Free Energy Perturbation Calculation of Relative Binding Free Energy between Broadly Neutralizing Antibodies and the gp120 Glycoprotein of HIV-1

A Method for Treating Significant Conformational Changes in Alchemical Free Energy Simulations of Protein-Ligand Binding

Relative binding free energy (RBFE) simulation is a rigorous approach to the calculation of quantitatively accurate binding free energy values for protein-ligand binding in which a reference binder is gradually converted to a target binder through alchemical transformation during the simulation. The success of such simulations relies on being able to accurately sample the correct conformational phase space for each alchemical state; however, this becomes a challenge when a significant conformation change occurs between the reference and target binder-receptor complexes. Increasing the simulation time and using enhanced sampling methods can be helpful, but effects can be limited, especially when the free energy barrier between conformations is high or when the correct target complex conformation is difficult to find and maintain. Current RBFE protocols seed the reference complex structure into every alchemical window of the simulation. In our study, we describe an improved protocol in which the reference structure is seeded into the first half of the alchemical windows, and the target structure is seeded into the second half of the alchemical windows. By applying information about the relevant correct end point conformations to different simulation windows from the beginning, the need for large barrier crossings or simulation prediction of the correct structures during an alchemical simulation is in many cases obviated. In the diverse cases we examine below, the simulations yielded free energy predictions that are satisfactory as compared to experiment and superior to running the simulations utilizing the conventional protocol. The method is straightforward to implement for publicly available FEP workflows.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery

INTRODUCTION

For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

AREAS COVERED

End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.

EXPERT OPINION

The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

An Ensemble Docking Approach for Analyzing and Designing Aptamer Heterodimers Targeting VEGF165

Vascular endothelial growth factor 165 (VEGF165) is a prominent isoform of the VEGF-A protein that plays a crucial role in various angiogenesis-related diseases. It is homodimeric, and each of its monomers is composed of two domains connected by a flexible linker. DNA aptamers, which have emerged as potent therapeutic molecules for many proteins with high specificity and affinity, can also work for VEGF165. A DNA aptamer heterodimer composed of monomers of V7t1 and del5-1 connected by a flexible linker (V7t1:del5-1) exhibits a greater binding affinity with VEGF165 compared to either of the two monomers alone. Although the structure of the complex formed between the aptamer heterodimer and VEGF165 is unknown due to the highly flexible linkers, gaining structural information will still be valuable for future developments. Toward this end of accessing structural information, we adopt an ensemble docking approach here. We first obtain an ensemble of structures for both VEGF165 and the aptamer heterodimer by considering both small- and large-scale motions. We then proceed through an extraction process based on ensemble docking, molecular dynamics simulations, and binding free energy calculations to predict the structures of the VEGF165/V7t1:del5-1 complex. Through the same procedures, we reach a new aptamer heterodimer that bears a locked nucleic acid-modified counterpart of V7t1, namely RNV66:del5-1, which also binds well with VEGF165. We apply the same protocol to the monomeric units V7t1, RNV66, and del5-1 to target VEGF165. We observe that V7t1:del5-1 and RNV66:del5-1 show higher binding affinities with VEGF165 than any of the monomers, consistent with experiments that support the notion that aptamer heterodimers are more effective anti-VEGF165 aptamers than monomeric aptamers. Among the five different aptamers studied here, the newly designed RNV66:del5-1 shows the highest binding affinity with VEGF165. We expect that our ensemble docking approach can help in de novo designs of homo/heterodimeric anti-angiogenic drugs to target the homodimeric VEGF165.

Flavonostilbenes natural hybrids from Rhamnoneuron balansae as potential antitumors targeting ALDH1A1: molecular docking, ADMET, MM-GBSA calculations and molecular dynamics studies

Several studies have linked Cancer stem cells (CSCs) to cancer resistance development to chemotherapy and radiotherapy. ALDH1A1 is a key enzyme that regulates the gene expression of CSCs and creates an immunosuppressive tumor microenvironment. It was reported that quercetin and resveratrol were among the inhibitors of ALDH1A1. In early 2022, it was reported that new 11 flavonostilbenes (rhamnoneuronal D-N) were isolated from Rhamnoneuron balansae as potential antiaging natural products. Rhamnoneuronal H (5) could be envisioned as a natural hybrid of quercetin and resveratrol. It was therefore hypothesized that 5 and its analogous isolates rhamnoneuronal D-G (1-4) and rhamnoneuronal I-N (6-11) would have potential ALDH1A1 inhibitory activity. To this end, all isolates were subjected to molecular docking, MM-GBSA, ADMET, and molecular dynamics simulations studies to assess their potential as new leads for cancer treatment targeting ALDH1A1. In silico findings revealed that natural hybrid 5 has a similar binding affinity, judged by MM-GBSA, to the ALDH1A1 active site when compared to the co-crystalized ligand (-64.71 kcal/mole and -64.12 kcal/mole, respectively). Despite having lesser affinity than that of the co-crystalized ligand, the rest of the flavonostilbenes, except 2-4, displayed better binding affinities (-37.55 kcal/mole to -58.6 kcal/mole) in comparison to either resveratrol (-34.44 kcal/mole) or quercetin (-36.48 kcal/mole). Molecular dynamic simulations showed that the natural hybrids 1, 5-11 are of satisfactory stability up to 100 ns. ADMET outcomes indicate that these hybrids displayed acceptable properties and hence could represent an ideal starting point for the development of potent ALDH1A1 inhibitors for cancer treatment.Communicated by Ramaswamy H. Sarma.

Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation

Free energy perturbation (FEP) remains an indispensable method for computationally assaying prospective compounds in advance of synthesis. However, before FEP can be deployed prospectively, it must demonstrate retrospective recapitulation of known experimental data where the subtle details of the atomic ligand-receptor model are consequential. An open question is whether AlphaFold models can serve as useful initial models for FEP in the regime where there exists a congeneric series of known chemical matter but where no experimental structures are available either of the target or of close homologues. As AlphaFold structures are provided without a bound ligand, we employ induced fit docking to refine the AlphaFold models in the presence of one or more congeneric ligands. In this work, we first validate the performance of our latest induced fit docking technology, IFD-MD, on a retrospective set of public experimental GPCR structures with 95% of cross-docks producing a pose with a ligand RMSD ≤ 2.5 Å in the top two predictions. We then apply IFD-MD and FEP on AlphaFold models of the somatostatin receptor family of GPCRs. We use AlphaFold models produced prior to the availability of any experimental structure from this family. We arrive at FEP-validated models for SSTR2, SSTR4, and SSTR5, with RMSE around 1 kcal/mol, and explore the challenges of model validation under scenarios of limited ligand data, ample ligand data, and categorical data.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Alchemical Calculation of Relative Free Energies for Charge-Changing Mutations at Protein-Protein Interfaces Considering Fixed and Variable Protonation States

The calculation of relative free energies (ΔΔG) for charge-changing mutations at protein-protein interfaces through alchemical methods remains challenging due to variations in the system's net charge during charging steps, the possibility of mutated and contacting ionizable residues occurring in various protonation states, and undersampling issues. In this study, we present a set of strategies, collectively termed TIRST/TIRST-H+, to address some of these challenges. Our approaches combine thermodynamic integration (TI) with the prediction of pKa shifts to calculate ΔΔG values. Moreover, special sets of restraints are employed to keep the alchemically transformed molecules separated. The accuracy of the devised approaches was assessed on a large and diverse data set comprising 164 point mutations of charged residues (Asp, Glu, Lys, and Arg) to Ala at the protein-protein interfaces of complexes with known three-dimensional structures. Mean absolute and root-mean-square errors ranging from 1.38 to 1.66 and 1.89 to 2.44 kcal/mol, respectively, and Pearson correlation coefficients of ∼0.6 were obtained when testing the approaches on the selected data set using the GPU-TI module of Amber18 suite and the ff14SB force field. Furthermore, the inclusion of variable protonation states for the mutated acid residues improved the accuracy of the predicted ΔΔG values. Therefore, our results validate the use of TIRST/TIRST-H+ in prospective studies aimed at evaluating the impact of charge-changing mutations to Ala on the stability of protein-protein complexes.

Molecular dynamics and free energy calculations of clozapine bound to D2 and H1 receptors reveal a cardiometabolic mitigated derivative

Most atypical antipsychotics derive from a high dropout of drug treatments due to adverse cardiometabolic side effects. These side effects are caused, in part, by the H1 receptor blockade. The current work sought a clozapine derivative with a reduced affinity for the H1 receptor while maintaining its therapeutic effect linked to D2 receptor binding. Explicit molecular dynamics simulations and end-point free energy calculations of clozapine in complex with the D2 and H1 receptors embedded in cholesterol-rich lipid bilayers were performed to analyze the intermolecular interactions and address the relevance of clozapine-functional groups. Based on that, free energy perturbation calculations were performed to measure the change in free energy of clozapine structural modifications. Our results indicate the best clozapine derivative is the iodine atom substitution for chlorine. The latter is mainly due to electrostatic interaction loss for the H1 receptor, while the halogen orientation out of the D2 active site reduces the impact on the affinity.Communicated by Ramaswamy H. Sarma.

Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

Free Energy Perturbation Calculations of Mutation Effects on SARS-CoV-2 RBD::ACE2 Binding Affinity

The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein-protein binding affinities and, hence, in a variety of practical applications such as the optimization of neutralizing antibodies.

Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .

An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design

Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.

Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction

Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.

In-silico Structural Modeling of Human Immunodeficiency Virus Proteins

Human immunodeficiency virus (HIV) is an infectious virus that depletes the CD4⁺ T lymphocytes of the immune system and causes a chronic life-treating disease-acquired immunodeficiency syndrome (AIDS). The HIV genome encodes different structural and accessory proteins involved in viral entry and life cycle. Determining the 3D structure of HIV proteins is essential for new target position finding, structure-based drug designing, and future planning for computational and laboratory experimentations. Hence, the study aims to predict the 3D structures of all the HIV structural and accessory proteins using computational homology modeling to understand better the structural basis of HIV proteins interacting with host cells and viral replication. The sequences of HIV capsid, matrix, nucleocapsid, p6, reverse transcriptase, invertase, protease, gp120, gp41, virus protein r, viral infectivity factor, virus protein unique, RNA splicing regulator, transactivator protein, negative regulating factor, and virus protein x proteins were retrieved from UniProt. The primary and secondary structures of HIV proteins were predicted by Expasy ProtParam and SOPMA web servers. For the homology modeling, the MODELLER predicted the 3D structures of HIV proteins using templates. Then, the modeled structures were validated by the Ramachandran plot, local and global quality estimation scores, QMEAN scores, and Z-scores. Most of the amino acid residues of HIV proteins were present in the most favored and generously allowed regions in the Ramachandran plots. The local and global quality scores and Z-scores of the HIV proteins confirmed the good quality of modeled structures. The 3D modeled structures of HIV proteins might help further investigate the possible treatment.

Role of Dynamics and Mutations in Interactions of a Zinc Finger Antiviral Protein with CG-rich Viral RNA

