Enthalpy-Based Screening of Focused Combinatorial Libraries for the

Nov 2, 2017 - In modern drug discovery, the ability of biophysical methods, including nuclear magnetic resonance spectroscopy or surface plasmon reson...
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Enthalpy-Based Screening of Focused Combinatorial Libraries for the Identification of Potent and Selective Ligands Carlo Baggio, Parima Udompholkul, Elisa Barile, and Maurizio Pellecchia* Division of Biomedical Sciences, School of Medicine, University of California, Riverside, 900 University Avenue, Riverside, California 92521, United States S Supporting Information *

ABSTRACT: In modern drug discovery, the ability of biophysical methods, including nuclear magnetic resonance spectroscopy or surface plasmon resonance, to detect and characterize ligand− protein interactions accurately and unambiguously makes these approaches preferred versus conventional biochemical highthroughput screening of large collections of compounds. Nonetheless, ligand screening strategies that address simultaneously potency and selectivity have not yet been fully developed. In this work, we propose a novel method for screening large collections of combinatorial libraries using enthalpy measurements as a primary screening technique. We demonstrate that selecting binders that are driven by enthalpy (ΔH) results in agents that are not only potent but also more selective for a given target. This general and novel approach, we termed ΔH screening of f POS (enthalpy screening of focused positional scanning library), combines the principles of focused combinatorial chemistry with rapid calorimetry measurements to efficiently identify potent and selective inhibitors.

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of the target and/or its relevance for studies in a disease model. Hence, novel screening strategies that directly and simultaneously address potency and selectivity at earlier stages of discovery or during the hit-to-lead optimization process are urgently needed. Recently, accurate calorimetry measurements using isothermal titration calorimetry (ITC) have been increasingly deployed to measure the direct dissociation constant between optimized ligands to a given target.4−12 ITC studies provide valuable orthogonal and independent measurements of the affinity of the selected ligand group for the given target and, in our opinion and experience, can be used to effectively eliminate false positives as we recently demonstrated.13 In addition to providing an accurate measure of the dissociation constant by titration, calorimetry studies also offer a direct measure of the enthalpy (ΔH) and entropy (ΔS) of binding.4 While most ligands bind to their targets with a relatively balanced mix of enthalpy and entropy contributions to ΔG (ΔG = ΔH − TΔS), ligands whose binding is driven primarily by enthalpy, or conversely that do not present significant entropy-driven binding, tend to be more selective for the given target. A large ΔH of binding is likely a result of a numerous specific intermolecular interactions that are less likely to be present on many targets. Hence, arguably, ligand screening strategies that select for compounds with the largest ΔH could result in more selective agents.4 Unfortunately, ligands displaying larger negative ΔH values often also display a

he discovery of potent and selective ligands for a given drug target is obviously the most difficult first step in the lengthy, unpredictable, and torturous path toward the development of novel therapeutics. Most commonly, screening techniques are aimed at the identification of hit molecules that are suitable for stepwise optimizations. The evolution of hit molecules into potential drug lead candidates, an iterative progression known as the hit-to-lead optimization process, aims to increase the affinity of the agents against the given target while keeping in check empirical druglike characteristics, to ensure that the resulting optimized compounds are pharmacologically viable (i.e., they cross cell membranes, are stable in plasma, etc.). Hence, the selectivity of the lead compounds is often not directly engineered earlier in the process, but addressed at later stages of the hit-to-lead optimization process. Once the hit compounds have undergone extensive modifications, engineering selectivity without altering too much the chemical structure of the optimized agents, albeit possible, is surely an arduous task. This may lead to the selection of compounds that are not optimally selective for the given target and may also produce off-target effects that complicate the interpretation of the agents’ activity in cellular and animal studies, and even in humans. In modern medicinal chemistry, some molecular properties can be determined, such as cLogP (calculated logarithm of the partition coefficient), tPSA (total polar surface area), ligand efficiency, and the LLE (lipophilic ligand efficiency), as predictors of “druglike” properties or resulting agents.1−3 In target validation studies conducted via the design of novel pharmacological probes (or chemical probes), the selectivity of such agents is arguably particularly important for making definitive assessments of the druggability © XXXX American Chemical Society

Received: August 17, 2017 Accepted: October 18, 2017

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DOI: 10.1021/acschembio.7b00717 ACS Chem. Biol. XXXX, XXX, XXX−XXX

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Figure 1. Schematic representation of the ΔH screening of f POS approach. (a) A positional scanning (POS) library of compounds needs to be assembled by first selecting an anchoring moiety (orange triangle). This can be any preferred scaffold that is essential for binding to and recognition of a given target, such as, for example, an optimized fragment hit, identified by screening methods and/or by defragmentation of known endogenous or synthetic inhibitors, etc. In the example, a four-position synthetic combinatorial library is then prepared with the first position fixed by an anchoring fragment (orange triangle). With a library of n elements, there will be 3 × n mixtures, each containing n × n compounds. Hence, rather than synthesizing and testing n × n × n individual compounds, the approach would result in testing 3 × n mixtures. For example, a library of 50 fragments assembled at three different positions could be sampled by synthetizing and screening 150 mixtures (50 × 3), rather than by synthesizing and testing 125000 (503) individual compounds. (b) Enthalpy (ΔH) screening of the 3 × n mixtures can be performed by one or more injections of the target protein into the mixture solutions. (c) The ΔH of each mixture is measured and plotted as a function of the fixed fragment at each position, thus potentially identifying elements that present the highest enthalpy of binding for a given target at each position. (d) Preferential fragments for each position are therefore selected, and final individual test compounds are synthesized. The dissociation constant (Kd) and the relative thermodynamics of binding for the resulting compounds are determined by isothermal titration calorimetry (ITC) analysis, while selectivity can be accomplished by displacement biochemical assays with a series of related countertargets.

