomes101 at near-atomic resolution, providing potential applications in drug discovery.102 Both X-ray crystallography and cryo-EM can provide detailed essential information about the three-dimensional (3D) structures of ligand-bound (holo) and unbound (apo) forms. These methods have enabled a structural view of allostery. In an allosterically regulated protein, the conformational change at the orthosteric site that occurs upon modulator binding can be generally uncovered by structural comparison of the holo and apo forms of the protein. By contrast, in the case of dynamics-driven allosteric proteins such as CAP33,34 and PDZ domains,35 the orthosteric site and whole protein exhibit no structural change between the holo and apo forms. In fact, X-ray and cryo-EM crystal structures are static average snapshots of macromolecules. However, these molecules can interconvert along many distinct conformations under physiological conditions to mediate different biological functions. The lack of conformational change based on the static crystal structure occasionally limits the usefulness of X-ray crystallography and cryo-EM in studies of allostery at the molecular level. In nature, allostery is a dynamic process. NMR spectroscopy, on the other hand, can probe the dynamic processes of biomolecules on a wide range of ps-ms timescales and observe conformational states in solution that are sparsely populated, yielding detailed dynamical information that is very suitable for the characterization of their allosteric mechanisms.103–106 It can be used to study allosterically regulated proteins 16


Page 16 of 72

Page 17 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


with significant conformational changes when bound to allosteric modulators. Most notably, it is particularly suited to explore protein allostery when conformational change of the protein is rarely detected in response to allosteric modulator binding, thus providing direct experimental evidence to establish the dynamics or ensemble view of allostery. As an example, using chemical shift and relaxation dispersion NMR analyses, Tzeng and Kalodimos revealed that one cAMP molecule binding to the first homodimeric CAP has a minor effect on the fast dynamics of CAP, while the binding of another cAMP molecule to the second homodimeric CAP quenches such dynamics, further highlighting the role of conformational entropy in the allosteric regulation of CAP, which was validated by ITC measurements.34 Traditionally, solution NMR spectroscopy has focused on relatively small proteins. Recent advances in the amide-/methyl-TROSY

approaches,

isotope

labeling,

and

pulse

sequence

techniques107 have made it possible to characterize large-molecular-weight proteins such as the 20S proteasome core particle108 and molecular chaperones such as HSP90109 and GroEL.110 X-ray crystallography, cryo-EM, and NMR spectroscopy can provide direct experimental proof for allosteric modulation of biological macromolecule function. In addition to these direct approaches, a few indirect experimental approaches have been developed to explore allostery in a particular protein. These include site-directed mutagenesis such as disulfide trapping (or tethering)111 and alanine scanning,112 fluorescent113,114 and photoaffinity115 labeling, and hydrogen/deuterium exchange (HDX) mass spectrometry.116 17



4.2. Computational Approaches It is typically time consuming and frequently unsatisfactory to determine allosteric sites in proteins and the mechanism of allostery using experimental approaches such as X-ray crystallography and NMR because of the complex nature of allostery in biological processes. As a viable alternative to experimental methods, computational approaches for the study of allostery provide robust tools to aid in drug design, which has been an area of intensive research.21,89,90,117 For example, AlloSteric Database (ASD)20,118,119 and ASBench120 benchmark are two major developments that have facilitated the development of computational methods to predict allosteric sites. A large amount of the state-of-the-art computational methods have emerged, facilitated by ASD and ASBench data in the last few years.21,89,90,117 Most critically, many of the theoretical predictions have been confirmed by experimental observations, which will be described in the following section, suggesting the potential usefulness of computational methods in structure-based drug discovery. As such, a combined computational and experimental approach should be become a new strategy in allosteric modulator discovery. Table 1 summarizes the current availability of representative computational allosteric prediction methods. Such methods include the prediction of allosteric sites, allosteric mutations, allosteric communication in proteins, assessment of allosteric protein-modulator interactions, and virtual screening of allosteric modulators. Remarkably, most computational methods focus on the identification of allosteric sites because the determination of the location of allosteric sites is the first step for 18


Page 18 of 72

Page 19 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


structure-based allosteric modulator discovery. A wealth of predictive models for allosteric sites have been created based on different methodologies, including structure-based methods (e.g., Allosite,121 AllositePro,122 ALLO,123 and a random forest model124), evolution-based methods (e.g., statistical coupling analysis (SCA)125), normal

model

analysis-based

methods

(e.g.,

AlloPred126

and

PARS127),

dynamics-based methods (a two-state Gō model,128 ExProSE,129 molecular dynamics (MD) simulations with enhanced conformational sampling,130 and AllosMod131), energy-based

perturbation

methods

(e.g.,

reverse

perturbation),132

and

correlation-based methods (e.g., CorrSite).133 Moreover, some of these programs, such as Allosite,121 PARS,127 AllosMod,131 and CorrSite133, have been deployed as web servers, facilitating medicinal chemists to find allosteric modulators. In addition to allosteric site prediction, web servers or tools such as MCPath,134 SPACER,135 DynOmics ENM,136 AlloSigMA137, AlloMAPS138, and AlloDriver139 for allosteric communication and mutation analysis, STRESS140 for allosteric hotspot residues prediction, Alloscore141 for allosteric interaction evaluation, and CovCys142 for covalent allosteric modulator design have also been developed.

Table 1: Databases of structures containing allosteric sites and software to detect and predict them name

refs

web servers available

contents, methods, applications

and

20, 118, 119

http://mdl.shsmu.edu.cn/ASD

collection of experimentally determined allosteric proteins, modulators, and

database ASD

19



Page 20 of 72

pathways benchmark ASBench

120

http://mdl.shsmu.edu.cn/asbench

two benchmark sets of allosteric sites: ‘Core set’ and ‘Core-diversity set’

allosteric site identification Allosite

121

http://mdl.shsmu.edu.cn/AST

detection of allosteric sites based on a structure-based machine learning method

AllositePro

122

No

detection of allosteric sites based on a combined structure- and normal mode analysis-based method

ALLO

123

No


random forest model

124

No


SCA

125

No

detection of allosteric sites based on an evolutionary method

AlloPred

126

http://www.sbg.bio.ic.ac.uk/allopred/home

detection of allosteric sites based on the normal mode analysis method

PARS

127

http://bioinf.uab.cat/pars

detection of allosteric sites based on the normal mode analysis method

two-state Gō model

128

No

detection of allosteric sites based on an ensemble generated by coarse-grained simulations

ExProSE

129

No

detection of allosteric sites based on an ensemble generated by a distance geometry-based method

AllosMod

131

http://modbase.compbio.ucsf.edu/allosmod

detection of allosteric sites and allosteric mechanism research from MD simulations based on a modeled energy landscape

reverse perturbation

132

No

detection of allosteric sites based on analysis of 20


Page 21 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


approach

CorrSite

energetics of reverse allosteric communication from orthosteric to allosteric sites 133, 142

http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus

detection of allosteric sites based on motion correlation analysis of allosteric and orthosteric sites

allosteric hotspot residues prediction STRESS

140

http://stress.molmovdb.org

Prediction of allosteric hotspot residues based on models of conformational change generated by Monte Carlo simulations

allosteric communication and mutation analysis MCPath

134

http://safir.prc.boun.edu.tr/clbet_server

prediction of allosteric communication and allosteric residues based on an ensemble generated by Monte Carlo path simulation

SPACER

135

http://allostery.bii.a-star.edu.sg

prediction of allosteric communication and allosteric sites based on the normal mode analysis method

DynOmics ENM

136

http://dyn.life.nthu.edu.tw/oENM

prediction of allosteric communication and functional residues based on elastic network model

AlloSigMA

137

http://allosigma.bii.a-star.edu.sg/home

assessment of the energetics of allosteric communication induced by ligand binding and mutation

AlloMAPS

138

http://allomaps.bii.a-star.edu.sg

evaluation of allosteric effects of mutations and prediction of latent regulatory exosites

AlloDriver

139

No

analysis of clinic high-throughput data on allosteric mutations

allosteric interaction scoring Alloscore

141

http://mdl.shsmu.edu.cn/alloscore

21


prediction affinities

of of

binding allosteric


Page 22 of 72

protein-modulator interactions based on machine learning method

a

allosteric modulator screening and design AlloFinder

26

http://mdl.shsmu.edu.cn/ALF

automatic discovery of allosteric modulators and allosteric mechanism research

CovCys

142

http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus

automatic detection of druggable cysteine residues for allosteric covalent drug design

5. PRINCIPLES

FOR

ALLOSTERIC

MODULATOR

DISCOVERY The latest release of ASD (Nov. 2018) contains 1,800 allosteric proteins regardless of species (Figure 4), which are mainly distributed among eight protein families: kinases, GPCRs, transcription factors, ion channels, oxidoreductases, lyases, hydrolases, and transferases (Figure 5). At the nascent stage of allosteric research, the allosteric regulatory phenomena were observed in multimeric proteins with hemoglobin representing the prototype of this class. Owing to the progress in chemical and structural biology techniques, allosteric binding has been observed in monomeric proteins such as GPCRs, kinases, and enzymes, greatly expanding the repertoire of available allosteric drug targets.

22


Page 23 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 4. The total number of X-ray or NMR structures of allosteric proteins and yearly growth of total structures in the AlloSteric Database (ASD). Representative structures of allosteric proteins are shown.

Figure 5. Major class distribution of the structures of allosteric proteins in ASD. The number of allosteric modulators has been rapidly increasing in recent years due to the growing investment of the scientific community in allosteric modulator discovery (Figure 6). ASD now contains 80,000 allosteric modulators that target 23



more than 1300 proteins. Among the allosteric modulators, five chemical entities have been approved by the U.S. FDA as marketed drugs, including three GPCR (Cinacalcet®, Maraviroc®, and Plerixafor®) and two kinase (Gleevec® and Mekinist®) allosteric drugs. Additionally, many promising candidates are now entering clinical trials.

Figure 6. Yearly growth of allosteric modulators in ASD. The chemical structures of the marketed allosteric drugs are indicated in the chart.

5.1. Characteristics of Allosteric Sites The molecular evolution of allosteric and orthosteric sites is different. Kim and coworkers analyzed the sequence conservation of allosteric and orthosteric sites in 56 allosterically regulated enzymes.88 The results showed that allosteric site residues (average conservation score=0.58) are significantly less conserved than orthosteric site residues (average conservation score=0.94; P=1.310-67). Similarly, comparing 24


Page 24 of 72

Page 25 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


the sequence conservation of ATP binding sites in the allosteric and orthosteric datasets, we found that residues in the orthosteric ATP binding site (average conservation score=0.63) are less conserved than residues in the orthosteric ATP binding site (average conservation score=0.83; P=1.210-3).87 Furthermore, the shapes calculated by the pocket similarity score (PS-score) between the allosteric and orthosteric ATP binding sites are distinct. The global structural similarity of the allosteric ATP binding site is lower (average PS-score=0.3) than that of the orthosteric ATP binding site (average PS-score=0.6).87 Collectively, these data are consistent with the higher target selectivity observed for allosteric modulators relative to orthosteric ligands. The physicochemical properties of allosteric and orthosteric sites in an individual protein were further explored by comparison of the amino acid compositions of the two sets of binding sites curated from ASD v3.0.20 The results showed that hydrophobic residues such as isoleucine and tyrosine are highly enriched in allosteric sites (Figure 7). By contrast, polar residues such as aspartic acid, glutamine, histidine, and glycine are highly enriched in orthosteric sites. These data suggest that allosteric sites are more hydrophobic than orthosteric sites.

25



Figure 7. Residue compositions in the orthosteric (light blue) and allosteric (blue) sites calculated from ASD v3.0.

