The Fragment Molecular Orbital Method Reveals New Insight into the

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The Fragment Molecular Orbital Method Reveals New Insight into the Chemical Nature of GPCR-Ligand Interactions Alexander Heifetz, Ewa Chudyk, Laura Gleave, Matteo Aldeghi, Vadim Cherezov, Dmitri G. Fedorov, Philip C. Biggin, and Mike Bodkin J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00644 • Publication Date (Web): 07 Dec 2015 Downloaded from http://pubs.acs.org on December 12, 2015

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Journal of Chemical Information and Modeling

The Fragment Molecular Orbital Method Reveals New Insight into the Chemical Nature of GPCR-Ligand Interactions Alexander Heifetz1*, Ewa I. Chudyk1*, Laura Gleave1, Matteo Aldeghi2, Vadim Cherezov3,4, Dmitri G. Fedorov5, Philip C. Biggin2 and Mike J. Bodkin1 1

Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, United Kingdom

2

;

Department of Biochemistry, University of Oxford, South Parks Road Oxford OX1 3QU, United Kingdom

3

;

Department of Chemistry, Bridge Institute, University of Southern California, Los Angeles, California, 90089, USA

4

Laboratory for Structural Biology of GPCRs, Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia

5

NRI, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan

ABSTRACT: Our interpretation of ligand-protein interactions is often informed by high-resolution structures, which represent the cornerstone of structure-based drug design. However, visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum Mechanics approaches are often too computationally expensive, but one method, Fragment Molecular Orbital (FMO), offers an excellent compromise and has the potential to reveal key interactions that would otherwise be hard to detect. To illustrate this we have applied the FMO method to 18 Class A GPCR-ligand crystal structures, representing different branches of the GPCR genome. Our work reveals key interactions that are often omitted from structure-based descriptions, including hydrophobic interactions, non-classical hydrogen bonds and the involvement of backbone atoms. This approach provides a more comprehensive picture of receptor-ligand interactions than is currently used, and should prove useful for evaluation of the chemical nature of ligand binding and to support structure-based drug design.

INTRODUCTION Chemical interactions between a receptor and its ligand form the basis of biomolecular recognition and are central to the vast majority of processes that exist within all living organisms. Proteins have evolved to adopt high specificity to bind small molecules with affinities that reflect the precise needs of the cell. Our understanding of these interactions is often greatly enhanced by an atomic resolution crystal structure, where the interpretation is typically made along simple molecular mechanics-based arguments. Indeed, this is the whole premise behind rational structurebased drug-design. The availability of structural information on any given target receptor protein plays a key role in the rationalization, efficiency and cost-effectiveness of the drug design process1-4. However, even with the crystal structure of receptor-ligand complex in hand, “visual inspection” and molecular mechanics (MM) calculations traditionally used for the rationalization of receptor-ligand affinity cannot always explain the full complexity or precise chemical nature of the molecular interactions 5.

Experimental methods, such as calorimetry, provide accurate estimations of the free energy of binding that can be separated into enthalpic and entropic terms 5, but interpreting that in terms of contributions from discrete parts of the molecules is nearly impossible. This factor limits the ability to rationalize receptor-ligand affinity in a chemically intuitive way that can contribute to structurebased drug design (SBDD)5. Over the past few decades numerous attempts have been made to optimise molecular mechanics (MM) based methods6 via the use of improved force fields, sampling algorithms and parallel computing. MM methods rely on predetermined parameters optimised for reproducing average values7, whose transferability and accuracy for a wide variety of bonding types is uncertain. The use of quantum mechanical (QM) methods was always considered promising for reliable prediction of receptor-ligand enthalpic binding contributions8, 9 as it can overcome many of the limitations of MM. Compared to MM energy functions that use fixed atomic charges to model electrostatic interactions, QM has the advantage that it can represent charge fluctuations and dynamic polarization that are essential in assessing molecular interactions. In spite of their many advantages on accuracy improvement, ACS Paragon Plus Environment


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traditional QM methods are not feasible for large biological systems, such as proteins, due to their high computational cost10. The fragment molecular orbital (FMO) method9, 11, 12 offers a considerable computational speed-up over traditional QM methods13. It is achieved by dividing the system into smaller pieces called fragments (Figure 1). For example, in proteins, each residue can be represented by a fragment. Similarly, the ligand can be represented by single or multiple fragments as necessary. By performing QM calculations on fragments, one can achieve high efficiency in much shorter time. FMO has been efficiently parallelized for PC clusters.13 Another key advantage of FMO is that it can provide accurate information on the individual contribution of each residue to the ligand binding, and not just an overall interaction energy10. It provides unique insight into the chemical nature of non-covalent interactions between proteins and ligands. Such information is essential for medicinal chemists to explore protein-ligand binding and rationally design modification of lead compounds in order to increase favourable interactions. Pair interaction energy (PIE) between any two fragments calculated by FMO is a sum of four energy terms: electrostatics, exchange-repulsion, charge transfer and dispersion, provided by pair interaction energy decomposition analysis (PIEDA – Figure 1)14. The electrostatics and charge transfer are important in saltbridges, hydrogen bonds or polar interactions whilst dispersion is more hydrophobic in nature. The role of hydrophobic interactions is vital for biomolecular recognition but there is still no reliably predictive method for its quantification5. The exchange-repulsion term describes the steric repulsion between electrons10 that prevents atoms from collapsing into each other. The total PIE calculated by FMO describes the stability of receptor-ligand complex. Identifying the interactions between ligand and protein was never straightforward. Some types of interactions like classical H-bonds and salt bridges can be easily identified even by visual inspection. However, it is quite clear today5, 15-17 that there is a large number of hidden from eye interactions such as CH/π11, 18, halogen/π19, cation/π20, non-classical H-bonds21 and others, that play vital roles in receptor-ligand binding and are not properly parameterized in currently available force fields (FF).16 The major difference between FMO and MM methods originates from the fact that FMO takes into account polarization (in the self-consistent mutual polarization of fragments) and charge transfer (whereby charge is allowed to flow between fragments)11, 22. The description of electrostatics in most popular force fields is based on static charges which neglect polarization and in polar systems such as proteins they are an approximation to the actual state. The van-der-Waals forces, although perhaps reasonably well parametrized on the average, fail to capture the directional nature of the dispersion terms involving halogens.23 As for a specific example24 of comparing FMO and MM, the performance of the FMO was compared with those of docking scoring functions and the values obtained using MMFF94x force field with AM1-BCC charges on the

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ligand atoms. The FMO method clearly outperformed the other FF-based scoring functions and demonstrated high correlation (r2 = 0.80) with experimentally measured values of protein-ligand affinity24. In performing detail analysis of protein-ligand structure there is no need today to compromise in performing MM calculations while a similar analysis can be done with FMO that is reasonably quick.

Figure 1. Workflow for PIEDA calculations and details on 10 each of PIE terms that are computed . The electrostatic component arises from the Coulomb interaction between polarized charge distributions of fragments. The exchange repulsion term is derived from the interaction between fragments situated in close proximity and is always repulsive; it is due to the Pauli repulsion and is related to the overlap of two occupied orbitals. The charge transfer term arises from the interaction between occupied orbitals of a

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donor and unoccupied orbitals of an acceptor. The dispersion arises as the interaction between instantaneous dipole moments of two fragments, it is hydrophobic (non-polar) in nature and is obtained in PIEDA from the correlation energy of electrons.

