Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands.

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods.

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint.

Conformational sampling of complex biomolecules is an emerging frontier in drug discovery. Advances in lab-based structural biology and related computational approaches like AlphaFold have made great strides in obtaining static protein structures for biologically relevant targets. However, biology is in constant motion, and many important biological processes rely on conformationally driven events. Conventional molecular dynamics (MD) simulations run on standard hardware are impractical for many drug design projects, where conformationally driven biological events can take microseconds to milliseconds or longer. An alternative approach is to focus the search on a limited region of conformational space defined by a putative reaction coordinate (i.e., path collective variable). The search space is typically limited by applying restraints, which can be guided by insights about the underlying biological process of interest. The challenge is striking a balance between the degree to which the system is constrained and still allowing for natural motions along the path. A plethora of restraints exist to limit the size of conformational search space, although each has drawbacks when simulating complex biological motions. In this work, we present a three-stage procedure to construct realistic path collective variables (PCVs) and introduce a new kind of barrier restraint that is particularly well suited for complex conformationally driven biological events, such as allosteric modulations and conformational signaling. The PCV presented here is all-atom (as opposed to C-alpha or backbone only) and is derived from all-atom MD trajectory frames. The new restraint relies on a barrier function (specifically, the scaled reciprocal function), which we show is particularly beneficial in the context of molecular dynamics, where near-hard-wall restraints are needed with zero tolerance to restraint violation. We have implemented our PCV and barrier restraint within a hybrid sampling framework that combines well-tempered metadynamics and extended-Lagrangian adaptive biasing force (meta-eABF). We use three particular examples of high pharmaceutical interest to demonstrate the value of this approach: (1) sampling the distance from ubiquitin to a protein of interest within the supramolecular cullin-RING ligase complex, (2) stabilizing the wild-type conformation of the oncogenic mutant JAK2-V617F pseudokinase domain, and (3) inducing an activated state of the stimulator of interferon genes (STING) protein observed upon ligand binding. For examples 2 and 3, we present statistical analysis of meta-eABF free energy estimates and, for each case, code for reproducing this work.

Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry.

Targeted protein degradation (TPD) is a promising approach in drug discovery for degrading proteins implicated in diseases. A key step in this process is the formation of a ternary complex where a heterobifunctional molecule induces proximity of an E3 ligase to a protein of interest (POI), thus facilitating ubiquitin transfer to the POI. In this work, we characterize 3 steps in the TPD process. (1) We simulate the ternary complex formation of SMARCA2 bromodomain and VHL E3 ligase by combining hydrogen-deuterium exchange mass spectrometry with weighted ensemble molecular dynamics (MD). (2) We characterize the conformational heterogeneity of the ternary complex using Hamiltonian replica exchange simulations and small-angle X-ray scattering. (3) We assess the ubiquitination of the POI in the context of the full Cullin-RING Ligase, confirming experimental ubiquitinomics results. Differences in degradation efficiency can be explained by the proximity of lysine residues on the POI relative to ubiquitin.

Precise Binding Free Energy Calculations for Multiple Molecules Using an Optimal Measurement Network of Pairwise Differences.

Alchemical binding free energy (BFE) calculations offer an efficient and thermodynamically rigorous approach to in silico binding affinity predictions. As a result of decades of methodological improvements and recent advances in computer technology, alchemical BFE calculations are now widely used in drug discovery research. They help guide the prioritization of candidate drug molecules by predicting their binding affinities for a biomolecular target of interest (and potentially selectivity against undesirable antitargets). Statistical variance associated with such calculations, however, may undermine the reliability of their predictions, introducing uncertainty both in ranking candidate molecules and in benchmarking their predictive accuracy. Here, we present a computational method that substantially improves the statistical precision in BFE calculations for a set of ligands binding to a common receptor by dynamically allocating computational resources to different BFE calculations according to an optimality objective established in a previous work from our group and extended in this work. Our method, termed Network Binding Free Energy (NetBFE), performs adaptive BFE calculations in iterations, re-optimizing the allocations in each iteration based on the statistical variances estimated from previous iterations. Using examples of NetBFE calculations for protein binding of congeneric ligand series, we demonstrate that NetBFE approaches the optimal allocation in a small number (≤5) of iterations and that NetBFE reduces the statistical variance in the BFE estimates by approximately a factor of 2 when compared to a previously published and widely used allocation method at the same total computational cost.

