Comparing pharmacophore models derived from crystal structures and from molecular dynamics simulations

A combination of structure-based virtual screening and experimental strategies to identify the potency of caffeic acid ester derivatives as SARS-CoV-2 3CLpro inhibitor from an in-house database

Drug development requires significant time and resources, and computer-aided drug discovery techniques that integrate chemical and biological spaces offer valuable tools for the process. This study focused on the field of COVID-19 therapeutics and aimed to identify new active non-covalent inhibitors for 3CLpro, a key protein target. By combining in silico and in vitro approaches, an in-house database was utilized to identify potential inhibitors. The drug-likeness criteria were considered to pre-filter 553 compounds from 12 groups of natural products. Using structure-based virtual screening, 296 compounds were identified that matched the chemical features of SARS-CoV-2 3CLpro peptidomimetic inhibitor pharmacophore models. Subsequent molecular docking resulted in 43 hits with high binding affinities. Among the hits, caffeic acid analogs showed significant interactions with the 3CLpro active site, indicating their potential as promising candidates. To further evaluate their efficacy, enzyme-based assays were conducted, revealing that two ester derivatives of caffeic acid (4k and 4l) exhibited more than a 30% reduction in 3CLpro activity. Overall, these findings suggest that the screening approach employed in this study holds promise for the discovery of novel anti-SARS-CoV-2 therapeutics. Furthermore, the methodology could be extended for optimization or retrospective evaluation to enhance molecular targeting and antiviral efficacy of potential drug candidates.

Identification of potential COVID-19 main protease inhibitors using structure-based pharmacophore approach, molecular docking and repurposing studies

The current outbreak of novel coronavirus (COVID-19) infections urges the need to identify potential therapeutic agents. Therefore, the repurposing of FDA-approved drugs against today's diseases involves the use of de-risked compounds with potentially lower costs and shorter development timelines. In this study, the recently resolved X-ray crystallographic structure of COVID-19 main protease (Mpro) was used to generate a pharmacophore model and to conduct a docking study to capture antiviral drugs as new promising COVID-19 main protease inhibitors. The developed pharmacophore successfully captured five FDA-approved antiviral drugs (lopinavir, remdesivir, ritonavir, saquinavir and raltegravir). The five drugs were successfully docked into the binding site of COVID-19 Mpro and showed several specific binding interactions that were comparable to those tying the co-crystallized inhibitor X77 inside the binding site of COVID-19 Mpro. Three of the captured drugs namely, remdesivir, lopinavir and ritonavir, were reported to have promising results in COVID-19 treatment and therefore increases the confidence in our results. Our findings suggest an additional possible mechanism of action for remdesivir as an antiviral drug inhibiting COVID-19 Mpro. Additionally, a combination of structure-based pharmacophore modeling with a docking study is expected to facilitate the discovery of novel COVID-19 Mpro inhibitors.

Rutin: A Potential Antiviral for Repurposing as a SARS-CoV-2 Main Protease (Mpro) Inhibitor

Various computational studies, including in silico ones, have identified several existing compounds that could serve as effective inhibitors of the SARS-CoV-2 main protease (Mpro), and thus preventing replication of the virus. Among these, rutin has been identified as a potential hit, having prominent binding affinity to the virus. Moreover, its presence in several traditional antiviral medicines prescribed in China to infected patients with mild to moderate symptoms of COVID-19 justify its promise as a repurposed bioactive secondary metabolite against SARS-CoV-2.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Insights of structure-based pharmacophore studies and inhibitor design against Gal3 receptor through molecular dynamics simulations

Our present work studies the structure-based pharmacophore modeling and designing inhibitor against Gal3 receptor through molecular dynamics (MD) simulations extensively. Pharmacophore models play a key role in computer-aided drug discovery like in the case of virtual screening of chemical databases, de novo drug design and lead optimization. Structure-based methods for developing pharmacophore models are important, and there have been a number of studies combining such methods with the use of MD simulations to model protein's flexibility. The two potential antagonists SNAP 37889 and SNAP 398299 were docked and simulated for 250 ns and the results are analyzed and carried for the structure-based pharmacophore studies. This helped in identification of the subtype selectivity of the binding sites of the Gal3 receptor. Our work mainly focuses on identifying these binding site residues and to design more potent inhibitors compared to the previously available inhibitors through pharmacophore models. The study provides crucial insight into the binding site residues Ala2, Asp3, Ala4, Gln5, Phe24, Gln79, Ala80, Ile82, Tyr83, Trp88, His99, Ile102, Tyr103, Met106, Tyr157, Tyr161, Pro174, Trp176, Arg181, Ala183, Leu184, Asp185, Thr188, Trp248, His251, His252, Ile255, Leu256, Phe258, Trp259, Tyr270, Arg273, Leu274 and His277, which plays a significant role in the conformational changes of the receptor and helps to understand the inhibition mechanism. Communicated by Ramaswamy H. Sarma.

Exploring Novel N-Myristoyltransferase Inhibitors: A Molecular Dynamics Simulation Approach

N-Myristoyltransferase (NMT) is a cytosolic monomeric enzyme involved in the allocation of the myristoyl group to the aminoterminal of glycine in several viral and eukaryotic cellular proteins. NMT has been validated as a potential drug target against kinetoplastid for parasitic protozoa. A multistep virtual screening protocol based on the pharmacophore modeling, molecular docking, and molecular dynamics simulation was carried out. Initially, Maybridge database was virtually screened via a validated pharmacophore model. The effective pharmacophore models were accompanied with exclusion volumes to improve their receiver operating characteristic curve to identify potential NMT inhibitors. The hits identified as actives based on the 3D-pharmacophore model were evaluated by molecular docking studies. In stepwise screening, six compounds were shortlisted for the dynamic simulation to get insights into their binding mode. In conclusion, this study provides fundamental information about the architecture of the binding site and some crucial residues that may provide insights into the development of new antiparasitic agents.