compensatory loss of entropy upon binding, making the correlation between ΔH and ΔG unpredictable.4,6,10,14 Nonetheless, in a series of congeneric ligands, it may still be advantageous in principle to select those agents with greater ΔH, if selectivity is a major goal.4−11,15 However, ITC is a technique that until now has required a considerable amount of protein for a given ligand titration (∼300−500 μL of protein at a concentration of ≳200 μM for a reverse titration), and each ligand needs to be tested one at a time in a relatively lengthy experiment (≳1 h for a full titration). Therefore, the method is not practically suitable for testing tens of thousands of compounds to be used as a primary screening strategy. Recently, however, ΔH screening has been proposed as a method for determining the enthalpy of binding for several compounds, using a 96-well plate and automation, by considering only the initial titration point(s), hence consuming

a relatively small amount of reagents and acquiring the information more effectively.4 In this way, it is possible to obtain thermodynamic information at any stage of development or in support to the hit identification or optimization process for several tens or even hundreds of test agents.4 It is worth noting, however, that the sensitivity of the method is still relatively low, requiring these agents to already possess a sizable binding affinity (usually low micromolar or better) to be reproducibly detected. Recently, we proposed use of an initial fragment as a common scaffold for the design of focused combinatorial libraries of approximately 100000 elements that if arranged in a positional scanning (POS) fashion would reduce the number of samples to be synthesized and tested enormously.16−18 We demonstrate here that the binding affinity for the elements of the focused library is increased by the anchoring fragment, making it feasible to detect binding events B

DOI: 10.1021/acschembio.7b00717 ACS Chem. Biol. XXXX, XXX, XXX−XXX

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Figure 2. Identification of the BIR3 consensus binding motif using the ΔH screening of f POS approach. (a) Structure of the BIR3 domain of XIAP in complex with the N-terminal amino acid residues of SMAC of an AVPI amino acid sequence. (b) ΔH screening data for the AVPI peptide and the known inhibitor GDC-0152. The measurements were performed by injecting four times 2.5 μL of a solution of 200 μM BIR3 domain of XIAP into the cell containing the test inhibitor at a concentration of 50 μM. The value of ΔH was calculated as the average of injections 2−4. (c) ΔH screening of f POS data for three positive mixtures (one for each position), which identified the known BIR3 consensus binding motif with an AVPI sequence (or AVPF). The ΔH was calculated using the first point obtained by injecting 2.5 μL of 200 μM BIR3 domain of XIAP into the cell containing each mixture (1 mM) consisting of 2116 peptoids. (d) ΔH screening of f POS data for three negative mixtures for each position. The measurements were performed as indicated in panel c. Because of the focused nature of the library containing the anchoring element, a minimum ΔH value of approximately −2 kcal/mol is generally observed for most mixtures.

the third position, and so on for all 50 amino acids and for all positions in the tripeptides. Hence, rather than synthesizing and testing individually 503 = 125000 tripeptides, this approach entails the synthesis and testing of 50 × 3 = 150 mixtures. An emerging drug discovery approach that is finding widespread applications is fragment-based lead discovery (FBLD) in which initial fragment hits of weak affinity is identified by using sensitive biophysical screening techniques and small libraries (usually 500−1000 compounds) of diverse low-molecular weight compounds (the fragments).24−29 While the identification of such hit fragment molecules is relatively straightforward, their optimization into more potent compounds is often quite difficult and unsuccessful. The most effective approaches for hit optimization are as follows: the fragment growing in which the initial fragment hit is further iteratively derivatized into a more potent hit molecule, often guided by structural information about the binding mode of the compound, and the use of DNA-encoded methods suitable for both fragment screening and optimization.30 Recently, we proposed to utilize the combinatorial library approach as described above for hit optimizations by generating a focused positional scanning library in which one position is fixed and occupied by the given fragment hit.17 When possible, we proposed to use structural information to identify a suitable way to link the fragment to the rest of the library; however, in the absence of such structural studies, structure−activity relationship (SAR) data on the fragment hit can be used to develop a hypothesis about a possible site of derivatization. The approach in which libraries of compounds are produced with

of the resulting test agents using ITC measurements. In addition, we demonstrate that enthalpy screening of such compound mixtures can be attained with an automated ITC system using a relatively small amount of protein and reagents. We also report that selection of combinatorial library elements using ΔH as a ranking method can identify potent and selective agents. We illustrate the method in targeting the BIR3 domain of the anti-apoptotic protein XIAP.

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RESULTS AND DISCUSSION Enthalpy Screening of Focused Positional Scanning Libraries (ΔH screening of f POS). We have recently proposed a novel approach, termed high-throughput screening (HTS) by nuclear magnetic resonance (NMR),18 in which the principles of positional scanning combinatorial chemistry19−22 and fragment-based drug design are combined with protein NMR spectroscopy23 to iteratively identify and optimize ligands from collections of >100000 peptide mimetics. To render the synthesis and testing of this library feasible and practical, the agents are synthesized and tested in mixtures. A powerful pooling technique in combinatorial chemistry is positional scanning (POS)19−22 in which compound mixtures are systematically assembled with one element fixed at each given position while the other positions comprise all the combinations. For example, let us consider a library comprised of tripeptides made up of 50 natural and non-natural amino acids. The Pro-XX mixture of 2500 compounds (502) is composed of all possible tripeptides starting with proline, while the XX-Pro mixture is composed of all possible tripeptides with proline in C

