Predicting the Impact of Deleterious Mutations in the Protein Kinase Domain of FGFR2 in the Context of Function, Structure, and Pathogenesis—a Bioinformatics Approach

Fibroblast growth factor receptor 2 (FGFR2) controls a wide range of biological functions by regulating the cellular proliferation, survival, migration and differentiation. A growing body of preclinical data demonstrated that deregulation of the FGFR signalling through genetic modification was observed in various types of cancers. However, the extent to which genetic modifications interfere with gene regulation and their involvement in cancer susceptibility remains largely unknown. In this work, we performed in silico profiling of harmful non-synonymous single nucleotide polymorphisms (SNPs) in the protein kinase domain of FGFR2. Tolerance index, position-specific independent count score, change in free energy score (ΔΔG), Eris and FoldX indicated that seven mutations were found to be deleterious and may alter the protein function and structure. Furthermore, based on physico-chemical properties, two mutations K659N and R747H were found to be most deleterious in protein kinase domain and taken for further structural analysis. Docking study showed a complete loss of binding affinity followed by interference in hydrogen bonding and surrounding residues due to K659N and R747H mutations. In order to elucidate the mechanism behind the impact of mutation that can generate a ripple effect throughout the protein structure and ultimately affect the function, in-depth molecular dynamics simulation and principal component analysis were performed. The obtained results indicate that K659N and R747H mutations have a distinct effect on the dynamic behaviour of FGFR2 protein. Our strategy may be helpful for understanding SNP effects on proteins with function and their role in human genetic diseases and for the development of novel pharmacological strategies.     

Prediction and analysis of binding affinities for chemically diverse HIV-1 PR inhibitors by the modified SAFE_p approach

One of the biggest challenges in the "in silico" screening of enzyme ligands is to have a protocol that could predict the ligand binding free energies. In our group we have developed a very simple screening function (referred to as solvent accessibility free energy of binding predictor, SAFE_p) which we have applied previously to the study of peptidic HIV-1 protease (HIV-1 PR) inhibitors and later to cyclic urea type HIV-1 PR inhibitors. In this work, we have extended the SAFE_p protocol to a chemically diverse set of HIV-1 PR inhibitors with binding constants that differ by several orders of magnitude. The resulting function is able to reproduce the ranking and in many cases the value of the inhibitor binding affinities for the HIV-1 PR, with accuracy comparable with that of costlier protocols. We also demonstrate that the binding pocket SAFE_p analysis can contribute to the understanding of the physical forces that participate in ligand binding. The analysis tools afforded by our protocol have allowed us to identify an induced fit phenomena mediated by the inhibitor and have demonstrated that larger fragments do not necessarily contribute the most to the binding free energy, an outcome partially brought about by the substantial role the desolvation penalty plays in the energetics of binding. Finally, we have revisited the effect of the Asp dyad protonation state on the predicted binding affinities.     

Rational in silico design of aptamers for organophosphates based on the example of paraoxon

Poisoning by organophosphates (OPs) takes one of the leading places in the total number of exotoxicoses. Detoxication of OPs at the first stage of the poison entering the body could be achieved with the help of DNA- or RNA-aptamers, which are able to bind poisons in the bloodstream. The aim of the research was to develop an approach to rational in silico design of aptamers for OPs based on the example of paraoxon. From the published sequence of an aptamer binding organophosphorus pesticides, its three-dimensional model has been constructed. The most probable binding site for paraoxon was determined by molecular docking and molecular dynamics (MD) methods. Then the nucleotides of the binding site were mutated consequently and the values of free binding energy have been calculated using MD trajectories and MM-PBSA approach. On the basis of the energy values, two sequences that bind paraoxon most efficiently have been selected. The value of free binding energy of paraoxon with peripheral anionic site of acetylcholinesterase (AChE) has been calculated as well. It has been revealed that the aptamers found bind paraoxon more effectively than AChE. The peculiarities of paraoxon interaction with the aptamers nucleotides have been analyzed. The possibility of improving in silico approach for aptamer selection is discussed.     

A critical cross-validation of high throughput structural binding prediction methods for pMHC

T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces. The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000 binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools. Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely, the threading of the peptide, energy minimization and scoring.     