5.2. Characteristics of Allosteric Modulators Based on the allosteric modulators from ASD v3.0 and orthosteric ligands from ChEMBL, Carlson and coworkers compared the physicochemical properties of allosteric modulators and orthosteric ligands.143 The results showed that allosteric modulators are more rigid and aromatic than orthosteric ligands, in accordance with previous studies by Overington and coworkers.93 This principle can be applied to help select potential allosteric modulators for a protein after virtual screening—that is, in cases where it is known there are differences in polarity between orthosteric and allosteric sites, choosing compounds that are rigid and more aromatic can increase the likelihood of occupying an allosteric site.

6. EXAMPLES OF THE STRUCTURE-BASED DESIGN OF ALLOSTERIC MODULATORS 26


Page 26 of 72

Page 27 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Due to the recent progress in the molecular understanding of the allosteric process in recent years, many computational approaches have been developed for various aspects of allosteric applications. Such approaches proved successful in finding putative allosteric modulators, confirmed by subsequent crystallographic/NMR or biological experiments. In this section, we will present some representative examples of using combined computational and experimental tactics to identify allosteric modulators. 6.1. SIRT6 Allosteric Activators Sirtuin 6 (SIRT6), a member of the human sirtuin family (SIRT1-7), is a histone deacetylase that transfers the acetyl group from the lysine side chain of a protein or peptide substrate to nicotinamide adenine dinucleotide (NAD+).144 The regulation of SIRT6 has been closely associated with multiple biological processes, encompassing DNA damage repair, organ metabolism, aging, and tumorigenesis.145 Accumulating evidence strongly indicates that SIRT6 knockout mice are accompanied by a severe premature aging syndrome, while mice containing SIRT6 overexpression display a dramatically longer lifespan. They also show massive apoptosis in various cancer cells but not in normal cells.146–148 Pharmacological activation of SIRT6 by small-molecule compounds may, therefore, provide a potential new avenue in ageand cancer-related treatment.149,150 To increase SIRT6 catalytic activity, the design of compounds to directly compete with endogenous NAD+ is infeasible because NAD+ is the SIRT6 cofactor involved in the catalytic reaction. As such, it is advisable and effective to target 27



allosteric sites outside of the NAD+ orthosteric site. However, the allosteric sites of SIRT6 were unavailable at the onset of our studies on SIRT6 allosteric activators in 2013. To address this issue, we employed our previously developed structure-based Allosite121 approach to computationally predict potential allosteric sites (Figure 8). The prediction informed us of a potential allosteric site formed by residues Phe82 and Phe86 adjacent to the N-terminus of SIRT6. Based on this predicted allosteric site, virtual screening of seven commercial chemical libraries (SPECS, ChemBridge, ChemDiv, Enamine, InterBioScreen, Life Chemicals, and Maybridge) consisting of more than 5,000,000 compounds was performed. According to the principle that allosteric modulators are rigid and more aromatic than orthosteric ligands, we chose and purchased 20 rigid and aromatic compounds from the top-ranked compounds calculated by our Alloscore141 approach to assess allosteric interactions. After experimental testing, two initial hits from the SPECS chemical library, AN-988/40889624 (2) and AH-487/41802661 (3) (Figure 7), were found to dose dependently activate SIRT6 deacetylation, with half-maximal effective concentration values of 173.81.3 μM and 217.61.1 μM, respectively.25 To improve the activation potency of the hits, medicinal chemistry optimizations of 3 led to two activators: MDL-800 (4) and MDL-801 (5) (Figure 8), both of which significantly enhanced the deacetylation activity of SIRT6, with EC50 values of 10.30.3 μM and 5.70.3 μM, respectively. Crystallographic studies further confirmed that 5 binds to the allosteric site located at the surface exit of the long SIRT6 channel pocket but not to the 28


Page 28 of 72

Page 29 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


substrate site of SIRT6. This allosteric site is formed by the N-terminal residues Val3-Leu9, Val70, Glu74, Phe82, Phe86, Val153, and Met157 (Figure 9),25 in agreement with the computationally predicted allosteric site by Allosite121 and supporting the feasibility of Allosite in the identification of as-yet-unknown allosteric sites. Cellular and animal model studies on the growth inhibitory effects of human hepatocellular carcinoma (HCC) further demonstrated that allosteric SIRT6 activators can suppress the proliferation of HCC.25 These findings should provide a starting point to facilitate the exploration of allosteric SIRT6 activators as therapeutic agents or as probe compounds to understand SIRT6 biology.

Figure 8. Workflow of the rational discovery of SIRT6 allosteric activators. The potential

allosteric

site

of

SIRT6

was

predicted

using

Allosite

(http://mdl.shsmu.edu.cn/AST). SIRT6 is shown as surface colored in gray, with the orthosteric and predicted allosteric sites highlighted in blue and red, respectively.

29



Figure 9. Detailed interactions between MDL-801 and SIRT6 at the allosteric site determined by the cocrystal structure (PDB code 5Y2F). SIRT6 is shown in gray, and MDL-801 is depicted by sticks with carbon atoms colored in yellow. The key residues within the allosteric site of MDL-801 are represented by sticks with carbon atoms colored in cyan.

6.2. STAT3 Allosteric Inhibitors Signal transducer and activator of transcription 3 (STAT3) plays an important role in many biological processes, including differentiation, survival, proliferation, and angiogenesis.151 However, constitutive or aberrant activation of STAT3 has been linked to malignant transformation and tumorigenesis.152 As such, inhibition of STAT3 activity represents an attractive strategy for anticancer drug discovery.153 STAT3 is a multidomain protein consisting of six distinct functional domains (Figure 10). Many STAT3 inhibitors were previously developed that targeted to the 30


Page 30 of 72

Page 31 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Src homology 2 (SH2) domain, and, to a lesser extent, the DNA-binding domain (DBD).154–157 Such inhibitors often encounter poor pharmacokinetic properties and cytotoxicity during clinical trials, making them ineffective drug candidates.158 Unlike the SH2 domain and DBD, inhibitors targeted to other domains such as the coiled-coil domain (CCD) are scarce. The CCD can be regarded as an allosteric domain to regulate STAT3 functional activity.85 Based on the STAT3 CCD, we used our developed AlloFinder26 approach to screen in silico for potential allosteric inhibitors. This approach can automatically predict allosteric sites by Allosite,121 perform molecular docking by AutoDock Vina,159 and rank the docked poses by Alloscore.141 We chose and purchased the top 15 hits for bioassays; these hits are bound to the predicted allosteric site formed by residues Asp171, Asn175, Gln202, and Met213 (Figure 10). After biological testing, we observed that the small-molecule compound K116 (AH-034/11936955) (6) from the SPECS chemical library can bind to STAT3 CCD, with a Kd value of 3.220.85 μM.26 Site-directed mutagenesis experiments showed that both double mutants (N175G/K177G and Q212A/M213G) decreased the binding affinity of 6 to the mutants by 10 fold compared with wild-type STAT3.26 These data support the possibility of the discovery of STAT3 allosteric inhibitors by the AlloFinder approach. However, a further crystallographic structural complex is required to confirm the allosteric inhibition of STAT3 by 6 directly. Currently, medicinal chemistry optimization of 6 is in progress, and structural determination of STAT3 in complex with 6 derivatives will be performed soon. 31



Figure 10. Workflow of the rational discovery of a STAT3 allosteric inhibitor. An X-ray structure of STAT3 (residues 130 to 722) (PDB code 1BG1) is colored by the domain. The predicted allosteric site at the coiled-coil domain (CCD), large-scale molecular docking of compound libraries at the CCD allosteric site, and allosteric interaction evaluation after docking were executed automatically by AlloFinder (http://mdl.shsmu.edu.cn/ALF). Enlarged figure showing the potential interactions between the allosteric inhibitor K116 (6) and STAT3, consistent with mutagenesis experiments.

6.3. GPX4 Allosteric Activators By analysis of the correlations between allosteric sites and corresponding orthosteric sites in monomer or oligomeric allosteric proteins using a Gaussian network model, Lai and coworkers found that the motions of allosteric and orthosteric sites in proteins are highly correlated.133 According to this principle, they developed a correlation analysis method named CorrSite133 to predict potential allosteric sites in proteins. 32


Page 32 of 72

Page 33 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Glutathione peroxidase 4 (GPX4) controls many biological processes, including cell membrane repair, inflammation suppression, and ferroptosis inhibition.160 Upregulating GPX4 enzyme activity has a clinical benefit in the treatment of inflammation- and ferroptosis-associated diseases.161,162 As such, the development of GPX4 activators can provide a pharmacological benefit. To discover GPX4 activators, Lai and coworkers recently have combined the computational methods CAVITY163 and CorrSite133 to predict a potential allosteric site in GPX4.27 The predicted allosteric site, with a CorreSite score of 1.49 (cavities with CorreSite scores >0.5 are considered potential allosteric sites by the CorrSite method), is located at the opposite side of the protein from the substrate binding site (Figure 11). This allosteric site is formed by three acidic residues (Asp21, Asp23, and Asp101), two basic residues (Lys31 and Lys90), and seven hydrophobic residues (Ile22, Ala93, Ala94, Val98, Phe100, Met102, and Phe103). Virtual screening of the SPECS chemical library was then performed based on the predicted allosteric site, and 251 compounds were selected for biological testing with  one protein-ligand hydrogen bond and good hydrophobic interactions. Among these compounds, compound 1 (7) (Figure 11) was observed to dose dependently increase GPX4 activity to 260% at an activator concentration of 500 μM in the cell-free assay, with a Kd value of 635 μM. Based on the modeling of CPX4-7 protein-ligand interactions, SAR studies of 7 were further performed to generate the strongest compound, 1d4 (8) (Figure 11), which significantly increased GPX4 activity to 150% at 20 μM in the cell-free assay and 61 μM in cell extracts. A double mutant (D21A/D23A) and two 33



Page 34 of 72

single mutants (D21A and D23A) showed reduced activation of GPX4 in response to both 7 and 8, confirming the predicted allosteric site by the CorrSite133 approach.

Figure 11. Workflow of the rational discovery of GPX4 allosteric activators. The allosteric

site

of

GPX4

was

predicted

using

CAVITY

and

CorrSite

(http://www.pkumdl.cn/cavityplus) based on the crystal structure of GPX4 (PDB code 2OBI). The predicted allosteric site (red) is on the opposite side of the orthosteric site (blue). The predicted binding mode of 1d4 (8) represented by blue sticks to GPX4 is shown, with key residues within the binding site highlighted in orange by stick representations.

6.4. Cathepsin K Allosteric Inhibitors Evolution-based prediction of allosteric sites exploit sequence alignments and other genetics analysis methods to identify conserved residues throughout evolution.164 SCA is a prototype of an evolution-based prediction method, which utilizes multiple 34


Page 35 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


sequence alignment (MSA) to uncover evolutionarily conserved networks of residues within a protein family.46 Such networks, referred to as protein sectors, are proposed to mediate allosteric communications between functional orthosteric sites and regulatory allosteric sites.46,165 Nevertheless, the practicality of SCA has limitations.166–170 Major concerns include that this method is only applicable to residues in close physical contacts that are immediately subject to evolution selection,167,169,170 and whether its prediction of the evolutionarily constrained positions can completely reflect the thermodynamic or allosteric couplings within protein structures.166 Despite its advantages and disadvantages, the potential application of SCA has been exemplified in the discovery of the first low-molecular-weight allosteric inhibitor of cathepsin K, NSC13345 (9) (Figure 12A).171 Novinec and coworkers first constructed an MSA of 1,239 catalytic domains from the papain-like cysteine peptidase family.171 The pairwise correlations between all position pairs within the alignment were revealed through the positional correlation matrix calculations, which were then clustered using hierarchical clustering. A continuous network surrounding the orthosteric site and stretching around cathepsin K (i.e., the protein sector) was identified through automated sector identification. This sector comprises the coevolving residues in the protein family with functional importance. A few potential allosteric sites were subsequently detected using the AutoLigand172 cavity prediction method. 9 from the NCI Diversity Set III compound library was identified as a potential hit. A crystal structure of cathepsin K in complex with 9, solved to 35



determine the allosteric regulatory mechanism for its binding, showed that it was bound to a remote surface-exposed allosteric site (Figure 12B), Site 6. The latter mainly comprises helix α10 and adjacent β sheets and loops. Within this site, 9 makes hydrogen bonds with Gly113, Lys236, Arg237, and Gln313. Further biochemical assays revealed that 9 abolished the collagenolytic activity of cathepsin K, with an IC50 value of 80±30 μM. Together, these results suggest the possibility of applying an evolution-based allosteric method in the discovery of allosteric modulators. In fact, this method has been applied in other cases, such as the PDZ domain,173 G proteins,174 and TonB-dependent transporters.175

Figure 12. Structural overview of cathepsin K in complex with its allosteric inhibitor NSC13345 (9) and substrate TCO (PDB ID: 5J94). The backbone structure of 36


Page 36 of 72

Page 37 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


cathepsin K is shown in gray. The allosteric site (Site 6) and orthosteric site are highlighted in red and blue, respectively. The upper panel shows the chemical structure of 9, and the lower panel shows the detailed interactions between 9 and cathepsin K. Hydrogen bonds are displayed in red dashed lines and residues within the allosteric site are represented by sticks. Substrate TCO is manually docked into the orthosteric site based on the superposition of PDB 5J94 with PDB 1Q6K.

