Induced-fit Effects Research Articles

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

Large-Scale Virtual Screening for the Discovery of SARS-CoV-2 Papain-like Protease (PLpro) Non-covalent Inhibitors.

The rapid global spread of the SARS-CoV-2 virus facilitated the development of novel direct-acting antiviral agents (DAAs). The papain-like protease (PLpro) has been proposed as one of the major SARS-CoV-2 targets for DAAs due to its dual role in processing viral proteins and facilitating the host's immune suppression. This dual role makes identifying small molecules that can effectively neutralize SARS-CoV-2 PLpro activity a high-priority task. However, PLpro drug discovery faces a significant challenge due to the high mobility and induced-fit effects in the protease's active site. Herein, we virtually screened the ZINC20 database with Deep Docking (DD) to identify prospective noncovalent PLpro binders and combined ultra-large consensus docking with two pharmacophore (ph4)-filtering strategies. The analysis of active compounds revealed their somewhat-limited diversity, likely attributed to the induced-fit nature of PLpro's active site in the crystal structures, and therefore, the use of rigid docking protocols poses inherited limitations. The top hits were assessed against recombinant viral proteins and live viruses, demonstrating desirable inhibitory activities. The best compound VPC-300195 (IC50: 15 μM) ranks among the top noncovalent PLpro inhibitors discovered through in silico methodologies. In the search for novel SARS-CoV-2 PLpro-specific chemotypes, the identified inhibitors could serve as diverse templates for the development of effective noncovalent PLpro inhibitors.

Crystal structure of human acetylcholinesterase in complex with tacrine: Implications for drug discovery

Alzheimer's disease (AD) is one of the most common, progressive neurodegenerative disorders affecting the aged populations. Though various disease pathologies have been suggested for AD, the impairment of the cholinergic system is one of the critical factors for the disease progression. Restoration of the cholinergic transmission through acetylcholinesterase (AChE) inhibitors is a promising disease modifying therapy. Being the first marketed drug for AD, tacrine reversibly inhibits AChE and thereby slows the breakdown of the chemical messenger acetylcholine (ACh) in the brain. However, the atomic level of interactions of tacrine towards human AChE (hAChE) is unknown for years. Hence, in the current study, we report the X-ray structure of hAChE-tacrine complex at 2.85 Å resolution. The conformational heterogeneity of tacrine within the electron density was addressed with the help of molecular mechanics assisted methods and the low-energy ligand configuration is reported, which provides a mechanistic explanation for the high binding affinity of tacrine towards AChE. Additionally, structural comparison of reported hAChE structures sheds light on the conformational selection and induced fit effects of various active site residues upon binding to different ligands and provides insight for future drug design campaigns against AD where AChE is a drug target.

Subsite Ligand Recognition and Cooperativity in the TPP Riboswitch: Implications for Fragment-Linking in RNA Ligand Discovery.

RNA molecules can show high levels of cooperativity in their global folding and interactions with divalent ions. However, cooperativity at individual ligand-RNA interaction sites remains poorly understood. Here, we investigated the binding of thiamine and methylene diphosphonic acid (MDP, a soluble structural analogue of pyrophosphate) to the thiamine pyrophosphate riboswitch. These ligands each bind weakly at proximal subsites, with 10 μM and 1 mM affinities, respectively. The affinity of MDP moderately improves when thiamine or thiamine-like fragments are pre-bound to the RNA. Covalent linking of thiamine and MDP substantially increases riboswitch binding to a notable high affinity of 20 nM. Crystal structures and single-molecule correlated chemical probing revealed favorable induced fit effects upon binding of individual ligands and, unexpectedly, a substantial thermodynamically unfavorable RNA structural rearrangement upon binding of the linked thiamine-MDP ligand. Thus, linking of two ligands of modest affinity, accompanied by an unfavorable structural rearrangement, still yields a potent linked RNA-binding compound. Since complex ligands often bind riboswitches and other RNAs at proximal subsites, principles derived from this work inform and support fragment-linking strategies for identifying small molecules that interact with RNA specifically and with high affinity.

Enhanced Sampling Approach to the Induced-Fit Docking Problem in Protein-Ligand Binding: The Case of Mono-ADP-Ribosylation Hydrolase Inhibitors.