Functional assessment of “in vivo” and “in silico” mutations in the guanine binding site of RNase T1: A DFT study

Experimentally, the functional assessment of amino acid side chains in proteins is carried out by comparing parameters such as binding constants for the wild‐type protein and a mutant protein in which the considered side chain is deleted. In the present study, we apply a density functional theory (DFT) methodology to obtain changes in binding energy upon mutations in the enzyme ribonuclease T₁. Mutant structures were either taken directly from crystallographic data (“in vivo”) allowing for conformational changes upon mutation, or derived from the wild‐type (“in silico”). Excluding entropic contributions, the computed interaction energy changes upon mutation in vivo correlate qualitatively well with experimental binding free energy changes. In contrast, the in silico approach does not perform as well, especially for residues that contribute largely to binding. Subsequently, we assessed the applicability of the in vivo approach by analyzing the functional cooperativity between pairs of side chains. © 2004 Wiley Periodicals, Inc. Int J Quantum Chem, 2004     

Predicting Fixation Tendencies of the H3N2 Influenza Virus by Free Energy Calculation

Influenza virus evolves to escape from immune system antibodies that bind to it. We used free energy calculations with Einstein crystals as reference states to calculate the difference of antibody binding free energy (ΔΔG) induced by amino acid substitution at each position in epitope B of the H3N2 influenza hemagglutinin, the key target for antibody. A substitution with positive ΔΔG value decreases the antibody binding constant. On average an uncharged to charged amino acid substitution generates the highest ΔΔG values. Also on average, substitutions between small amino acids generate ΔΔG values near to zero. The 21 sites in epitope B have varying expected free energy differences for a random substitution. Historical amino acid substitutions in epitope B for the A/Aichi/2/1968 strain of influenza A show that most fixed and temporarily circulating substitutions generate positive ΔΔG values. We propose that the observed pattern of H3N2 virus evolution is affected by the free energy landscape, the mapping from the free energy landscape to virus fitness landscape, and random genetic drift of the virus. Monte Carlo simulations of virus evolution are presented to support this view.     

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.     

Fruit Bromelain-Derived Peptide Potentially Restrains the Attachment of SARS-CoV-2 Variants to hACE2: A Pharmacoinformatics Approach

Before entering the cell, the SARS-CoV-2 spike glycoprotein receptor-binding domain (RBD) binds to the human angiotensin-converting enzyme 2 (hACE2) receptor. Hence, this RBD is a critical target for the development of antiviral agents. Recent studies have discovered that SARS-CoV-2 variants with mutations in the RBD have spread globally. The purpose of this in silico study was to determine the potential of a fruit bromelain-derived peptide. DYGAVNEVK. to inhibit the entry of various SARS-CoV-2 variants into human cells by targeting the hACE binding site within the RBD. Molecular docking analysis revealed that DYGAVNEVK interacts with several critical RBD binding residues responsible for the adhesion of the RBD to hACE2. Moreover, 100 ns MD simulations revealed stable interactions between DYGAVNEVK and RBD variants derived from the trajectory of root-mean-square deviation (RMSD), radius of gyration (Rg), and root-mean-square fluctuation (RMSF) analysis, as well as free binding energy calculations. Overall, our computational results indicate that DYGAVNEVK warrants further investigation as a candidate for preventing SARS-CoV-2 due to its interaction with the RBD of SARS-CoV-2 variants.     

Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.     

Inhibitor docking screened by the modified SAFE_p scoring function: application to cyclic urea HIV-1 PR inhibitors

Our laboratory has in the past developed a method for the prediction of ligand binding free energies to proteins, referred to as SAFE_p (Solvent free energy predictor). Previously, we have applied this protocol for the prediction of the binding free energy of peptidic and cyclic urea HIV-1 PR inhibitors, whose X-ray structures bound to enzyme are known. In this work, we present the first account of a docking simulation, where the ligand conformations were screened and inhibitor ranking was predicted on the basis of a modified SAFE_p approach, for a set of cyclic urea-HIV-1 PR complexes whose structures are not known. We show that the optimal dielectric constant for docking is rather high, in line with the values needed to reproduce some protein residue properties, like pKa's. Our protocol is able to reproduce most of the observed binding ranking, even in the case that the components of the equation are not fitted to experimental data. Partition of the binding free energy into pocket and residue contributions sheds light into the importance of the inhibitor's fragments and on the prediction of "hot spots" for resistance mutations.     