DOI: 10.1021/acschembio.7b00717 ACS Chem. Biol. XXXX, XXX, XXX−XXX

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Figure 3. Library deconvolution and identification of the novel XIAP-BIR3-binding agent Ala(pY)Pro(4F-Phe). (a) Summary of ΔH values for the highest-ranking mixtures and selected low-ranking mixtures for each position. At positions P2 and P4, the mixtures with the fixed residue phosphotyrosine (pY) and 4-fluorophenylalanine (4F-Phe), respectively, ranked higher than the mixtures containing valine and isoleucine at positions P2 and P4, respectively. While for position P3, the mixture with proline as the fixed amino acid was confirmed to be the highest-ranking mixture. (b) ΔH screening of f POS data for the Ala-pY-XX and Ala-XX-4FPhe mixtures. The ΔH was calculated using the first point obtained by injecting 2.5 μL of 200 μM BIR3 domain of XIAP into the cell containing each mixture at 1 mM. (c) Isothermal titration calorimetry (ITC) data for the binding of the BIR3 domain of XIAP to the tetrapeptide with an AVPI sequence and to the novel peptide with an A(pY)P(4F-Phe) sequence. The measurements were performed as described in Methods.

the final individual compounds to be synthesized and tested in full ITC titrations and additional biochemical assays to assess potency and selectivity with orthogonal methods (Figure 1d). Application of the ΔH Screening of f POS for the Design of Potent and Selective XIAP Antagonists Targeting Its BIR3 Domain. As an application, we derived a POS library of tetrapeptides using natural and non-natural amino acids, which was designed to target the BIR3 domain of the anti-apoptotic protein XIAP. BIR3 domains recognize tetrapeptides containing the consensus motif with an AϕPϕ sequence, where ϕ represents generally a hydrophobic residue. While agents with various replacements of the Pro have been obtained, the Ala residue at the N-terminus has proven to be essential for binding to the BIR3 domains from various proteins based on SAR and structural studies (Figure 2a).31−41 Hence, we synthesized a positional scanned combinatorial library with an Ala-XXX sequence that consisted of 46 natural and nonnatural amino acids (Supplementary Figure 2); each mixture comprised two fixed elements with the Ala at position P1 and systematically one of the 46 elements in each of the other three positions. For example, the Ala-Ile-XX mixture is composed of 46 × 46 = 2116 peptides all having Ala at position P1 and Ile at position P2; the Ala-XX-Trp mixture contains 2116 peptides all having Ala at position P1 and Trp at position P4, and so on. Hence, the synthesis and testing of only 3 × 46 = 138 mixtures would cover a chemical space of ∼100000 Ala-XXX

one fixed anchoring fragment while the other positions are randomized can be very effective in the hit optimization process, and the mixtures containing the initial anchoring element (Figure 1a) may be enriched in compounds with increased binding affinity for the target. Hence, to select among these elements for those that contribute to potency and selectivity, we sought to screen the focused combinatorial library using calorimetry measurements. In our implementation, mixtures, containing 2116 peptoids each, are automatically transferred to the reaction vessel at a total mixture concentration of 1 mM in a volume of 180 μL, and the protein target is titrated in a volume of 2−3 μL of a 100−300 μM solution, to obtain ΔH measurements (Figure 1b). Practically, one to four titration points were added to assess the instrument’s reproducibility and stability, but our experimental studies supported the idea that one point is sufficient to obtain accurate measurements for ranking purposes (Figure 1). Hence, testing ∼125000 compounds arranged in 150 mixtures would require approximately 2.5 μL × 2 points × 150 samples ∼ 750 μL of protein at concentrations of 100−300 μM. Considering dead volumes required for liquid handling and automation with our Affinity ITC instrument (TA Instruments), in practice ∼1.5 mL of a 100−300 μM protein solution should suffice to conduct the ΔH screening of a focused POS library covering ∼125000 compounds. Ranking mixtures by ΔH (Figure 1c) would provide suggestions about the possible ideal combination of elements to be assembled in D

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Figure 4. Thermodynamic analysis of A(pY)P(4F-Phe), GDC-0152, and AVPI followed by selectivity studies against the BIR3 domains of XIAP, cIAP1, and cIAP2. (a) Isothermal titration calorimetry (ITC) data for the binding of the BIR3 domain of XIAP to the known inhibitor GDC-0152 (structure reported). The measured thermodynamic parameters for GDC-0152, AVPI, and A(pY)P(4F-Phe) are also reported. (b) DELFIA displacement curves relative to binding agents GDC-0152, AVPI, and A(pY)P(4F-Phe) as tested against the BIR3 domain of XIAP, cIAP1, or cIAP2. (c) The docking pose of A(pY)P(4F-Phe) in the binding site of the XIAP-BIR3 domain is reported in the top panel; in the bottom panel, the structure of GDC-0152 bound to the cIAP1 BIR3 domain [Protein Data Bank (PDB) entry 3UW4]40 is reported superimposed on the XIAP-BIR3 domain (PDB entry 1G73).44 According to these models, the pY residue interacts directly with Lys311 on the binding surface of XIAP-BIR3. Such interaction is not present in GDC-0152. (d) Sequence alignment of the BIR3 domains of XIAP, cIAP1, and cIAP2 showing that cIAP1 and cIAP2 contain a glutamic acid residue instead of Lys311, identifying this amino acid as a potential residue for the design of selective binding agents.

Table 1. BIR3-Binding Agents and Relative Binding Affinities, Selectivities, and Thermodynamic Parameters XIAP

IC50 (nM) determined by a DELFIA

selectivityb

LLEc

agent

ΔHa (kcal/mol)

Kd (nM) by ITC

XIAP

cIAP1

cIAP2

cIAP/XIAP

XIAP

GDC-0152 AVPI AVPF A(pY)P(4F-Phe)

−5.16 −4.30 −7.64 −12.17

94.7 824.6 174.6 204.6

22.1 957.0 60.0 40.1

7.0 289.1 50.9 124.1

9.9 320.0 168.2 142.7

0.4 0.3 1.8 3.3

4.18 5.34 6.06 7.35

a

Measured from a full curve titration as described herein. bRatio of the average IC50 values for cIAP1-BIR3 and cIAP2-BIR3 versus the IC50 value for XIAP-BIR3. cLLE defined as pKd(XIAP-BIR3) − cLogP.