Enhanced sampling methods can predict free-energy landscapes associated with protein/ligand binding, characterizing the involved intermolecular interactions in a precise way. However, these in silico approaches can be challenged by induced-fit effects. Here, we present a variant of volume-based metadynamics tailored to tackle this problem in a general and efficient way. The validity of the approach is established by applying it to substrate/enzyme complexes of pharmacological relevance: mono-ADP-ribose (ADPr) in complex with mono-ADP-ribosylation hydrolases (MacroD1 and MacroD2), where induced-fit phenomena are known to be significant. The calculated binding free energies are consistent with experiments, with an absolute error smaller than 0.5 kcal/mol. Our simulations reveal that in all circumstances, the active loops, delimiting the boundaries of the binding site, undergo significant conformation rearrangements upon ligand binding. The calculations further provide, for the first time, the molecular basis of ADPr specificity and the relative changes in its experimental binding affinity on passing from MacroD1 to MacroD2 and all its mutants. Our study paves the way to the quantitative description of induced-fit events in molecular recognition.

CIFDock: A novel CHARMM-based flexible receptor-flexible ligand docking protocol.

Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data.

Subtle Chemical Changes Cross the Boundary between Agonist and Antagonist: New A3 Adenosine Receptor Homology Models and Structural Network Analysis Can Predict This Boundary.

Distinguishing compounds' agonistic or antagonistic behavior would be of great utility for the rational discovery of selective modulators. We synthesized truncated nucleoside derivatives and discovered 6c (Ki = 2.40 nM) as a potent human A3 adenosine receptor (hA3AR) agonist, and subtle chemical modification induced a shift from antagonist to agonist. We elucidated this shift by developing new hA3AR homology models that consider the pharmacological profiles of the ligands. Taken together with molecular dynamics (MD) simulation and three-dimensional (3D) structural network analysis of the receptor-ligand complex, the results indicated that the hydrogen bonding with Thr943.36 and His2727.43 could make a stable interaction between the 3'-amino group with TM3 and TM7, and the corresponding induced-fit effects may play important roles in rendering the agonistic effect. Our results provide a more precise understanding of the compounds' actions at the atomic level and a rationale for the design of new drugs with specific pharmacological profiles.

Pre‐ and post‐docking sampling of conformational changes using ClustENM and HADDOCK for protein‐protein and protein‐DNA systems

Incorporating the dynamic nature of biomolecules in the modeling of their complexes is a challenge, especially when the extent and direction of the conformational changes taking place upon binding is unknown. Estimating whether the binding of a biomolecule to its partner(s) occurs in a conformational state accessible to its unbound form (“conformational selection”) and/or the binding process induces conformational changes (“induced‐fit”) is another challenge. We propose here a method combining conformational sampling using ClustENM—an elastic network‐based modeling procedure—with docking using HADDOCK, in a framework that incorporates conformational selection and induced‐fit effects upon binding. The extent of the applied deformation is estimated from its energetical costs, inspired from mechanical tensile testing on materials. We applied our pre‐ and post‐docking sampling of conformational changes to the flexible multidomain protein‐protein docking benchmark and a subset of the protein‐DNA docking benchmark. Our ClustENM‐HADDOCK approach produced acceptable to medium quality models in 7/11 and 5/6 cases for the protein‐protein and protein‐DNA complexes, respectively. The conformational selection (sampling prior to docking) has the highest impact on the quality of the docked models for the protein‐protein complexes. The induced‐fit stage of the pipeline (post‐sampling), however, improved the quality of the final models for the protein‐DNA complexes. Compared to previously described strategies to handle conformational changes, ClustENM‐HADDOCK performs better than two‐body docking in protein‐protein cases but worse than a flexible multidomain docking approach. However, it does show a better or similar performance compared to previous protein‐DNA docking approaches, which makes it a suitable alternative.

