Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Peptide Affinity Chromatography Applied to Therapeutic Antibodies Purification

The interest in therapeutic monoclonal antibodies (mAbs) has significantly grown in the pharmaceutical industry, exceeding 100 FDA mAbs approved. Although the upstream processing of their industrial production has been significantly improved in the last years, the downstream processing still depends on immobilized protein A affinity chromatography. The high cost, low capacity and short half-life of immobilized protein A chromatography matrices, encouraged the design of alternative short-peptide ligands for mAb purification. Most of these peptides have been obtained by screening combinatorial peptide libraries. These low-cost ligands can be easily produced by solid-phase peptide synthesis and can be immobilized on chromatographic supports, thus obtaining matrices with high capacity and selectivity. Furthermore, matrices with immobilized peptide ligands have longer half-life than those with protein A due to the higher stability of the peptides. In this review the design and synthesis of peptide ligands, their immobilization on chromatographic supports and the evaluation of the affinity supports for their application in mAb purification is described.

Molecular Modeling of Signal Peptide Recognition by Eukaryotic Sec Complexes

Here, we review recent molecular modelling and simulation studies of the Sec translocon, the primary component/channel of protein translocation into the endoplasmic reticulum (ER) and bacterial periplasm, respectively. Our focus is placed on the eukaryotic Sec61, but we also mention modelling studies on prokaryotic SecY since both systems operate in related ways. Cryo-EM structures are now available for different conformational states of the Sec61 complex, ranging from the idle or closed state over an inhibited state with the inhibitor mycolactone bound near the lateral gate, up to a translocating state with bound substrate peptide in the translocation pore. For all these states, computational studies have addressed the conformational dynamics of the translocon with respect to the pore ring, the plug region, and the lateral gate. Also, molecular simulations are addressing mechanistic issues of insertion into the ER membrane vs. translocation into the ER, how signal-peptides are recognised at all in the translocation pore, and how accessory proteins affect the Sec61 conformation in the co- and post-translational pathways.

Peptide-Based Inhibitors for SARS-CoV-2 and SARS-CoV

The COVID-19 (coronavirus disease) global pandemic, caused by the spread of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus, currently has limited treatment options which include vaccines, anti-virals, and repurposed therapeutics. With their high specificity, tunability, and biocompatibility, small molecules like peptides are positioned to act as key players in combating SARS-CoV-2, and can be readily modified to match viral mutation rate. A recent expansion of the understanding of the viral structure and entry mechanisms has led to the proliferation of therapeutic viral entry inhibitors. In this comprehensive review, inhibitors of SARS and SARS-CoV-2 are investigated and discussed based on therapeutic design, inhibitory mechanistic approaches, and common targets. Peptide therapeutics are highlighted, which have demonstrated in vitro or in vivo efficacy, discuss advantages of peptide therapeutics, and common strategies in identifying targets for viral inhibition.

Glioma-Targeted Therapeutics: Computer-Aided Drug Design Prospective

Amongst the several types of brain cancers known to humankind, glioma is one of the most severe and life-threatening types of cancer, comprising 40% of all primary brain tumors. Recent reports have shown the incident rate of gliomas to be 6 per 100,000 individuals per year globally. Despite the various therapeutics used in the treatment of glioma, patient survival rate remains at a median of 15 months after undergoing first-line treatment including surgery, radiation, and chemotherapy with Temozolomide. As such, the discovery of newer and more effective therapeutic agents is imperative for patient survival rate. The advent of computer-aided drug design in the development of drug discovery has emerged as a powerful means to ascertain potential hit compounds with distinctively high therapeutic effectiveness against glioma. This review encompasses the recent advances of bio-computational in-silico modeling that have elicited the discovery of small molecule inhibitors and/or drugs against various therapeutic targets in glioma. The relevant information provided in this report will assist researchers, especially in the drug design domains, to develop more effective therapeutics against this global disease.

