Detailed computational study of the active site of the hepatitis C viral RNA polymerase to aid novel drug design

Combining machine learning, molecular dynamics, and free energy analysis for (5HT)-2A receptor modulator classification

The 5-Hydroxytryptamine (5HT)-2A receptor, a key target in psychoactive drug development, presents significant challenges in the design of selective compounds. Here, we describe the construction, evaluation and validation of two machine learning (ML) models for the classification of bioactivity mechanisms against the (5HT)-2A receptor. Employing neural networks and XGBoost models, we achieved an overall accuracy of around 87 %, which was further enhanced through molecular modelling (MM) (e.g. molecular dynamics simulations) and binding free energy analysis. This ML-MM integration provided insights into the mechanisms of direct modulators and prodrugs. A significant outcome of the current study is the development of a 'binding free energy fingerprint' specific to (5HT)-2A modulators, offering a novel metric for evaluating drug efficacy against this target. Our study demonstrates the prospective of employing a successful workflow combining AI with structural biology, offering a powerful tool for advancing psychoactive drug discovery.

"Hit" to lead optimization and chemoinformatic studies for a new series of Autotaxin inhibitors

Robust experimental evidence has highlighted the role of Autotaxin (ATX)/Lysophosphatidic acid (LPA) axis not only in the pathogenesis of chronic inflammatory conditions and especially in fibroproliferative diseases but also in several types of cancer. As a result, different series of substrate-, lipid-based and small-molecule ATX inhibitors have been identified thus far by both academia and pharma. The "crowning achievement" of these drug discovery campaigns was the development and entry of the first-in-class ATX inhibitor (ziritaxestat, GLPG-1690) in advanced clinical trials against idiopathic pulmonary fibrosis. Herein, the potency optimization efforts of a new series of Autotaxin inhibitors, namely 2-substituted-2,6-dihydro-4H-thieno[3,4-c]pyrazol-1-substituted amide, is described using a previously identified novel chemical scaffold as a "hit". The mode of inhibition of the most promising ATX inhibitors was investigated, while their cellular activity, aqueous solubility and cytotoxicity were evaluated. Our pharmacological results were corroborated by chemoinformatic tools (molecular docking and molecular dynamics simulations) deployed, to provide insight into the binding mechanism of the synthesized inhibitors to ATX.

The Inhibitory Potential of 2'-dihalo Ribonucleotides against HCV: Molecular Docking, Molecular Simulations, MM-BPSA, and DFT Studies

Sofosbuvir is the first approved direct-acting antiviral (DAA) agent that inhibits the HCV NS5B polymerase, resulting in chain termination. The molecular models of the 2′-dihalo ribonucleotides used were based on experimental biological studies of HCV polymerase inhibitors. They were modeled within HCV GT1a and GT1b to understand the structure–activity relationship (SAR) and the binding interaction of the halogen atoms at the active site of NS5B polymerase using different computational approaches. The outputs of the molecular docking studies indicated the correct binding mode of the tested compounds against the active sites in target receptors, exhibiting good binding free energies. Interestingly, the change in the substitution at the ribose sugar was found to produce a mild effect on the binding mode. In detail, increasing the hydrophobicity of the substituted moieties resulted in a better binding affinity. Furthermore, in silico ADMET investigation implied the general drug likeness of the examined derivatives. Specifically, good oral absorptions, no BBB penetration, and no CYP4502D6 inhibitions were expected. Likely, the in silico toxicity studies against several animal models showed no carcinogenicity and high predicted TD50 values. The DFT studies exhibited a bioisosteric effect between the substituents at the 2′-position and the possible steric clash between 2′-substituted nucleoside analogs and the active site in the target enzyme. Finally, compound 6 was subjected to several molecular dynamics (MD) simulations and MM-PBSA studies to examine the protein-ligand dynamic and energetic stability.

A 'deep dive' into the SARS-Cov-2 polymerase assembly: identifying novel allosteric sites and analyzing the hydrogen bond networks and correlated dynamics