6.5 c-Src Allosteric Inhibitors Protein allostery is rooted in protein dynamics.176 The proper understanding of allosteric effects and accurate prediction of allostery cannot be achieved unless the conformational dynamics and energetic landscape of protein structures are thoroughly dissected. MD simulations can characterize both large-scale conformational changes in overall structures and subtle alterations in residue orientations, as well as provide energetic insights into conformational dynamics.177–180 Thus, MD-based approaches, coupled with subsequent correlated motions and community analyses, can uncover allosteric sites coupled to orthosteric sites with a high accuracy.181–184 Furthermore, MD simulations enable the production of complete dynamic biological processes, facilitating the detection of the highly flexible or even transient, so-called “hidden” allosteric sites.73,74 Pande and coworkers performed 550 μs of massively distributed MD simulations and Markov state model (MSM) analyses to identify potential allosteric sites on the anti-cancer drug target c-Src.185 Snapshot structures from simulated trajectories were 37



clustered using MSM analysis, which separated the dynamic process into a series of discrete states and calculated the population of each state and transitions between them. Two intermediate states (I1 and I2) were thereby identified along the activation pathway of c-Src. A high similarity was observed between the I2 conformational state and previously reported structure of an allosteric inhibitor (ANS) (10)-bound cyclin-dependent kinase 2 (CDK2).186 In these two complexes, the activation loop (A-loop) adopted an unfolded conformation, forming a potential allosteric site with the C-helix and adjacent β-sheets (Figure 13A), which cannot be found in both the active (Figures 13B and 13C) and inactive (Figure 13D) states of c-Src. The binding of 10 to the c-Src was further confirmed by simulations of a 10-bound I2 structure. 10 was bound to the pocket formed by the A-loop, C-helix, and β3, 5, 6 of c-Src, similar to that in the crystal structure of the CDK2-10 complex (Figure 13A). Through blocking the displacement of the C-helix, 10 allosterically inhibited c-Src by trapping it in a partially active intermediate state. Collectively, this study highlighted that large-scale MD simulations can detect a novel, cryptic allosteric site in the intermediate state structure of c-Src. Due to their ability to characterize the kinetic, thermodynamic, and structural features of the protein ensembles, MD simulations, coupled with subsequent MSM analysis, have become a popular, robust tool for allosteric site and modulator discovery.187–190

38


Page 38 of 72

Page 39 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 13. (A) Chemical structure of ANS (10) and structural overview of CDK2 in complex with 10 (PDB code 3PXF). (B) The binding site of 10 is in a closed conformation in the active state of c-Src (PDB code 1Y57). (C) Structural comparison between the 10-bound CDK2 (yellow) and active state of c-Src (blue) shows that the allosteric site is in a closed conformation mainly due to the displacements of the C-helix and A-loop. (D) The binding site of 10 is also in a closed conformation in the inactive state of c-Src (PDB code 2SRC). The C-helix, A-loop, and β-sheets involved 39



in the binding site of 10 are colored in orange, pink, and cyan, respectively.

7. CONCLUSIONS AND PERSPECTIVES Allostery underlies a myriad of biological processes that regulate protein functions, where binding of an effector at the allosteric site affects catalytic processing at an orthosteric site. From a medicinal chemistry perspective, allostery represents an effective tool for the medicinal chemist to drug ‘undruggable’ proteins or alternatively to expand the repertoire of the available drug targets. Allosteric drugs differ from orthosteric drugs in the mode of drug action, with the possibility of achieving higher selectivity, fewer adverse effects, and lower toxicity than orthosteric drugs. Allosterism in drug discovery has, therefore, emerged as a promising strategy to develop efficient and safe therapeutic agents. Because the nature of allosteric modulation is complicated, allosteric modulators were traditionally discovered serendipitously by high-throughput screening. Over the past decade, understanding of the basic mechanisms of allosteric modulation, technology advances in the experimental approaches to study allostery, and remarkable availability of structural data have contributed to the development of various computational approaches to predict allosteric phenomena.191 Such predictive models were created based on the structure-, evolution-, or dynamics-based methods to detect and discover allosteric sites and modulators.89,90 Some of these computational predictions have been confirmed by experimental observations, highlighting the possibility of computational approaches in the discovery of allosteric 40


Page 40 of 72

Page 41 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


modulators. Despite decades of allosteric research and the development of many models for allosteric applications, allosteric modulator discovery continues to face challenges. One of the biggest challenges originates from identifying putative allosteric sites. Particularly, in the case of cryptic allosteric sites that are invisible in apo crystal structures but can emerge in the conformational transition of proteins such as in holo crystal structures, current structure- and evolution-based allosteric computational methods cannot detect such sites in the apo structures.73 Large-scale MD simulations, coupled with MSM analyses, can provide a potential opportunity to identify cryptic allosteric sites, but such simulations with an effective conformational sampling are extremely time consuming.192,193 Alternatively, using normal mode analysis to rapidly capture the conformational ensemble of a protein and then employing computational approaches to identify cryptic allosteric sites can offer guidelines to help to circumvent this dilemma. Despite the current understanding of the general characteristics of known allosteric sites and modulators, the guidelines for the structural modifications of initial allosteric hits to increase affinities (hit-to-lead optimization) are lacking. Because the properties of allosteric sites and modulators differ from the corresponding orthosteric sites and ligands, software traditionally used for orthosteric drug design faces challenges in guiding allosteric modulator design. There is a strong demand for insights into the unique protein-modulator interactions underlying allosteric binding. Our previously created ASBench120 benchmark of high-quality data of allosteric 41



protein-modulator complexes can assist in uncovering interactions useful to design allosteric-active compounds. Many reports of analyses of allostery on the single-molecule level have emerged, leaving the role of allostery at the cellular level unexplored.194 Allostery is a fundamental cellular phenomenon. Thus, it is required to develop multiscale approaches that unite computational and experimental methods to explore allosteric effects on cellular networks. The system-centric method will identify new targets to broaden the allosteric modulator discovery scope.195–197 For example, the allo-network drug concept has recently been proposed to design allosteric modulators in system-based drug design.198 Overall, recently developed computational allosteric prediction approaches have shown some success in designing allosteric modulators for known protein targets. These techniques, coupled with advances in experimental approaches to understand allosteric activation or inhibition, indicate that allosteric modulator discovery has emerged as a new strategy for drug design.

AUTHOR INFORMATION Corresponding Author *J.Z.: E-mail: [email protected]. Phone: +86-21-63846590-776922 Author Contributions §S.L.,

X.H., and D.N. contributed equally to this work.

Notes 42


Page 42 of 72

Page 43 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The authors declare no competing financial interest. Biographies Shaoyong Lu received his undergraduate Applied Chemistry degree from Hangzhou Normal University in 2007. Thereafter, he obtained a Ph.D. degree in Chemistry from Zhejiang University, China, in 2012. He then continued with postdoctoral research at Shanghai Jiao Tong University, School of Medicine in Professor Jian Zhang’s laboratory and is now an associate professor. During 2014-2015, he studied as a visiting scholar in Professor Ruth Nussinov’s group at the National Cancer Institute-Frederick. His main interests include the study of allosteric protein-ligand interactions and the development of allosteric methods and their applications. Xinheng He is an undergraduate student from the Discipline of Biomedical Science, Shanghai Jiao Tong University, School of Medicine in Professor Jian Zhang’s group. He mainly focuses on allosteric mechanism research and allosteric drug design. Duan Ni is an undergraduate student from the Discipline of Biomedical Science, Shanghai Jiao Tong University, School of Medicine, in Professor Jian Zhang’s group. He mainly focuses on using computational methods to investigate the allosteric mechanisms and predict allosteric sites. Jian Zhang received a B.M. degree in Pharmacology in 2002 from Peking University and a Ph.D. in 2007 from Shanghai Institute of Materia Medica, Chinese Academy of Sciences. After receiving his Ph.D., he moved to the University of Michigan to conduct postdoctoral research in Professor Shaomeng Wang’s laboratory. In 2009, he joined Shanghai Jiao Tong University, School of Medicine, where he is a full-time 43



Page 44 of 72

researcher and doctoral supervisor. Now, he is also the director of the Medicinal Bioinformatics Center. His fields of research include first-in-class drug discovery and chemical biology that mainly pertain to the repertoire of allostery. His website provides further details: http://mdl.shsmu.edu.cn/.

ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China (21778037, 81603023, 81322046, 91753117, 81473137), the National Basic Research Program of China (973 Program) (2015CB910403), and the Innovation Team “Diagnosis and Treatment of Major Cardiovascular Diseases” of High-Level Universities in Shanghai.

ABBREVIATIONS USED A-loop, activation loop; ASD, AlloSteric Database; CAP, catabolite activator protein; cryo-EM,

cryo-electron

microscopy;

CCD,

coiled-coil

domain;

CDK2,

cyclin-dependent kinase 2; 3D, three-dimensional; DBD, DNA-binding domain; GPX4, Glutathione peroxidase 4; GPCRs, G protein-coupled receptors; HCC, human hepatocellular

carcinoma;

HDX,

hydrogen/deuterium

exchange;

KNF,

Koshland-Nemethy-Filmer; MSM, Markov State Model; MD, molecule dynamics; MWC, Monod-Wyman-Changeux; MSA, multiple sequence alignment; NAD+, nicotinamide adenine dinucleotide; NMR, nuclear magnetic resonance; PPI, protein-protein interaction; STAT3, signal transducer and activator of transcription 3; 44


Page 45 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


SH2, Src homology 2; SCA, statistical coupling analysis; SIRT6, sirtuin 6

REFERENCES (1) (2) (3) (4) (5) (6) (7) (8) (9)

(10) (11) (12) (13)

(14)

(15)