Zinc finger antiviral protein (ZAP) is a host antiviral factor that selectively inhibits the replication of a variety of viruses. ZAP recognizes the CG-enriched RNA sequences and activates the viral RNA degradation machinery. In this work, we investigated the dynamics of a ZAP/RNA complex and computed the energetics of mutations in ZAP that affect its binding to the viral RNA. The crystal structure of a mouse-ZAP/RNA complex showed that RNA interacts with the zinc finger 2 (ZF2) and ZF3 domains. However, we found that due to the dynamic behavior of the single-stranded RNA, the terminal nucleotides C1 and G2 of RNA change their positions from the ZF3 to the ZF1 domain. Moreover, the electrostatic interactions between the zinc ions and the viral RNA provide further stability to the ZAP/RNA complex. We also provide structural and thermodynamic evidence for seven residue pairs (C1-Arg74, C1-Arg179, G2-Arg74, U3-Lys76, C4-Lys76, G5-Arg95, and U6-Glu204) that show favorable ZAP/RNA interactions, although these interactions were not observed in the ZAP/RNA crystal structure. Consistent with the observations from the mouse-ZAP/RNA crystal structure, we found that four residue pairs (C4-Lys89, C4-Leu90, C4-Tyr108, and G5-Lys107) maintained stable interactions in MD simulations. Based on experimental mutagenesis studies and our residue-level interaction analysis, we chose seven residues (Arg74, Lys76, Lys89, Arg95, Lys107, Tyr108, and Arg179) for individual alanine mutations. In addition, we studied mutations in those residues that are only observed in the crystal structures as interacting with RNA (Tyr98, Glu148, and Arg170). Out of these 10 mutations, we found that the Ala mutation in each of the five residues Arg74, Lys76, Lys89, Lys107, and Glu148 significantly reduced the binding affinity of ZAP to RNA.

Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations

Improving protein thermostability has been a labor- and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of stabilizing mutants. Among these is FEP+, a free energy perturbation implementation that uses a thoroughly tested physics-based method to achieve unparalleled accuracy in predicting changes in protein thermostability. To gauge the applicability of FEP+ to situations where crystal structures are unavailable, here we have applied the FEP+ approach to homology models of 12 different proteins covering 316 mutations. By comparing predictions obtained with homology models to those obtained using crystal structures, we have identified that local rather than global sequence conservation between target and template sequence is a determining factor in the accuracy of predictions. By excluding mutation sites with low local sequence identity (<40%) to a template structure, we have obtained predictions with comparable performance to crystal structures (R2 of 0.67 and 0.63 and an RMSE of 1.20 and 1.16 kcal/mol for crystal structure and homology model predictions, respectively) for identifying stabilizing mutations when incorporating residue scanning into a cascade screening strategy. Additionally, we identify and discuss inherent limitations in sequence alignments and homology modeling protocols that translate into the poor FEP+ performance of a few select examples. Overall, our retrospective study provides detailed guidelines for the application of the FEP+ approach using homology models for protein thermostability predictions, which will greatly extend this approach to studies that were previously limited by structure availability.

Residues E53, L55, H59, and G70 of the cellular receptor protein Tva mediate cell binding and entry of the novel subgroup K avian leukosis virus

Subgroup K avian leukosis virus (ALV-K) is a novel subgroup of ALV isolated from Chinese native chickens. As for a retrovirus, the interaction between its envelope protein and cellular receptor is a crucial step in ALV-K infection. Tva, a protein previously determined to be associated with vitamin B₁₂/cobalamin uptake, has been identified as the receptor of ALV-K. However, the molecular mechanism underlying the interaction between Tva and the envelope protein of ALV-K remains unclear. In this study, we identified the C-terminal loop of the LDL-A module of Tva as the minimal functional domain that directly interacts with gp85, the surface component of the ALV-K envelope protein. Further point-mutation analysis revealed that E53, L55, H59, and G70, which are exposed on the surface of Tva and are spatially adjacent, are key residues for the binding of Tva and gp85 and facilitate the entry of ALV-K. Homology modeling analysis indicated that the substitution of these four residues did not significantly impact the Tva structure but impaired the interaction between Tva and gp85 of ALV-K. Importantly, the gene-edited DF-1 cell line with precisely substituted E53, L55, H59, and G70 was completely resistant to ALV-K infection and did not affect vitamin B₁₂/cobalamin uptake. Collectively, these findings not only contribute to a better understanding of the mechanism of ALV-K entry into host cells but also provide an ideal gene-editing target for antiviral study.

An Open Source Graph-Based Weighted Cycle Closure Method for Relative Binding Free Energy Calculations

Free energy perturbation-relative binding free energy (FEP-RBFE) prediction has shown its reliability and accuracy in the prediction of protein-ligand binding affinities, which plays a fundamental role in structure-based drug design. In FEP-RBFE predictions, the calculation of each mutation path is associated with a statistical error, and cycle closure (cc) has proven to be an effective method in improving the calculation accuracy by correcting the hysteresis (summation of errors) of each closed cycle to the theoretical value 0. However, a primary hypothesis was made in the current cycle closure method that the hysteresis is evenly distributed to all paths, which is unlikely to be true in practice and may limit the further improvement of the calculation accuracy when better error estimation methods are available. Moreover, being a closed source software makes the current cycle closure method unachievable in many studies. In this paper, a newly implemented open source graph-based weighted cycle closure (wcc) algorithm was developed and introduced, not only including functions from the original cc method but also containing a new wcc method which can consider different error contributions from different paths and further improve the calculation accuracy. The wcc program also provides a new path-independent molecular error calculation method, which can be quite useful in many studies (like structure-activity relationship (SAR)) compared with the path-dependent method of the original cc program.

Exploring PROTAC Cooperativity with Coarse-Grained Alchemical Methods

Proteolysis targeting chimera (PROTAC) is a novel drug modality that facilitates the degradation of a target protein by inducing proximity with an E3 ligase. In this work, we present a new computational framework to model the cooperativity between PROTAC-E3 binding and PROTAC-target binding principally through protein-protein interactions (PPIs) induced by the PROTAC. Due to the scarcity and low resolution of experimental measurements, the physical and chemical drivers of these non-native PPIs remain to be elucidated. We develop a coarse-grained (CG) approach to model interactions in the target-PROTAC-E3 complexes, which enables converged thermodynamic estimations using alchemical free energy calculation methods despite an unconventional scale of perturbations. With minimal parametrization, we successfully capture fundamental principles of cooperativity, including the optimality of intermediate PROTAC linker lengths that originates from configurational entropy. We qualitatively characterize the dependency of cooperativity on PROTAC linker lengths and protein charges and shapes. Minimal inclusion of sequence- and conformation-specific features in our current force field, however, limits quantitative modeling to reproduce experimental measurements, but further development of the CG model may allow for efficient computational screening to optimize PROTAC cooperativity.