Replication of the SARS-CoV-2 genome is a fundamental step in the virus life cycle and inhibiting the SARS-CoV2 replicase machinery has been proven recently as a promising approach in combating the virus. Despite this recent success, there are still several aspects related to the structure, function and dynamics of the CoV-2 polymerase that still need to be addressed. This includes understanding the dynamicity of the various polymerase subdomains, analyzing the hydrogen bond networks at the active site and at the template entry in the presence of water, studying the binding modes of the nucleotides at the active site, highlighting positions for acceptable nucleotides' substitutions that can be tolerated at different positions within the nascent RNA strand, identifying possible allosteric sites within the polymerase structure and studying their correlated dynamics relative to the catalytic site. Here, we combined various cutting-edge modelling tools with the recently resolved SARS-CoV-2 cryo-EM polymerase structures to fill this gap in knowledge. Our findings provide a detailed analysis of the hydrogen bond networks at various parts of the polymerase structure and suggest possible nucleotides' substitutions that can be tolerated by the polymerase complex. We also report here three 'druggable' allosteric sites within the NSP12 RdRp that can be targeted by small molecule inhibitors. Our correlated motion analysis shows that the dynamics within one of the newly identified sites are linked to the active site, indicating that targeting this site can significantly impact the catalytic activity of the SARS-CoV-2 polymerase.Communicated by Ramaswamy H. Sarma.

The application of the MM/GBSA method in the binding pose prediction of FGFR inhibitors

The success of a structure-based drug is highly dependent on a known binding pose of the protein-ligand system. However, this is not always available. In this study, we set out to explore the applicability of the popular and easy-to-use MD-based MM/GBSA method to determine the binding poses of known FGFR inhibitors. It was found that MM/GBSA combined with 100 ns of MD simulation significantly improved the success rate of docking methods from 30-40% to 70%. This work demonstrates a way that the MM/GBSA method can be more accurate than it is in ligand ranking, filling a gap in structure-based drug discovery when the binding pose is unknown.

Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H

The rapid emergence of drug-resistant variants is one of the most common causes of highly active antiretroviral therapeutic (HAART) failure in patients infected with HIV-1. Compared with the existing HAART, the recently developed pyrrolyl diketo acid scaffold targeting both HIV-1 integrase (IN) and reverse transcriptase-associated ribonuclease H (RNase H) is an efficient approach to counteract the failure of anti-HIV treatment due to drug resistance. However, the binding mode and potential resistance profile of these inhibitors with important mechanistic principles remain poorly understood. To address this issue, an integrated computational method was employed to investigate the binding mode of inhibitor JMC6F with HIV-1 IN and RNase H. By using per-residue binding free energy decomposition analysis, the following residues: Asp64, Thr66, Leu68, Asp116, Tyr143, Gln148 and Glu152 in IN, Asp443, Glu478, Trp536, Lys541 and Asp549 in RNase H were identified as key residues for JMC6F binding. And then computational alanine scanning was carried to further verify the key residues. Moreover, the resistance profile of the currently known major mutations in HIV-1 IN and 2 mutations in RNase H against JMC6F was predicted by in silico mutagenesis studies. The results demonstrated that only three mutations in HIV-1 IN (Y143C, Q148R and N155H) and two mutations in HIV-1 RNase H (Y501R and Y501W) resulted in a reduction of JMC6F potency, thus indicating their potential role in providing resistance to JMC6F. These data provided important insights into the binding mode and resistance profile of the inhibitors with a pyrrolyl diketo acid scaffold in HIV-1 IN and RNase H, which would be helpful for the development of more effective dual HIV-1 IN and RNase H inhibitors.

Targeting HCV polymerase: a structural and dynamic perspective into the mechanism of selective covalent inhibition

Background: Concerns have been raised over the emerging pandemic status of hepatitis C virus (HCV). Current available drugs lack specificity, stability and potency. The HCV NS5B RNA-dependent RNA polymerase (RdRp) is a vital component in viral replication and is often targeted in antiviral therapies. Recent experimental procedures have led to the discovery of a novel covalent RdRp inhibitor, compound 47, which selectively targets cysteine 366 of the HCV RdRp and exhibits promising pharmacokinetic outcomes. Selective covalent inhibition of HCV is, however, a highly neglected subject in the literature, that is reinforced by the lack of efficient structure-based drug design protocols. In this paper, an atomistic insight into a novel selective approach to inhibit HCV RdRp is provided. Methodology/Results: Covalent molecular dynamic analyses revealed the inhibitory mechanism of compound 47 on the RdRp. Inhibitor binding induced distinctive internal movements resulting in the disruption of normal physiological interdomain interactions. Conclusion: Compound 47 stimulates reorganization of key protein elements required for RNA transcription, thus hampering viral replication as well as disrupting the overall conformation of HCV. This study will open new lines of approach for the design of novel selective inhibitors against HCV as well as other viral families.