Ribeiro, A. A. S. T.; Ortiz, V. A Chemical Perspective on Allostery. Chem. Rev. 2016, 116, 6488–6502. Changeux, J.-P. The Concept of Allosteric Modulation: An Overview. Drug Discov. Today. Technol. 2013, 10, e223–e228. Dokholyan, N. V. Controlling Allosteric Networks in Proteins. Chem. Rev. 2016, 116, 6463–6487. Nussinov, R.; Tsai, C. J.; Xin, F.; Radivojac, P. Allosteric Post-Translational Modification Codes. Trends Biochem. Sci. 2012, 37, 447–455. Nussinov, R.; Tsai, C. J. Allostery in Disease and in Drug Discovery. Cell 2013, 153, 293–305. Goodey, N. M.; Benkovic, S. J. Allosteric Regulation and Catalysis Emerge via a Common Route. Nat. Chem. Biol. 2008, 4, 474–482. Nussinov, R.; Ma, B.; Tsai, C. J.; Csermely, P. Allosteric Conformational Barcodes Direct Signaling in the Cell. Structure 2013, 21, 1509–1521. Changeux, J.-P. Allosteric Receptors: From Electric Organ to Cognition. Annu. Rev. Pharmacol. Toxicol. 2010, 50, 1–38. Lu, S.; Jang, H.; Muratcioglu, S.; Gursoy, A.; Keskin, O.; Nussinov, R.; Zhang, J. Ras Conformational Ensembles, Allostery, and Signaling. Chem. Rev. 2016, 116, 6607–6665. Lisi, G. P.; Loria, J. P. Allostery in Enzyme Catalysis. Curr. Opin. Struct. Biol. 2017, 47, 123–130. Fenton, A. W. Allostery: An Illustrated Definition for the “Second Secret of Life.” Trends Biochem. Sci. 2008, 33, 420–425. Changeux, J. P.; Christopoulos, A. Allosteric Modulation as a Unifying Mechanism for Receptor Function and Regulation. Cell 2016, 166, 1084–1102. Wootten, D.; Christopoulos, A.; Sexton, P. M. Emerging Paradigms in GPCR Allostery: Implications for Drug Discovery. Nat. Rev. Drug Discov. 2013, 12, 630–644. Rettenmaier, T. J.; Sadowsky, J. D.; Thomsen, N. D.; Chen, S. C.; Doak, A. K.; Arkin, M. R.; Wells, J. A. A Small-Molecule Mimic of a Peptide Docking Motif Inhibits the Protein Kinase PDK1. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 18590–18595. Zhang, J.; Adrián, F. J.; Jahnke, W.; Cowan-Jacob, S. W.; Li, A. G.; Iacob, R. E.; Sim, T.; Powers, J.; Dierks, C.; Sun, F.; Guo, G. R.; Ding, Q.; Okram, B.; Choi, Y.; Wojciechowski, A.; Deng, X.; Liu, G.; Fendrich, G.; Strauss, A.; Vajpai, N.; Grzesiek, S.; Tuntland, T.; Liu, Y.; Bursulaya, B.; Azam, M.; Manley, P. W.; Engen, J. R.; Daley, G. Q.; Warmuth, M.; Gray, N. S. Targeting Bcr-Abl by Combining Allosteric with ATP-Binding-Site Inhibitors. Nature 45



(16)

(17) (18)

(19) (20)

(21)

(22) (23)

(24)

(25)

(26)

(27)

(28) (29)

2010, 463, 501–506. Wenthur, C. J.; Gentry, P. R.; Mathews, T. P.; Lindsley, C. W. Drugs for Allosteric Sites on Receptors. Annu. Rev. Pharmacol. Toxicol. 2014, 54, 165–184. De Smet, F.; Christopoulos, A.; Carmeliet, P. Allosteric Targeting of Receptor Tyrosine Kinases. Nat. Biotechnol. 2014, 32, 1113–1120. Hauser, A. S.; Attwood, M. M.; Rask-Andersen, M.; Schiöth, H. B.; Gloriam, D. E. Trends in GPCR Drug Discovery: New Agents, Targets and Indications. Nat. Rev. Drug Discov. 2017, 16, 829–842. Hardy, J. A.; Wells, J. A. Searching for New Allosteric Sites in Enzymes. Curr. Opin. Struct. Biol. 2004, 14, 706–715. Shen, Q.; Wang, G.; Li, S.; Liu, X.; Lu, S.; Chen, Z.; Song, K.; Yan, J.; Geng, L.; Huang, Z.; Huang, W.; Chen, G.; Zhang, J. ASD v3.0: Unraveling Allosteric Regulation with Structural Mechanisms and Biological Networks. Nucleic Acids Res. 2016, 44, D527–D535. Wagner, J. R.; Lee, C. T.; Durrant, J. D.; Malmstrom, R. D.; Feher, V. A.; Amaro, R. E. Emerging Computational Methods for the Rational Discovery of Allosteric Drugs. Chem. Rev. 2016, 116, 6370–6390. Lu, S.; Zhang, J. Designed Covalent Allosteric Modulators: An Emerging Paradigm in Drug Discovery. Drug Discov. Today 2017, 22, 447–453. Perna, A. M.; Reisen, F.; Schmidt, T. P.; Geppert, T.; Pillong, M.; Weisel, M.; Hoy, B.; Simister, P. C.; Feller, S. M.; Wessler, S.; Schneider, G. Inhibiting Helicobacter Pylori HtrA Protease by Addressing a Computationally Predicted Allosteric Ligand Binding Site. Chem. Sci. 2014, 5, 3583–3590. Hocker, H. J.; Cho, K.-J.; Chen, C.-Y. K.; Rambahal, N.; Sagineedu, S. R.; Shaari, K.; Stanslas, J.; Hancock, J. F.; Gorfe, A. A. Andrographolide Derivatives Inhibit Guanine Nucleotide Exchange and Abrogate Oncogenic Ras Function. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 10201–10206. Huang, Z.; Zhao, J.; Deng, W.; Chen, Y.; Shang, J.; Song, K.; Zhang, L.; Wang, C.; Lu, S.; Yang, X.; He, B.; Min, J.; Hu, H.; Tan, M.; Xu, J.; Zhang, Q.; Zhong, J.; Sun, X.; Mao, Z.; Lin, H.; Xiao, M.; Chin, Y. E.; Jiang, H.; Xu, Y.; Chen, G.; Zhang, J. Identification of a Cellularly Active SIRT6 Allosteric Activator. Nat. Chem. Biol. 2018, 14, 1118-1126. Huang, M.; Song, K.; Liu, X.; Lu, S.; Shen, Q.; Wang, R.; Gao, J.; Hong, Y.; Li, Q.; Ni, D.; Xu, J.; Chen, G.; Zhang, J. AlloFinder: A Strategy for Allosteric Modulator Discovery and Allosterome Analyses. Nucleic Acids Res. 2018, 46, W451–W458. Li, C.; Deng, X.; Zhang, W.; Xie, X.; Conrad, M.; Liu, Y.; Angeli, J. P. F.; Lai, L. Novel Allosteric Activators for Ferroptosis Regulator Glutathione Peroxidase 4. J. Med. Chem. 2019, 62, 266-275. Monod, J.; Jacob, F. Telenomic Mechanisms in Cellular Metabolism, Growth, and Differentiation. Cold Spring Harb. Symp Quant Biol 1961, 26, 389–401. Monod, J.; Wyman, J.; Changeux, J.-P. On the Nature of Allosteric Transitions: A Plausible Model. J. Mol. Biol. 1965, 12, 88–188. 46


Page 46 of 72

Page 47 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(30)

(31) (32)

(33) (34) (35)

(36)

(37) (38)

(39) (40) (41)

(42)

(43)

(44) (45) (46) (47)

Koshland, D. E.; Nemethy, J. G.; Filmer, D. Comparison of Experimental Binding Data and Theoretical Models in Proteins Containing Subunits. Biochemistry 1966, 5, 365–385. Cooper, A.; Dryden, D. T. Allostery without Conformational Change. A Plausible Model. Eur. Biophys. J. 1984, 11, 103–109. Igumenova, T. I.; Frederick, K. K.; Wand, A. J. Characterization of the Fast Dynamics of Protein Amino Acid Side Chains Using NMR Relaxation in Solution. Chem. Rev. 2006, 106, 1672–1699. Tzeng, S. R.; Kalodimos, C. G. Dynamic Activation of an Allosteric Regulatory Protein. Nature 2009, 462, 368–372. Tzeng, S. R.; Kalodimos, C. G. Protein Activity Regulation by Conformational Entropy. Nature 2012, 488, 236–240. Petit, C. M.; Zhang, J.; Sapienza, P. J.; Fuentes, E. J.; Lee, A. L. Hidden Dynamic Allostery in a PDZ Domain. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 18249–18254. Lundström, P.; Mulder, F. A.; Akke, M. Correlated Dynamics of Consecutive Residues Reveal Transient and Cooperative Unfolding of Secondary Structure in Proteins. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 16984–16989. Frederick, K. K.; Marlow, M. S.; Valentine, K. G.; Wand, A. J. Conformational Entropy in Molecular Recognition by Proteins. Nature 2007, 448, 325–329. Lee, A. L.; Kinnear, S. A.; Wand, A. J. Redistribution and Loss of Side Chain Entropy upon Formation of a Calmodulin-Peptide Complex. Nat. Struct. Biol. 2000, 7, 72–77. Kornev, A. P.; Taylor, S. S. Dynamics-Driven Allostery in Protein Kinases. Trends Biochem. Sci. 2015, 40, 628–647. Wand, A. J. Bringing Disorder and Dynamics in Protein Allostery into Focus. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 4278–4280. Marlow, M. S.; Dogan, J.; Frederick, K. K.; Valentine, K. G.; Wand, J. A. The Role of Conformational Entropy in Molecular Recognition by Calmodulin. Nat. Chem. Biol. 2010, 6, 352–358. Kasinath, V.; Sharp, K. A.; Wand, A. J. Microscopic Insights into the NMR Relaxation-Based Protein Conformational Entropy Meter. J. Am. Chem. Soc. 2013, 135, 15092–15100. Kumar, S.; Ma, B.; Tsai, C. J.; Wolfson, H.; Nussinov, R. Folding Funnels and Conformational Transitions via Hinge-Bending Motions. Cell Biochem. Biophys. 1999, 31, 141–164. Ma, B.; Kumar, S.; Tsai, C. J.; Nussinov, R. Folding Funnels and Binding Mechanisms. Protein Eng. 1999, 12, 713–720. Tsai, C. J.; Kumar, S.; Ma, B.; Nussinov, R. Folding Funnels, Binding Funnels, and Protein Function. Protein Sci. 1999, 8, 1181–1190. Lockless, S. W.; Ranganathan, R. Evolutionarily Conserved Pathways of Energetic Connectivity in Protein Families. Science 1999, 286, 295–299. del Sol, A.; Tsai, C. J.; Ma, B.; Nussinov, R. The Origin of Allosteric Functional Modulation: Multiple Pre-Existing Pathways. Structure 2009, 17, 47



(48)

(49)

(50) (51) (52) (53) (54) (55) (56) (57)

(58)

(59)

(60)

(61)