Fast Equilibration of Water between Buried Sites and the Bulk by Molecular Dynamics with Parallel Monte Carlo Water Moves on Graphical Processing Units.

Molecular dynamics (MD) simulations of proteins are commonly used to sample from the Boltzmann distribution of conformational states, with wide-ranging applications spanning chemistry, biophysics, and drug discovery. However, MD can be inefficient at equilibrating water occupancy for buried cavities in proteins that are inaccessible to the surrounding solvent. Indeed, the time needed for water molecules to equilibrate between the bulk solvent and the binding site can be well beyond what is practical with standard MD, which typically ranges from hundreds of nanoseconds to a few microseconds. We recently introduced a hybrid Monte Carlo/MD (MC/MD) method, which speeds up the equilibration of water between buried cavities and the surrounding solvent, while sampling from the thermodynamically correct distribution of states. While the initial implementation of the MC functionality led to considerable slowing of the overall simulations, here we address this problem with a parallel MC algorithm implemented on graphical processing units. This results in speed-ups of 10-fold to 1000-fold over the original MC/MD algorithm, depending on the system and simulation parameters. The present method is available for use in the AMBER simulation software.

Physics-based modeling provides predictive understanding of selectively promiscuous substrate binding by Hsp70 chaperones.

To help cells cope with protein misfolding and aggregation, Hsp70 molecular chaperones selectively bind a variety of sequences ("selective promiscuity"). Statistical analyses from substrate-derived peptide arrays reveal that DnaK, the E. coli Hsp70, binds to sequences containing three to five branched hydrophobic residues, although otherwise the specific amino acids can vary considerably. Several high-resolution structures of the substrate -binding domain (SBD) of DnaK bound to peptides reveal a highly conserved configuration of the bound substrate and further suggest that the substrate-binding cleft consists of five largely independent sites for interaction with five consecutive substrate residues. Importantly, both substrate backbone orientations (N- to C- and C- to N-) allow essentially the same backbone hydrogen-bonding and side-chain interactions with the chaperone. In order to rationalize these observations, we performed atomistic molecular dynamics simulations to sample the interactions of all 20 amino acid side chains in each of the five sites of the chaperone in the context of the conserved substrate backbone configurations. The resulting interaction energetics provide the basis set for deriving a predictive model that we call Paladin (Physics-based model of DnaK-Substrate Binding). Trained using available peptide array data, Paladin can distinguish binders and nonbinders of DnaK with accuracy comparable to existing predictors and further predicts the detailed configuration of the bound sequence. Tested using existing DnaK-peptide structures, Paladin correctly predicted the binding register in 10 out of 13 substrate sequences that bind in the N- to C- orientation, and the binding orientation in 16 out of 22 sequences. The physical basis of the Paladin model provides insight into the origins of how Hsp70s bind substrates with a balance of selectivity and promiscuity. The approach described here can be extended to other Hsp70s where extensive peptide array data is not available.

Accounting for the Central Role of Interfacial Water in Protein-Ligand Binding Free Energy Calculations.

Rigorous binding free energy methods in drug discovery are growing in popularity because of a combination of methodological advances, improvements in computer hardware, and workflow automation. These calculations typically use molecular dynamics (MD) to sample from the Boltzmann distribution of conformational states. However, when part or all of the binding sites is inaccessible to the bulk solvent, the time needed for water molecules to equilibrate between bulk solvent and the binding site can be well beyond what is practical with standard MD. This sampling limitation is problematic in relative binding free energy calculations, which compute the reversible work of converting ligand 1 to ligand 2 within the binding site. Thus, if ligand 1 is smaller and/or more polar than ligand 2, the perturbation may allow additional water molecules to occupy a region of the binding site. However, this change in hydration may not be captured by standard MD simulations and may therefore lead to errors in the computed free energy. We recently developed a hybrid Monte Carlo/MD (MC/MD) method, which speeds up the equilibration of water between bulk solvent and buried cavities, while sampling from the intended distribution of states. Here, we report on the use of this approach in the context of alchemical binding free energy calculations. We find that using MC/MD markedly improves the accuracy of the calculations and also reduces hysteresis between the forward and reverse perturbations, relative to matched calculations using only MD with or without the crystallographic water molecules. The present method is available for use in AMBER simulation software.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.