Effects of protein-protein interactions and ligand binding on the ion permeation in KCNQ1 potassium channel

The voltage-gated KCNQ1 potassium ion channel interacts with the type I transmembrane protein minK (KCNE1) to generate the slow delayed rectifier (I_Ks) current in the heart. Mutations in these transmembrane proteins have been linked with several heart-related issues, including long QT syndromes (LQTS), congenital atrial fibrillation, and short QT syndrome. Off-target interactions of several drugs with that of KCNQ1/KCNE1 ion channel complex have been known to cause fatal cardiac irregularities. Thus, KCNQ1/KCNE1 remains an important avenue for drug-design and discovery research. In this work, we present the structural and mechanistic details of potassium ion permeation through an open KCNQ1 structural model using the combined molecular dynamics and steered molecular dynamics simulations. We discuss the processes and key residues involved in the permeation of a potassium ion through the KCNQ1 ion channel, and how the ion permeation is affected by (i) the KCNQ1-KCNE1 interactions and (ii) the binding of chromanol 293B ligand and its derivatives into the complex. The results reveal that interactions between KCNQ1 with KCNE1 causes a pore constriction in the former, which in-turn forms small energetic barriers in the ion-permeation pathway. These findings correlate with the previous experimental reports that interactions of KCNE1 dramatically slows the activation of KCNQ1. Upon ligand-binding onto the complex, the energy-barriers along ion permeation path are more pronounced, as expected, therefore, requiring higher force in our steered-MD simulations. Nevertheless, pulling the ion when a weak blocker is bound to the channel does not necessitate high force in SMD. This indicates that our SMD simulations have been able to discern between strong and week blockers and reveal their influence on potassium ion permeation. The findings presented here will have some implications in understanding the potential off-target interactions of the drugs with the KCNQ1/KCNE1 channel that lead to cardiotoxic effects.

Quantum molecular modeling of hepatitis C virus inhibition through non-structural protein 5B polymerase receptor binding of C5-arylidene rhodanines

We have carried out high-level quantum chemical computations followed by molecular docking studies on a set of 17C₅-arylidene rhodanine isomers to provide insights into the binding modes with different reported binding pockets of the nonstructural protein 5B (NS5B) polymerase that contribute to the hepatitis C virus (HCV) inhibition. We optimized the multi-target profile of the selected rhodanine analogs to investigate potential non-nucleotide inhibitors (NNIs) by quantum chemical optimization of the 18 isomers followed by docking with quantum chemically optimized structures of each isomer with NS5B polymerase at multiple binding pockets. The binding affinities of the PP-I, PP-II and TP-II pockets of NS5B polymerase were analyzed for all the 17 isomers of 2-[(5Z)-5-(2,4-dichlorobenzylidene)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]-3-phenylpropanoic acid. On the basis of binding propensity at the different pockets and inhibitor constants, we ranked these isomers as potential candidates for the HCV inhibition. We have identified four isomers as promising NNIs of NS5B polymerase with comparable binding and inhibition to the standard (1,3) dichloro substituted isomer that exhibits in vitro activity and several other isomers as candidates in a "multi-targeted drug" approach.

Molecular Dynamics Simulation and Prediction of Druggable Binding Sites

Binding site identification and druggability evaluation are two essential steps in structure-based drug design. A druggable binding site tends to have high binding affinity to drug-like molecules. Predicting such sites can have a significant impact on a drug design campaign. This chapter focuses on summarizing the different methods that are used to predict druggable binding sites. The chapter also discusses the importance of including protein flexibility in the search process and the use of molecular dynamics simulations to address this aspect. Case studies from the literature are also summarized and discussed. We hope that this chapter would provide an overview on the different methods employed in binding site identification evaluation.

Natural products used as a chemical library for protein-protein interaction targeted drug discovery

Protein-protein interactions (PPIs), which are essential for cellular processes, have been recognized as attractive therapeutic targets. Therefore, the construction of a PPI-focused chemical library is an inevitable necessity for future drug discovery. Natural products have been used as traditional medicines to treat human diseases for millennia; in addition, their molecular scaffolds have been used in diverse approved drugs and drug candidates. The recent discovery of the ability of natural products to inhibit PPIs led us to use natural products as a chemical library for PPI-targeted drug discovery. In this study, we collected natural products (NPDB) from non-commercial and in-house databases to analyze their similarities to small-molecule PPI inhibitors (iPPIs) and FDA-approved drugs by using eight molecular descriptors. Then, we evaluated the distribution of NPDB and iPPIs in the chemical space, represented by the molecular fingerprint and molecular scaffolds, to identify the promising scaffolds, which could interfere with PPIs. To investigate the ability of natural products to inhibit PPI targets, molecular docking was used. Then, we predicted a set of high-potency natural products by using the iPPI-likeness score based on a docking score-weighted model. These selected natural products showed high binding affinities to the PPI target, namely XIAP, which were validated in an in vitro experiment. In addition, the natural products with novel scaffolds might provide a promising starting point for further medicinal chemistry developments. Overall, our study shows the potency of natural products in targeting PPIs, which might help in the design of a PPI-focused chemical library for future drug discovery.