1042–1050. Csermely, P.; Korcsmáros, T.; Kiss, H. J. M.; London, G.; Nussinov, R. Structure and Dynamics of Molecular Networks: A Novel Paradigm of Drug Discovery: A Comprehensive Review. Pharmacol. Ther. 2013, 138, 333–408. Lu, S.; Deng, R.; Jiang, H.; Song, H.; Li, S.; Shen, Q.; Huang, W.; Nussinov, R.; Yu, J.; Zhang, J. The Mechanism of ATP-Dependent Allosteric Protection of Akt Kinase Phosphorylation. Structure 2015, 23, 1725–1734. Changeux, J.-P.; Edelstein, S. J. Allosteric Mechanisms of Signal Transduction. Science 2005, 308, 1424–1428. Hilser, V. J. An Ensemble View of Allostery. Science 2010, 327, 653–654. Motlagh, H. N.; Wrabl, J. O.; Li, J.; Hilser, V. J. The Ensemble Nature of Allostery. Nature 2014, 508, 331–339. Hilser, V. J.; Wrabl, J. O.; Motlagh, H. N. Structural and Energetic Basis of Allostery. Annu. Rev. Biophys. 2012, 41, 585–609. Liu, J.; Nussinov, R. Allostery: An Overview of Its History, Concepts, Methods, and Applications. PLoS Comput. Biol. 2016, 12, 3–7. Tsai, C.-J.; Nussinov, R. A Unified View of “How Allostery Works”. PLoS Comput. Biol. 2014, 10, e1003394. Cui, Q.; Karplus, M. Allostery and Cooperativity Revisited. Protein Sci. 2008, 17, 1295–1307. Bagal, S. K.; Andrews, M.; Bechle, B. M.; Bian, J.; Bilsland, J.; Blakemore, D. C.; Braganza, J. F.; Bungay, P. J.; Corbett, M. S.; Cronin, C. N.; Cui, J. J.; Dias, R.; Flanagan, N. J.; Greasley, S. E.; Grimley, R.; James, K.; Johnson, E.; Kitching, L.; Kraus, M. L.; McAlpine, I.; Nagata, A.; Ninkovic, S.; Omoto, K.; Scales, S.; Skerratt, S. E.; Sun, J.; Tran-Dubé, M.; Waldron, G. J.; Wang, F.; Warmus, J. S. Discovery of Potent, Selective, and Peripherally Restricted Pan-Trk Kinase Inhibitors for the Treatment of Pain. J. Med. Chem. 2018, 61, 6779–6800. Kaniskan, H. Ü.; Eram, M. S.; Zhao, K.; Szewczyk, M. M.; Yang, X.; Schmidt, K.; Luo, X.; Xiao, S.; Dai, M.; He, F.; Zang, I.; Lin, Y.; Li, F.; Dobrovetsky, E.; Smil, D.; Min, S. J.; Lin-Jones, J.; Schapira, M.; Atadja, P.; Li, E.; Barsyte-Lovejoy, D.; Arrowsmith, C. H.; Brown, P. J.; Liu, F.; Yu, Z.; Vedadi, M.; Jin, J. Discovery of Potent and Selective Allosteric Inhibitors of Protein Arginine Methyltransferase 3 (PRMT3). J. Med. Chem. 2018, 61, 1204–1217. Ortiz Zacarías, N. V.; van Veldhoven, J. P. D.; Portner, L.; van Spronsen, E.; Ullo, S.; Veenhuizen, M.; van der Velden, W. J. C.; Zweemer, A. J. M.; Kreekel, R. M.; Oenema, K.; Lenselink, E. B.; Heitman, L. H.; IJzerman, A. P. Pyrrolone Derivatives as Intracellular Allosteric Modulators for Chemokine Receptors: Selective and Dual-Targeting Inhibitors of CC Chemokine Receptors 1 and 2. J. Med. Chem. 2018, 61, 9146–9161. Davis, M. I.; Hunt, J. P.; Herrgard, S.; Ciceri, P.; Wodicka, L. M.; Pallares, G.; Hocker, M.; Treiber, D. K.; Zarrinkar, P. P. Comprehensive Analysis of Kinase Inhibitor Selectivity. Nat. Biotechnol. 2011, 29, 1046–1051. Wu, P.; Clausen, M. H.; Nielsen, T. E. Allosteric Small-Molecule Kinase 48


Page 48 of 72

Page 49 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(62)

(63)

(64)

(65)

(66)

(67)

(68) (69) (70) (71)

(72)

Inhibitors. Pharmacol. Ther. 2015, 156, 59–68. Lewis, J. A.; Lebois, E. P.; Lindsley, C. W. Allosteric Modulation of Kinases and GPCRs: Design Principles and Structural Diversity. Curr. Opin. Chem. Biol. 2008, 12, 269–280. Comess, K. M.; Sun, C.; Abad-Zapatero, C.; Goedken, E. R.; Gum, R. J.; Borhani, D. W.; Argiriadi, M.; Groebe, D. R.; Jia, Y.; Clampit, J. E.; Haasch, D. L.; Smith, H. T.; Wang, S.; Song, D.; Coen, M. L.; Cloutier, T. E.; Tang, H.; Cheng, X.; Quinn, C.; Liu, B.; Xin, Z.; Liu, G.; Fry, E. H.; Stoll, V.; Ng, T. I.; Banach, D.; Marcotte, D.; Burns, D. J.; Calderwood, D. J.; Hajduk, P. J. Discovery and Characterization of Non-ATP Site Inhibitors of the Mitogen Activated Protein (MAP) Kinases. ACS Chem. Biol. 2011, 6, 234–244. Sherbenou, D. W.; Hantschel, O.; Kaupe, I.; Willis, S.; Bumm, T.; Turaga, L. P.; Lange, T.; Dao, K. H.; Press, R. D.; Druker, B. J.; Superti-Furga, G.; Deininger, M. W. BCR-ABL SH3-SH2 Domain Mutations in Chronic Myeloid Leukemia Patients on Imatinib. Blood 2010, 116, 3278–3285. Schoepfer, J.; Jahnke, W.; Berellini, G.; Buonamici, S.; Cotesta, S.; Cowan-Jacob, S. W.; Dodd, S.; Drueckes, P.; Fabbro, D.; Gabriel, T.; Groell, J. M.; Grotzfeld, R. M.; Hassan, A. Q.; Henry, C.; Iyer, V.; Jones, D.; Lombardo, F.; Loo, A.; Manley, P. W.; Pellé, X.; Rummel, G.; Salem, B.; Warmuth, M.; Wylie, A. A.; Zoller, T.; Marzinzik, A. L.; Furet, P. Discovery of Asciminib (ABL001), an Allosteric Inhibitor of the Tyrosine Kinase Activity of BCR-ABL1. J. Med. Chem. 2018, 61, 8120–8135. Wylie, A. A.; Schoepfer, J.; Jahnke, W.; Cowan-Jacob, S. W.; Loo, A.; Furet, P.; Marzinzik, A. L.; Pelle, X.; Donovan, J.; Zhu, W.; Buonamici, S.; Hassan, A. Q.; Lombardo, F.; Iyer, V.; Palmer, M.; Berellini, G.; Dodd, S.; Thohan, S.; Bitter, H.; Branford, S.; Ross, D. M.; Hughes, T. P.; Petruzzelli, L.; Vanasse, K. G.; Warmuth, M.; Hofmann, F.; Keen, N. J.; Sellers, W. R. The Allosteric Inhibitor ABL001 Enables Dual Targeting of BCR-ABL1. Nature 2017, 543, 733–737. Wei, G.; Xi, W.; Nussinov, R.; Ma, B. Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell. Chem. Rev. 2016, 116, 6516–6551. Weis, W. I.; Kobilka, B. K. The Molecular Basis of G Protein–Coupled Receptor Activation. Annu. Rev. Biochem. 2018, 87, 897–919. Lazo, J. S.; Sharlow, E. R. Drugging Undruggable Molecular Cancer Targets. Annu. Rev. Pharmacol. Toxicol. 2016, 56, 23–40. Dang, C. V.; Reddy, E. P.; Shokat, K. M.; Soucek, L. Drugging the “Undruggable” Cancer Targets. Nat. Rev. Cancer 2017, 17, 502–508. Shao, H.; Li, X.; Moses, M. A.; Gilbert, L. A.; Kalyanaraman, C.; Young, Z. T.; Chernova, M.; Journey, S. N.; Weissman, J. S.; Hann, B.; Jacobson, M. P.; Neckers, L.; Gestwicki, J. E. Exploration of Benzothiazole Rhodacyanines as Allosteric Inhibitors of Protein-Protein Interactions with Heat Shock Protein 70 (Hsp70). J. Med. Chem. 2018, 61, 6163–6177. Pricer, R.; Gestwicki, J. E.; Mapp, A. K. From Fuzzy to Function: The New 49



(73) (74)

(75)

(76)

(77)

(78)

(79)

(80)

(81)

(82)

(83)

(84)

(85)

Frontier of Protein-Protein Interactions. Acc. Chem. Res. 2017, 50, 584–589. Lu, S.; Ji, M.; Ni, D.; Zhang, J. Discovery of Hidden Allosteric Sites as Novel Targets for Allosteric Drug Design. Drug Discov. Today 2018, 23, 359–365. Brogi, S.; Ramunno, A.; Savi, L.; Chemi, G.; Alfano, G.; Pecorelli, A.; Pambianchi, E.; Galatello, P.; Compagnoni, G.; Focher, F.; Biamonti, G.; Valacchi, G.; Butini, S.; Gemma, S.; Campiani, G.; Brindisi, M. First Dual AK/GSK-3β Inhibitors Endowed with Antioxidant Properties as Multifunctional, Potential Neuroprotective Agents. Eur. J. Med. Chem. 2017, 138, 438–457. An, X.; Lu, S.; Song, K.; Shen, Q.; Huang, M.; Yao, X.; Liu, H.; Zhang, J. Are the Apo Proteins Suitable for the Rational Discovery of Allosteric Drugs? J. Chem. Inf. Model. 2018, DOI:10.1021/acs.jcim.8b00735. Lu, S.; Jang, H.; Gu, S.; Zhang, J.; Nussinov, R. Drugging Ras GTPase: A Comprehensive Mechanistic and Signaling Structural View. Chem. Soc. Rev. 2016, 45, 4929–4952. Cox, A. D.; Fesik, S. W.; Kimmelman, A. C.; Luo, J.; Der, C. J. Drugging the Undruggable RAS: Mission Possible? Nat. Rev. Drug Discov. 2014, 13, 828–851. Gustafson, W. C.; Meyerowitz, J. G.; Nekritz, E. A.; Chen, J.; Benes, C.; Charron, E.; Simonds, E. F.; Seeger, R.; Matthay, K. K.; Hertz, N. T.; Eilers, M.; Shokat, K. M.; Weiss, W. A. Drugging MYCN through an Allosteric Transition in Aurora Kinase A. Cancer Cell 2014, 26, 414–427. Liu, F.; Rehmani, I.; Esaki, S.; Fu, R.; Chen, L.; de Serrano, V.; Liu, A. Pirin Is an Iron-Dependent Redox Regulator of NF- B. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 9722–9727. Krishnan, N.; Konidaris, K. F.; Gasser, G.; Tonks, N. K. A Potent, Selective, and Orally Bioavailable Inhibitor of the Protein-Tyrosine Phosphatase PTP1B Improves Insulin and Leptin Signaling in Animal Models. J. Biol. Chem. 2018, 293, 1517–1525. Xie, J.; Si, X.; Gu, S.; Wang, M.; Shen, J.; Li, H.; Li, D.; Fang, Y.; Liu, C.; Zhu, J. Allosteric Inhibitors of SHP2 with Therapeutic Potential for Cancer Treatment. J. Med. Chem. 2017, 60, 10205–10219. Krishnan, N.; Koveal, D.; Miller, D. H.; Xue, B.; Akshinthala, S. D.; Kragelj, J.; Jensen, M. R.; Gauss, C. M.; Page, R.; Blackledge, M.; Muthuswamy, S. K.; Peti, W.; Tonks, N. K. Targeting the Disordered C Terminus of PTP1B with an Allosteric Inhibitor. Nat. Chem. Biol. 2014, 10, 558–566. Jiang, L.; Stanevich, V.; Satyshur, K. A.; Kong, M.; Watkins, G. R.; Wadzinski, B. E.; Sengupta, R.; Xing, Y. Structural Basis of Protein Phosphatase 2A Stable Latency. Nat. Commun. 2013, 4, 1611–1699. Scheuermann, T. H.; Li, Q.; Ma, H. W.; Key, J.; Zhang, L.; Chen, R.; Garcia, J. A.; Naidoo, J.; Longgood, J.; Frantz, D. E.; Tambar, U. K.; Gardner, K. H.; Bruick, R. K. Allosteric Inhibition of Hypoxia Inducible Factor-2 with Small Molecules. Nat. Chem. Biol. 2013, 9, 271–276. Minus, M. B.; Liu, W.; Vohidov, F.; Kasembeli, M. M.; Long, X.; Krueger, M. 50