binding of −4.30 and −5.16 kcal/mol, respectively (Figure 2b), calculated as the average of three points.4 Systematic ΔH screening of the Ala-XXX library revealed that the Ala-Val-XX, Ala-X-Pro-X, and Ala-X-X-Ile mixtures presented similarly high ΔH values of −7.05, −8.06, and −4.43 kcal/mol, respectively (Figure 2c), while the majority of the mixtures presented significantly smaller values. For example, the Ala-a-XX (with a D-Ala at P2), Ala-X-p-X (with a D-Pro at P3), and Ala-X-X-Glu mixtures presented ΔH values of only approximately −2 kcal/ mol, reaffirming the non-optimal nature of the residues at these positions (Figure 2d). Enthalpy ranking of the Ala-XXX mixtures revealed, surprisingly, that while the highest-ranking amino acids at positions P3 and P4 are in agreement with the known consensus, i.e., Pro at position P3 and hydrophobic residues such as 4-fluoro-Phe (4F-Phe) or Trp at position P4 (Figure 3a), other possible residues at position P2 also produced sizable ΔH values (Figure 3a), one of which is the cyclo-hexyl glycine (ChG) (Figure 3a) present at position P2 of the inhibitor GDC-0152 (Figure 4a). Moreover, and perhaps

tetrapeptides in which the essential Ala residue at position P1 represents the anchoring element. Each mixture was subsequently placed in a 96-well plate in dimethyl sulfoxide (DMSO) at a concentration of 200 mM (assuming the total mixture concentration, or approximately 100 μM per individual compound in the mixture) and subsequently transferred to a 96-deep well plate at a concentration of 1 mM (total mixture concentration, or ∼0.5 μM per individual peptoid) dissolved in the same buffer of the target protein (1% total DMSO). As positive controls, the tetrapeptide AVPI and the mimetic compound GDC-0152 were also tested separately at concentrations of 50 μM each. In parallel, a solution containing 200 μM BIR3 of XIAP was placed in the titrating syringe and the ΔH of binding for each mixture was tested using a fully automated ITC microcalorimetry instrument (Affinity ITC from TA Instruments). For the positive controls, 2.5 μL of a protein solution was injected four times into the cell containing reference compounds AVPI and GDC-0152, revealing values for ΔH of E

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A parameter that has been recently introduced to predict the drug-likeness character of a given ligand is the lipophilic ligand efficiency (LLE), defined as LLE = pKd − LogP, where pKd is the negative logarithm of the dissociation constant (or any inhibition constant available for the given ligand studies) and LogP is the logarithm of the partition coefficient of the compound between n-octanol and water (often calculated, cLogP).1,2 This parameter dissects the binding potency from hydrophobicity, and compounds with high values of LLE, being more selective, present in principle greater drug-like properties. Recently, it has been reported that a possible correlation can be observed between LLE and binding enthalpy when ligand binding is not associated with large conformational changes.42 Intuitively, this is expected given that binding events driven by hydrophobicity are often entropically driven while significant specific interactions, including salt bridges or hydrogen bonding, are driven by enthalpy. Using our experimentally determined Kd values and calculated LogP values, the LLE values for our agent are orders of magnitude greater than the values for GDG-0152 [or even AVPI or AVPF (Table 1)], suggesting that the identified ligand is in principle a more suitable starting point for obtaining potent and selective XIAP inhibitors compared to GDC-0152 or AVPI. To further assess the selectivity of the agent compared to the reference molecules, we developed a panel of DELFIA (dissociation-enhanced lanthanide fluorescent immunoassay) displacement assays using a biotinylated AVPI peptide and recombinant six-His-tagged BIR3 domains of XIAP, cIAP1, and cIAP2. In these assays, AVPI, AVPF, and GDG-0152 were very potent in targeting cIAP1 and cIAP2, with IC50 values that closely resembled the data reported in literature for these agents.40 In particular, GDC-0152, similar to most other SMAC mimetics, is orders of magnitude more potent for cIAP1 and cIAP2 than for XIAP (Table 1 and Figure 4b). On the other hand, Ala-pTyr-Pro-4FPhe was similarly fairly potent for the BIR3 domain of XIAP and its selectivity trend for the other BIR3 domains was inverted (Table 1 and Figure 4b). In an attempt to rationalize the observed selectivity, we used modeling studies based on the available X-ray structure of known SMAC mimetics in complex with various BIR3 domains (Figure 4c). The binding sites of the BIR3 domains of XIAP, cIAP1, and cIAP2 are highly conserved (Figure 4d). However, docking of Ala-pTyr-Pro-4FPhe placed the position P2 residue in the proximity of Lys311 in the XIAP-BIR3 domain (Figure 4c). It is worth noting that Lys311 corresponds to a glutamic acid residue in both cIAP1 and cIAP2 (Figure 4d), hence, identifying these differences as potential targets for engineering potency and selectivity against one target or the other. In conclusion, we believe we have developed a powerful method that could be of general use in most drug discovery academic or industrial laboratories. The approach uses accessible benchtop instrumentation and requires only several days of experimentation and relatively limited amounts of unlabeled protein with no molecular weight limitations compared to other biophysical methods to fairly accurately derive ΔH values of potentially a large number of agents, provided that these are assembled using the positional scanning method. Hence, the approach can be used not only to discover potent and selective peptide mimetics but also to find applicability in deriving fragment-inspired libraries or target focused libraries to accelerate hit finding and/or the hit-to-lead optimization process, taking into account selectivity in a direct fashion. In addition, we envision that the approach could also