Combining Electrospray Mass Spectrometry (ESI-MS) and Computational Techniques in the Assessment of G-Quadruplex Ligands: A Hybrid Approach to Optimize Hit Discovery

Guanine-rich sequences forming G-quadruplexes (GQs) are present in several genomes, ranging from viral to human. Given their peculiar localization, the induction of GQ formation or GQ stabilization with small molecules represents a strategy for interfering with crucial biological functions. Investigating the recognition event at the molecular level, with the aim of fully understanding the triggered pharmacological effects, is challenging. Native electrospray ionization mass spectrometry (ESI-MS) is being optimized to study these noncovalent assemblies. Quantitative parameters retrieved from ESI-MS studies, such as binding affinity, the equilibrium binding constant, and sequence selectivity, will be overviewed. Computational experiments supporting the ESI-MS investigation and boosting its efficiency in the search for GQ ligands will also be discussed with practical examples. The combination of ESI-MS and in silico techniques in a hybrid high-throughput-screening workflow represents a valuable tool for the medicinal chemist, providing data on the quantitative and structural aspects of ligand–GQ interactions.

Characteristics of Food Protein-Derived Antidiabetic Bioactive Peptides: A Literature Update

Diabetes, a glucose metabolic disorder, is considered one of the biggest challenges associated with a complex complication of health crises in the modern lifestyle. Inhibition or reduction of the dipeptidyl peptidase IV (DPP-IV), alpha-glucosidase, and protein-tyrosine phosphatase 1B (PTP-1B) enzyme activities or expressions are notably considered as the promising therapeutic strategies for the management of type 2 diabetes (T2D). Various food protein-derived antidiabetic bioactive peptides have been isolated and verified. This review provides an overview of the DPP-IV, PTP-1B, and α-glucosidase inhibitors, and updates on the methods for the discovery of DPP-IV inhibitory peptides released from food-protein hydrolysate. The finding of novel bioactive peptides involves studies about the strategy of separation fractionation, the identification of peptide sequences, and the evaluation of peptide characteristics in vitro, in silico, in situ, and in vivo. The potential of bioactive peptides suggests useful applications in the prevention and management of diabetes. Furthermore, evidence of clinical studies is necessary for the validation of these peptides’ efficiencies before commercial applications.

Three novel piperidones exhibit tumor-selective cytotoxicity on leukemia cells via protein degradation and stress-mediated mechanisms

Background

Cancer is an ongoing worldwide health problem. Although chemotherapy remains the mainstay therapy for cancer, it is not always effective and has detrimental side effects. Here, we present piperidone compounds P3, P4, and P5 that selectively target cancer cells via protein- and stress-mediated mechanisms.

Methods

We assessed typical apoptotic markers including phosphatidylserine externalization, caspase-3 activation, and DNA fragmentation through flow cytometry. Then, specific markers of the intrinsic pathway of apoptosis including the depolarization of the mitochondria and the generation of reactive oxygen species (ROS) were investigated. Finally, we utilized western blot techniques, RT-qPCR, and observed the cell cycle profile after compound treatment to evaluate the possible behavior of these compounds as proteasome inhibitors. For statistical analyses, we employed the one-way ANOVA followed by Bonferroni post hoc test.

Results

P3, P4, and P5 induce cytotoxic effects towards tumorigenic cells, as opposed to non-cancerous cells, at the low micromolar range. Compound treatment leads to the activation of the intrinsic pathway of apoptosis. The accumulation of poly-ubiquitinated proteins and the pro-apoptotic protein Noxa, both typically observed after proteasome inhibition, occurs after P3, P4, and P5 treatment. The stress-related genes PMAIP1, ATF3, CHAC1, MYC, and HMOX-1 were differentially regulated to contribute to the cytotoxic activity of P3–P5. Finally, compound P5 causes cell cycle arrest at the G₂/M phase.

Conclusion

Taken together, compounds P3, P4, and P5 exhibit strong potential as anticancer drug candidates as shown by strong cytotoxic potential, activation of the intrinsic pathway of apoptosis, and show typical proteasome inhibitor characteristics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43440-021-00322-3.