Computational alanine scanning mutagenesis--an improved methodological approach

Alanine scanning mutagenesis of protein-protein interfacial residues can be applied to a wide variety of protein complexes to understand the structural and energetic characteristics of the hot-spots. Binding free energies have been estimated with reasonable accuracy with empirical methods, such as Molecular Mechanics/Poisson-Boltzmann surface area (MM-PBSA), and with more rigorous computational approaches like Free Energy Perturbation (FEP) and Thermodynamic Integration (TI). The main objective of this work is the development of an improved methodological approach, with less computational cost, that predicts accurately differences in binding free energies between the wild-type and alanine mutated complexes (DeltaDeltaG(binding)). The method was applied to three complexes, and a mean unsigned error of 0.80 kcal/mol was obtained in a set of 46 mutations. The computational method presented here achieved an overall success rate of 80% and an 82% success rate in residues for which alanine mutation causes an increase in the binding free energy > 2.0 kcal/mol (warm- and hot-spots). This fully atomistic computational methodological approach consists in a computational Molecular Dynamics simulation protocol performed in a continuum medium using the Generalized Born model. A set of three different internal dielectric constants, to mimic the different degree of relaxation of the interface when different types of amino acids are mutated for alanine, have to be used for the proteins, depending on the type of amino acid that is mutated. This method permits a systematic scanning mutagenesis of protein-protein interfaces and it is capable of anticipating the experimental results of mutagenesis, thus guiding new experimental investigations.     

In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions

The notion of a contribution of a specific group in an organic molecule’s property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a molecule. The fragment- based drug discovery (FBDD) approach has proven to be fruitful in addressing the above notions. The main difficulty of the FBDD, however, is in its reliance on the low throughput and expensive experimental means of determining the fragment-sized molecules binding. In this article we propose a way to enhance the throughput and availability of the FBDD methods by judiciously using an in silico means of assessing the contribution to ligand-receptor binding energy of fragments of a molecule under question using a previously developed in silico Reverse Fragment Based Drug Discovery (R-FBDD) approach. It has been shown that the proposed structure-based drug discovery (SBDD) type of approach fills in the vacant niche among the existing in silico approaches, which mainly stem from the ligand-based drug discovery (LBDD) counterparts. In order to illustrate the applicability of the approach, our work retrospectively repeats the findings of the use case of an FBDD hit-to-lead project devoted to the experimentally based determination of additive group efficiency (GE)—an analog of ligand efficiency (LE) for a group in the molecule—using the Free-Wilson (FW) decomposition. It is shown that in using our in silico approach to evaluate fragment contributions of a ligand and to estimate GE one can arrive at similar decisions as those made using the experimentally determined activity-based FW decomposition. It is also shown that the approach is rather robust to the choice of the scoring function, provided the latter demonstrates a decent scoring power. We argue that the proposed approach of in silico assessment of GE has a wider applicability domain and expect that it will be widely applicable to enhance the net throughput of drug discovery based on the FBDD paradigm.     

X-Ray Crystallography and Free Energy Calculations Reveal the Binding Mechanism of A2A Adenosine Receptor Antagonists

We present a robust protocol based on iterations of free energy perturbation (FEP) calculations, chemical synthesis, biophysical mapping and X-ray crystallography to reveal the binding mode of an antagonist series to the A_2A adenosine receptor (AR). Eight A_2AAR binding site mutations from biophysical mapping experiments were initially analyzed with sidechain FEP simulations, performed on alternate binding modes. The results distinctively supported one binding mode, which was subsequently used to design new chromone derivatives. Their affinities for the A_2AAR were experimentally determined and investigated through a cycle of ligand-FEP calculations, validating the binding orientation of the different chemical substituents proposed. Subsequent X-ray crystallography of the A_2AAR with a low and a high affinity chromone derivative confirmed the predicted binding orientation. The new molecules and structures here reported were driven by free energy calculations, and provide new insights on antagonist binding to the A_2AAR, an emerging target in immuno-oncology.     

Engineering the Pseudomonas aeruginosa II lectin: designing mutants with changed affinity and specificity

This article focuses on designing mutations of the PA-IIL lectin from Pseudomonas aeruginosa that lead to change in specificity. Following the previous results revealing the importance of the amino acid triad 22–23–24 (so-called specificity-binding loop), saturation in silico mutagenesis was performed, with the intent of finding mutations that increase the lectin’s affinity and modify its specificity. For that purpose, a combination of docking, molecular dynamics and binding free energy calculation was used. The combination of methods revealed mutations that changed the performance of the wild-type lectin and its mutants to their preferred partners. The mutation at position 22 resulted in 85 % in inactivation of the binding site, and the mutation at 23 did not have strong effects thanks to the side chain being pointed away from the binding site. Molecular dynamics simulations followed by binding free energy calculation were performed on mutants with promising results from docking, and also at those where the amino acid at position 24 was replaced for bulkier or longer polar chain. The key mutants were also prepared in vitro and their binding properties determined by isothermal titration calorimetry. Combination of the used methods proved to be able to predict changes in the lectin performance and helped in explaining the data observed experimentally.     