Improved Alchemical Free Energy Calculations with Optimized Smoothstep Softcore Potentials.

Progress in the development of GPU-accelerated free energy simulation software has enabled practical applications on complex biological systems and fueled efforts to develop more accurate and robust predictive methods. In particular, this work re-examines concerted (a.k.a., one-step or unified) alchemical transformations commonly used in the prediction of hydration and relative binding free energies (RBFEs). We first classify several known challenges in these calculations into three categories: endpoint catastrophes, particle collapse, and large gradient-jumps. While endpoint catastrophes have long been addressed using softcore potentials, the remaining two problems occur much more sporadically and can result in either numerical instability (i.e., complete failure of a simulation) or inconsistent estimation (i.e., stochastic convergence to an incorrect result). The particle collapse problem stems from an imbalance in short-range electrostatic and repulsive interactions and can, in principle, be solved by appropriately balancing the respective softcore parameters. However, the large gradient-jump problem itself arises from the sensitivity of the free energy to large values of the softcore parameters, as might be used in trying to solve the particle collapse issue. Often, no satisfactory compromise exists with the existing softcore potential form. As a framework for solving these problems, we developed a new family of smoothstep softcore (SSC) potentials motivated by an analysis of the derivatives along the alchemical path. The smoothstep polynomials generalize the monomial functions that are used in most implementations and provide an additional path-dependent smoothing parameter. The effectiveness of this approach is demonstrated on simple yet pathological cases that illustrate the three problems outlined. With appropriate parameter selection, we find that a second-order SSC(2) potential does at least as well as the conventional approach and provides vast improvement in terms of consistency across all cases. Last, we compare the concerted SSC(2) approach against the gold-standard stepwise (a.k.a., decoupled or multistep) scheme over a large set of RBFE calculations as might be encountered in drug discovery.

Rigorous Free Energy Simulations in Virtual Screening.

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

Mechanism of biomolecular recognition of trimethyllysine by the fluorinated aromatic cage of KDM5A PHD3 finger.

The understanding of biomolecular recognition of posttranslationally modified histone proteins is centrally important to the histone code hypothesis. Despite extensive binding and structural studies on the readout of histones, the molecular language by which posttranslational modifications on histone proteins are read remains poorly understood. Here we report physical-organic chemistry studies on the recognition of the positively charged trimethyllysine by the electron-rich aromatic cage containing PHD3 finger of KDM5A. The aromatic character of two tryptophan residues that solely constitute the aromatic cage of KDM5A was fine-tuned by the incorporation of fluorine substituents. Our thermodynamic analyses reveal that the wild-type and fluorinated KDM5A PHD3 fingers associate equally well with trimethyllysine. This work demonstrates that the biomolecular recognition of trimethyllysine by fluorinated aromatic cages is associated with weaker cation-π interactions that are compensated by the energetically more favourable trimethyllysine-mediated release of high-energy water molecules that occupy the aromatic cage.

Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at Protein-Protein Interaction Interfaces.