The FMO protocol has been previously used for both membrane and soluble proteins: for exploration of the role of none-classical CH/π hydrogen bonds in ligand recognition and the equilibrium between active and inactive states of the β2-adrenergic GPCR receptor18, in electroncorrelated calculations for biomolecular and nano systems25, for detection of the CH/π hydrogen bonds that determent Src homology 2 domain selectivity of tyrosine phosphotyrosyl peptides26, for analysis of interaction energy on specific binding of the influenza virus hemagglutinin to avian and human sialosaccharide receptors27, for correlation analyses on binding affinity of sialic acid analogues and anti-influenza drugs with human Neuraminidase structures28, in analysis of protein-ligand interactions in pheromone binding protein29, for drug-discovery against kinase24, Hsp9013, 30 targets and for fragment-based drug discovery.13

transfer dominance coefficients calculated in Eq. 5. The is very intuitive, eases the interpretation of the data and helps to figure out which forces (electrostatics, dispersion or charge-transfer) drive receptor-ligand binding. We revealed many non-obvious interactions that are essential for GPCR-ligand binding and that had been previously impossible to detect with any other methods except for QM. In particular, FMO was able to identify hydrophobic interactions, non-classical hydrogen bonds21, involvement of backbone atoms and key water molecules for GPCR-ligand binding. Furthermore, in many systems there is a good correlation between experimentally measured affinity and the FMO calculated PIE, providing further confidence in PIE values. The results shown here support previous suggestions11, 13 that FMO can be a useful tool for structure analysis and for structure-based drug design.

A particularly well-studied family of target receptors are the G-protein coupled receptors (GPCRs). Not only are they a large and important group of signalling proteins, but they are also the targets for about 40% of all drugs currently on the market31-33. The UniProt database has records for about 800 proteins classified as human GPCRs, but drugs have only been developed against 0.7) between the experimental values and the PIE. In the case of 4DJH, the correlation was lower (r2=0.57) and probably due to the fact that the binding site of the κ-opioid receptor is highly solvent exposed. Furthermore this crystal structure is of lower resolution (2.9Å) and so one might expect larger error margins in the measured data. The structure of Suvorexant in complex with the OX2 receptor (4S0V) is a particularly interesting case for FMO analysis. Suvorexant was arrived at through extensive SAR exploration in conjunction with small-molecule X-ray crystallography and NMR 70, 71 on closely related analogs. This work suggested that the bioactive conformation of Suvorexant is a face-to-face (F2F)-‘U’ shape 71 and this was later confirmed by the crystal structure of the complex. It was proposed that the F2F bioactive conformation is maintained by the intra-molecular interaction between the left-hand side (LHS) and right-hand side (RHS) of the ligand (Figure 5A). To explore this hypothesis we initially used FMO to calculate the PIE between the Suvorexant SAR and OX2 receptor. These calculations indicated strong binding between these ligands and the receptor with PIE in the range of -113.0 and -99 kcal/mol (Supporting information Figure S18) however no correlation with pKi was observed. This phenomenon could be explained by the fact that all these close Suvorexant analogs share a similar number of interactions with the receptor and the differences in their potencies are dictated by intramolecular interactions between LHS and RHS of the ligand as suggested by X-ray crystallography and NMR 70, 71 studies. To explore this hypothesis further, we used FMO to calculate the PIE between the LHS and the RHS of the ligand by fragmenting Suvorexant analogs into three parts: LHS, middle and RHS fragments. A strong correlation (r2 >0.7, Figure 5A) was observed between pKi’s and the PIE of the LHS-RHS interaction, consistent with the hypothesis concerning bioactive conformation. This observation gives us confidence that, similar to a recent kinase case study 24 and a fragment based drugdiscovery13, this approach can be used to provide additional insight into structure-based drug discovery against GPCR targets.


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Figure 5. Correlation plots between experimentally measured affinity and PIE for 4 systems. In (A) the calculated PIEs is between RHS and LHS of the ligand and in (B) - (D) the PIE is between the ligand and the receptor: (A) 4S0V, (B) 3SN6, (C) 4DJH and (D) 4NTJ. The experimental SAR data collected from the literature and summarized in Supporting information Tables S2-S5.

Discussion GPCRs are among the most attractive and intensively explored biological and drug-discovery targets. The understanding of receptor-ligand interactions is vital for successful SBDD against these targets. However, even after solving the crystal structure of a GPCR-ligand complex, it quite often still remains challenging to account for the high affinity of some ligands. Visual inspection or conventional molecular mechanics approaches do not always give a straightforward answer as highlighted by the OX2 antagonist Suvorexant. In this study we have demonstrated that the FMO approach can be highly useful for deep analysis of the crystal-structures and the chemical nature of interactions between a receptor and a ligand. The majority of the interactions detected by FMO are consistent with the published reports and the SDM data. However the key advantage of FMO is that it provides insight into the chemical nature of the interaction that normally are difficult to detect with any other method except for QM. Applying the FMO analysis can result in two considerable benefits: (a) complex QM theories are condensed into four simple and intuitive quantities, and (b) calculations become much faster than traditional QM approaches. This knowledge can be used to understand the chemical nature of existing receptor-ligand complexes which in turn can be used to suggest mutagenesis or to help

optimize the ligand in ways that were previously not considered. FMO brings the power of general ab initio QM approaches to molecular biochemical research: the calculations are reasonably easy to set up and can be performed on moderate PC clusters on an acceptable time scale. The exact calculation time depends on many factors and for the type of calculations done here, one PIEDA run took only several hours on 32 CPU cores. FMO and PIEDA also deliver a wide physicochemical insight into the nature of interactions in complex biochemical systems, which can be easily visualised and aid in rationalizing the protein function. The FMO/PIEDA analysis is a highly useful tool for rational structure-based drug design11, 30, 72, as it provides an accurate and comprehensive list of the proteinligand interactions. It allows us to distinguish between strong, weak, repulsive or missing interactions between the ligand and its neighbouring residues. This information is highly important in the design of the next generations of compounds in hit-to-lead and lead optimization stages of drug discovery. The insights provided by FMO are highly useful in design and testing of the modifications, replacement (scaffold hoping) linking (particular in case of fragment-based drug discovery) or extending of the different portions of the ligand structure to form better or new inter-



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actions with the protein. The FMO analysis of the ligandwater-protein network helps to distinguish between energetically favourable and unfavourable water molecules and to design analogues that can interact or displace certain waters. The high correlation between protein-ligand affinity and FMO energy terms24 indicates that they can be efficiently used as descriptors in QSAR modelling to predict the binding affinities of new targets for synthesis. Application of the FMO method can be highly useful for design, evaluation and filtering of new ideas that significantly decrease the effort and cost of chemical synthesis. FMO has

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been successfully applied in our confidential drug discovery programs including GPCR and kinases targets and, as been reported previously30, in the discovery of a novel Hsp90 inhibitors by fragment linking. In this work PIEDA calculations across all 18 GPCR-ligand complexes (Figure 6A) show the utility of FMO and highlight the importance of hydrophobic interactions, non-classical hydrogen bonds21, interactions with the backbone atoms and water molecules for GPCR-ligand binding.