The role of water and protein flexibility in the structure-based virtual screening of allosteric GPCR modulators: an mGlu5 receptor case study

Stabilizing unique receptor conformations, allosteric modulators of G-protein coupled receptors (GPCRs) might open novel treatment options due to their new pharmacological action, their enhanced specificity and selectivity in both binding and signaling. Ligand binding occurs at intrahelical allosteric sites and involves significant induced fit effects that include conformational changes in the local protein environment and water networks. Based on the analysis of available crystal structures of metabotropic glutamate receptor 5 (mGlu5) we investigated these effects in the binding of mGlu5 receptor negative allosteric modulators. A large set of retrospective virtual screens revealed that the use of multiple protein structures and the inclusion of selected water molecules improves virtual screening performance compared to conventional docking strategies. The role of water molecules and protein flexibility in ligand binding can be taken into account efficiently by the proposed docking protocol that provided reasonable enrichment of true positives. This protocol is expected to be useful also for identifying intrahelical allosteric modulators for other GPCR targets.

Structural insights into ligand binding features of dual FABP4/5 inhibitors by molecular dynamics simulations

The fatty acid binding protein (FABP) 4 and 5 have been considered as potential targets for the treatment of metabolic diseases. A compensatory upregulation of FABP5 due to the gene ablation of FABP4 in adipocytes indicated the importance of dual FABP4/5 inhibitors. A few compounds have been discovered as dual FABP4/5 inhibitors. However, none exhibited equivalent inhibitory activity against both FABP4 and FABP5, and almost all compounds showed weaker inhibition against FABP5. To provide a better structural understanding for the design of potent dual FABP4/5 inhibitors, molecular dynamics simulations have been performed for 100 ns to disclose the ligand binding features in FABP4 and FABP5 using Amber14, respectively. Key residues were identified by analysis of close contact, hydrogen bond occupancy, binding free energy and alanine scanning mutagenesis. In addition, induced-fit effects have been observed upon ligand binding in the process of simulations. The shifted alkyl chain of ligand in FABP4 was significantly different from that in FABP5 due to the corresponding residues (Phe58FABP4 and Leu60FABP5). Thus, to avoid different steric effects made by these two residues, hydrophobic groups of suitable size should be taken into account. Besides, electrostatic and steric effects with Arg107FABP4 and Arg109FABP5 should be paid more attention to. The results will facilitate the rational design of dual FABP4/5 inhibitors.

Determination of the native features of the exoglucanase Cel48S from Clostridium thermocellum

BackgroundClostridium thermocellum is considered one of the most efficient natural cellulose degraders because of its cellulosomal system. As the major exoglucanase of cellulosome in C. thermocellum, Cel48S plays key roles and influences the activity and features of cellulosome to a great extent. Thus, it is of great importance to reveal the enzymatic features of Cel48S. However, Cel48S has not been well performed due to difficulties in purifying either recombinant or native Cel48S proteins.ResultsWe observed that the soluble fraction of the catalytic domain of Cel48S (Cel48S_CD) obtained by heterologous expression in Escherichia coli and denaturation-refolding treatment contained a large portion of incorrectly folded proteins with low activity. Using a previously developed seamless genome-editing system for C. thermocellum, we achieved direct purification of Cel48S_CD from the culture supernatant of C. thermocellum DSM1313 by inserting a sequence encoding 12 successive histidine residues and a TAA stop codon immediately behind the GH domain of Cel48S. Based on the fully active protein, biochemical and structural analyses were performed to reveal its innate characteristics. The native Cel48S_CD showed high activity of 117.61 ± 2.98 U/mg and apparent substrate preference for crystalline cellulose under the assay conditions. The crystal structure of the native GH48 protein revealed substrate-coupled changes in the residue conformation, indicating induced-fit effects between Cel48S_CD and substrates. Mass spectrum and crystal structural analyses suggested no significant posttranslational modification in the native Cel48S_CD protein.ConclusionOur results confirmed that the high activity and substrate specificity of Cel48S_CD from C. thermocellum were consistent with its importance in the cellulosome. The structure of the native Cel48S_CD protein revealed evidence of conformational changes during substrate binding. In addition, our study provided a reliable method for in situ purification of cellulosomal and other secretive proteins from C. thermocellum.

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

Are induced fit protein conformational changes caused by ligand-binding predictable? A molecular dynamics investigation.