Accuracy and precision of binding free energy prediction for a tacrine related lead inhibitor of acetylcholinesterase with an arsenal of supercomputerized molecular modelling methods: a comparative study

Nowadays, advanced computational chemistry methods offer various strategies for revealing prospective hit structures in drug development essentially through accurate binding free energy predictions. After the era of molecular docking and quantitative structure-activity relationships, much interest has been lately oriented to perturbed molecular dynamic approaches like replica exchange with solute tempering and free energy perturbation (REST/FEP) and the potential of the mean force with adaptive biasing and accelerated weight histograms (PMF/AWH). Both of these receptor-based techniques can exploit exascale CPU&GPU supercomputers to achieve high throughput performance. In this fundamental study, we have compared the predictive power of a panel of supercomputerized molecular modelling methods to distinguish the major binding modes and the corresponding binding free energies of a promising tacrine related potential antialzheimerics in human acetylcholinesterase. The binding free energies were estimated using flexible molecular docking, molecular mechanics/generalized Born surface area/Poisson-Boltzmann surface area (MM/GBSA/PBSA), transmutation REST/FEP with 12 x 5 ns/λ windows, annihilation FEP with 20 x 5 ns/λ steps, PMF with weight histogram analysis method (WHAM) and 40 x 5 ns samples, and PMF/AWH with 10 x 100 ns replicas. Confrontation of the classical approaches such as canonical molecular dynamics and molecular docking with alchemical calculations and steered molecular dynamics enabled us to show how large errors in ΔG predictions can be expected if these in silico methods are employed in the elucidation of a common case of enzyme inhibition.Communicated by Ramaswamy H. Sarma.

Reliable and Accurate Prediction of Single-Residue pKa Values through Free Energy Perturbation Calculations

Accurate prediction of the pK_a's of protein residues is crucial to many applications in biological simulation and drug discovery. Here, we present the use of free energy perturbation (FEP) calculations for the prediction of single-protein residue pK_a values. We begin with an initial set of 191 residues with experimentally determined pK_a values. To isolate sampling limitations from force field inaccuracies, we develop an algorithm to classify residues whose environments are significantly affected by crystal packing effects. We then report an approach to identify buried histidines that require significant sampling beyond what is achieved in typical FEP calculations. We therefore define a clean data set not requiring algorithms capable of predicting major conformational changes on which other pK_a prediction methods can be tested. On this data set, we report an RMSE of 0.76 pK_a units for 35 ASP residues, 0.51 pK_a units for 44 GLU residues, and 0.67 pK_a units for 76 HIS residues.

Residues L55 and W69 of Tva Mediate Entry of Subgroup A Avian Leukosis Virus

The receptor of the subgroup A avian leukosis virus (ALV-A) in chicken is Tva, which is the homologous protein of human CD320 (huCD320), contains a low-density lipoprotein (LDL-A) module and is involved in the uptake of transcobalamin bound vitamin B₁₂/cobalamin (Cbl). To map the functional determinants of Tva responsible for ALV-A receptor activity, a series of chimeric receptors were created by swapping the LDL-A module fragments between huCD320 and Tva. These chimeric receptors were then used for virus entry and binding assays to map the minimal ALV-A functional domain of Tva. The results showed that Tva residues 49 to 71 constituted the minimal functional domain that directly interacted with the ALV-A gp85 protein to mediate ALV-A entry. Single-residue substitution analysis revealed that L55 and W69, which were spatially adjacent on the surface of the Tva structure, were key residues that mediate ALV-A entry. Structural alignment results indicated that L55 and W69 substitutions did not affect the Tva protein structure but abolished the interaction force between Tva and gp85. Furthermore, substituting the corresponding residues of huCD320 with L55 and W69 of Tva converted huCD320 into a functional receptor of ALV-A. Importantly, soluble huCD320 harboring Tva L55 and W69 blocked ALV-A entry. Finally, we constructed a Tva gene-edited cell line with L55R and W69L substitutions that could fully resist ALV-A entry, while Cbl uptake was not affected. Collectively, our findings suggested that amino acids L55 and W69 of Tva were key for mediating virus entry. IMPORTANCE Retroviruses bind to cellular receptors through their envelope proteins, which is a crucial step in infection. While most retroviruses require two receptors for entry, ALV-A requires only one. Various Tva alleles conferring resistance to ALV-A, including Tva^r1 (C40W substitution), Tva^r2 (frame-shifting four-nucleotide insertion), Tva^r3, Tva^r4, Tva^r5, and Tva^r6 (deletion in the first intron), are known. However, the detailed entry mechanism of ALV-A in chickens remains to be explored. We demonstrated that Tva residues L55 and W69 were key for ALV-A entry and were important for correct interaction with ALV-A gp85. Soluble Tva and huCD320 harboring the Tva residues L55 and W69 effectively blocked ALV-A infection. Additionally, we constructed gene-edited cell lines targeting these two amino acids, which completely restricted ALV-A entry without affecting Cbl uptake. These findings contribute to a better understanding of the infection mechanism of ALV-A and provided novel insights into the prevention and control of ALV-A.

Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models

Homology models have been used for virtual screening and to understand the binding mode of a known active compound; however, rarely have the models been shown to be of sufficient accuracy, comparable to crystal structures, to support free-energy perturbation (FEP) calculations. We demonstrate here that the use of an advanced induced-fit docking methodology reliably enables predictive FEP calculations on congeneric series across homology models ≥30% sequence identity. Furthermore, we show that retrospective FEP calculations on a congeneric series of drug-like ligands are sufficient to discriminate between predicted binding modes. Results are presented for a total of 29 homology models for 14 protein targets, showing FEP results comparable to those obtained using experimentally determined crystal structures for 86% of homology models with template structure sequence identities ranging from 30 to 50%. Implications for the use and validation of homology models in drug discovery projects are discussed.

ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be used to predict various properties of interest, such as protein-protein binding affinities, or inhibitory concentrations (IC₅₀ ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC₅₀ of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

Large-scale application of free energy perturbation calculations for antibody design

Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.