X‐Ray Crystallography and Free Energy Calculations Reveal the Binding Mechanism of A2A Adenosine Receptor Antagonists

We present a robust protocol based on iterations of free energy perturbation (FEP) calculations, chemical synthesis, biophysical mapping and X‐ray crystallography to reveal the binding mode of an antagonist series to the A_2A adenosine receptor (AR). Eight A_2AAR binding site mutations from biophysical mapping experiments were initially analyzed with sidechain FEP simulations, performed on alternate binding modes. The results distinctively supported one binding mode, which was subsequently used to design new chromone derivatives. Their affinities for the A_2AAR were experimentally determined and investigated through a cycle of ligand‐FEP calculations, validating the binding orientation of the different chemical substituents proposed. Subsequent X‐ray crystallography of the A_2AAR with a low and a high affinity chromone derivative confirmed the predicted binding orientation. The new molecules and structures here reported were driven by free energy calculations, and provide new insights on antagonist binding to the A_2AAR, an emerging target in immuno‐oncology.     

Sampling and convergence in free energy calculations of protein-ligand interactions: The binding of triphenoxypyridine derivatives to factor Xa and trypsin

The binding of a set of 10 triphenoxypyridine derivatives to two serine proteases, factor Xa and trypsin, has been used to analyze factors related to sampling and convergence in free energy calculations based on molecular dynamics simulation techniques. The inhibitors investigated were initially proposed as part of the Critical Assessment of Techniques for Free Energy Evaluation (CATFEE) project for which no experimental results nor any assessment of the predictions submitted by various groups have ever been published. The inhibitors studied represent a severe challenge for explicit free energy calculations. The mutations from one compound to another involve up to 19 atoms, the creation and annihilation of net charge and several alternate binding modes. Nevertheless, we demonstrate that it is possible to obtain highly converged results (+/- 5-10 kJ/mol) even for such complex multi-atom mutations by simulating on a nanosecond time scale. This is achieved by using soft-core potentials to facilitate the creation and deletion of atoms and by a careful choice of mutation pathway. The results show that given modest computational resources, explicit free energy calculations can be successfully applied to realistic problems in drug design.     

The Confine-and-Release Method: Obtaining Correct Binding Free Energies in the Presence of Protein Conformational Change

Free energy calculations are increasingly being used to estimate absolute and relative binding free energies of ligands to proteins. However, computed free energies often appear to depend on the initial protein conformation, indicating incomplete sampling. This is especially true when proteins can change conformation on ligand binding, as free energies associated with these conformational changes are either ignored or assumed to be included by virtue of the sampling performed in the calculation. Here, we show that, in a model protein system (a designed binding site in T4 Lysozyme), conformational changes can make a difference of several kcal/mol in computed binding free energies, and that they are neglected in computed binding free energies if the system remains kinetically trapped in a particular metastable state on simulation timescales. We introduce a general "confine-and-release" framework for free energy calculations that accounts for these free energies of conformational change. We illustrate its use in this model system by demonstrating that an umbrella sampling protocol can obtain converged binding free energies that are independent of the starting protein structure and include these conformational change free energies.     

Electrostatic component of binding energy: Interpreting predictions from poisson–boltzmann equation and modeling protocols

Macromolecular interactions are essential for understanding numerous biological processes and are typically characterized by the binding free energy. Important component of the binding free energy is the electrostatics, which is frequently modeled via the solutions of the Poisson–Boltzmann Equations (PBE). However, numerous works have shown that the electrostatic component (ΔΔG_elec) of binding free energy is very sensitive to the parameters used and modeling protocol. This prompted some researchers to question the robustness of PBE in predicting ΔΔG_elec. We argue that the sensitivity of the absolute ΔΔG_elec calculated with PBE using different input parameters and definitions does not indicate PBE deficiency, rather this is what should be expected. We show how the apparent sensitivity should be interpreted in terms of the underlying changes in several numerous and physical parameters. We demonstrate that PBE approach is robust within each considered force field (CHARMM‐27, AMBER‐94, and OPLS‐AA) once the corresponding structures are energy minimized. This observation holds despite of using two different molecular surface definitions, pointing again that PBE delivers consistent results within particular force field. The fact that PBE delivered ΔΔG_elec values may differ if calculated with different modeling protocols is not a deficiency of PBE, but natural results of the differences of the force field parameters and potential functions for energy minimization. In addition, while the absolute ΔΔG_elec values calculated with different force field differ, their ordering remains practically the same allowing for consistent ranking despite of the force field used. © 2016 Wiley Periodicals, Inc.