Figure 6. Relative fractions to electrostatic (yellow), dispersion (blue) and charge-transfer (red) energy terms in the PIE attraction according (A) Column plot of the contribution fractions for 18 crystal structures (B) Average fractions contributions

According to this analysis, the average ratio for these 18 crystal structures (Figure 6B) was 1:1:0.5, which indicates equal electrostatic and hydrophobic contributions to the ligand binding, and the charge transfer contribution is about half the size. Historically, hydrophobic interactions have been accounted for with terms that are delocalized, typically in the form of either a shape complementarity term or solvation/entropy penalty. Such approaches are useful in providing an overall estimation of the contribution to the affinity, but do not readily provide spatial decomposition that can be readily incorporated into a design strategy for future compounds. Furthermore, an understanding of the regions of the binding site that have the most prominent contributions is likely to be useful in understanding the origins of efficacy. By analysing a wide range of GPCR-ligand crystal structures we were also able to demonstrate general trends in ligand binding for different proteins belonging to the same family. For example, residues in positions 3.32, 3.33, 6.48, 6.51, 6.52, 7.39 and 7.43 located on TM3, TM6 and TM7 have considerable contributions to receptor-ligand binding and are quite conserved (>70%, Figure 7). Interestingly residues in positions 7.39 and 7.43 of the two branchδ GPCRs (4PHU and 4NTJ) were not detected as signifi-

cant for ligand binding. No other obvious differences in general trends between other branches were observed. The residues in the positions 3.33, 6.48, 6.51, 6.52 and 7.43 form interaction with mainly hydrophobic nature while the residues in the position 3.32 and 7.39 form mainly electrostatic interactions. Published mutations in these positions frequently affect selectivity 73 and ligand binding affinity 74 . This observation is also in agreement with a previous report 73 that residues in these positions highly frequently make contact with diverse ligands across nearly all class A GPCRs. Residues in the other positions are less frequently involved in interactions only with specific ligands 73. These key positions in TM3, TM6 and TM7 form a consensus core of the ligand-binding pocket with any variation in the amino acids occupying the topologically equivalent positions contributing to ligand specificity in different GPCRs. This is in spite of the observation that amino-acids present in these positions are not conserved (average similarity < 41%) (see multiple - sequence alignment, Supporting information Figure S15). According to PIEDA, the predominant contribution in the majority of these conserved positions is in fact hydrophobic. Some of the newly discovered interactions detected by FMO are difficult to validate experimentally. How-


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ever, the confidence in the existence of these interactions is derived from the fact that our FMO results are in agreement with all available experimental data including SDM and affinity measurements. The residues that are predicted to be involved in these interactions have been part of general trends of ligand-protein interactions in GPCRs as shown in Figure 7 and can be subjected to further SDM experiments. A large amount of reported evidence emphasizes the high importance of water molecules for GPCR-ligand binding and function 49, 75, 76. In this work we analysed only these water molecules that were resolved in crystal structures and explained their role in the individual systems. However, since this analysis depend on the resolution, it is difficult to compare water molecules networks between different crystal structures. The FMO method is not de-

signed to predict the positions of water molecules. However it can be applied as a post-factum analysis tool for already predicted water molecules that have been placed by any other computational methods.75-83 Overall, we have shown, using a well-studied receptor family as a test case how the FMO method can provide new insight into the nature of key chemical interactions required for recognition. We anticipate this approach can be used to provide insight into many protein-ligand interactions, and also specifically to the design of new compounds.

Figure 7. Comparison of residues involved in ligand-binding. Receptor–ligand information is shown as rows, and the Ballesteros–Weinstein numbers of residues that contact the ligand are shown as columns. In the matrix, the presence of a contact between the ligand and the residue is shown as a colored box, and the absence of a contact is shown as grey box. Boxes are otherwise colored according to their PIEDA type: from dark blue (100% of dispersion contribution) to yellow (100% of sum electrostatics + charge-transfer). An equal contribution mix is light blue as shown by the right spectrum. The level of interaction consensus is shown above with the bar color-coded according to average PIEDA type, from dark blue (100% of dispersion contribution) to yellow (100% of sum electrostatics + charge-transfer).

generic number proposed by Ballesteros and Weinstein 54, in superscript. In B&W nomenclature, amino acid residues in the 7TM are given two numbers separated by a period; the first is the trans-membrane (TM) helix number (1 to 7), while the second number indicates the residue position relative to the most conserved residue across class A GPCRs. For that particular TM it is arbitrarily assigned to 55

EXPERIMENTAL SECTION Residue Numbering The position of each TM’s amino acid residue was identified by its unique sequence number as well as by its

11



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be 50. For the loop residues we used the loop identity (ECL – extracellular loop; ICL – intracellular loop) and its number in superscript. Structure Preparation Hydrogen atoms were added to the crystal structures at physiological pH (7.0) with the Protonate 3D84 tool implemented in MOE version 2014.09 (Chemical Computing Group). The purpose of the Protonate3D function is to assign ionization states and position hydrogens in proteins, ligands and solvents given 3D coordinates (typically from a crystal structure). For practical reasons84 the protonation of water molecules and their orientation is performed by the Protonate3D algorithm at the end of the process, after the hydrogens are already added to the rest of the system. This is performed by orienting the waters one by one in a recursive manner starting from the water molecules in the strongest electrostatic field (generated by the protein, ligand and previously oriented waters) to the weakest. The protonated structures were subject to constrained minimisation with the main purpose being to optimise the interaction geometries required for accurate FMO calculations. In this work we used a constrained minimisation procedure with the semi empirical Amber10:EHT forcedfield85, 86 implemented in MOE version 2014.09, where each atom was allowed to deviate up to 0.5 Å from its original position in crystal structure. Atom positions in crystal structures have certain errors associated with them, which depend on the B-factor of the atom and overall resolution of the structure. Small errors in the positions of atoms could translate in large deviations in energy terms 3. Therefore it is important to optimize crystal structures before applying any type of calculations to them 3. The binding poses for SAR ligands for exploration of PIE vs. experimental activity correlation were generated in two steps: (1) Shape-based overlay of SAR ligand on top of the binding pose of the parent ligand (taken from the crystal structure) using OMEGA-ROCS tool as implemented in the OpenEye software package (version 3.0.0). These SAR ligands are close analogs to the parent ligand and so our assumption was that their binding orientation would be also very similar. (2) Receptor – SAR ligand minimisation as described above. FMO Calculation Protocol The FMO method is a general quantum mechanical method that can be applied to any set of atoms, no matter if it is a soluble or membrane protein. Here, the FMO method 11 was applied to GPCRs using FMO code13 version 5.1 as embedded in General Atomic and Molecular Electronic Structure System (GAMESS)87, which is a general ab initio quantum chemistry package. In FMO calculations a large biological system is divided into smaller pieces called fragments (Figure 1). 9, 11 For example: in a protein each residue can be represented as a fragment. The ligand can be also represented as an

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individual fragment or can be fragmented; some ligands can be very large and dividing a ligand into several fragments has the benefit of both reducing the computational cost and providing a more detailed analysis. The detailed description of the fragmentation strategy and the basic methodology can be found in the published reviews 9, 11, 14 including detailed mathematical formulation that are beyond the scope of this manuscript. In this work we used a well-established FMO protocol that was previously described and tested11, 18, 24, 88, 89 to calculate the pair interaction energy (PIE) between the ligand and the rest of the system (receptor and water molecules). The FMO calculation consists of the following basic steps: (a) Fragmentation (i.e., assigning atoms in a system to a fragment); (b) Fragment self-consistent field (SCF) calculations in the embedding polarizable potential, so that fragments mutually polarize each other in a selfconsistent fashion whereby intrafragment charge transfer and other quantum effects are accounted for; (c) Fragment pair SCF calculations, bringing in interfragment charge transfer; (d) Total property (energy, gradient, etc.) evaluation. By performing QM computations on fragments one can achieve high efficiency, often resulting from linear scaling and computational speed. The FMO method has been efficiently parallelized for PC clusters13. In our calculations we used the MP2 method (2nd order Møller-Plesset perturbation theory,90) with the 6-31G* basis set. Residues within a radius of ≤ 4.5Å around the ligand atoms where included in the FMO calculations. PIE – interaction energy ( ∆E int ) between fragments i and j is a sum of 4 PIE terms: electrostatics ( ∆E es ), exchange-repulsion ( ∆E ex ), charge transfer ( ∆E ct ) and dispersion ( ∆E di ) – see Eq. 1, while the chemical definition of each term is described in Figure 1. ∆Eijint = ∆Eijes + ∆Eijex + ∆Eijct + ∆Eijdi