Protein DEK and DTA Aptamers: Insight Into the Interaction Mechanisms and the Computational Aptamer Design

By blocking the DEK protein, DEK-targeted aptamers (DTAs) can reduce the formation of neutrophil extracellular traps (NETs) to reveal a strong anti-inflammatory efficacy in rheumatoid arthritis. However, the poor stability of DTA has greatly limited its clinical application. Thus, in order to design an aptamer with better stability, DTA was modified by methoxy groups (DTA_OMe) and then the exact DEK–DTA interaction mechanisms were explored through theoretical calculations. The corresponding 2′-OCH3-modified nucleotide force field was established and the molecular dynamics (MD) simulations were performed. It was proved that the 2′-OCH3-modification could definitely enhance the stability of DTA on the premise of comparative affinity. Furthermore, the electrostatic interaction contributed the most to the binding of DEK–DTA, which was the primary interaction to maintain stability, in addition to the non-specific interactions between positively-charged residues (e.g., Lys and Arg) of DEK and the negatively-charged phosphate backbone of aptamers. The H-bond network analysis reminded that eight bases could be mutated to probably enhance the affinity of DTA_OMe. Therein, replacing the 29th base from cytosine to thymine of DTA_OMe was theoretically confirmed to be with the best affinity and even better stability. These research studies imply to be a promising new aptamer design strategy for the treatment of inflammatory arthritis.

Alchemical Free Energy Calculations to Investigate Protein-Protein Interactions: the Case of the CDC42/PAK1 Complex

Here, we show that alchemical free energy calculations can quantitatively compute the effect of mutations at the protein–protein interface. As a test case, we have used the protein complex formed by the small Rho-GTPase CDC42 and its downstream effector PAK1, a serine/threonine kinase. Notably, the CDC42/PAK1 complex offers a wealth of structural, mutagenesis, and binding affinity data because of its central role in cellular signaling and cancer progression. In this context, we have considered 16 mutations in the CDC42/PAK1 complex and obtained excellent agreement between computed and experimental data on binding affinity. Importantly, we also show that a careful analysis of the side-chain conformations in the mutated amino acids can considerably improve the computed estimates, solving issues related to sampling limitations. Overall, this study demonstrates that alchemical free energy calculations can conveniently be integrated into the design of experimental mutagenesis studies.

Higher Affinity Antibodies Bind With Lower Hydration and Flexibility in Large Scale Simulations

We have carried out a long-timescale simulation study on crystal structures of nine antibody-antigen pairs, in antigen-bound and antibody-only forms, using molecular dynamics with enhanced sampling and an explicit water model to explore interface conformation and hydration. By combining atomic level simulation and replica exchange to enable full protein flexibility, we find significant numbers of bridging water molecules at the antibody-antigen interface. Additionally, a higher proportion of interactions excluding bulk waters and a lower degree of antigen bound CDR conformational sampling are correlated with higher antibody affinity. The CDR sampling supports enthalpically driven antibody binding, as opposed to entropically driven, in that the difference between antigen bound and unbound conformations do not correlate with affinity. We thus propose that interactions with waters and CDR sampling are aspects of the interface that may moderate antibody-antigen binding, and that explicit hydration and CDR flexibility should be considered to improve antibody affinity prediction and computational design workflows.

Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV

The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.

Vaccination has saved more lives than any other medical procedure. But, we do not have robust ways to develop vaccines against highly mutable pathogens. For example, there is no effective vaccine against HIV, and a universal vaccine against diverse strains of influenza is also not available. The development of immunization strategies to elicit antibodies that can neutralize diverse strains of highly mutable pathogens (so-called ‘broadly neutralizing antibodies’, or bnAbs) would enable the design of universal vaccines against such pathogens, as well as other viruses that may emerge in the future. In this paper, we present an agent-based model of affinity maturation–the Darwinian process by which antibodies evolve against a pathogen–that, for the first time, enables the in silico investigation of real germline nucleotide sequences of antibodies known to evolve into potent bnAbs, evolving against real amino acid sequences of HIV-based vaccine-candidate proteins. Our results provide new insights into bnAb evolution against HIV, and can be used to qualitatively guide the future design of vaccines against highly mutable pathogens.

Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation

Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of research, along with the complexity of the algorithm, time-consuming procedures, and reproducibility issues. In this review, we have discussed the advantages and disadvantages of diverse free energy methods like Thermodynamic Integration (TI), Bennett's Acceptance Ratio (BAR), Free Energy Perturbation (FEP), alchemical methods. Moreover, we discussed the possible application of the machine learning method in protein-ligand binding free energy estimation.

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

Free Energy-Based Computational Methods for the Study of Protein-Peptide Binding Equilibria

This chapter discusses the theory and application of physics-based free energy methods to estimate protein-peptide binding free energies. It presents a statistical mechanics formulation of molecular binding, which is then specialized in three methodologies: (1) alchemical absolute binding free energy estimation with implicit solvation, (2) alchemical relative binding free energy estimation with explicit solvation, and (3) potential of mean force binding free energy estimation. Case studies of protein-peptide binding application taken from the recent literature are discussed for each method.

Exploring space-energy matching via quantum-molecular mechanics modeling and breakage dynamics-energy dissipation via microhydrodynamic modeling to improve the screening efficiency of nanosuspension prepared by wet media milling

Introduction: The preparation of nanosuspensions by wet media milling is a promising technique that increases the bioavailability of insoluble drugs. The nanosuspension is thermodynamically unstable, where its stability might be influenced by the interaction energy between the stabilizers and the drugs after milling at a specific collision energy. However, it is difficult to screen the stabilizers and the parameters of milling accurately and quickly by using traditional analysis methods. Quantum-molecular mechanics and microhydrodynamic modeling can be applied to improve screening efficiency.Areas covered: Quantum-molecular mechanics model, which includes molecular docking, molecular dynamics simulations, and data on binding energy, provides insights into screening stabilizers based on their molecular behavior at the atomic level. The microhydrodynamic model explores the mechanical processes and energy dissipation in nanomilling, and even combines information on the mechanical modulus and an energy vector diagram for the milling parameters screening of drug crystals.Expert opinion: These modeling methods improve screening efficiency and support screening theories based on thermodynamics and physical dynamics. However, how to reasonably combine different modeling methods with their theoretical characteristics and further multidimensional and cross-scale simulations of nanosuspension formation remain challenges.