most intriguingly, a phosphotyrosine (pY) at position P2 produced a ΔH value of −8.65 kcal/mol (Figure 3a). Hence, we synthesized an optimal tetrapeptide based on the ΔH screening of the Ala-pTyr-Pro-(4F)Phe sequence and tested it by ITC side by side with Ala-Val-Pro-Ile (AVPI), Ala-Val-ProPhe (AVPF), and GDC-0152 (Figures 3c and 4a, Supplementary Figure 3, and Table 1). Under the same experimental conditions, the Kd values for AVPI, AVPF, GDC-0152, and AlapTyr-Pro-(4F)Phe were 824.6, 175, 94.7, and 204.6 nM, respectively (Figures 3c and 4a, Supplementary Figure 3, and Table 1), indicating that the ΔH approach resulted in a fairly potent agent with a single iteration. Moreover, in agreement with the ΔH measurements of the mixtures, the compound AlapTyr-Pro-(4F)Phe presented the largest ΔH of binding (−12.2 kcal/mol) compared to those of the positive controls [−4.3, −7.6, and −5.2 kcal/mol measured for AVPI, AVPF, and GDC0152, respectively (Figure 4a, Supplementary Figure 3, and Table 1)], suggesting that selecting elements on the basis of ΔH screening is robust and reproducible. However, as observed previously, the increased −ΔH values resulted in a compensatory increased losses of entropy upon binding; hence, large negative ΔH values did not directly translate to an increased affinity.4,8,10,14 Hence, to further assess if the increased −ΔH of binding on the selected agent corresponded to an increased selectivity for the intended target, we derived a biochemical displacement assay using a biotinylated AVPI peptide (AVPIAQKSEKBiotin) and the DELFIA [dissociation-enhanced lanthanide fluorescent immunoassay (PerkinElmer)] assay platform and applied it to three closely related BIR3 domains of XIAP, cIAP1, and cIAP2. Dose−response curves with these displacement assays revealed that GDC-0152, AVPI, and AVPF were able to displace the biotinylated peptide equally well from all three proteins (Table 1, Figure 4b, and Supplementary Figure 3). In particular, we observed that these agents were more potent against cIAP1 and cIAP2 than against XIAP. On the other hand, the ΔH screening of the f POS-derived agent [AlapTyr-Pro-(4F)Phe (Table 1)] was significantly more potent for the intended target (BIR3 of XIAP) than for the other BIR3 domains (Table 1, Figure 4b, and Supplementary Figure 3). The data strongly suggested that the approach was suitable for deriving novel potent agents and that choosing these on the basis of ΔH resulted in increased selectivity for the intended target. In summary, when we applied the ΔH screening of f POS approach testing the focused library, a consensus Ala-(cyclohexyl-Gly)-Pro-(4F-Phe) motif that closely resembled the structure of the clinical candidate GDC-0152 was identified (Figure 3a). These agents followed the general IAP-binding motif AϕPϕ, where ϕ represents a hydrophobic residue (Figure 2a); hence, they are not surprisingly cross-reactive with various members of the IAP family, including cIAP1 and cIAP2. It is worth noting that recent surveys of the patent literature of SMAC mimetics revealed that most reported agents (if not all) were not XIAP-BIR3 selective and presented at position P2 a Val or a Val mimetic.39,41 However, our ΔH screening against XIAP revealed unexpectedly that pTyr at position P2 conferred upon the resulting molecule an increased enthalpy of binding of −12.9 kcal/mol, which was remarkably greater than the values of −5.16, −4.30, and −7.64 kcal/mol observed with GDC0152, AVPI, and AVPF, respectively (Figures 2b, 3c, and 4a and Supplementary Figures 3 and 4). F

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of the first point for the ΔH screening of f POS measurements, which we found to be sufficiently accurate for the purpose of ranking. It is worth noting, however, that when three or four points can be measured, a titration effect may be observable in positive mixtures (Supplementary Figure 1), while for the mixtures with low ΔH values (∼2 kcal/mol) all the injections were of the same magnitude (Supplementary Figure 1). Hence, in principle, a single-point titration could have the benefit of limiting protein consumption, while a threeor four-point titration could, in principle, also provide some rough indication of the relative binding affinity. ITC Measurements. Isothermal titration calorimetry measurements were performed using the Affinity ITC Autosampler from TA Instruments. To determine dissociation constants, the titrations were performed in a reverse fashion by titrating the protein into the ligand solution. All the measurements were performed at 25 °C in a buffer composed of 50 mM MES (pH 6.0), 100 mM NaCl, 50 μM Zn(Ac)2, and 1 mM DTT, and a final DMSO concentration of 1%. The syringe was filled with a 200 μM solution of the XIAP-BIR3 domain, and 15 additions of 2.5 μL each were injected into the cell containing a 25 μM solution of the compounds. The injections were made at a 200 s interval with a stirring speed of 75 rpm. All the solutions were kept in the autosampler at 4 °C in two different 96-well plates for the reaction cell solutions and syringe solutions. The volume of the reaction cell was 180 μL, but 630 μL was loaded as an excess volume was needed for the cell conditioning and to avoid the introduction of air. The analysis of the raw data was performed using the NanoAnalyze software (TA Instruments), and the data were subsequently exported into Microsoft Excel. For the evaluation of the thermodynamic signatures (ΔH, −TΔS, and ΔG), GraphPad Prism version 7 was used. Molecular Modeling. The selected docked conformation for the binding of Ala-pTyr-Pro-4FPhe was obtained using Gold [Cambridge Crystallographic Data Centre (www.ccdc.cam.ac.uk)] and Protein Data Bank entry 1G73.44 Prior to docking, the protein and the ligands were prepared using SYBYL-X 1.2 (Certara, Princeton, NJ). The surface figures were prepared using MOLCAD45 as implemented in SYBYL-X 2.1.1. DELFIA (dissociation-enhanced lanthanide fluorescent immunoassay). A solution containing 100 μL of 100 nM AVPI-Biotin (AVPIAQKSEK-Biotin) was added to each well of a 96-well streptavidin-coated plate (PerkinElmer) and the plate incubated for 1 h, followed by the three washing steps to remove the unbound AVPI-Biotin. Subsequently, 89 μL of 1.56 nM (for XIAP-BIR3 and cIAP1-BIR3) or 2.08 nM (for cIAP2-BIR3) solutions of the Eu-N1labeled anti-six-His antibody (PerkinElmer) and a mixture containing 11 μL of the protein and a serial dilution of the test compounds were added to each well. Following incubation for 1 h, the unbound protein−Eu antibody complexes, which were displaced by a test compound, were eliminated through the second washing step, and 200 μL of the DELFIA enhancement solution (PerkinElmer) was then added to each well and the plate incubated for 10 min. The fluorescence was measured using the VICTOR X5 microplate reader (PerkinElmer) with excitation and emission wavelengths of 340 and 615 nm, respectively. The final protein concentrations were 30 nM for XIAP-BIR3 and cIAP1-BIR3 and 15 nM for cIAP2-BIR3. The final antibody concentration used for XIAP-BIR3 and cIAP1-BIR3 was 22.2 ng/well and for cIAP2-BIR3 29.7 ng/well. DELFIA assay buffer (PerkinElmer) was used to prepare the protein, peptide, and antibody solutions, and the incubations were performed at room temperature. All samples were normalized to 1% DMSO and reported as percent inhibition. The IC50 values were calculated with GraphPad Prism version 6.