(1)

It is important to emphasize that PIE is not a difference between energies of ‘free’ and ‘bound’ ligand but it rather represents the ‘strength’ of the interaction between the ligand and protein residues in the complex (the ∆ signs refer to the differences in the total QM energy of a fragment pair ij and two individual fragments i and j; both computed in the receptor-ligand complex). In our work the usual fragment i was the ligand and the other n fragments were receptor residues and water molecules. In the equations below there is no self-interaction (the sums exclude j=i). The total PIE calculated by FMO describes the stability of receptor-ligand complex; this stability correlates to, but is not the same as, the binding energy 24: the difference lies in (a) the desolvation penalty (the energy cost to remove solvent from the binding pocket and the ligand91 and (b) in the polarization factors (the ligand is polarized by the protein and vice versa, 13).

12


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Journal of Chemical Information and Modeling The total attraction energy ( ∆E attr ) between frag-

ment i and the other n fragments is a sum of the sums of the individual energy terms as shown in Eq. 2 (the exchangerepulsion term is always repulsive and since not included in equation 2). n

n

n

(2)

∆Eiattr = ∑ ∆Eijes + ∑ ∆Eijct + ∑ ∆Eijdi j =1

j =1

j =1

EaS = ES lig : DI lig : CT lig

(5)

Note that the exchange repulsion term is not included in the signature, because it correlates well with the charge transfer component (r2 >0.7) as both can be related to the overlap integrals (Supporting information Figure S17). The three attraction PIE terms (electrostatics, dispersion and charge-transfer) describe receptor-ligand interactions while, the exchange-repulsion term describe the steric repulsion between atoms. 10.

We used Eq. 3 to calculate the fractions of the electrostatic, dispersion and charge-transfer energies ( f i es , ct f i di and f i respectively) in the overall attraction energy.

∑ ∆E

∑ ∆E

fi =

j =1

di

fi =

∆Eiattr

∑ ∆E

ct ij

di ij

es ij

es

Supporting Information describes FMO results for additional 14 crystal structures, SAR tables, multiple sequence alignment, electrostatic complementarity of human OX2-Suvorexant complex and the overall results

n

n

n

j =1

ct

∆Eiattr

fi =

j =1

∆Eiattr

(3)

To estimate dominance coefficients of dispersion (hydrophobic) and charge-transfer energy terms compared to the electrostatics we used Eq. 4: n

∑ ∆E ES i =

j =1 n

∑ ∆E j =1

es ij

di ij

j =1 n

∑ ∆E

es ij

j =1

Corresponding Authors

n

∑ ∆E DI i =

AUTHOR INFORMATION [email protected] and [email protected]

n

es ij

ASSOCIATED CONTENT

CTi =

∑ ∆E

ct ij

∑ ∆E

es ij

j =1 n

j =1

(4)

ES i , DI i and CT i are dominance coefficients of electrostatic, dispersion and charge- transfer (electrostatic coefficient is used as reference and hence always 1).

The fragmentation in FMO92 results in residue fragments shifted by a carboxyl group relative to conventional residues, as driven by the accuracy considerations for fragmentation. This does not create problems in discussing the interactions of residue fragments except the case when the carboxyl is the group that interacts. The structure was examined to determine such cases and the interaction energies involving carboxyl groups were assigned to the residue to which the carboxyl belongs, not to the residue fragment it is assigned to in FMO. Thereby, the interactions reported in this work correspond to the actual residues, not residue fragments.

ACKNOWLEDGMENT A.H. thanks Royal Society (grant no. IF100104). We thank the BBSRC (grant no. BB/L026287/1) for support (PCB and AH). D.G.F. was supported by the Next Generation Super Computing Project, Nanoscience Program (MEXT, Japan) and Computational Materials Science Initiative (CMSI, Japan). VC acknowledges support from the Russian Federation Ministry of Education and Science Project 5-100. M. A. is supported by the EPSRC and Evotec via the Systems Approaches to Biomedical Sciences Doctoral Training Centre. We thank Dr. Inaki Morao for his advice regarding setting up the FMO calculations and Dr. Tim James for useful discussions. We are grateful to Dr. Roger Robinson for supporting the major computing part of this project.

ABBREVIATIONS FMO, Fragment Molecular Orbitals method; GPCR, 7TMD, 7 transmembrane domain; TM, transmembrane helix; ECL, extracellular loop; PDB, Protein Data Bank; SAR, structureactivity relationship; SBDD, structure-based drug discovery.

Attraction Energy “Signature” () Ratio We introduced a new term: the attraction energy signature (), which is the ratio between the 3 dominance factors calculated for the ligand - Eq. 5. allows in a simple and convenient way to figure out which of these 3 attraction terms dominates receptor-ligand binding.

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REFERENCES 1. Tautermann, C. S., GPCR Structures in Drug Design, Emerging Opportunities with New Structures. Bioorg. Med. Chem. Lett. 2014, 24, 4073-9. 2. Shonberg, J.; Kling, R. C.; Gmeiner, P.; Lober, S., Gpcr Crystal Structures: Medicinal Chemistry in the Pocket. Bioorg. Med. Chem. 2015, 23, 3880-906. 3. Jazayeri, A.; Dias, J. M.; Marshall, F. H., From G Protein-Coupled Receptor Structure Resolution to Rational Drug Design. J. Biol. Chem. 2015, 290, 19489-95. 4. Jacobson, K. A., New Paradigms in Gpcr Drug Discovery. Biochem. Pharmacol. 2015. 5. Bissantz, C.; Kuhn, B.; Stahl, M., A Medicinal Chemist's Guide to Molecular Interactions. J. Med. Chem. 2010, 53, 5061-84. 6. Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M. K.; Greenwood, J.; Romero, D. L.; Masse, C.; Knight, J. L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D. L.; Jorgensen, W. L.; Berne, B. J.; Friesner, R. A.; Abel, R., Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern FreeEnergy Calculation Protocol and Force Field. J. Am. Chem. Soc. 2015, 137, 2695-703. 7. Aldeghi, M.; Heifetz, A.; Bodkin, M. J.; Knapp, S.; Biggin, P. C., Accurate Calculation of the Absolute Free Energy of Binding for Drug Molecules. Chemical Science 2015. 8. Yu, N.; Li, X.; Cui, G.; Hayik, S. A.; Merz, K. M., 2nd, Critical Assessment of Quantum Mechanics Based Energy Restraints in Protein Crystal Structure Refinement. Protein Sci. 2006, 15, 2773-84. 9. Fedorov, D. G.; Kitaura, K., Extending the Power of Quantum Chemistry to Large Systems with the Fragment Molecular Orbital Method. J. Phys. Chem. A 2007, 111, 6904-14. 10. Phipps, M. J.; Fox, T.; Tautermann, C. S.; Skylaris, C. K., Energy Decomposition Analysis Approaches and Their Evaluation on Prototypical ProteinDrug Interaction Patterns. Chem. Soc. Rev. 2015, 44, 3177211. 11. Fedorov, D. G.; Nagata, T.; Kitaura, K., Exploring Chemistry with the Fragment Molecular Orbital Method. Phys. Chem. Chem. Phys. 2012, 14, 7562-77. 12. Kitaura, K.; Ikeo, E.; Asada, T.; Nakano, T.; Uebayasi, M., Fragment Molecular Orbital Method: An Approximate Computational Method for Large Molecules. Chemical Physics Letters 1999, 313, 701-706. 13. Alexeev, Y.; Mazanetz, M. P.; Ichihara, O.; Fedorov, D. G., Gamess as a Free Quantum-Mechanical Platform for Drug Research. Curr. Top. Med. Chem. 2012, 12, 2013-33.