Antiviral Potential of Plants against Noroviruses

Human noroviruses, which belong to the enterovirus family, are one of the most common etiological agents of food-borne diseases. In recent years, intensive research has been carried out regarding the antiviral activity of plant metabolites that could be used for the preservation of fresh food, because they are safer for consumption when compared to synthetic chemicals. Plant preparations with proven antimicrobial activity differ in their chemical compositions, which significantly affects their biological activity. Our review aimed to present the results of research related to the characteristics, applicability, and mechanisms of the action of various plant-based preparations and metabolites against norovirus. New strategies to combat intestinal viruses are necessary, not only to ensure food safety and reduce infections in humans but also to lower the direct health costs associated with them.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: the Main Protease (Mpro) Case Study

The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Corilagin and 1,3,6-Tri-O-galloy-β-D-glucose: potential inhibitors of SARS-CoV-2 variants

The COVID-19 disease caused by the virus SARS-CoV-2, first detected in December 2019, is still emerging through virus mutations. Although almost under control in some countries due to effective vaccines that are mitigating the worldwide pandemic, the urgency to develop additional vaccines and therapeutic treatments is imperative. In this work, the natural polyphenols corilagin and 1,3,6-tri-O-galloy-β-d-glucose (TGG) are investigated to determine the structural basis of inhibitor interactions as potential candidates to inhibit SARS-CoV-2 viral entry into target cells. First, the therapeutic potential of the ligands are assessed on the ACE2/wild-type RBD. We first use molecular docking followed by molecular dynamics, to take into account the conformational flexibility that plays a significant role in ligand binding and that cannot be captured using only docking, and then analyze more precisely the affinity of these ligands using MMPBSA binding free energy. We show that both ligands bind to the ACE2/wild-type RBD interface with good affinities which might prevent the ACE2/RBD association. Second, we confirm the potency of these ligands to block the ACE2/RBD association using a combination of surface plasmon resonance and biochemical inhibition assays. These experiments confirm that TGG and, to a lesser extent, corilagin, inhibit the binding of RBD to ACE2. Both experiments and simulations show that the ligands interact preferentially with RBD, while weak binding is observed with ACE2, hence, avoiding potential physiological side-effects induced by the inhibition of ACE2. In addition to the wild-type RBD, we also study numerically three RBD mutations (E484K, N501Y and E484K/N501Y) found in the main SARS-CoV-2 variants of concerns. We find that corilagin could be as effective for RBD/E484K but less effective for the RBD/N501Y and RBD/E484K-N501Y mutants, while TGG strongly binds at relevant locations to all three mutants, demonstrating the significant interest of these molecules as potential inhibitors for variants of SARS-CoV-2.

Computational Chemistry to Repurposing Drugs for the Control of COVID-19

Thus far, in 2021, 219 countries with over 175 million people have been infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is a positive sense, single-stranded RNA virus, and is the causal agent for coronavirus disease (COVID-19). Due to the urgency of the situation, virtual screening as a computational modeling method offers a fast and effective modality of identifying drugs that may be effective against SARS-CoV-2. There has been an overwhelming abundance of molecular docking against SARS-CoV-2 in the last year. Due to the massive volume of computational studies, this systematic review has been created to evaluate and summarize the findings of existing studies. Herein, we report on computational articles of drugs which target, (1) viral protease, (2) Spike protein-ACE 2 interaction, (3) RNA-dependent RNA polymerase, and (4) other proteins and nonstructural proteins of SARS-CoV-2. Based on the studies presented, there are 55 identified natural or drug compounds with potential anti-viral activity. The next step is to show anti-viral activity in vitro and translation to determine effectiveness into human clinical trials.

Identification of Natural Compounds to Inhibit Sonic Hedgehog Pathway in Oral Cancer

BACKGROUND

Conventional treatment resistance remains a significant problem in cancer care. Cancer stem cells might play a major role in treatment resistance, and as a result, basic stem cell pathways are instrumental in cancer. Sonic Hedgehog signaling has not been widely studied in oral cancer, and being one of the major cancer stem cell pathways, targeting it with natural compounds could open many opportunities in the treatment scenario.