be used to obtain rapid SAR data on a series of congeneric agents, as also recently demonstrated,4 at any stage of the hitto-lead optimization process. Recently, the use of thermodynamic measurements as primary drivers for drug discovery has been somewhat questioned given the complex and unpredictable interplay between ΔH and ΔS.42 Nonetheless, we are confident that the approach should be given serious consideration in the arsenal of strategies for drug discovery, perhaps most effectively in cases in which attaining selectivity is a stumbling block for a given series of compounds. In particular, we believe that the approach will be more effective when the data are used in conjunction with structural information about the binding mode, to interpret and utilize the observed thermodynamic measurements more effectively.

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METHODS

Compounds and Combinatorial Library Preparation. The positional scanning libraries were prepared with Pepscan as described previously 18,22 using the simultaneous multipeptide synthesis method.43 Hence, the library was arranged in an Ala-OXX format, where O represents one of the components in a defined position and X represents a mixture of all the components. The tetrapeptide library contained Ala at position P1 for all agents and 46 components at each of the three diversity positions, P2−P4. The components chosen represented natural (14) and unnatural (32) amino acids (Supplementary Figure 2). Each mixture was lyophilized three times, and compounds had free N-termini and amide C-termini. Mixtures were dissolved in DMSO at a concentration of 200 mM and used in the HTS by NMR and ΔH screening of f POS strategies. Individual agents were prepared using conventional solid phase peptide synthesis. GDC0152 was obtained from MedChem Express. Protein Expression and Purification. The cDNA fragment encoding the human BIR3 domain of XIAP (residues 253−347) was inserted into the pET-15b vector and consequently transformed into Escherichia coli BL21(DE3) Gold cells. The protein was overexpressed by growing the transformed cells in LB medium at 37 °C with 100 μg L−1 ampicillin until an OD600 of 0.6−0.7 was reached, followed by induction with 1 mM isopropyl β-D-1-thiogalactopyranoside overnight at 25 °C. Bacteria were collected and lysed by being sonicated at 4 °C. The overexpressed protein containing an N-terminal His tag was purified using immobilized metal ion affinity chromatography (IMAC). The buffer of the eluted protein was exchanged with a desalting column into an aqueous buffer composed of 50 mM MES (pH 6.0), 100 mM NaCl, 50 μM Zn(Ac)2, and 1 mM DTT. The recombinant BIR3 domains of cIAP1 and cIAP2 with an N-terminal six-His tag were obtained from Reaction Biology Corp. ΔH Screening of f POS. The ΔH screening of f POS was performed using the Affinity ITC Autosampler from TA Instruments in a reverse fashion by injecting the protein into the ligand solution. Each of the 138 mixtures (3 × 46) was dissolved in 630 μL of a buffer containing 50 mM MES [2-(N-morpholino)ethanesulfonic acid] (pH 6.0), 100 mM NaCl, 50 μM Zn(Ac)2, and 1 mM DTT (dithiothreitol), at a concentration of 1 mM, with a final concentration of DMSO (dimethyl sulfoxide) of 1%. Each solution was placed in each well of a 96-well plate inside the autosampler at a temperature of 4 °C. The analysis was conducted by performing four injections of 2.5 μL per run with a solution containing 200 μM human XIAP-BIR3 domain in the same buffer as the mixtures containing 1% DMSO. Under these experimental conditions, the maximum number of mixtures to be analyzed with a single syringe loading is 21 (>200 μL of the protein solution is used for syringe conditioning and to avoid the introduction of air). As a control, a well containing buffer with 1% DMSO was used to determine the heat due to sample dilution. All the measurements were performed at 25 °C, with an injection interval of 200 s, and a stirring speed of 75 rpm. As each mixture contained 2116 peptoids, and the total concentration in the reaction cell was 1 mM, the concentration of each single peptoid was ∼500 nM. As opposed to the previously proposed ΔH screening,4 we decided to use just the value

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acschembio.7b00717. Additional ITC and ΔH measurements and DELFIA dose−response curves (PDF) G