Page 50 of 72

Page 51 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


J.; Stevens, A.; Kolosov, M. I.; Tweardy, D. J.; Sison, E. A. R.; Redell, M. S.; Ball, Z. T. Rhodium(II) Proximity-Labeling Identifies a Novel Target Site on STAT3 for Inhibitors with Potent Anti-Leukemia Activity. Angew. Chemie Int. Ed. 2015, 54, 13085–13089. (86) Lu, S.; Zhang, J. Allosteric Modulators. Compr. Med. Chem. III 2017, 2, 276–296. (87) Lu, S.; Huang, W.; Wang, Q.; Shen, Q.; Li, S.; Nussinov, R.; Zhang, J. The Structural Basis of ATP as an Allosteric Modulator. PLoS Comput. Biol. 2014, 10, e1003831. (88) Yang, J.-S.; Seo, S. W.; Jang, S.; Jung, G. Y.; Kim, S. Rational Engineering of Enzyme Allosteric Regulation through Sequence Evolution Analysis. PLoS Comput. Biol. 2012, 8, e1002612. (89) Lu, S.; Huang, W.; Zhang, J. Recent Computational Advances in the Identification of Allosteric Sites in Proteins. Drug Discov. Today 2014, 19, 1595–1600. (90) Schueler-Furman, O.; Wodak, S. J. Computational Approaches to Investigating Allostery. Curr. Opin. Struct. Biol. 2016, 41, 159–171. (91) Pendergrass, D. C.; Williams, R.; Blair, J. B.; Fenton, A. W. Mining for Allosteric Information: Natural Mutations and Positional Sequence Conservation in Pyruvate Kinase. IUBMB Life 2006, 58, 31–38. (92) Smith, N. J.; Milligan, G. Allostery at G Protein-Coupled Receptor Homo- and Heteromers: Uncharted Pharmacological Landscapes. Pharmacol. Rev. 2010, 62, 701–725. (93) van Westen, G. J. P.; Gaulton, A.; Overington, J. P. Chemical, Target, and Bioactive Properties of Allosteric Modulation. PLoS Comput. Biol. 2014, 10, e1003559. (94) Shi, Y. A Glimpse of Structural Biology through X-Ray Crystallography. Cell 2014, 159, 995–1014. (95) Lu, S.; Zhang, J. Small Molecule Allosteric Modulators of G-Protein-Coupled Receptors: Drug–Target Interactions. J. Med. Chem. 2019, 62, 24-45. (96) Beck, F.; Unverdorben, P.; Bohn, S.; Schweitzer, A.; Pfeifer, G.; Sakata, E.; Nickell, S.; Plitzko, J. M.; Villa, E.; Baumeister, W.; Forster, F. Near-Atomic Resolution Structural Model of the Yeast 26S Proteasome. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 14870–14875. (97) Chen, S.; Oldham, M. L.; Davidson, A. L.; Chen, J. Carbon Catabolite Repression of the Maltose Transporter Revealed by X-Ray Crystallography. Nature 2013, 499, 364–368. (98) Zhou, L.; Hang, J.; Zhou, Y.; Wan, R.; Lu, G.; Yin, P.; Yan, C.; Shi, Y. Crystal Structures of the Lsm Complex Bound to the 3′ End Sequence of U6 Small Nuclear RNA. Nature 2014, 506, 116–120. (99) Stass, R.; Ilca, S. L.; Huiskonen, J. T. Beyond Structures of Highly Symmetric Purified Viral Capsids by Cryo-EM. Curr. Opin. Struct. Biol. 2018, 52, 25–31. (100) Pospich, S.; Raunser, S. Single Particle Cryo-EM — an Optimal Tool to Study Cytoskeletal Proteins. Curr. Opin. Struct. Biol. 2018, 52, 16–24. 51



(101) Fica, S. M.; Nagai, K. Cryo-Electron Microscopy Snapshots of the Spliceosome: Structural Insights into a Dynamic Ribonucleoprotein Machine. Nat. Struct. Mol. Biol. 2017, 24, 791–799. (102) Renaud, J. P.; Chari, A.; Ciferri, C.; Liu, W. T.; Rémigy, H. W.; Stark, H.; Wiesmann, C. Cryo-EM in Drug Discovery: Achievements, Limitations and Prospects. Nat. Rev. Drug Discov. 2018, 17, 471–492. (103) Grutsch, S.; Brüschweiler, S.; Tollinger, M. NMR Methods to Study Dynamic Allostery. PLoS Comput. Biol. 2016, 12, 1–20. (104) Tzeng, S. R.; Kalodimos, C. G. Protein Dynamics and Allostery: An NMR View. Curr. Opin. Struct. Biol. 2011, 21, 62–67. (105) Boulton, S.; Melacini, G. Advances in NMR Methods to Map Allosteric Sites: From Models to Translation. Chem. Rev. 2016, 116, 6267–6304. (106) Lisi, G. P.; Loria, J. P. Solution NMR Spectroscopy for the Study of Enzyme Allostery. Chem. Rev. 2016, 116, 6323–6369. (107) Jiang, Y.; Kalodimos, C. G. NMR Studies of Large Proteins. J. Mol. Biol. 2017, 429, 2667–2676. (108) Velyvis, A.; Kay, L. E. Measurement of Active Site Ionization Equilibria in the 670 kDa Proteasome Core Particle Using Methyl-TROSY NMR. J. Am. Chem. Soc. 2013, 135, 9259–9262. (109) Karagöz, G. E.; Duarte, A. M. S.; Akoury, E.; Ippel, H.; Biernat, J.; Morán Luengo, T.; Radli, M.; Didenko, T.; Nordhues, B. A.; Veprintsev, D. B.; Dickey, C. A.; Mandelkow, E.; Zweckstetter, M.; Boelens, R.; Madl, T.; Rüdiger, S. G. D. Hsp90-Tau Complex Reveals Molecular Basis for Specificity in Chaperone Action. Cell 2014, 156, 963–974. (110) Libich, D. S.; Fawzi, N. L.; Ying, J.; Clore, G. M. Probing the Transient Dark State of Substrate Binding to GroEL by Relaxation-Based Solution NMR. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 11361–11366. (111) Ostrem, J. M.; Peters, U.; Sos, M. L.; Wells, J. A.; Shokat, K. M. K-Ras(G12C) Inhibitors Allosterically Control GTP Affinity and Effector Interactions. Nature 2013, 503, 548–551. (112) Boehlein, S. K.; Shaw, J. R.; Hannah, L. C.; Stewart, J. D. Probing Allosteric Binding Sites of the Maize Endosperm ADP-Glucose Pyrophosphorylase. Plant Physiol. 2010, 152, 85–95. (113) Schneider, R.; Becker, C.; Simard, J. R.; Getlik, M.; Bohlke, N.; Janning, P.; Rauh, D. Direct Binding Assay for the Detection of Type IV Allosteric Inhibitors of Abl. J. Am. Chem. Soc. 2012, 134, 9138–9141. (114) Schneider, R.; Beumer, C.; Simard, J. R.; Grütter, C.; Rauh, D. Selective Detection of Allosteric Phosphatase Inhibitors. J. Am. Chem. Soc. 2013, 135, 6838–6841. (115) Magnaghi, P.; D’Alessio, R.; Valsasina, B.; Avanzi, N.; Rizzi, S.; Asa, D.; Gasparri, F.; Cozzi, L.; Cucchi, U.; Orrenius, C.; Polucci, P.; Ballinari, D.; Perrera, C.; Leone, A.; Cervi, G.; Casale, E.; Xiao, Y.; Wong, C.; Anderson, D. J.; Galvani, A.; Donati, D.; O’Brien, T.; Jackson, P. K.; Isacchi, A. Covalent and Allosteric Inhibitors of the ATPase VCP/p97 Induce Cancer Cell Death. 52


Page 52 of 72

Page 53 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Nat. Chem. Biol. 2013, 9, 548–559. (116) de Vera, I. M. S.; Zheng, J.; Novick, S.; Shang, J.; Hughes, T. S.; Brust, R.; Munoz-Tello, P.; Gardner, W. J.; Marciano, D. P.; Kong, X.; Griffin, P. R.; Kojetin, D. J. Synergistic Regulation of Coregulator/Nuclear Receptor Interaction by Ligand and DNA. Structure 2017, 25, 1506–1518. (117) Greener, J. G.; Sternberg, M. J. Structure-Based Prediction of Protein Allostery. Curr. Opin. Struct. Biol. 2017, 50, 1–8. (118) Huang, Z.; Zhu, L.; Cao, Y.; Wu, G.; Liu, X.; Chen, Y.; Wang, Q.; Shi, T.; Zhao, Y.; Wang, Y.; Li, W.; Li, Y.; Chen, H.; Chen, G.; Zhang, J. ASD: A Comprehensive Database of Allosteric Proteins and Modulators. Nucleic Acids Res. 2011, 39, D663–D669. (119) Huang, Z.; Mou, L.; Shen, Q.; Lu, S.; Li, C.; Liu, X.; Wang, G.; Li, S.; Geng, L.; Liu, Y.; Wu, J.; Chen, G.; Zhang, J. ASD v2.0: Updated Content and Novel Features Focusing on Allosteric Regulation. Nucleic Acids Res. 2014, 42, D510–D516. (120) Huang, W.; Wang, G.; Shen, Q.; Liu, X.; Lu, S.; Geng, L.; Huang, Z.; Zhang, J. ASBench: Benchmarking Sets for Allosteric Discovery. Bioinformatics 2015, 31, 2598–2600. (121) Huang, W.; Lu, S.; Huang, Z.; Liu, X.; Mou, L.; Luo, Y.; Zhao, Y.; Liu, Y.; Chen, Z.; Hou, T.; Zhang, J. Allosite: A Method for Predicting Allosteric Sites. Bioinformatics 2013, 29, 2357–2359. (122) Song, K.; Liu, X.; Huang, W.; Lu, S.; Shen, Q.; Zhang, L.; Zhang, J. Improved Method for the Identification and Validation of Allosteric Sites. J. Chem. Inf. Model. 2017, 57, 2358–2363. (123) Akbar, R.; Helms, V. ALLO: A Tool to Discriminate and Prioritize Allosteric Pockets. Chem. Biol. Drug Des. 2018, 91, 845–853. (124) Chen, A. S. Y.; Westwood, N. J.; Brear, P.; Rogers, G. W.; Mavridis, L.; Mitchell, J. B. O. A Random Forest Model for Predicting Allosteric and Functional Sites on Proteins. Mol. Inform. 2016, 35, 125–135. (125) Smock, R. G.; Rivoire, O.; Russ, W. P.; Swain, J. F.; Leibler, S.; Ranganathan, R.; Gierasch, L. M. An Interdomain Sector Mediating Allostery in Hsp70 Molecular Chaperones. Mol. Syst. Biol. 2010, 6, 1–10. (126) Greener, J. G.; Sternberg, M. J. AlloPred: Prediction of Allosteric Pockets on Proteins Using Normal Mode Perturbation Analysis. BMC Bioinformatics 2015, 16, 335. (127) Panjkovich, A.; Daura, X. PARS: A Web Server for the Prediction of Protein Allosteric and Regulatory Sites. Bioinformatics 2014, 30, 1314–1315. (128) Qi, Y.; Wang, Q.; Tang, B.; Lai, L. Identifying Allosteric Binding Sites in Proteins with a Two-State Go̅ Model for Novel Allosteric Effector Discovery. J. Chem. Theory Comput. 2012, 8, 2962–2971. (129) Greener, J. G.; Filippis, I.; Sternberg, M. J. E. Predicting Protein Dynamics and Allostery Using Multi-Protein Atomic Distance Constraints. Structure 2017, 25, 546–558. (130) De Vivo, M.; Masetti, M.; Bottegoni, G.; Cavalli, A. Role of Molecular 53



(131)

(132)

(133)

(134)

(135)

(136)

(137) (138)

(139)

(140)

(141)

(142)

(143)