OBJECTIVE

The objective of the study was to identify the role of various natural compounds as an anti-cancer agent for oral cancer by targeting the Hedgehog signaling pathway.

METHODS

The selection of natural compounds were identified through literature review and NPACT database. The protein (3M1N and 3MXW) and ligand molecules were retrieved through the PDB and PubChem database. To carry out docking experiments, the AutoDock 4.2 program was used to study the interaction between the identified protein and ligand.

RESULTS

Among the 13 identified natural compounds, the top three were selected based on their binding energy. The higher the binding energy on the negative side, the better the interaction formed between protein and ligand. The natural compound showing best results with 3M1N protein were Butein, Biochanin-A, and Curcumin, whereas, with 3MXW, Zerumbone, Curcumin, and Butein were identified.

CONCLUSION

The identified natural compounds have shown better binding energy to bind the Hh ligands in the absence/presence of a known Sonic Hedgehog inhibitor. Based on the results, natural compounds can be utilized in the current treatment modality for oral cancer either as an individual anti-cancer agent or in combination with the known Sonic Hedgehog inhibitor to curb the increasing incidence rate. Yet, in-vitro evidence in lab setup is required.

Potential drugs against COVID-19 revealed by gene expression profile, molecular docking and molecular dynamic simulation

Aim: SARS-CoV-2, an emerging betacoronavirus, is the causative agent of COVID-19. Currently, there are few specific and selective antiviral drugs for the treatment and vaccines to prevent contagion. However, their long-term effects can be revealed after several years, and new drugs for COVID-19 should continue to be investigated. Materials & methods: In the first step of our study we identified, through a gene expression analysis, several drugs that could act on the biological pathways altered in COVID-19. In the second step, we performed a docking simulation to test the properties of the identified drugs to target SARS-CoV-2. Results: The drugs that showed a higher binding affinity are bardoxolone (-8.78 kcal/mol), irinotecan (-8.40 kcal/mol) and pyrotinib (-8.40 kcal/mol). Conclusion: We suggested some drugs that could be efficient in treating COVID-19.

Detection of Binding Sites on SARS-CoV-2 Spike Protein Receptor-Binding Domain by Molecular Dynamics Simulations in Mixed Solvents

The novel SARS-CoV-2 uses ACE2 (Angiotensin-Converting Enzyme 2) receptor as an entry point. Insights on S protein receptor-binding domain (RBD) interaction with ACE2 receptor and drug repurposing has accelerated drug discovery for the novel SARS-CoV-2 infection. Finding small molecule binding sites in S protein and ACE2 interface is crucial in search of effective drugs to prevent viral entry. In this study, we employed molecular dynamics simulations in mixed solvents together with virtual screening to identify small molecules that could be potential inhibitors of S protein -ACE2 interaction. Observation of organic probe molecule localization during the simulations revealed multiple sites at the S protein surface related to small molecule, antibody, and ACE2 binding. In addition, a novel conformation of the S protein was discovered that could be stabilized by small molecules to inhibit attachment to ACE2. The most promising binding site on RBD-ACE2 interface was targeted with virtual screening and top-ranked compounds (DB08248, DB02651, DB03714, and DB14826) are suggested for experimental testing. The protocol described here offers an extremely fast method for characterizing key proteins of a novel pathogen and for the identification of compounds that could inhibit or accelerate spreading of the disease.

In silico modeling of the interaction between TEX19 and LIRE1, and analysis of TEX19 gene missense SNPs

BACKGROUND

Testis expressed 19 (TEX19) is a specific human stem cell gene identified as cancer-testis antigen (CTA), which emerged as a potential therapeutic drug target. TEX19.1, a mouse paralog of human TEX19, can interact with LINE-1 retrotransposable element ORF1 protein (LIRE1) and subsequently restrict mobilization of LINE-1 elements in the genome.