In this work, the ability of molecular dynamics simulations (MD) to prospectively predict regions of ligand binding sites that could undergo induced fit effects was investigated. Conventional MD was run on 39 apo structures (no ligand), and the resulting trajectories were compared to a set of 147 holo X-ray structures (ligand-bound). It was observed from the simulations, in the absence of the ligands, that structures exhibiting large residue conformational changes indicated higher likelihood of induced fit effects. Nevertheless, the simulation results did not perform better than using the normalized crystallographic structural factors as predictors of active-site rigid residues (87% predictive power) and mobile residues (47% predictive power). While the simulations could not produce full active sites conformations similar to holo-like states, it was found that the simulations could reproduce bound state conformations of individual residues. These results suggest potential issues in the use of unligated simulation frames directly for drug design applications such as ligand docking, and an overall caution in the use of protein flexibility in docking protocols should be emphasized. © 2017 Wiley Periodicals, Inc.

WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand-Receptor Docking.

We have developed a new methodology for protein-ligand docking and scoring, WScore, incorporating a flexible description of explicit water molecules. The locations and thermodynamics of the waters are derived from a WaterMap molecular dynamics simulation. The water structure is employed to provide an atomic level description of ligand and protein desolvation. WScore also contains a detailed model for localized ligand and protein strain energy and integrates an MM-GBSA scoring component with these terms to assess delocalized strain of the complex. Ensemble docking is used to take into account induced fit effects on the receptor conformation, and protein reorganization free energies are assigned via fitting to experimental data. The performance of the method is evaluated for pose prediction, rank ordering of self-docked complexes, and enrichment in virtual screening, using a large data set of PDB complexes and compared with the Glide SP and Glide XP models; significant improvements are obtained.

CNMA: a framework of encounter complex-based normal mode analysis to model conformational changes in protein interactions.

Motivation: It remains both a fundamental and practical challenge to understand and anticipate motions and conformational changes of proteins during their associations. Conventional normal mode analysis (NMA) based on anisotropic network model (ANM) addresses the challenge by generating normal modes reflecting intrinsic flexibility of proteins, which follows a conformational selection model for protein–protein interactions. But earlier studies have also found cases where conformational selection alone could not adequately explain conformational changes and other models have been proposed. Moreover, there is a pressing demand of constructing a much reduced but still relevant subset of protein conformational space to improve computational efficiency and accuracy in protein docking, especially for the difficult cases with significant conformational changes.Method and results: With both conformational selection and induced fit models considered, we extend ANM to include concurrent but differentiated intra- and inter-molecular interactions and develop an encounter complex-based NMA (cNMA) framework. Theoretical analysis and empirical results over a large data set of significant conformational changes indicate that cNMA is capable of generating conformational vectors considerably better at approximating conformational changes with contributions from both intrinsic flexibility and inter-molecular interactions than conventional NMA only considering intrinsic flexibility does. The empirical results also indicate that a straightforward application of conventional NMA to an encounter complex often does not improve upon NMA for an individual protein under study and intra- and inter-molecular interactions need to be differentiated properly. Moreover, in addition to induced motions of a protein under study, the induced motions of its binding partner and the coupling between the two sets of protein motions present in a near-native encounter complex lead to the improved performance. A study to isolate and assess the sole contribution of intermolecular interactions toward improvements against conventional NMA further validates the additional benefit from induced-fit effects. Taken together, these results provide new insights into molecular mechanisms underlying protein interactions and new tools for dimensionality reduction for flexible protein docking.Availability and implementation: Source codes are available upon request.Contact: yshen@tamu.edu

Pharmacophore Modeling, Ensemble Docking, Virtual Screening, and Biological Evaluation on Glycogen Synthase Kinase-3β.

Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which is engaged in a variety of signaling pathways, regulating a wide range of cellular processes. GSK-3β, also known as tau protein kinase I (TPK-I), is one of the most important kinases implicated in the hyperphosphorylation of tau that leads to neurodegenerative diseases. Hence, GSK-3β has emerged as an important therapeutic target. To identify compounds that are structurally novel and diverse compared to previously reported ATP-competitive GSK-3β inhibitors, we performed virtual screening by implementing a mixed ligand/structure-based approach, which included pharmacophore modeling, diversity analysis, and ensemble docking. The sensitivities of different docking protocols to induced-fit effects were explored. An enrichment study was employed to verify the robustness of ensemble docking, using 13 X-ray structures of GSK-3β, compared to individual docking in terms of retrieving active compounds from a decoy dataset. A total of 24 structurally diverse compounds obtained from the virtual screening underwent biological validation. The bioassay results showed that 15 out of the 24 hit compounds are indeed GSK-3β inhibitors, and among them, one compound exhibiting sub-micromolar inhibitory activity is a reasonable starting point for further optimization.