Page 14 of 24

14. Fedorov, D. G.; Kitaura, K., Pair Interaction Energy Decomposition Analysis. J. Comput. Chem. 2007, 28, 222-37. 15. Tong, Y.; Mei, Y.; Li, Y. L.; Ji, C. G.; Zhang, J. Z., Electrostatic Polarization Makes a Substantial Contribution to the Free Energy of Avidin-Biotin Binding. J. Am. Chem. Soc. 2010, 132, 5137-42. 16. Raha, K.; Peters, M. B.; Wang, B.; Yu, N.; Wollacott, A. M.; Westerhoff, L. M.; Merz, K. M., Jr., The Role of Quantum Mechanics in Structure-Based Drug Design. Drug. Discov. Today. 2007, 12, 725-31. 17. Beratan, D. N.; Liu, C.; Migliore, A.; Polizzi, N. F.; Skourtis, S. S.; Zhang, P.; Zhang, Y., Charge Transfer in Dynamical Biosystems, or the Treachery of (Static) Images. Acc. Chem. Res. 2015, 48, 474-81. 18. Ozawa, T.; Okazaki, K.; Kitaura, K., Ch/Pi Hydrogen Bonds Play a Role in Ligand Recognition and Equilibrium between Active and Inactive States of the Beta2 Adrenergic Receptor: An Ab Initio Fragment Molecular Orbital (Fmo) Study. Bioorg. Med. Chem. 2011, 19, 5231-7. 19. Lu, Y.-X.; Zou, J.-W.; Wang, Y.-H.; Yu, Q.-S., Substituent Effects on Noncovalent Halogen/Π Interactions: Theoretical Study. International Journal of Quantum Chemistry 2007, 107, 1479-1486. 20. Gallivan, J. P.; Dougherty, D. A., Cation-Pi Interactions in Structural Biology. Proc. Natl. Acad. Sci. U S A 1999, 96, 9459-64. 21. Johnston, R. C.; Cheong, P. H., C-H...O NonClassical Hydrogen Bonding in the Stereomechanics of Organic Transformations: Theory and Recognition. Org. Biomol. Chem. 2013, 11, 5057-64. 22. Fedorov, D. G.; Kitaura, K., Energy Decomposition Analysis in Solution Based on the Fragment Molecular Orbital Method. J. Phys. Chem. A 2012, 116, 704-19. 23. El Kerdawy, A.; Murray, J. S.; Politzer, P.; Bleiziffer, P.; Hesselmann, A.; Gorling, A.; Clark, T., Directional Noncovalent Interactions: Repulsion and Dispersion. J. Chem. Theory. Comput. 2013, 9, 2264-75. 24. Mazanetz, M. P.; Ichihara, O.; Law, R. J.; Whittaker, M., Prediction of Cyclin-Dependent Kinase 2 Inhibitor Potency Using the Fragment Molecular Orbital Method. J. Cheminform. 2011, 3, 2. 25. Tanaka, S.; Mochizuki, Y.; Komeiji, Y.; Okiyama, Y.; Fukuzawa, K., Electron-Correlated FragmentMolecular-Orbital Calculations for Biomolecular and Nano Systems. Phys. Chem. Chem. Phys. 2014, 16, 10310-44. 26. Ozawa, T.; Okazaki, K., Ch/Pi Hydrogen Bonds Determine the Selectivity of the Src Homology 2 Domain to Tyrosine Phosphotyrosyl Peptides: An Ab Initio Fragment Molecular Orbital Study. J. Comput. Chem. 2008, 29, 2656-66. 27. Anzaki, S.; Watanabe, C.; Fukuzawa, K.; Mochizuki, Y.; Tanaka, S., Interaction Energy Analysis on Specific Binding of Influenza Virus Hemagglutinin to Avian and Human Sialosaccharide Receptors: Importance

14


Page 15 of 24

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


of Mutation-Induced Structural Change. J. Mol. Graph. Model. 2014, 53, 48-58. 28. Hitaoka, S.; Matoba, H.; Harada, M.; Yoshida, T.; Tsuji, D.; Hirokawa, T.; Itoh, K.; Chuman, H., Correlation Analyses on Binding Affinity of Sialic Acid Analogues and Anti-Influenza Drugs with Human Neuraminidase Using Ab Initio Mo Calculations on Their Complex Structures-Lere-Qsar Analysis (Iv). J. Chem. Inf. Model. 2011, 51, 2706-16. 29. Nemoto, T.; Fedorov, D. G.; Uebayasi, M.; Kanazawa, K.; Kitaura, K.; Komeiji, Y., Ab Initio Fragment Molecular Orbital (Fmo) Method Applied to Analysis of the Ligand-Protein Interaction in a PheromoneBinding Protein. Comput. Biol. Chem. 2005, 29, 434-9. 30. Barker, J. J.; Barker, O.; Courtney, S. M.; Gardiner, M.; Hesterkamp, T.; Ichihara, O.; Mather, O.; Montalbetti, C. A.; Muller, A.; Varasi, M.; Whittaker, M.; Yarnold, C. J., Discovery of a Novel Hsp90 Inhibitor by Fragment Linking. ChemMedChem 2010, 5, 1697-700. 31. Rask-Andersen, M.; Masuram, S.; Schioth, H. B., The Druggable Genome: Evaluation of Drug Targets in Clinical Trials Suggests Major Shifts in Molecular Class and Indication. Annu. Rev. Pharmacol. Toxicol. 2014, 54, 9-26. 32. Wise, A.; Gearing, K.; Rees, S., Target Validation of G-Protein Coupled Receptors. Drug Discov. Today 2002, 7, 235-46. 33. Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L., How Many Drug Targets Are There? Nat. Rev. Drug Discov. 2006, 5, 993-6. 34. Dohlman, H. G., Thematic Minireview Series: New Directions in G Protein-Coupled Receptor Pharmacology. J. Biol. Chem. 2015, 290, 19469-70. 35. Heifetz, A.; Schertler, G. F.; Seifert, R.; Tate, C. G.; Sexton, P. M.; Gurevich, V. V.; Fourmy, D.; Cherezov, V.; Marshall, F. H.; Storer, R. I.; Moraes, I.; Tikhonova, I. G.; Tautermann, C. S.; Hunt, P.; Ceska, T.; Hodgson, S.; Bodkin, M. J.; Singh, S.; Law, R. J.; Biggin, P. C., Gpcr Structure, Function, Drug Discovery and Crystallography: Report from Academia-Industry International Conference (Uk Royal Society) Chicheley Hall, 1-2 September 2014. Naunyn Schmiedebergs Arch. Pharmacol. 2015. 36. Shonberg, J.; Kling, R. C.; Gmeiner, P.; Lober, S., Gpcr Crystal Structures: Medicinal Chemistry in the Pocket. Bioorg. Med. Chem. 2014. 37. Liu, W.; Chun, E.; Thompson, A. A.; Chubukov, P.; Xu, F.; Katritch, V.; Han, G. W.; Roth, C. B.; Heitman, L. H.; AP, I. J.; Cherezov, V.; Stevens, R. C., Structural Basis for Allosteric Regulation of Gpcrs by Sodium Ions. Science 2012, 337, 232-6. 38. Miller-Gallacher, J. L.; Nehme, R.; Warne, T.; Edwards, P. C.; Schertler, G. F.; Leslie, A. G.; Tate, C. G., The 2.1 a Resolution Structure of Cyanopindolol-Bound Beta1-Adrenoceptor Identifies an Intramembrane Na+ Ion That Stabilises the Ligand-Free Receptor. PLoS One 2014, 9, e92727. 39. Cherezov, V.; Rosenbaum, D. M.; Hanson, M. A.; Rasmussen, S. G.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.;