Modeling the human Nav1.5 sodium channel: structural and mechanistic insights of ion permeation and drug blockade

Abnormalities in the human Nav1.5 (hNav1.5) voltage-gated sodium ion channel (VGSC) are associated with a wide range of cardiac problems and diseases in humans. Current structural models of hNav1.5 are still far from complete and, consequently, their ability to study atomistic interactions of this channel is very limited. Here, we report a comprehensive atomistic model of the hNav1.5 ion channel, constructed using homology modeling technique and refined through long molecular dynamics simulations (680 ns) in the lipid membrane bilayer. Our model was comprehensively validated by using reported mutagenesis data, comparisons with previous models, and binding to a panel of known hNav1.5 blockers. The relatively long classical MD simulation was sufficient to observe a natural sodium permeation event across the channel's selectivity filters to reach the channel's central cavity, together with the identification of a unique role of the lysine residue. Electrostatic potential calculations revealed the existence of two potential binding sites for the sodium ion at the outer selectivity filters. To obtain further mechanistic insight into the permeation event from the central cavity to the intracellular region of the channel, we further employed "state-of-the-art" steered molecular dynamics (SMD) simulations. Our SMD simulations revealed two different pathways through which a sodium ion can be expelled from the channel. Further, the SMD simulations identified the key residues that are likely to control these processes. Finally, we discuss the potential binding modes of a panel of known hNav1.5 blockers to our structural model of hNav1.5. We believe that the data presented here will enhance our understanding of the structure-property relationships of the hNav1.5 ion channel and the underlying molecular mechanisms in sodium ion permeation and drug interactions. The results presented here could be useful for designing safer drugs that do not block the hNav1.5 channel.

Applications of computer-aided approaches in the development of hepatitis C antiviral agents

INTRODUCTION

Hepatitis C virus (HCV) is a global health problem that causes several chronic life-threatening liver diseases. The numbers of people affected by HCV are rising annually. Since 2011, the FDA has approved several anti-HCV drugs; while many other promising HCV drugs are currently in late clinical trials. Areas covered: This review discusses the applications of different computational approaches in HCV drug design. Expert opinion: Molecular docking and virtual screening approaches have emerged as a low-cost tool to screen large databases and identify potential small-molecule hits against HCV targets. Ligand-based approaches are useful for filtering-out compounds with rich physicochemical properties to inhibit HCV targets. Molecular dynamics (MD) remains a useful tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in HCV. Despite their varied roles, the application of in-silico approaches in HCV drug design is still in its infancy. A more mature application should aim at modelling the whole HCV replicon in its active form and help to identify new effective druggable sites within the replicon system. With more technological advancements, the roles of computer-aided methods are only going to increase several folds in the development of next-generation HCV drugs.

Role of R292K mutation in influenza H7N9 neuraminidase toward oseltamivir susceptibility: MD and MM/PB(GB)SA study

The H7N9 avian influenza virus is a novel re-assortment from at least four different strains of virus. Neuraminidase, which is a glycoprotein on the surface membrane, has been the target for drug treatment. However, some H7N9 strains that have been isolated from patient after drug treatment have a R292K mutation in neuraminidase. This substitution was found to facilitate drug resistance using protein- and virus- assays, in particular it gave a high resistance to the most commonly used drug, oseltamivir. The aim of this research is to understand the source of oseltamivir resistance using MD simulations and the MM/PB(GB)SA binding free energy approaches. Both methods can predict the reduced susceptibility of oseltamivir in good agreement to the IC ₅₀ binding energy, although MM/GBSA underestimates this prediction compared to the MM/PBSA calculation. Electrostatic interaction is the main contribution for oseltamivir binding in terms of both interaction and solvation. We found that the source of the drug resistance is a decrease in the binding interaction combined with the reduction of the dehydration penalty. The smaller K292 mutated residue has a larger binding pocket cavity compared to the wild-type resulting in the loss of drug carboxylate-K292 hydrogen bonding and an increased accessibility for water molecules around the K292 mutated residue. In addition, oseltamivir does not bind well to the R292K mutant complex as shown by the high degree of fluctuation in ligand RMSD during the simulation and the change in angular distribution of bulky side chain groups.