Surface salt bridges contribute to the extreme thermal stability of an FN3-like domain from a thermophilic bacterium

This study uses differential scanning calorimetry, X-ray crystallography, and molecular dynamics simulations to investigate the structural basis for the high thermal stability (melting temperature 97.5°C) of a FN3-like protein domain from thermophilic bacteria Thermoanaerobacter tengcongensis (FN3tt). FN3tt adopts a typical FN3 fold with a three-stranded beta sheet packing against a four-stranded beta sheet. We identified three solvent exposed arginine residues (R23, R25, and R72), which stabilize the protein through salt bridge interactions with glutamic acid residues on adjacent strands. Alanine mutation of the three arginine residues reduced melting temperature by up to 22°C. Crystal structures of the wild type (WT) and a thermally destabilized (∆Tm -19.7°C) triple mutant (R23L/R25T/R72I) were found to be nearly identical, suggesting that the destabilization is due to interactions of the arginine residues. Molecular dynamics simulations showed that the salt bridge interactions in the WT were stable and provided a dynamical explanation for the cooperativity observed between R23 and R25 based on calorimetry measurements. In addition, folding free energy changes computed using free energy perturbation molecular dynamics simulations showed high correlation with melting temperature changes. This work is another example of surface salt bridges contributing to the enhanced thermal stability of thermophilic proteins. The molecular dynamics simulation methods employed in this study may be broadly useful for in silico surface charge engineering of proteins.

The binding and specificity of chemokine binding proteins, through the lens of experiment and computation

Chemokines are small proteins that are critical for immune function, being primarily responsible for the activation and chemotaxis of leukocytes. As such, many viruses, as well as parasitic arthropods, have evolved systems to counteract chemokine function in order to maintain virulence, such as binding chemokines, mimicking chemokines, or producing analogs of transmembrane chemokine receptors that strongly bind their targets. The focus of this review is the large group of chemokine binding proteins (CBP) with an emphasis on those produced by mammalian viruses. Because many chemokines mediate inflammation, these CBP could possibly be used pharmaceutically as anti-inflammatory agents. In this review, we summarize the structural properties of a diverse set of CBP and describe in detail the chemokine binding properties of the poxvirus-encoded CBP called vCCI (viral CC Chemokine Inhibitor). Finally, we describe the current and emerging capabilities of combining computational simulation, structural analysis, and biochemical/biophysical experimentation to understand, and possibly re-engineer, protein–protein interactions.

Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations

Nicotinic acetylcholine receptor (nAChR) subtypes are key drug targets, but it is challenging to pharmacologically differentiate between them because of their highly similar sequence identities. Furthermore, α-conotoxins (α-CTXs) are naturally selective and competitive antagonists for nAChRs and hold great potential for treating nAChR disorders. Identifying selectivity-enhancing mutations is the chief aim of most α-CTX mutagenesis studies, although doing so with traditional docking methods is difficult due to the lack of α-CTX/nAChR crystal structures. Here, we use homology modeling to predict the structures of α-CTXs bound to two nearly identical nAChR subtypes, α3β2 and α3β4, and use free-energy perturbation (FEP) to re-predict the relative potency and selectivity of α-CTX mutants at these subtypes. First, we use three available crystal structures of the nAChR homologue, acetylcholine-binding protein (AChBP), and re-predict the relative affinities of twenty point mutations made to the α-CTXs LvIA, LsIA, and GIC, with an overall root mean square error (RMSE) of 1.08 ± 0.15 kcal/mol and an R2 of 0.62, equivalent to experimental uncertainty. We then use AChBP as a template for α3β2 and α3β4 nAChR homology models bound to the α-CTX LvIA and re-predict the potencies of eleven point mutations at both subtypes, with an overall RMSE of 0.85 ± 0.08 kcal/mol and an R2 of 0.49. This is significantly better than the widely used molecular mechanics-generalized born/surface area (MM-GB/SA) method, which gives an RMSE of 1.96 ± 0.24 kcal/mol and an R2 of 0.06 on the same test set. Next, we demonstrate that FEP accurately classifies α3β2 nAChR selective LvIA mutants while MM-GB/SA does not. Finally, we use FEP to perform an exhaustive amino acid mutational scan of LvIA and predict fifty-two mutations of LvIA to have greater than 100X selectivity for the α3β2 nAChR. Our results demonstrate the FEP is well-suited to accurately predict potency- and selectivity-enhancing mutations of α-CTXs for nAChRs and to identify alternative strategies for developing selective α-CTXs.

A strategy for proline and glycine mutations to proteins with alchemical free energy calculations

Computation of the thermodynamic consequences of protein mutations holds great promise in protein biophysics and design. Alchemical free energy methods can give improved estimates of mutational free energies, and are already widely used in calculations of relative and absolute binding free energies in small molecule design problems. In principle, alchemical methods can address any amino acid mutation with an appropriate alchemical pathway, but identifying a strategy that produces such a path for proline and glycine mutations is an ongoing challenge. Most current strategies perturb only side chain atoms, while proline and glycine mutations also alter the backbone parameters and backbone ring topology. Some strategies also perturb backbone parameters and enable glycine mutations. This work presents a strategy that enables both proline and glycine mutations and comprises two key elements: a dual backbone with restraints and scaling of bonded terms, facilitating backbone parameter changes, and a soft bond in the proline ring, enabling ring topology changes in proline mutations. These elements also have utility for core hopping and macrocycle studies in computer-aided drug design. This new strategy shows slight improvements over an alternative side chain perturbation strategy for a set T4 lysozyme mutations lacking proline and glycine, and yields good agreement with experiment for a set of T4 lysozyme proline and glycine mutations not previously studied. To our knowledge this is the first report comparing alchemical predictions of proline mutations with experiment. With this strategy in hand, alchemical methods now have access to the full palette of amino acid mutations.