(144)

Dynamics and Related Methods in Drug Discovery. J. Med. Chem. 2016, 59, 4035–4061. Weinkam, P.; Pons, J.; Sali, A. Structure-Based Model of Allostery Predicts Coupling between Distant Sites. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, 4875–4880. Tee, W.-V.; Guarnera, E.; Berezovsky, I. N. Reversing Allosteric Communication: From Detecting Allosteric Sites to Inducing and Tuning Targeted Allosteric Response. PLOS Comput. Biol. 2018, 14, e1006228. Ma, X.; Meng, H.; Lai, L. Motions of Allosteric and Orthosteric Ligand-Binding Sites in Proteins Are Highly Correlated. J. Chem. Inf. Model. 2016, 56, 1725–1733. Kaya, C.; Armutlulu, A.; Ekesan, S.; Haliloglu, T. MCPath: Monte Carlo Path Generation Approach to Predict Likely Allosteric Pathways and Functional Residues. Nucleic Acids Res. 2013, 41, W249–W255. Goncearenco, A.; Mitternacht, S.; Yong, T.; Eisenhaber, B.; Eisenhaber, F.; Berezovsky, I. N. SPACER: Server for Predicting Allosteric Communication and Effects of Regulation. Nucleic Acids Res. 2013, 41, 266–272. Li, H.; Chang, Y. Y.; Lee, J. Y.; Bahar, I.; Yang, L. W. DynOmics: Dynamics of Structural Proteome and Beyond. Nucleic Acids Res. 2017, 45, W374–W380. Guarnera, E.; Tan, Z. W.; Zheng, Z.; Berezovsky, I. N. AlloSigMA: Allosteric Signaling and Mutation Analysis Server. Bioinformatics 2017, 33, 3996–3998. Tan, Z. W.; Tee, W.-V.; Guarnera, E.; Booth, L.; Berezovsky, I. N. AlloMAPS: Allosteric Mutation Analysis and Polymorphism of Signaling Database. Nucleic Acids Res. 2019, 47, D265-D270. Shen, Q.; Cheng, F.; Song, H.; Lu, W.; Zhao, J.; An, X.; Liu, M.; Chen, G.; Zhao, Z.; Zhang, J. Proteome-Scale Investigation of Protein Allosteric Regulation Perturbed by Somatic Mutations in 7,000 Cancer Genomes. Am. J. Hum. Genet. 2017, 100, 5–20. Clarke, D.; Sethi, A.; Li, S.; Kumar, S.; Chang, R. W. F.; Chen, J.; Gerstein, M. Identifying Allosteric Hotspots with Dynamics: Application to Inter- and Intra-Species Conservation. Structure 2016, 24, 826–837. Li, S.; Shen, Q.; Su, M.; Liu, X.; Lu, S.; Chen, Z.; Wang, R.; Zhang, J. Alloscore: A Method for Predicting Allosteric Ligand–protein Interactions. Bioinformatics 2016, 32, 1574–1576. Xu, Y.; Wang, S.; Hu, Q.; Gao, S.; Ma, X.; Zhang, W.; Shen, Y.; Chen, F.; Lai, L.; Pei, J. CavityPlus: A Web Server for Protein Cavity Detection with Pharmacophore Modelling, Allosteric Site Identification and Covalent Ligand Binding Ability Prediction. Nucleic Acids Res. 2018, 46, W374–W379. Smith, R. D.; Lu, J.; Carlson, H. A. Are There Physicochemical Differences between Allosteric and Competitive Ligands? PLoS Comput. Biol. 2017, 13, e1005813. Beauharnois, J. M.; Bolívar, B. E.; Welch, J. T. Sirtuin 6: A Review of Biological Effects and Potential Therapeutic Properties. Mol. Biosyst. 2013, 9, 54


Page 54 of 72

Page 55 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1789–1806. (145) Tennen, R. I.; Chua, K. F. Chromatin Regulation and Genome Maintenance by Mammalian SIRT6. Trends Biochem. Sci. 2011, 36, 39–46. (146) Mostoslavsky, R.; Chua, K. F.; Lombard, D. B.; Pang, W. W.; Fischer, M. R.; Gellon, L.; Liu, P.; Mostoslavsky, G.; Franco, S.; Murphy, M. M.; Mills, K. D.; Patel, P.; Hsu, J. T.; Hong, A. L.; Ford, E.; Cheng, H. L.; Kennedy, C.; Nunez, N.; Bronson, R.; Frendewey, D.; Auerbach, W.; Valenzuela, D.; Karow, M.; Hottiger, M. O.; Hursting, S.; Barrett, J. C.; Guarente, L.; Mulligan, R.; Demple, B.; Yancopoulos, G. D.; Alt, F. W. Genomic Instability and Aging-like Phenotype in the Absence of Mammalian SIRT6. Cell 2006, 124, 315–329. (147) Kanfi, Y.; Naiman, S.; Amir, G.; Peshti, V.; Zinman, G.; Nahum, L.; Bar-Joseph, Z.; Cohen, H. Y. The Sirtuin SIRT6 Regulates Lifespan in Male Mice. Nature 2012, 483, 218–221. (148) Sebastián, C.; Zwaans, B. M. M.; Silberman, D. M.; Gymrek, M.; Goren, A.; Zhong, L.; Ram, O.; Truelove, J.; Guimaraes, A. R.; Toiber, D.; Cosentino, C.; Greenson, J. K.; MacDonald, A. I.; McGlynn, L.; Maxwell, F.; Edwards, J.; Giacosa, S.; Guccione, E.; Weissleder, R.; Bernstein, B. E.; Regev, A.; Shiels, P. G.; Lombard, D. B.; Mostoslavsky, R. The Histone Deacetylase SIRT6 Is a Tumor Suppressor That Controls Cancer Metabolism. Cell 2012, 151, 1185–1199. (149) Schiedel, M.; Robaa, D.; Rumpf, T.; Sippl, W.; Jung, M. The Current State of NAD+ -Dependent Histone Deacetylases (Sirtuins) as Novel Therapeutic Targets. Med. Res. Rev. 2018, 38, 147–200. (150) S. Sanchez-Fidalgo; I. Villegas; M. Sanchez-Hidalgo; C. Alarcon de la Lastra. Sirtuin Modulators: Mechanisms and Potential Clinical Implications. Curr. Med. Chem. 2012, 19, 2414–2441. (151) Wang, X.; Crowe, P. J.; Goldstein, D.; Yang, J. L. STAT3 Inhibition, a Novel Approach to Enhancing Targeted Therapy in Human Cancers (Review). Int. J. Oncol. 2012, 41, 1181–1191. (152) Zhao, C.; Li, H.; Lin, H. J.; Yang, S.; Lin, J.; Liang, G. Feedback Activation of STAT3 as a Cancer Drug-Resistance Mechanism. Trends Pharmacol. Sci. 2016, 37, 47–61. (153) Sgrignani, J.; Garofalo, M.; Matkovic, M.; Merulla, J.; Catapano, C. V.; Cavalli, A. Structural Biology of STAT3 and Its Implications for Anticancer Therapies Development. Int. J. Mol. Sci. 2018, 19, 1591. (154) Debnath, B.; Xu, S.; Neamati, N. Small Molecule Inhibitors of Signal Transducer and Activator of Transcription 3 (stat3) Protein. J. Med. Chem. 2012, 55, 6645–6648. (155) Miklossy, G.; Youn, U. J.; Yue, P.; Zhang, M.; Chen, C. H.; Hilliard, T. S.; Paladino, D.; Li, Y.; Choi, J.; Sarkaria, J. N.; Kawakami, J. K.; Wongwiwatthananukit, S.; Chen, Y.; Sun, D.; Chang, L. C.; Turkson, J. Hirsutinolide Series Inhibit Stat3 Activity, Alter GCN1, MAP1B, Hsp105, G6PD, Vimentin, TrxR1, and Importin α-2 Expression, and Induce Antitumor 55



Effects against Human Glioma. J. Med. Chem. 2015, 58, 7734–7748. (156) Escobar, Z.; Bjartell, A.; Canesin, G.; Evans-Axelsson, S.; Sterner, O.; Hellsten, R.; Johansson, M. H. Preclinical Characterization of 3β-(N-Acetyl L -Cysteine Methyl Ester)-2aβ,3-Dihydrogaliellalactone (GPA512), a Prodrug of a Direct STAT3 Inhibitor for the Treatment of Prostate Cancer. J. Med. Chem. 2016, 59, 4551–4562. (157) Yu, W.; Li, C.; Zhang, W.; Xia, Y.; Li, S.; Lin, J. yuh; Yu, K.; Liu, M.; Yang, L.; Luo, J.; Chen, Y.; Sun, H.; Kong, L. Discovery of an Orally Selective Inhibitor of Signal Transducer and Activator of Transcription 3 Using Advanced Multiple Ligand Simultaneous Docking. J. Med. Chem. 2017, 60, 2718–2731. (158) Beebe, J. D.; Liu, J. Y.; Zhang, J. T. Two Decades of Research in Discovery of Anticancer Drugs Targeting STAT3, How Close Are We? Pharmacol. Ther. 2018, 191, 74–91. (159) Trott, O.; Olson, A. J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. J. Comput. Chem. 2010, 31, 455–461. (160) Savaskan, N. E.; Ufer, C.; Kühn, H.; Borchert, A. Molecular Biology of Glutathione Peroxidase 4: From Genomic Structure to Developmental Expression and Neural Function. Biol. Chem. 2007, 388, 1007–1017. (161) Yang, W. S.; Sriramaratnam, R.; Welsch, M. E.; Shimada, K.; Skouta, R.; Viswanathan, V. S.; Cheah, J. H.; Clemons, P. A.; Shamji, A. F.; Clish, C. B.; Brown, L. M.; Girotti, A. W.; Cornish, V. W.; Schreiber, S. L.; Stockwell, B. R. Regulation of Ferroptotic Cancer Cell Death by GPX4. Cell 2014, 156, 317–331. (162) Li, C.; Deng, X.; Xie, X.; Liu, Y.; Friedmann Angeli, J. P.; Lai, L. Activation of Glutathione Peroxidase 4 as a Novel Anti-Inflammatory Strategy. Front. Pharmacol. 2018, 9, 1–12. (163) Yuan, Y.; Pei, J.; Lai, L. LigBuilder 2: A Practical de Novo Drug Design Approach. J. Chem. Inf. Model. 2011, 51, 1083–1091. (164) Lee, J.; Natarajan, M.; Nashine, V. C.; Socolich, M.; Vo, T.; Russ, W. P.; Benkovic, S. J.; Ranganathan, R. Surface Sites for Engineering Allosteric Control in Proteins. Science 2008, 322, 438–442. (165) Reynolds, K. A.; McLaughlin, R. N.; Ranganathan, R. Hot Spots for Allosteric Regulation on Protein Surfaces. Cell 2011, 147, 1564–1575. (166) Fodor, A. A.; Aldrich, R. W. On Evolutionary Conservation of Thermodynamic Coupling in Proteins. J. Biol. Chem. 2004, 279, 19046–19050. (167) Chi, C. N.; Elfström, L.; Shi, Y.; Snäll, T.; Engström, A.; Jemth, P. Reassessing a Sparse Energetic Network within a Single Protein Domain. Proc. Natl. Acad. Sci. U. S. A. U. S. A. 2008, 105, 4679–4684. (168) Liu, Z.; Chen, J.; Thirumalai, D. On the Accuracy of Inferring Energetic Coupling between Distant Sites in Protein Families from Evolutionary Imprints: Illustrations Using Lattice Model. Proteins: Struct. Funct. Bioinformatics 2009, 77, 823–831. 56