The ability to target protein-protein interactions (PPIs) with small molecule inhibitors offers great promise in expanding the druggable target space and addressing a broad range of untreated diseases. However, due to their nature and function of interacting with protein partners, PPI interfaces tend to extend over large surfaces without the typical pockets of enzymes and receptors. These features present unique challenges for small molecule inhibitor design. As such, determining whether a particular PPI of interest could be pursued with a small molecule discovery strategy requires an understanding of the characteristics of the PPI interface and whether it has hotspots that can be leveraged by small molecules to achieve desired potency. Here, we assess the ability of mixed-solvent molecular dynamic (MSMD) simulations to detect hotspots at PPI interfaces. MSMD simulations using three cosolvents (acetonitrile, isopropanol, and pyrimidine) were performed on a large test set of 21 PPI targets that have been experimentally validated by small molecule inhibitors. We compare MSMD, which includes explicit solvent and full protein flexibility, to a simpler approach that does not include dynamics or explicit solvent (SiteMap) and find that MSMD simulations reveal additional information about the characteristics of these targets and the ability for small molecules to inhibit the PPI interface. In the few cases were MSMD simulations did not detect hotspots, we explore the shortcomings of this technique and propose future improvements. Finally, using Interleukin-2 as an example, we highlight the advantage of the MSMD approach for detecting transient cryptic druggable pockets that exists at PPI interfaces.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

A Water-Bridged Cysteine-Cysteine Redox Regulation Mechanism in Bacterial Protein Tyrosine Phosphatases.

The emergence of multidrug-resistant Mycobacterium tuberculosis (Mtb) strains highlights the need to develop more efficacious and potent drugs. However, this goal is dependent on a comprehensive understanding of Mtb virulence protein effectors at the molecular level. Here, we used a post-expression cysteine (Cys)-to-dehydrolanine (Dha) chemical editing strategy to identify a water-mediated motif that modulates accessibility of the protein tyrosine phosphatase A (PtpA) catalytic pocket. Importantly, this water-mediated Cys-Cys non-covalent motif is also present in the phosphatase SptpA from Staphylococcus aureus, which suggests a potentially preserved structural feature among bacterial tyrosine phosphatases. The identification of this structural water provides insight into the known resistance of Mtb PtpA to the oxidative conditions that prevail within an infected host macrophage. This strategy could be applied to extend the understanding of the dynamics and function(s) of proteins in their native state and ultimately aid in the design of small-molecule modulators.

The Hsp70 interdomain linker is a dynamic switch that enables allosteric communication between two structured domains.

Hsp70 molecular chaperones play key roles in cellular protein homeostasis by binding to exposed hydrophobic regions of incompletely folded or aggregated proteins. This crucial Hsp70 function relies on allosteric communication between two well-structured domains: an N-terminal nucleotide-binding domain (NBD) and a C-terminal substrate-binding domain (SBD), which are tethered by an interdomain linker. ATP or ADP binding to the NBD alters the substrate-binding affinity of the SBD, triggering functionally essential cycles of substrate binding and release. The interdomain linker is a well-structured participant in the interdomain interface in ATP-bound Hsp70s. By contrast, in the ADP-bound state, exemplified by the Escherichia coli Hsp70 DnaK, the interdomain linker is flexible. Hsp70 interdomain linker sequences are highly conserved; moreover, mutations in this region compromise interdomain allostery. To better understand the role of this region in Hsp70 allostery, we used molecular dynamics simulations to explore the conformational landscape of the interdomain linker in ADP-bound DnaK and supported our simulations by strategic experimental data. We found that while the interdomain linker samples many conformations, it behaves as three relatively ordered segments connected by hinges. As a consequence, the distances and orientations between the NBD and SBD are limited. Additionally, the C-terminal region of the linker forms previously unreported, transient interactions with the SBD, and the predominant linker-docking site is available in only one allosteric state, that with high affinity for substrate. This preferential binding implicates the interdomain linker as a dynamic allosteric switch. The linker-binding site on the SBD is a potential target for small molecule modulators of the Hsp70 allosteric cycle.

Deciphering Cryptic Binding Sites on Proteins by Mixed-Solvent Molecular Dynamics.

In recent years, molecular dynamics simulations of proteins in explicit mixed solvents have been applied to various problems in protein biophysics and drug discovery, including protein folding, protein surface characterization, fragment screening, allostery, and druggability assessment. In this study, we perform a systematic study on how mixtures of organic solvent probes in water can reveal cryptic ligand binding pockets that are not evident in crystal structures of apo proteins. We examine a diverse set of eight PDB proteins that show pocket opening induced by ligand binding and investigate whether solvent MD simulations on the apo structures can induce the binding site observed in the holo structures. The cosolvent simulations were found to induce conformational changes on the protein surface, which were characterized and compared with the holo structures. Analyses of the biological systems, choice of probes and concentrations, druggability of the resulting induced pockets, and application to drug discovery are discussed here.