Entropy in bimolecular simulations: A comprehensive review of atomic fluctuations-based methods

Entropy of binding constitutes a major, and in many cases a detrimental, component of the binding affinity in biomolecular interactions. While the enthalpic part of the binding free energy is easier to calculate, estimating the entropy of binding is further more complicated. A precise evaluation of entropy requires a comprehensive exploration of the complete phase space of the interacting entities. As this task is extremely hard to accomplish in the context of conventional molecular simulations, calculating entropy has involved many approximations. Most of these golden standard methods focused on developing a reliable estimation of the conformational part of the entropy. Here, we review these methods with a particular emphasis on the different techniques that extract entropy from atomic fluctuations. The theoretical formalisms behind each method is explained highlighting its strengths as well as its limitations, followed by a description of a number of case studies for each method. We hope that this brief, yet comprehensive, review provides a useful tool to understand these methods and realize the practical issues that may arise in such calculations.

The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes

Among all tools available to design new drugs, molecular dynamics (MD) simulations have become an essential technique. Initially developed to investigate molecular models with a limited number of atoms, computers now enable investigations of large macromolecular systems with a simulation time reaching the microsecond range. The reviewed articles cover four years of research to give an overview on the actual impact of MD on the current medicinal chemistry landscape with a particular emphasis on studies of ligand-protein interactions. With a special focus on studies combining computational approaches with data gained from other techniques, this review shows how deeply embedded MD simulations are in drug design strategies and articulates what the future of this technique could be.

Daclatasvir-like inhibitors of NS5A block early biogenesis of hepatitis C virus-induced membranous replication factories, independent of RNA replication

BACKGROUND & AIMS

Direct-acting antivirals that target nonstructural protein 5A (NS5A), such as daclatasvir, have high potency against the hepatitis C virus (HCV). They are promising clinical candidates, yet little is known about their antiviral mechanisms. We investigated the mechanisms of daclatasvir derivatives.

METHODS

We used a combination of biochemical assays, in silico docking models, and high-resolution imaging to investigate inhibitor-induced changes in properties of NS5A, including its interaction with phosphatidylinositol-4 kinase IIIα and induction of the membranous web, which is the site of HCV replication. Analyses were conducted with replicons, infectious virus, and human hepatoma cells that express a HCV polyprotein. Studies included a set of daclatasvir derivatives and HCV variants with the NS5A inhibitor class-defining resistance mutation Y93H.

RESULTS

NS5A inhibitors did not affect NS5A stability or dimerization. A daclatasvir derivative interacted with NS5A and molecular docking studies revealed a plausible mode by which the inhibitor bound to NS5A dimers. This interaction was impaired in mutant forms of NS5A that are resistant to daclatavir, providing a possible explanation for the reduced sensitivity of the HCV variants to this drug. Potent NS5A inhibitors were found to block HCV replication by preventing formation of the membranous web, which was not linked to an inhibition of phosphatidylinositol-4 kinase IIIα. Correlative light-electron microscopy revealed unequivocally that NS5A inhibitors had no overall effect on the subcellular distribution of NS5A, but completely prevented biogenesis of the membranous web.

CONCLUSIONS

Highly potent inhibitors of NS5A, such as daclatasvir, block replication of HCV RNA at the stage of membranous web biogenesis-a new paradigm in antiviral therapy.

A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity

Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-á-go-go-related (hERG) K(+) ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model.

A Refined Model of the HCV NS5A protein bound to daclatasvir explains drug-resistant mutations and activity against divergent genotypes

Many direct-acting antiviral agents (DAAs) that selectively block hepatitis C virus (HCV) replication are currently under development. Among these agents is Daclatasvir, a first-in-class inhibitor targeting the NS5A viral protein. Although Daclatasvir is the most potent HCV antiviral molecule yet developed, its binding location and mode of binding remain unknown. The drug exhibits a low barrier to resistance mutations, particularly in genotype 1 viruses, but its efficacy against other genotypes is unclear. Using state-of-the-art modeling techniques combined with the massive computational power of Blue Gene/Q, we identified the atomic interactions of Daclatasvir within NS5A for different HCV genotypes and for several reported resistant mutations. The proposed model is the first to reveal the detailed binding mode of Daclatasvir. It also provides a tool to facilitate design of second generation drugs, which may confer less resistance and/or broader activity against HCV.