Efficient In Silico Saturation Mutagenesis of a Member of the Caspase Protease Family

Rational-design methods have proven to be a valuable toolkit in the field of protein design. Numerical approaches such as free-energy calculations or QM/MM methods are fit to widen the understanding of a protein-sequence space but require large amounts of computational time and power. Here, we apply an efficient method for free-energy calculations that combines the one-step perturbation (OSP) with the third-power-fitting (TPF) approach. It is fit to calculate full free energies of binding from three different end states only. The nonpolar contribution to the free energies are calculated for a set of chosen amino acids from a single simulation of a judiciously chosen reference state. The electrostatic contributions, on the other hand, are predicted from simulations of the neutral and charged end states of the individual amino acids. We used this method to perform in silico saturation mutagenesis of two sites in human Caspase-2. We calculated relative binding free energies toward two different substrates that differ in their P1' site and in their affinity toward the unmutated protease. Although being approximate, our approach showed very good agreement upon validation against experimental data. 76% of the predicted relative free energies of amino acid mutations was found to be true positives or true negatives. We observed that this method is fit to discriminate amino acid mutations because the rate of false negatives is very low (<1.5%). The approach works better for a substrate with medium/low affinity with a Matthews correlation coefficient (MCC) of 0.63, whereas for a substrate with very low affinity, the MCC was 0.38. In all cases, the combined TPF + OSP approach outperformed the linear interaction energy method.

Potency-Enhancing Mutations of Gating Modifier Toxins for the Voltage-Gated Sodium Channel NaV1.7 Can Be Predicted Using Accurate Free-Energy Calculations

Gating modifier toxins (GMTs) isolated from venomous organisms such as Protoxin-II (ProTx-II) and Huwentoxin-IV (HwTx-IV) that inhibit the voltage-gated sodium channel NaV1.7 by binding to its voltage-sensing domain II (VSDII) have been extensively investigated as non-opioid analgesics. However, reliably predicting how a mutation to a GMT will affect its potency for NaV1.7 has been challenging. Here, we hypothesize that structure-based computational methods can be used to predict such changes. We employ free-energy perturbation (FEP), a physics-based simulation method for predicting the relative binding free energy (RBFE) between molecules, and the cryo electron microscopy (cryo-EM) structures of ProTx-II and HwTx-IV bound to VSDII of NaV1.7 to re-predict the relative potencies of forty-seven point mutants of these GMTs for NaV1.7. First, FEP predicted these relative potencies with an overall root mean square error (RMSE) of 1.0 ± 0.1 kcal/mol and an R2 value of 0.66, equivalent to experimental uncertainty and an improvement over the widely used molecular-mechanics/generalized born-surface area (MM-GB/SA) RBFE method that had an RMSE of 3.9 ± 0.8 kcal/mol. Second, inclusion of an explicit membrane model was needed for the GMTs to maintain stable binding poses during the FEP simulations. Third, MM-GB/SA and FEP were used to identify fifteen non-standard tryptophan mutants at ProTx-II[W24] predicted in silico to have a at least a 1 kcal/mol gain in potency. These predicted potency gains are likely due to the displacement of high-energy waters as identified by the WaterMap algorithm for calculating the positions and thermodynamic properties of water molecules in protein binding sites. Our results expand the domain of applicability of FEP and set the stage for its prospective use in biologics drug discovery programs involving GMTs and NaV1.7.

Computational Modulation of the V3 Region of Glycoprotein gp125 of HIV-2

HIV-2 infection is frequently neglected in HIV/AIDS campaigns. However, a special emphasis must be given to HIV-2 as an untreated infection that also leads to AIDS and death, and for which the efficacy of most available drugs is limited against HIV-2. HIV envelope glycoproteins mediate binding to the receptor CD4 and co-receptors at the surface of the target cell, enabling fusion with the cell membrane and viral entry. Here, we developed and optimized a computer-assisted drug design approach of an important HIV-2 glycoprotein that allows us to explore and gain further insights at the molecular level into protein structures and interactions crucial for the inhibition of HIV-2 cell entry. The 3D structure of a key HIV-2ROD gp125 region was generated by a homology modeling campaign. To disclose the importance of the main structural features and compare them with experimental results, 3D-models of six mutants were also generated. These mutations revealed the selective impact on the behavior of the protein. Furthermore, molecular dynamics simulations were performed to optimize the models, and the dynamic behavior was tackled to account for structure flexibility and interactions network formation. Structurally, the mutations studied lead to a loss of aromatic features, which is very important for the establishment of π-π interactions and could induce a structural preference by a specific coreceptor. These new insights into the structure-function relationship of HIV-2 gp125 V3 and surrounding regions will help in the design of better models and the design of new small molecules capable to inhibit the attachment and binding of HIV with host cells.

The Bipartite Sequence Motif in the N and C Termini of gp85 of Subgroup J Avian Leukosis Virus Plays a Crucial Role in Receptor Binding and Viral Entry