Calculating Water Thermodynamics in the Binding Site of Proteins - Applications of WaterMap to Drug Discovery.

The ability to accurately characterize the solvation properties (water locations and thermodynamics) of biomolecules is of great importance to drug discovery. While crystallography, NMR, and other experimental techniques can assist in determining the structure of water networks in proteins and protein-ligand complexes, most water molecules are not fully resolved and accurately placed. Furthermore, understanding the energetic effects of solvation and desolvation on binding requires an analysis of the thermodynamic properties of solvent involved in the interaction between ligands and proteins. WaterMap is a molecular dynamics-based computational method that uses statistical mechanics to describe the thermodynamic properties (entropy, enthalpy, and free energy) of water molecules at the surface of proteins. This method can be used to assess the solvent contributions to ligand binding affinity and to guide lead optimization. In this review, we provide a comprehensive summary of published uses of WaterMap, including applications to lead optimization, virtual screening, selectivity analysis, ligand pose prediction, and druggability assessment.

Water-Restructuring Mutations Can Reverse the Thermodynamic Signature of Ligand Binding to Human Carbonic Anhydrase.

This study uses mutants of human carbonic anhydrase (HCAII) to examine how changes in the organization of water within a binding pocket can alter the thermodynamics of protein-ligand association. Results from calorimetric, crystallographic, and theoretical analyses suggest that most mutations strengthen networks of water-mediated hydrogen bonds and reduce binding affinity by increasing the enthalpic cost and, to a lesser extent, the entropic benefit of rearranging those networks during binding. The organization of water within a binding pocket can thus determine whether the hydrophobic interactions in which it engages are enthalpy-driven or entropy-driven. Our findings highlight a possible asymmetry in protein-ligand association by suggesting that, within the confines of the binding pocket of HCAII, binding events associated with enthalpically favorable rearrangements of water are stronger than those associated with entropically favorable ones.

Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability-Large-Scale Validation of MD-Based Relative Free Energy Calculations.

The stability of folded proteins is critical to their biological function and for the efficacy of protein therapeutics. Predicting the energetic effects of protein mutations can improve our fundamental understanding of structural biology, the molecular basis of diseases, and possible routes to addressing those diseases with biological drugs. Identifying the effect of single amino acid point mutations on the thermodynamic equilibrium between the folded and unfolded states of a protein can pinpoint residues of critical importance that should be avoided in the process of improving other properties (affinity, solubility, viscosity, etc.) and suggest changes at other positions for increasing stability in protein engineering. Multiple computational tools have been developed for in silico predictions of protein stability in recent years, ranging from sequence-based empirical approaches to rigorous physics-based free energy methods. In this work, we show that FEP+, which is a free energy perturbation method based on all-atom molecular dynamics simulations, can provide accurate thermal stability predictions for a wide range of biologically relevant systems. Significantly, the FEP+ approach, while originally developed for relative binding free energies of small molecules to proteins and not specifically fitted for protein stability calculations, performs well compared to other methods that were fitted specifically to predict protein stability. Here, we present the broadest validation of a rigorous free energy-based approach applied to protein stability reported to date: 700+ single-point mutations spanning 10 different protein targets. Across the entire data set, we correctly classify the mutations as stabilizing or destabilizing in 84% of the cases, and obtain statistically significant predictions as compared with experiment [average error of ~1.6kcal/mol and coefficient of determination (R2) of 0.40]. This study demonstrates, for the first time in a large-scale validation, that rigorous free energy calculations can be used to predict changes in protein stability from point mutations without parameterization or system-specific customization, although further improvements should be possible with additional sampling and a better representation of the unfolded state of the protein. Here, we describe the FEP+ method as applied to protein stability calculations, summarize the large-scale retrospective validation results, and discuss limitations of the method, along with future directions for further improvements.