Kuhn, P.; Weis, W. I.; Kobilka, B. K.; Stevens, R. C., High-Resolution Crystal Structure of an Engineered Human Beta2-Adrenergic G Protein-Coupled Receptor. Science 2007, 318, 1258-65. 40. Rasmussen, S. G.; DeVree, B. T.; Zou, Y.; Kruse, A. C.; Chung, K. Y.; Kobilka, T. S.; Thian, F. S.; Chae, P. S.; Pardon, E.; Calinski, D.; Mathiesen, J. M.; Shah, S. T.; Lyons, J. A.; Caffrey, M.; Gellman, S. H.; Steyaert, J.; Skiniotis, G.; Weis, W. I.; Sunahara, R. K.; Kobilka, B. K., Crystal Structure of the Beta2 Adrenergic Receptor-Gs Protein Complex. Nature 2011, 477, 549-55. 41. Chien, E. Y.; Liu, W.; Zhao, Q.; Katritch, V.; Han, G. W.; Hanson, M. A.; Shi, L.; Newman, A. H.; Javitch, J. A.; Cherezov, V.; Stevens, R. C., Structure of the Human Dopamine D3 Receptor in Complex with a D2/D3 Selective Antagonist. Science 2010, 330, 1091-5. 42. Shimamura, T.; Shiroishi, M.; Weyand, S.; Tsujimoto, H.; Winter, G.; Katritch, V.; Abagyan, R.; Cherezov, V.; Liu, W.; Han, G. W.; Kobayashi, T.; Stevens, R. C.; Iwata, S., Structure of the Human Histamine H1 Receptor Complex with Doxepin. Nature 2011, 475, 65-70. 43. Haga, K.; Kruse, A. C.; Asada, H.; YurugiKobayashi, T.; Shiroishi, M.; Zhang, C.; Weis, W. I.; Okada, T.; Kobilka, B. K.; Haga, T.; Kobayashi, T., Structure of the Human M2 Muscarinic Acetylcholine Receptor Bound to an Antagonist. Nature 2012, 482, 54751. 44. Kruse, A. C.; Hu, J.; Pan, A. C.; Arlow, D. H.; Rosenbaum, D. M.; Rosemond, E.; Green, H. F.; Liu, T.; Chae, P. S.; Dror, R. O.; Shaw, D. E.; Weis, W. I.; Wess, J.; Kobilka, B. K., Structure and Dynamics of the M3 Muscarinic Acetylcholine Receptor. Nature 2012, 482, 552-6. 45. Wang, C.; Jiang, Y.; Ma, J.; Wu, H.; Wacker, D.; Katritch, V.; Han, G. W.; Liu, W.; Huang, X. P.; Vardy, E.; McCorvy, J. D.; Gao, X.; Zhou, X. E.; Melcher, K.; Zhang, C.; Bai, F.; Yang, H.; Yang, L.; Jiang, H.; Roth, B. L.; Cherezov, V.; Stevens, R. C.; Xu, H. E., Structural Basis for Molecular Recognition at Serotonin Receptors. Science 2013, 340, 610-4. 46. Yin, J.; Mobarec, J. C.; Kolb, P.; Rosenbaum, D. M., Crystal Structure of the Human Ox2 Orexin Receptor Bound to the Insomnia Drug Suvorexant. Nature 2015, 519, 247-50. 47. Wu, B.; Chien, E. Y.; Mol, C. D.; Fenalti, G.; Liu, W.; Katritch, V.; Abagyan, R.; Brooun, A.; Wells, P.; Bi, F. C.; Hamel, D. J.; Kuhn, P.; Handel, T. M.; Cherezov, V.; Stevens, R. C., Structures of the Cxcr4 Chemokine Gpcr with Small-Molecule and Cyclic Peptide Antagonists. Science 2010, 330, 1066-71. 48. Tan, Q.; Zhu, Y.; Li, J.; Chen, Z.; Han, G. W.; Kufareva, I.; Li, T.; Ma, L.; Fenalti, G.; Li, J.; Zhang, W.; Xie, X.; Yang, H.; Jiang, H.; Cherezov, V.; Liu, H.; Stevens, R. C.; Zhao, Q.; Wu, B., Structure of the Ccr5 Chemokine Receptor-Hiv Entry Inhibitor Maraviroc Complex. Science 2013, 341, 1387-90.

15



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

49. Fenalti, G.; Giguere, P. M.; Katritch, V.; Huang, X. P.; Thompson, A. A.; Cherezov, V.; Roth, B. L.; Stevens, R. C., Molecular Control of Delta-Opioid Receptor Signalling. Nature 2014, 506, 191-6. 50. Wu, H.; Wacker, D.; Mileni, M.; Katritch, V.; Han, G. W.; Vardy, E.; Liu, W.; Thompson, A. A.; Huang, X. P.; Carroll, F. I.; Mascarella, S. W.; Westkaemper, R. B.; Mosier, P. D.; Roth, B. L.; Cherezov, V.; Stevens, R. C., Structure of the Human Kappa-Opioid Receptor in Complex with Jdtic. Nature 2012, 485, 327-32. 51. Zhang, H.; Unal, H.; Gati, C.; Han, G. W.; Liu, W.; Zatsepin, N. A.; James, D.; Wang, D.; Nelson, G.; Weierstall, U.; Sawaya, M. R.; Xu, Q.; Messerschmidt, M.; Williams, G. J.; Boutet, S.; Yefanov, O. M.; White, T. A.; Wang, C.; Ishchenko, A.; Tirupula, K. C.; Desnoyer, R.; Coe, J.; Conrad, C. E.; Fromme, P.; Stevens, R. C.; Katritch, V.; Karnik, S. S.; Cherezov, V., Structure of the Angiotensin Receptor Revealed by Serial Femtosecond Crystallography. Cell 2015. 52. Srivastava, A.; Yano, J.; Hirozane, Y.; Kefala, G.; Gruswitz, F.; Snell, G.; Lane, W.; Ivetac, A.; Aertgeerts, K.; Nguyen, J.; Jennings, A.; Okada, K., High-Resolution Structure of the Human Gpr40 Receptor Bound to Allosteric Agonist Tak-875. Nature 2014, 513, 124-7. 53. Zhang, K.; Zhang, J.; Gao, Z. G.; Zhang, D.; Zhu, L.; Han, G. W.; Moss, S. M.; Paoletta, S.; Kiselev, E.; Lu, W.; Fenalti, G.; Zhang, W.; Muller, C. E.; Yang, H.; Jiang, H.; Cherezov, V.; Katritch, V.; Jacobson, K. A.; Stevens, R. C.; Wu, B.; Zhao, Q., Structure of the Human P2y12 Receptor in Complex with an Antithrombotic Drug. Nature 2014, 509, 115-8. 54. Ballesteros, J. A.; Weinstein, H., Integrated Methods for the Construction of Three-Dimensional Models and Computational Probing of Structure-Function Relations in G Protein-Coupled Receptors. Methods Neurosci. 1995, 25, 366-428. 55. Prioleau, C.; Visiers, I.; Ebersole, B. J.; Weinstein, H.; Sealfon, S. C., Conserved Helix 7 Tyrosine Acts as a Multistate Conformational Switch in the 5ht2c Receptor. Identification of a Novel "Locked-on" Phenotype and Double Revertant Mutations. J. Biol. Chem. 2002, 277, 36577-36584. 56. Dunwiddie, T. V.; Masino, S. A., The Role and Regulation of Adenosine in the Central Nervous System. Annu Rev Neurosci 2001, 24, 31-55. 57. Xu, F.; Wu, H.; Katritch, V.; Han, G. W.; Jacobson, K. A.; Gao, Z. G.; Cherezov, V.; Stevens, R. C., Structure of an Agonist-Bound Human A2a Adenosine Receptor. Science 2011, 332, 322-7. 58. Jaakola, V. P.; Lane, J. R.; Lin, J. Y.; Katritch, V.; Ijzerman, A. P.; Stevens, R. C., Ligand Binding and Subtype Selectivity of the Human a(2a) Adenosine Receptor: Identification and Characterization of Essential Amino Acid Residues. J Biol Chem 2010, 285, 13032-44. 59. Malherbe, P.; Roche, O.; Marcuz, A.; Kratzeisen, C.; Wettstein, J. G.; Bissantz, C., Mapping the Binding Pocket of Dual Antagonist Almorexant to Human Orexin 1 and Orexin 2 Receptors: Comparison with the Selective