Subgroup J avian leukemia virus (ALV-J), belonging to the genus Alpharetrovirus, enters cells through its envelope surface unit (gp85) via specifically recognizing the cellular receptor chicken Na⁺/H⁺ exchanger type I (chNHE1), the 28 to 39 N-terminal residues of which were characterized as the minimal receptor functional domain in our previous studies. In this study, to further clarify the precise organization and properties of the interaction between ALV-J gp85 and chNHE1, we identified the chNHE1-binding domain of ALV-J gp85 using a series of gp85 mutants with segment substitutions and evaluating their effects on chNHE1 binding in protein-cell binding assays. Our results showed that hemagglutinin (HA) substitutions of amino acids (aa) 38 to 131 (N terminus of gp85) and aa 159 to 283 (C terminus of gp85) significantly inhibited the interaction between gp85 and chNHE1/chNHE1 loop 1. In addition, these HA-substituted chimeric gp85 proteins could not effectively block the entry of ALV-J into chNHE1-expressing cells. Furthermore, analysis of various N-linked glycosylation sites and cysteine mutants in gp85 revealed that glycosylation sites (N6 and N11) and cysteines (C3 and C9) were directly involved in receptor-gp85 binding and important for the entry of ALV-J into cells. Taken together, our findings indicated that the bipartite sequence motif, spanning aa 38 to 131 and aa 159 to 283, of ALV-J gp85 was essential for binding to chNHE1, with its two N-linked glycosylation sites and two cysteines being important for its receptor-binding function and subsequent viral infection steps.IMPORTANCE Infection of a cell by retroviruses requires the attachment and fusion of the host and viral membranes. The specific adsorption of envelope (Env) surface proteins to cell receptors is a key step in triggering infections and has been the target of antiviral drug screening. ALV-J is an economically important avian pathogen that belongs to the genus Alpharetrovirus and has a wider host range than other ALV subgroups. Our results showed that the amino acids 38 to 131 of the N terminus and 159 to 283 of the C terminus of ALV-J gp85 controlled the efficiency of gp85 binding to chNHE1 and were critical for viral infection. In addition, the glycosylation sites (N6 and N11) and cysteines (C3 and C9) of gp85 played a crucial role in the receptor binding and viral entry. These findings might help elucidate the mechanism of the entry of ALV-J into host cells and provide antiviral targets for the control of ALV-J.

Structure-based design and discovery of novel anti-tissue factor antibodies with cooperative double-point mutations, using interaction analysis

The generation of a wide range of candidate antibodies is important for the successful development of drugs that simultaneously satisfy multiple requirements. To find cooperative mutations and increase the diversity of mutants, an in silico double-point mutation approach, in which 3D models of all possible double-point mutant/antigen complexes are constructed and evaluated using interaction analysis, was developed. Starting from an antibody with very high affinity, four double-point mutants were designed in silico. Two of the double-point mutants exhibited improved affinity or affinity comparable to that of the starting antibody. The successful identification of two active double-point mutants showed that a cooperative mutation could be found by utilizing information regarding the interactions. The individual single-point mutants of the two active double-point mutants showed decreased affinity or no expression. These results suggested that the two active double-point mutants cannot be obtained through the usual approach i.e. a combination of improved single-point mutants. In addition, a triple-point mutant, which combines the distantly located active double-point mutation and an active single-point mutation collaterally obtained in the process of the double-point mutation strategy, was designed. The triple-point mutant showed improved affinity. This finding suggested that the effects of distantly located mutations are independent and additive. The double-point mutation approach using the interaction analysis of 3D structures expands the design repertoire for mutants, and hopefully paves a way for the identification of cooperative multiple-point mutations.

Advances in the calculation of binding free energies

In recent years, calculations of binding affinities from molecular simulations seem to have matured significantly. While the number of applications of such methods in drug design and biotechnology increases, the number of truly new methodological developments decreases. This review provides an overview of the current status of the field as reflected in recent publications. The focus is on the challenges that remain when using endstate, alchemical and pathway methods. For endstate methods this is the calculation of entropic contributions. For alchemical methods there are unsolved problems associated with the solvation of the active site, sampling slow degrees of freedom and when modifying the net charge. For pathway methods achieving sufficient sampling remains challenging. New trends are also highlighted, including the use of pathway methods for the quantification of protein-protein interactions.

Optimal designs for pairwise calculation: An application to free energy perturbation in minimizing prediction variability

Pairwise-based methods such as the free energy perturbation (FEP) method have been widely deployed to compute the binding free energy differences between two similar host-guest complexes. The calculated pairwise free energy difference is either directly adopted or transformed to absolute binding free energy for molecule rank ordering. We investigated, through both analytic derivations and simulations, how the selection of pairs in the experiment could impact the overall prediction precision. Our studies showed that (1) the estimated absolute binding free energy ( Δ G ^ ) derived from calculated pairwise differences (ΔΔG) through weighted least squares fitting is more precise in prediction than the pairwise difference values when the number of pairs is more than the number of ligands and (2) prediction precision is influenced by both the total number of pairs and the specifically selected pairs, the latter being critically important when the number of calculated pairs is limited. Furthermore, we applied optimal experimental design in pair selection and found that the optimally selected pairs can outperform randomly selected pairs in prediction precision. In an illustrative example, we showed that, upon weighing ligand structure similarity into design optimization, the weighted optimal designs are more efficient than the literature reported designs. This work provides a new approach to assess retrospective pairwise-based prediction results, and a method to design new prospective pairwise-based experiments for molecular lead optimization. © 2019 Wiley Periodicals, Inc.

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

The estimation of changes in free energy upon mutation is central to the problem of protein design. Modern protein design methods have had remarkable success over a wide range of design targets, but are reaching their limits in ligand binding and enzyme design due to insufficient accuracy in mutational free energies. Alchemical free energy calculations have the potential to supplement modern design methods through more accurate molecular dynamics based prediction of free energy changes, but suffer from high computational cost. Multisite λ dynamics (MSλD) is a particularly efficient and scalable free energy method with potential to explore combinatorially large sequence spaces inaccessible with other free energy methods. This work aims to quantify the accuracy of MSλD and demonstrate its scalability. We apply MSλD to the classic problem of calculating folding free energies in T4 lysozyme, a system with a wealth of experimental measurements. Single site mutants considering 32 mutations show remarkable agreement with experiment with a Pearson correlation of 0.914 and mean unsigned error of 1.19 kcal/mol. Multisite mutants in systems with up to five concurrent mutations spanning 240 different sequences show comparable agreement with experiment. These results demonstrate the promise of MSλD in exploring large sequence spaces for protein design.

SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation

Motivation

Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.

Results

We present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations, which abolish detectable binding.

Availability and implementation

The database is available as supplementary data and at https://life.bsc.es/pid/skempi2/.

Supplementary information

Supplementary data are available at Bioinformatics online.