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Guided Identification of Novel Agents Targeting the Protein Docking Domain of YopH. ChemMedChem 11, 919−927. (17) Wu, B., Barile, E., De, S. K., Wei, J., Purves, A., and Pellecchia, M. (2015) High-Throughput Screening by Nuclear Magnetic Resonance (HTS by NMR) for the Identification of PPIs Antagonists. Curr. Top. Med. Chem. 15, 2032−2042. (18) Wu, B., Zhang, Z., Noberini, R., Barile, E., Giulianotti, M., Pinilla, C., Houghten, R. A., Pasquale, E. B., and Pellecchia, M. (2013) HTS by NMR of combinatorial libraries: a fragment-based approach to ligand discovery. Chem. Biol. 20, 19−33. (19) Dooley, C. T., and Houghten, R. A. (1998) Synthesis and screening of positional scanning combinatorial libraries. Methods Mol. Biol. 87, 13−24. (20) Dooley, C. T., and Houghten, R. A. (1993) The use of positional scanning synthetic peptide combinatorial libraries for the rapid determination of opioid receptor ligands. Life Sci. 52, 1509− 1517. (21) Houghten, R. A., Ostresh, J. M., and Pratt, S. M. (1991) Modified solid-phase methods for the rapid synthesis of opioid peptides. NIDA Res. Monogr. 112, 239−255. (22) Pinilla, C., Appel, J. R., Blanc, P., and Houghten, R. A. (1992) Rapid identification of high affinity peptide ligands using positional scanning synthetic peptide combinatorial libraries. BioTechniques 13, 901−905. (23) Barile, E., and Pellecchia, M. (2014) NMR-based approaches for the identification and optimization of inhibitors of protein-protein interactions. Chem. Rev. 114, 4749−4763. (24) Fischer, M., and Hubbard, R. E. (2009) Fragment-based ligand discovery. Mol. Interventions 9, 22−30. (25) Erlanson, D. A. (2011) Introduction to fragment-based drug discovery. Top. Curr. Chem. 317, 1−32. (26) Harner, M. J., Frank, A. O., and Fesik, S. W. (2013) Fragmentbased drug discovery using NMR spectroscopy. J. Biomol. NMR 56, 65−75. (27) Wang, T., Wu, M. B., Chen, Z. J., Chen, H., Lin, J. P., and Yang, L. R. (2015) Fragment-based drug discovery and molecular docking in drug design. Curr. Pharm. Biotechnol. 16, 11−25. (28) Mello, J., Gomes, R. A., Vital-Fujii, D. G., Ferreira, G. M., and Trossini, G. H. G. (2017) Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases. Chem. Biol. Drug Des., n/a. (29) Neri, D. (2017) Twenty-five Years of DNA-Encoded Chemical Libraries. ChemBioChem 18, 827−828. (30) Wichert, M., Krall, N., Decurtins, W., Franzini, R. M., Pretto, F., Schneider, P., Neri, D., and Scheuermann, J. (2015) Dual-display of small molecules enables the discovery of ligand pairs and facilitates affinity maturation. Nat. Chem. 7, 241−249. (31) Park, C. M., Sun, C., Olejniczak, E. T., Wilson, A. E., Meadows, R. P., Betz, S. F., Elmore, S. W., and Fesik, S. W. (2005) Non-peptidic small molecule inhibitors of XIAP. Bioorg. Med. Chem. Lett. 15, 771− 775. (32) Oost, T. K., Sun, C., Armstrong, R. C., Al-Assaad, A. S., Betz, S. F., Deckwerth, T. L., Ding, H., Elmore, S. W., Meadows, R. P., Olejniczak, E. T., Oleksijew, A., Oltersdorf, T., Rosenberg, S. H., Shoemaker, A. R., Tomaselli, K. J., Zou, H., and Fesik, S. W. (2004) Discovery of potent antagonists of the antiapoptotic protein XIAP for the treatment of cancer. J. Med. Chem. 47, 4417−4426. (33) Sun, C., Cai, M., Meadows, R. P., Xu, N., Gunasekera, A. H., Herrmann, J., Wu, J. C., and Fesik, S. W. (2000) NMR structure and mutagenesis of the third Bir domain of the inhibitor of apoptosis protein XIAP. J. Biol. Chem. 275, 33777−33781. (34) Liu, Z., Sun, C., Olejniczak, E. T., Meadows, R. P., Betz, S. F., Oost, T., Herrmann, J., Wu, J. C., and Fesik, S. W. (2000) Structural basis for binding of Smac/DIABLO to the XIAP BIR3 domain. Nature 408, 1004−1008. (35) Sun, C., Cai, M., Gunasekera, A. H., Meadows, R. P., Wang, H., Chen, J., Zhang, H., Wu, W., Xu, N., Ng, S. C., and Fesik, S. W. (1999) NMR structure and mutagenesis of the inhibitor-of-apoptosis protein XIAP. Nature 401, 818−822.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: (951) 827-7829. ORCID

Maurizio Pellecchia: 0000-0001-5179-470X Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Financial support to M.P. was provided in part by National Institutes of Health Grants CA168517 and CA081534 and the City of Hope-UCR Biomedical Research Initiative Award. M.P. holds the Daniel Hays Chair in Cancer Research at the School of Medicine at the University of California, Riverside (UCR). P.U. is a recipient of the 2017−2018 Pease Cancer Fellowship through the Division of Biomedical Sciences of UCR.