Page 16 of 24

Ox1 Antagonist Sb-674042 and the Selective Ox2 Antagonist N-Ethyl-2-[(6-Methoxy-Pyridin-3-Yl)(Toluene-2-Sulfonyl)-Amino]-N-Pyridin-3-Ylmet HylAcetamide (Empa). Mol. Pharmacol. 2010, 78, 81-93. 60. Van Arnam, E. B.; Lester, H. A.; Dougherty, D. A., Dissecting the Functions of Conserved Prolines within Transmembrane Helices of the D2 Dopamine Receptor. ACS Chem. Biol. 2011, 6, 1063-8. 61. Isberg, V.; de Graaf, C.; Bortolato, A.; Cherezov, V.; Katritch, V.; Marshall, F. H.; Mordalski, S.; Pin, J. P.; Stevens, R. C.; Vriend, G.; Gloriam, D. E., Generic Gpcr Residue Numbers - Aligning Topology Maps While Minding the Gaps. Trends Pharmacol. Sci. 2015, 36, 2231. 62. van der Kant, R.; Vriend, G., Alpha-Bulges in G Protein-Coupled Receptors. Int. J. Mol. Sci. 2014, 15, 7841-64. 63. Davenport, A. J.; Moller, C.; Heifetz, A.; Mazanetz, M. P.; Law, R. J.; Ebneth, A.; Gemkow, M. J., Using Electrophysiology and in Silico Three-Dimensional Modeling to Reduce Human Ether-a-Go-Go Related Gene K(+) Channel Inhibition in a Histamine H3 Receptor Antagonist Program. Assay Drug. Dev. Technol. 2010, 8, 781-9. 64. Bach, P.; Antonsson, T.; Bylund, R.; Bjorkman, J. A.; Osterlund, K.; Giordanetto, F.; van Giezen, J. J.; Andersen, S. M.; Zachrisson, H.; Zetterberg, F., Lead Optimization of Ethyl 6-Aminonicotinate Acyl Sulfonamides as Antagonists of the P2y12 Receptor. Separation of the Antithrombotic Effect and Bleeding for Candidate Drug Azd1283. J. Med. Chem. 2013, 56, 701524. 65. Wang, C.; Wu, H.; Evron, T.; Vardy, E.; Han, G. W.; Huang, X. P.; Hufeisen, S. J.; Mangano, T. J.; Urban, D. J.; Katritch, V.; Cherezov, V.; Caron, M. G.; Roth, B. L.; Stevens, R. C., Structural Basis for Smoothened Receptor Modulation and Chemoresistance to Anticancer Drugs. Nat. Protoc. 2014, 5, 4355. 66. Roecker, A. J.; Mercer, S. P.; Harrell, C. M.; Garson, S. L.; Fox, S. V.; Gotter, A. L.; Prueksaritanont, T.; Cabalu, T. D.; Cui, D.; Lemaire, W.; Winrow, C. J.; Renger, J. J.; Coleman, P. J., Discovery of Dual Orexin Receptor Antagonists with Rat Sleep Efficacy Enabled by Expansion of the Acetonitrile-Assisted/DiphosgeneMediated 2,4-Dichloropyrimidine Synthesis. Bioorg. Med. Chem. Lett. 2014, 24, 2079-85. 67. Roecker, A. J.; Mercer, S. P.; Bergman, J. M.; Gilbert, K. F.; Kuduk, S. D.; Harrell, C. M.; Garson, S. L.; Fox, S. V.; Gotter, A. L.; Tannenbaum, P. L.; Prueksaritanont, T.; Cabalu, T. D.; Cui, D.; Lemaire, W.; Winrow, C. J.; Renger, J. J.; Coleman, P. J., Discovery of Diazepane Amide Doras and 2-Soras Enabled by Exploration of Isosteric Quinazoline Replacements. Bioorg. Med. Chem. Lett. 2015. 68. Hoenke, C.; Bouyssou, T.; Tautermann, C. S.; Rudolf, K.; Schnapp, A.; Konetzki, I., Use of 5-Hydroxy4h-Benzo[1,4]Oxazin-3-Ones as Beta2-Adrenoceptor Agonists. Bioorg. Med. Chem. Lett. 2009, 19, 6640-4.

16


Page 17 of 24

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


69. Kormos, C. M.; Gichinga, M. G.; Maitra, R.; Runyon, S. P.; Thomas, J. B.; Brieaddy, L. E.; Mascarella, S. W.; Navarro, H. A.; Carroll, F. I., Design, Synthesis, and Biological Evaluation of (3r)-1,2,3,4-Tetrahydro-7Hydroxy-N-[(1s)-1-[[(3r,4r)-4-(3-Hydroxyphenyl)-3,4-Dim Ethyl-1-Piperidinyl]Methyl]-2-Methylpropyl]-3Isoquinolinecarboxamide (Jdtic) Analogues: In Vitro Pharmacology and Adme Profile. J. Med. Chem. 2014, 57, 7367-81. 70. McGaughey, G.; Bayly, C. I.; Cox, C. D.; Schreier, J. D.; Breslin, M. J.; Bogusky, M.; Pitzenberger, S.; Ball, R.; Coleman, P. J., Shaping Suvorexant: Application of Experimental and Theoretical Methods for Driving Synthetic Designs. J. Comput. Aided. Mol. Des. 2014, 28, 5-12. 71. Cox, C. D.; McGaughey, G. B.; Bogusky, M. J.; Whitman, D. B.; Ball, R. G.; Winrow, C. J.; Renger, J. J.; Coleman, P. J., Conformational Analysis of N,NDisubstituted-1,4-Diazepane Orexin Receptor Antagonists and Implications for Receptor Binding. Bioorg. Med. Chem. Lett 2009, 19, 2997-3001. 72. Sawada, T.; Fedorov, D. G.; Kitaura, K., Binding of Influenza a Virus Hemagglutinin to the Sialoside Receptor Is Not Controlled by the Homotropic Allosteric Effect. J. Phys. Chem. B 2010, 114, 15700-5. 73. Venkatakrishnan, A. J.; Deupi, X.; Lebon, G.; Tate, C. G.; Schertler, G. F.; Babu, M. M., Molecular Signatures of G-Protein-Coupled Receptors. Nature 2013, 494, 185-94. 74. Dore, A. S.; Robertson, N.; Errey, J. C.; Ng, I.; Hollenstein, K.; Tehan, B.; Hurrell, E.; Bennett, K.; Congreve, M.; Magnani, F.; Tate, C. G.; Weir, M.; Marshall, F. H., Structure of the Adenosine a(2a) Receptor in Complex with Zm241385 and the Xanthines Xac and Caffeine. Structure 2011, 19, 1283-93. 75. Mason, J. S.; Bortolato, A.; Congreve, M.; Marshall, F. H., New Insights from Structural Biology into the Druggability of G Protein-Coupled Receptors. Trends in Pharmacological Sciences 2012, 33, 249-260. 76. Yuan, S.; Filipek, S.; Palczewski, K.; Vogel, H., Activation of G-Protein-Coupled Receptors Correlates with the Formation of a Continuous Internal Water Pathway. Nat. Commun. 2014, 5, 4733. 77. Gerogiokas, G.; Southey, M. W.; Mazanetz, M. P.; Hefeitz, A.; Bodkin, M.; Law, R. J.; Michel, J., Correction: Evaluation of Water Displacement Energetics in Protein Binding Sites with Grid Cell Theory. Phys. Chem. Chem. Phys. 2015, 17, 16213. 78. Ross, G. A.; Morris, G. M.; Biggin, P. C., Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites. PLoS One 2012, 7, e32036. 79. Truchon, J. F.; Pettitt, B. M.; Labute, P., A Cavity Corrected 3d-Rism Functional for Accurate Solvation Free Energies. J. Chem. Theory. Comput. 2014, 10, 934-941. 80. Beuming, T.; Lenselink, B.; Pala, D.; McRobb, F.; Repasky, M.; Sherman, W., Docking and Virtual Screening Strategies for Gpcr Drug Discovery. Methods Mol. Biol. 2015, 1335, 251-76.