Applying conformational selection theory to improve crossdocking efficiency in 3‐phosphoinositide dependent protein kinase‐1

The emerging picture of biomolecular recognition is that of conformational selection followed by induced-fit. Conformational selection theory states that binding partners exist in various conformations in solution, with binding involving a "selection" between complementary conformers. In this study, we devise a docking protocol that mimics conformational selection in protein-ligand binding and demonstrate that it significantly enhances crossdocking accuracy over Glide's flexible docking protocol, which is widely used in the pharmaceutical industry. Our protocol uses a pregenerated conformational ensemble to simulate ligand flexibility. The ensemble was generated by thorough conformational sampling coupled with conformer minimization. The generated conformers were then rigidly docked in the active site of the protein along with a postdocking minimization step that allows limited induced fit effects to be modeled for the ligand. We illustrate the improved performance of our protocol through crossdocking of 31 ligands to cocomplexed proteins of the kinase 3-phosphoinositide dependent protein kinase-1 extracted from the crystal structures 1H1W (ATP bound), 1OKY (staurosporine bound) and 3QD0 (bound to a potent inhibitor). Consistent with conformational selection theory, the performance of our protocol was the best for crossdocking to the cognate protein bound to the natural ligand, ATP.

Fast Sampling of A-to-B Protein Global Conformational Transitions: From Galileo Galilei to Monte Carlo Anisotropic Network Modeling

Local and global conformational transitions underlie protein function. Local changes include induced-fit effects such as the reorientation of residues. Global motions may involve interdomain motion, allosteric switches, and the dynamics of secondary structure elements several nanometers distant from each other (1). Global conformational changes are activated processes, thus particularly challenging for traditional atomistic simulation, because sampling can be prohibitively slow or activated by rare events. The easiest simulation scenario arises when at least knowledge of two states of interest, A and B, is available; these states can be assumed the most relevant and the problem is reduced to two key questions: what are the most convenient pathways connecting A to B, and vice versa? Does one unique, most favorable pathway exist?

Structural Modeling of HCV NS3/4A Serine Protease Drug-Resistance Mutations Using End-Point Continuum Solvation and Side-Chain Flexibility Calculations

Computational methods of modeling protein-ligand interactions have gained widespread application in modern drug discovery. In continuum solvation-based methods of binding affinity estimation, limited description of solvent environment and protein flexibility is traded for a time scale that fits medicinal chemistry test cycles. The results of this speed-accuracy trade-off have been promising in terms of modeling structure-activity relationships of ligand series against protein targets. The potential of these approaches in recapitulating structural and energetic effects of resistance mutations, which involve large changes in binding affinity, remains relatively unexplored. We used continuum solvation binding affinity predictions and graph theory-based flexibility calculations to model thirteen drug resistance mutations in HCV NS3/4A serine protease, against three small-molecule inhibitors, with a 2-fold objective: quantitative assessment of binding energy predictions against experimental data and elucidation of structural/energetic determinants of resistance. The results show statistically significant correlation between predicted and experimental binding affinities, with R(2) and predictive index of up to 0.83 and 0.91, respectively. The level of accuracy was consistent with what has been reported for the inverse problem of binding affinity estimation of congeneric ligands against the same target. The quality of predictions was poor for mutations involving induced-fit effects, primarily, because of the lack of entropy terms. Flexibility analysis explained this discrepancy by indicating characteristic changes in side-chain mobility of a key binding site residue. The combined results from two approaches provide novel insights regarding the molecular mechanism of resistance. NS3/4A inhibitors, with large P2 substituents, derive high affinity with optimal van der Waals interactions in the S2 subsite, in order to overcome unfavorable desolvation and entropic cost of induced-fit effects. High-level resistance mutations tend to increase the desolvation and/or entropic barrier to ligand binding. The lead optimization strategies should, therefore, address the balance of these opposing energetic contributions in both the wild-type and mutant target.