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REFERENCES

(1) Shiao, H. Y., Coumar, M. S., Chang, C. W., Ke, Y. Y., Chi, Y. H., Chu, C. Y., Sun, H. Y., Chen, C. H., Lin, W. H., Fung, K. S., Kuo, P. C., Huang, C. T., Chang, K. Y., Lu, C. T., Hsu, J. T., Chen, C. T., Jiaang, W. T., Chao, Y. S., and Hsieh, H. P. (2013) Optimization of ligand and lipophilic efficiency to identify an in vivo active furano-pyrimidine Aurora kinase inhibitor. J. Med. Chem. 56, 5247−5260. (2) Jabeen, I., Pleban, K., Rinner, U., Chiba, P., and Ecker, G. F. (2012) Structure-activity relationships, ligand efficiency, and lipophilic efficiency profiles of benzophenone-type inhibitors of the multidrug transporter P-glycoprotein. J. Med. Chem. 55, 3261−3273. (3) Hopkins, A. L., Keseru, G. M., Leeson, P. D., Rees, D. C., and Reynolds, C. H. (2014) The role of ligand efficiency metrics in drug discovery. Nat. Rev. Drug Discovery 13, 105−121. (4) Schon, A., and Freire, E. (2016) Enthalpy screen of drug candidates. Anal. Biochem. 513, 1−6. (5) Ladbury, J. E., Klebe, G., and Freire, E. (2010) Adding calorimetric data to decision making in lead discovery: a hot tip. Nat. Rev. Drug Discovery 9, 23−27. (6) Kawasaki, Y., Chufan, E. E., Lafont, V., Hidaka, K., Kiso, Y., Mario Amzel, L., and Freire, E. (2010) How much binding affinity can be gained by filling a cavity? Chem. Biol. Drug Des. 75, 143−151. (7) Freire, E. (2009) A thermodynamic approach to the affinity optimization of drug candidates. Chem. Biol. Drug Des. 74, 468−472. (8) Freire, E. (2008) Do enthalpy and entropy distinguish first in class from best in class? Drug Discovery Today 13, 869−874. (9) Chaires, J. B. (2008) Calorimetry and thermodynamics in drug design. Annu. Rev. Biophys. 37, 135−151. (10) Lafont, V., Armstrong, A. A., Ohtaka, H., Kiso, Y., Mario Amzel, L., and Freire, E. (2007) Compensating enthalpic and entropic changes hinder binding affinity optimization. Chem. Biol. Drug Des. 69, 413−422. (11) Ruben, A. J., Kiso, Y., and Freire, E. (2006) Overcoming roadblocks in lead optimization: a thermodynamic perspective. Chem. Biol. Drug Des. 67, 2−4. (12) Carbonell, T., and Freire, E. (2005) Binding thermodynamics of statins to HMG-CoA reductase. Biochemistry 44, 11741−11748. (13) Wu, B., De, S. K., Kulinich, A., Salem, A. F., Koeppen, J., Wang, R., Barile, E., Wang, S., Zhang, D., Ethell, I., and Pellecchia, M. (2017) Potent and Selective EphA4 Agonists for the Treatment of ALS. Cell Chem. Biol. 24, 293−305. (14) Reynolds, C. H., and Holloway, M. K. (2011) Thermodynamics of ligand binding and efficiency. ACS Med. Chem. Lett. 2, 433−437. (15) Williams, G., Ferenczy, G. G., Ulander, J., and Keseru, G. M. (2017) Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragmentbased drug discovery. Drug Discovery Today 22, 681−689. (16) Bottini, A., Wu, B., Barile, E., De, S. K., Leone, M., and Pellecchia, M. (2016) High-Throughput Screening (HTS) by NMR H

DOI: 10.1021/acschembio.7b00717 ACS Chem. Biol. XXXX, XXX, XXX−XXX

Articles

ACS Chemical Biology (36) Monfardini, I., Huang, J. W., Beck, B., Cellitti, J. F., Pellecchia, M., and Domling, A. (2011) Screening multicomponent reactions for X-linked inhibitor of apoptosis-baculoviral inhibitor of apoptosis protein repeats domain binder. J. Med. Chem. 54, 890−900. (37) Huang, J. W., Zhang, Z., Wu, B., Cellitti, J. F., Zhang, X., Dahl, R., Shiau, C. W., Welsh, K., Emdadi, A., Stebbins, J. L., Reed, J. C., and Pellecchia, M. (2008) Fragment-based design of small molecule Xlinked inhibitor of apoptosis protein inhibitors. J. Med. Chem. 51, 7111−7118. (38) Hu, R., Li, J., Liu, Z., Miao, M., and Yao, K. (2015) GDC-0152 induces apoptosis through down-regulation of IAPs in human leukemia cells and inhibition of PI3K/Akt signaling pathway. Tumor Biol. 36, 577−584. (39) Dynek, J. N., and Vucic, D. (2013) Antagonists of IAP proteins as cancer therapeutics. Cancer Lett. 332, 206−214. (40) Flygare, J. A., Beresini, M., Budha, N., Chan, H., Chan, I. T., Cheeti, S., Cohen, F., Deshayes, K., Doerner, K., Eckhardt, S. G., Elliott, L. O., Feng, B., Franklin, M. C., Reisner, S. F., Gazzard, L., Halladay, J., Hymowitz, S. G., La, H., LoRusso, P., Maurer, B., Murray, L., Plise, E., Quan, C., Stephan, J. P., Young, S. G., Tom, J., Tsui, V., Um, J., Varfolomeev, E., Vucic, D., Wagner, A. J., Wallweber, H. J., Wang, L., Ware, J., Wen, Z., Wong, H., Wong, J. M., Wong, M., Wong, S., Yu, R., Zobel, K., and Fairbrother, W. J. (2012) Discovery of a potent small-molecule antagonist of inhibitor of apoptosis (IAP) proteins and clinical candidate for the treatment of cancer (GDC0152). J. Med. Chem. 55, 4101−4113. (41) Flygare, J. A., and Fairbrother, W. J. (2010) Small-molecule panIAP antagonists: a patent review. Expert Opin. Ther. Pat. 20, 251−267. (42) Geschwindner, S., Ulander, J., and Johansson, P. (2015) Ligand Binding Thermodynamics in Drug Discovery: Still a Hot Tip? J. Med. Chem. 58, 6321−6335. (43) Pinilla, C., Appel, J. R., and Houghten, R. A. (1994) Investigation of antigen-antibody interactions using a soluble, nonsupport-bound synthetic decapeptide library composed of four trillion (4 × 10(12) sequences. Biochem. J. 301 (Part 3), 847−853. (44) Wu, G., Chai, J., Suber, T. L., Wu, J. W., Du, C., Wang, X., and Shi, Y. (2000) Structural basis of IAP recognition by Smac/DIABLO. Nature 408, 1008−1012. (45) Teschner, M., Henn, C., Vollhardt, H., Reiling, S., and Brickmann, J. (1994) Texture mapping: a new tool for molecular graphics. J. Mol. Graphics 12, 98−105.

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DOI: 10.1021/acschembio.7b00717 ACS Chem. Biol. XXXX, XXX, XXX−XXX