81. Breiten, B.; Lockett, M. R.; Sherman, W.; Fujita, S.; Al-Sayah, M.; Lange, H.; Bowers, C. M.; Heroux, A.; Krilov, G.; Whitesides, G. M., Water Networks Contribute to Enthalpy/Entropy Compensation in Protein-Ligand Binding. J. Am. Chem. Soc. 2013, 135, 15579-84. 82. Gerogiokas, G.; Southey, M. W.; Mazanetz, M. P.; Heifetz, A.; Bodkin, M.; Law, R. J.; Michel, J., Evaluation of Water Displacement Energetics in Protein Binding Sites with Grid Cell Theory. Phys. Chem. Chem. Phys. 2015, 17, 8416-26. 83. Abel, R.; Young, T.; Farid, R.; Berne, B. J.; Friesner, R. A., Role of the Active-Site Solvent in the Thermodynamics of Factor Xa Ligand Binding. J. Am. Chem. Soc. 2008, 130, 2817-31. 84. Labute, P., Protonate3d: Assignment of Ionization States and Hydrogen Coordinates to Macromolecular Structures. Proteins 2009, 75, 187-205. 85. Gerber, P. R.; Muller, K., Mab, a Generally Applicable Molecular Force Field for Structure Modelling in Medicinal Chemistry. J. Comput. Aided Mol. Des. 1995, 9, 251-68. 86. Cerutti, D. S.; Swope, W. C.; Rice, J. E.; Case, D. A., Ff14ipq: A Self-Consistent Force Field for CondensedPhase Simulations of Proteins. J. Chem. Theory Comput. 2014, 10, 4515-4534. 87. Schmidt, M. W.; Baldridge, K. K.; Boatz, J. A.; Elbert, S. T.; Gordon, M. S.; Jensen, J. H.; Koseki, S.; Matsunaga, N.; Nguyen, K. A.; Su, S.; Windus, T. L.; Dupuis, M.; Montgomery, J. A., General Atomic and Molecular Electronic Structure System. Journal of Computational Chemistry 1993, 14, 1347-1363. 88. Yoshino, R.; Yasuo, N.; Inaoka, D. K.; Hagiwara, Y.; Ohno, K.; Orita, M.; Inoue, M.; Shiba, T.; Harada, S.; Honma, T.; Balogun, E. O.; da Rocha, J. R.; Montanari, C. A.; Kita, K.; Sekijima, M., Pharmacophore Modeling for Anti-Chagas Drug Design Using the Fragment Molecular Orbital Method. PLoS One 2015, 10, e0125829. 89. Hitaoka, S.; Chuman, H.; Yoshizawa, K., A Qsar Study on the Inhibition Mechanism of Matrix Metalloproteinase-12 by Arylsulfone Analogs Based on Molecular Orbital Calculations. Org Biomol Chem 2015, 13, 793-806. 90. Fedorov, D. G.; Kitaura, K., Second Order MollerPlesset Perturbation Theory Based Upon the Fragment Molecular Orbital Method. J. Chem. Phys. 2004, 121, 2483-90. 91. Murata, K.; Fedorov, D. G.; Nakanishi, I.; Kitaura, K., Cluster Hydration Model for Binding Energy Calculations of Protein-Ligand Complexes. J. Phys. Chem. B 2009, 113, 809-17. 92. Nakano, T.; Kaminuma, T.; Sato, T.; Akiyama, Y.; Uebayasi, M.; Kitaura, K., Fragment Molecular Orbital Method: Application to Polypeptides. Chemical Physics Letters 2000, 318, 614-618.

17



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Table of Contents artwork

18


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"

O

R2

O H N

N H R1

O

R4

O H N

N H R3

O

N H R5

Fragmentation of peptide chain

ΔEintij = ΔEesij + ΔEexij + ΔEctij + ΔEdiij Pair Interaction Energy (PIE)

Electrostatic (ΔEes) Forces between point charges, permanent and induced.

-

+

δ+

δ-

δ+ δ-

Dispersion(ΔEdi) Interaction forces due to instantaneous polarization multipoles caused by the movement of electrons in nearby molecules.

Charge transfer (ΔEct) Interactions between an occupied orbital of a donor and an unoccupied orbital of an acceptor. Orbital energy gap and overlap are important factors.

e-

Exchange repulsion (ΔEex) Repulsive forces between molecules that are close together, mainly due to the overlap of occupied orbitals.



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

A

4EIY

HOH2524

HOH2541

HOH2668

M2707.35

HOH2584

H264ECL3

HOH2521

L2496.51 N2536.55

E169ECL2

H2506.52

F168ECL2 Energy (kcal/mol)

HOH2585

B Q134

0

Y3547.43

-26

4S0V

H3507.39

HOH4025 I3206.51

3.32

P1313.29

N3246.55

Q1874.60

F2275.42

E212ECL2

Energy (kcal/mol) 0

-20


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B 4S0V A

Q1343.32 Sidechain

Fragmentation

Q1343.32 “War head”



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A

3ODU

W942.60

HOH1628

E321.26 HOH1650

D972.63

HOH1615

HOH1629 C186ECL2

E2887.39 Y1163.32 HOH1720

D187ECL2 H1133.29

B

ΔEint (kcal/mol) 0

-31

4NTJ

N159

4.60

V102

3.30

Y1093.37 Y1053.33

K2807.35

F2526.51

S1564.57 H1875.36 C1945.43 N1915.40

Q1955.44 0

Y2596.58 HOH1311 R2566.55 ΔEint (kcal/mol) -32


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100% disp

α

β γ

δ

TM1

TM2

TM3

TM4

TM5


TM6

TM7

100% elec+ct

Journal of Chemical Information and Page Modeling 24 of 24

1 2 3 4

ACS Paragon Plus Environment FMO

The Fragment Molecular Orbital Method Reveals New Insight into the

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