Computer-based techniques for lead identification and optimization II: Advanced search methods

This paper focuses on advanced computational techniques for identifying and optimizing lead molecules, such as metadynamics and a novel dynamic 3D pharmacophore analysis method called Dynophores. In this paper, the first application of the funnel metadynamics of the Berberine binding to G-quadruplex DNA is depicted, disclosing hints for drug design, in particular clarifying water’s role and suggesting the design of derivatives able to replace the solvent-mediated interactions between ligand and DNA to achieve more potent and selective activity. Secondly, the novel dynamic pharmacophore approach is an extension of the classic 3D pharmacophores, with statistical and sequential information about the conformational flexibility of a molecular system derived from molecular dynamics (MD) simulations.

Revisiting fish toxicity of active pharmaceutical ingredients: Mechanistic insights from integrated ligand-/structure-based assessments on acetylcholinesterase

The release of active pharmaceutical ingredients (APIs) into the environment is of great concern for aquatic ecosystem as many of these chemicals are designed to exert biological activity. Hence, their impact on non-target organisms like fish would not be surprising. In this respect, we revisited fish toxicity data of pharmaceuticals to generate linear and non-linear quantitative structure-toxicity relationships (QSTRs). We predicted fish lethality data from the validated QSTR models for 120 APIs with no experimental fish toxicity data. Toxicity of APIs on aquatic organisms is not fully characterized. Therefore, to provide a mechanistic insight for the assessment of API's toxicity to fish, the outcome of the derived QSTR models was integrated with structure-based toxicophore and molecular docking studies, utilizing the biomarker enzyme acetylcholinesterase originating from fish Torpedo californica (TcAChE). Toxicophore virtual screening of 60 chemicals with pT > 0 identified 23 hits as potential TcAChE binders with binding free energies ranging from -6.5 to -12.9 kcal/mol. The TcAChE-ligand interaction analysis revealed a good nesting of all 23 hits within TcAChE binding site through establishing strong lipophilic and hydrogen bonding interactions with the surrounding key amino acid residues. Among the chemicals passing the criteria of our integrated approach, majority of APIs belong noticeably to the Central Nervous System class. The screened chemicals displayed not only comprehensive toxicophore coverage, but also strong binding affinities according to the docking calculations, mainly due to interactions with TcAChE's key amino acid residues Tyr121, Tyr130, Tyr334, Trp84, Phe290, Phe330, Phe331, Ser122, and Ser200. Moreover, we propose here that binding of pharmaceuticals to AChE might have a potential in triggering molecular initiating events for adverse outcome pathways (AOPs), which in turn can play an important role for future screening of APIs lacking fish lethality data.

An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.

A Molecular Dynamics-Shared Pharmacophore Approach to Boost Early-Enrichment Virtual Screening: A Case Study on Peroxisome Proliferator-Activated Receptor α

Molecular dynamics (MD) simulations can be used, prior to virtual screening, to add flexibility to proteins and study them in a dynamic way. Furthermore, the use of multiple crystal structures of the same protein containing different co-crystallized ligands can help elucidate the role of the ligand on a protein's active conformation, and then explore the most common interactions between small molecules and the receptor. In this work, we evaluated the contribution of the combined use of MD on crystal structures containing the same protein but different ligands to examine the crucial ligand-protein interactions within the complexes. The study was carried out on peroxisome proliferator-activated receptor α (PPARα). Findings derived from the dynamic analysis of interactions were then used as features for pharmacophore generation and constraints for generating the docking grid for use in virtual screening. We found that information derived from short multiple MD simulations using different molecules within the binding pocket of the target can improve the early enrichment of active ligands in the virtual screening process for this receptor. In the end we adopted a consensus scoring based on docking score and pharmacophore alignment to rank our dataset. Our results showed an improvement in virtual screening performance in early recognition when screening was performed with the Molecular dYnamics SHAred PharmacophorE (MYSHAPE) approach.

All-Atom Molecular Dynamics Simulations of Entire Virus Capsid Reveal the Role of Ion Distribution in Capsid's Stability

Present experimental methods do not have sufficient resolution to investigate all processes in virus particles at atomistic details. We report the results of molecular dynamics simulations and analyze the connection between the number of ions inside an empty capsid of PCV2 virus and its stability. We compare the crystallographic structures of the capsids with unresolved N-termini and without them in realistic conditions (room temperature and aqueous solution) and show that the structure is preserved. We find that the chloride ions play a key role in the stability of the capsid. A low number of chloride ions results in loss of the native icosahedral symmetry, while an optimal number of chloride ions create a neutralizing layer next to the positively charged inner surface of the capsid. Understanding the dependence of the capsid stability on the distribution of the ions will help clarify the details of the viral life cycle that is ultimately connected to the role of packaged viral genome inside the capsid.

Structure-based discovery of potentially active semiochemicals for Cydia pomonella (L.)

The development of physiologically active semiochemicals is largely limited by the labor-consuming searching process. How to screen active semiochemicals efficiently is of significance to the extension of behavior regulation in pest control. Here pharmacophore modeling and shape-based virtual screening were combined to predict candidate ligands for Cydia pomonella pheromone binding protein 1 (CpomPBP1). Out of the predicted compounds, ETrME displayed the highest affinity to CpomPBP1. Further studies on the interaction between CpomPBP1 and ETrME, not only depicted the binding mode, but also revealed residues providing negative and positive contributions to the ETrME binding. Moreover, key residues involved in interacting with ETrME of CpomPBP1 were determined as well. These findings were significant to providing insights for the future searching and optimization of active semiochemicals.