Page 56 of 72

Page 57 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(169) Livesay, D. R.; Kreth, K. E.; Fodor, A. A. A Critical Evaluation of Correlated Mutation Algorithms and Coevolution within Allosteric Mechanisms. Methods Mol. Biol. 2012, 796, 385–398. (170) Anishchenko, I.; Ovchinnikov, S.; Kamisetty, H.; Baker, D. Origins of Coevolution between Residues Distant in Protein 3D Structures. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 9122–9127. (171) Novinec, M.; Korenč, M.; Caflisch, A.; Ranganathan, R.; Lenarčič, B.; Baici, A. A Novel Allosteric Mechanism in the Cysteine Peptidase Cathepsin K Discovered by Computational Methods. Nat. Commun. 2014, 5, 3287. (172) Harris, R.; Olson, A. J.; Goodsell, D. S. Automated Prediction of Ligand-Binding Sites in Proteins. Proteins 2008, 70, 1506–1517. (173) McLaughlin, R. N.; Poelwijk, F. J.; Raman, A.; Gosal, W. S.; Ranganathan, R. The Spatial Architecture of Protein Function and Adaptation. Nature 2012, 491, 138–142. (174) Hatley, M. E.; Lockless, S. W.; Gibson, S. K.; Gilman, A. G.; Ranganathan, R. Allosteric Determinants in Guanine Nucleotide-Binding Proteins. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 14445–14450. (175) Ferguson, A. D.; Amezcua, C. A.; Halabi, N. M.; Chelliah, Y.; Rosen, M. K.; Ranganathan, R.; Deisenhofer, J. Signal Transduction Pathway of TonB-Dependent Transporters. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 513–518. (176) Guo, J.; Zhou, H. X. Protein Allostery and Conformational Dynamics. Chem. Rev. 2016, 116, 6503–6515. (177) Perilla, J. R.; Goh, B. C.; Cassidy, C. K.; Liu, B.; Bernardi, R. C.; Rudack, T.; Yu, H.; Wu, Z.; Schulten, K. Molecular Dynamics Simulations of Large Macromolecular Complexes. Curr. Opin. Struct. Biol. 2015, 31, 64–74. (178) Elber, R. Simulations of Allosteric Transitions. Curr. Opin. Struct. Biol. 2011, 21, 167–172. (179) Ni, D.; Song, K.; Zhang, J.; Lu, S. Molecular Dynamics Simulations and Dynamic Network Analysis Reveal the Allosteric Unbinding of Monobody to H-Ras Triggered by R135K Mutation. Int. J. Mol. Sci. 2017, 18, 2249. (180) Ni, D.; Liu, D.; Zhang, J.; Lu, S. Computational Insights into the Interactions between Calmodulin and the c/nSH2 Domains of p85α Regulatory Subunit of PI3Kα: Implication for PI3Kα Activation by Calmodulin. Int. J. Mol. Sci. 2018, 19, 151. (181) Stank, A.; Kokh, D. B.; Fuller, J. C.; Wade, R. C. Protein Binding Pocket Dynamics. Acc. Chem. Res. 2016, 49, 809–815. (182) Dror, R. O.; Green, H. F.; Valant, C.; Borhani, D. W.; Valcourt, J. R.; Pan, A. C.; Arlow, D. H.; Canals, M.; Lane, J. R.; Rahmani, R.; Baell, J. B.; Sexton, P. M.; Christopoulos, A.; Shaw, D. E. Structural Basis for Modulation of a G-Protein-Coupled Receptor by Allosteric Drugs. Nature 2013, 503, 295–299. (183) Hertig, S.; Latorraca, N. R.; Dror, R. O. Revealing Atomic-Level Mechanisms of Protein Allostery with Molecular Dynamics Simulations. PLoS Comput. Biol. 2016, 12, e1004746. 57



(184) Lu, S.; Banerjee, A.; Jang, H.; Zhang, J.; Gaponenko, V.; Nussinov, R. GTP Binding and Oncogenic Mutations May Attenuate Hypervariable Region (HVR)-Catalytic Domain Interactions in Small GTPase K-Ras4B, Exposing the Effector Binding Site. J. Biol. Chem. 2015, 290, 28887–28900. (185) Shukla, D.; Meng, Y.; Roux, B.; Pande, V. S. Activation Pathway of Src Kinase Reveals Intermediate States as Targets for Drug Design. Nat. Commun. 2014, 5, 3397. (186) Betzi, S.; Alam, R.; Martin, M.; Lubbers, D. J.; Han, H.; Jakkaraj, S. R.; Georg, G. I.; Schönbrunn, E. Discovery of a Potential Allosteric Ligand Binding Site in CDK2. ACS Chem. Biol. 2011, 6, 492–501. (187) Sultan, M. M.; Kiss, G.; Pande, V. S. Towards Simple Kinetic Models of Functional Dynamics for a Kinase Subfamily. Nat. Chem. 2018, 10, 903–909. (188) Husic, B. E.; Pande, V. S. Markov State Models: From an Art to a Science. J. Am. Chem. Soc. 2018, 140, 2386–2396. (189) Fronik, P.; Gaiser, B. I.; Sejer Pedersen, D. Bitopic Ligands and Metastable Binding Sites: Opportunities for G Protein-Coupled Receptor (GPCR) Medicinal Chemistry. J. Med. Chem. 2017, 60, 4126–4134. (190) Bowman, G. R.; Bolin, E. R.; Hart, K. M.; Maguire, B. C.; Marqusee, S. Discovery of Multiple Hidden Allosteric Sites by Combining Markov State Models and Experiments. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 2734–2739. (191) Lu, S.; Li, S.; Zhang, J. Harnessing Allostery: A Novel Approach to Drug Discovery. Med. Res. Rev. 2014, 34, 1242–1285. (192) Dror, R. O.; Dirks, R. M.; Grossman, J. P.; Xu, H.; Shaw, D. E. Biomolecular Simulation: A Computational Microscope for Molecular Biology. Annu. Rev. Biophys. 2012, 41, 429–452. (193) Lee, Y.; Basith, S.; Choi, S. Recent Advances in Structure-Based Drug Design Targeting Class A G Protein-Coupled Receptors Utilizing Crystal Structures and Computational Simulations. J. Med. Chem. 2018, 61, 1–46. (194) Nussinov, R.; Tsai, C.-J.; Ma, B. The Underappreciated Role of Allostery in the Cellular Network. Annu. Rev. Biophys. 2013, 42, 169–189. (195) Pei, J.; Yin, N.; Ma, X.; Lai, L. Systems Biology Brings New Dimensions for Structure-Based Drug Design. J. Am. Chem. Soc. 2014, 136, 11556–11565. (196) Nussinov, R.; Tsai, C.; Liu, J. Principles of Allosteric Interactions in Cell Signaling. J. Am. Chem. Soc. 2014, 136, 17692–17701. (197) Csizmok, V.; Follis, A. V.; Kriwacki, R. W.; Forman-Kay, J. D. Dynamic Protein Interaction Networks and New Structural Paradigms in Signaling. Chem. Rev. 2016, 116, 6424–6462. (198) Nussinov, R.; Tsai, C. J.; Csermely, P. Allo-Network Drugs: Harnessing Allostery in Cellular Networks. Trends Pharmacol. Sci. 2011, 32, 686–693.

58


Page 58 of 72

Page 59 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1. Surface representation of the X-ray crystal structure of p38α complexed to an allosteric compound 10 (1) (PDB code 3NEW). The orthosteric ATP binding site and allosteric site at the C-lobe of the kinase are highlighted in blue and red, respectively. Multiple sequence alignments of allosteric site residues in p38α, p38β, p38γ, and p38δ are shown, with identical and similarity residues marked by black and gray, respectively.



Figure 2. Surface representation of the X-ray crystal structure of BCR-ABL1 complexed to both orthosteric drug imatinib and the allosteric inhibitor ABL001 (asciminib) (PDB code 5MO4), with orthosteric and allosteric sites highlighted in blue and red, respectively.


Page 60 of 72

Page 61 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3. Cartoon representation of the X-ray crystal structure of BCR-ABL1. The drug-resistant mutation residues at the orthosteric site (Thr315) and allosteric site (Ala337, Pro465, Val468, and Ile502) are depicted by sphere representations.



Figure 4. The total number of X-ray or NMR structures of allosteric proteins and yearly growth of total structures in the AlloSteric Database (ASD). Representative structures of allosteric proteins are shown.


Page 62 of 72

Page 63 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 5. Major class distribution of the structures of allosteric proteins in ASD.



Figure 6. Yearly growth of allosteric modulators in ASD. The chemical structures of the marketed allosteric drugs are indicated in the chart.


Page 64 of 72

Page 65 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 7. Residue compositions in the orthosteric (light blue) and allosteric (blue) sites calculated from ASD v3.0.



Figure 8. Workflow of the rational discovery of SIRT6 allosteric activators. The potential allosteric site of SIRT6 was predicted using Allosite (http://mdl.shsmu.edu.cn/AST). SIRT6 is shown as surface colored in gray, with the orthosteric and predicted allosteric sites highlighted in blue and red, respectively.


Page 66 of 72

Page 67 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 9. Detailed interactions between MDL-801 and SIRT6 at the allosteric site determined by the cocrystal structure (PDB code 5Y2F). SIRT6 is shown in gray, and MDL-801 is depicted by sticks with carbon atoms colored in yellow. The key residues within the allosteric site of MDL-801 are represented by sticks with carbon atoms colored in cyan.



Figure 10. Workflow of the rational discovery of a STAT3 allosteric inhibitor. An X-ray structure of STAT3 (residues 130 to 722) (PDB code 1BG1) is colored by the domain. The predicted allosteric site at the coiledcoil domain (CCD), large-scale molecular docking of compound libraries at the CCD allosteric site, and allosteric interaction evaluation after docking were executed automatically by AlloFinder (http://mdl.shsmu.edu.cn/ALF). Enlarged figure showing the potential interactions between the allosteric inhibitor K116 (6) and STAT3, consistent with mutagenesis experiments.


Page 68 of 72

Page 69 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 11. Workflow of the rational discovery of GPX4 allosteric activators. The allosteric site of GPX4 was predicted using CAVITY and CorrSite (http://www.pkumdl.cn/cavityplus) based on the crystal structure of GPX4 (PDB code 2OBI). The predicted allosteric site (red) is on the opposite side of the orthosteric site (blue). The predicted binding mode of 1d4 (8) represented by blue sticks to GPX4 is shown, with key residues within the binding site highlighted in orange by stick representations.



Figure 12. Structural overview of cathepsin K in complex with its allosteric inhibitor NSC13345 (9) and substrate TCO (PDB ID: 5J94). The backbone structure of cathepsin K is shown in gray. The allosteric site (Site 6) and orthosteric site are highlighted in red and blue, respectively. The upper panel shows the chemical structure of 9, and the lower panel shows the detailed interactions between 9 and cathepsin K. Hydrogen bonds are displayed in red dashed lines and residues within the allosteric site are represented by sticks. Substrate TCO is manually docked into the orthosteric site based on the superposition of PDB 5J94 with PDB 1Q6K. 221x152mm (300 x 300 DPI)


Page 70 of 72

Page 71 of 72 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 13. (A) Chemical structure of ANS (10) and structural overview of CDK2 in complex with 10 (PDB code 3PXF). (B) The binding site of 10 is in a closed conformation in the active state of c-Src (PDB code 1Y57). (C) Structural comparison between the 10-bound CDK2 (yellow) and active state of c-Src (blue) shows that the allosteric site is in a closed conformation mainly due to the displacements of the C-helix and A-loop. (D) The binding site of 10 is also in a closed conformation in the inactive state of c-Src (PDB code 2SRC). The C-helix, A-loop, and β-sheets involved in the binding site of 10 are colored in orange, pink, and cyan, respectively. 152x160mm (300 x 300 DPI)



Table of Contents 295x78mm (300 x 300 DPI)


Page 72 of 72

Allosteric Modulator Discovery: From ... - American Chemical Society

Recommend Documents