AIM

This study aimed to predict the interaction of TEX19 with LIRE1 and analyze TEX19 missense polymorphisms. TEX19 model was generated using I-TASSER and the interaction between TEX19 and LIRE1 was studied using the HADDOCK software.

METHODS

The stability of the docking formed complex was studied through the molecular dynamic simulation using GROMACS. Missense SNPs (n=102) of TEX19 were screened for their potential effects on protein structure and function using different software.

RESULTS

Outcomes of this study revealed amino acids that potentially stabilize the predicted interaction interface between TEX19 and LIRE1. Of these SNPs, 37 were predicted to play a probably damaging role for the protein, three of them (F35S, P61R, and E55L) located at the binding site of LIRE1 and could disturb this binding affinity.

CONCLUSION

This information can be verified by further in vitro and in vivo experimentations and could be exploited for potential therapeutic targets.

Binding Insight of Anti-HIV Phytocompounds with Prime Targets of HIV: A Molecular Dynamics Simulation Analysis

BACKGROUND

Despite intense efforts, AIDS is difficult to tackle by current anti-retroviral therapy (ART) due to its side effects; therefore, there is an urgent need to discover potential, multitarget and low-cost anti-HIV compounds.

OBJECTIVE

We have shown that few phytocompounds can potentially inhibit the prime targets of HIV namely GP120 envelope protein, reverse transcriptase, protease, integrase and ribonulcease. In this study, top ranked prioritized compounds were subjected to Molecular Dynamics (MD) simulation in order to study the conformational dynamics and integrity of crucial interaction in the receptor sites.

METHODS

The system was built for selected protein-ligand complex using TIP3P water model and OPLS_2005 force field. Trajectories were recorded up to 20 ns simulation time in Desmond module of Schrödinger software.

RESULTS

As a result of a comprehensive analysis of molecular properties and dynamics of the complexes, it has been concluded that Chebulic acid, Curcumin and Mulberroside C could be developed as envelope glycoprotein GP120 inhibitor, reverse transcriptase inhibitor and protease inhibitor respectively. However, the fluctuation of Chebulic acid with respect to integrase and ribonuclease protein was higher during the simulation.

CONCLUSION

These findings can aid in the designing of the structural properties for more effective anti-HIV compounds against the given targets.

CLoNe: automated clustering based on local density neighborhoods for application to biomolecular structural ensembles

Motivation

Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies, are now commonplace and enable the study of molecular conformational landscapes in many contexts. Resulting structural ensembles increase in size as technological and algorithmic advancements take place, making their analysis increasingly demanding. In this regard, cluster analysis remains a go-to approach for their classification. However, many state-of-the-art algorithms are restricted to specific cluster properties. Combined with tedious parameter fine-tuning, cluster analysis of protein structural ensembles suffers from the lack of a generally applicable and easy to use clustering scheme.

Results

We present CLoNe, an original Python-based clustering scheme that builds on the Density Peaks algorithm of Rodriguez and Laio. CLoNe relies on a probabilistic analysis of local density distributions derived from nearest neighbors to find relevant clusters regardless of cluster shape, size, distribution and amount. We show its capabilities on many toy datasets with properties otherwise dividing state-of-the-art approaches and improves on the original algorithm in key aspects. Applied to structural ensembles, CLoNe was able to extract meaningful conformations from membrane binding events and ligand-binding pocket opening as well as identify dominant dimerization motifs or inter-domain organization. CLoNe additionally saves clusters as individual trajectories for further analysis and provides scripts for automated use with molecular visualization software.

Availability and implementation

www.epfl.ch/labs/lbm/resources, github.com/LBM-EPFL/CLoNe.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Multi-target Drug Designing for BTK, MMP9, Proteasome and TAK1 for the Clinical Treatment of Mantle Cell Lymphoma

BACKGROUND

Mantle cell lymphoma (MCL) is a type of non-Hodgkin lymphoma characterized by the mutation and overexpression of the cyclin D1 protein by the reciprocal chromosomal translocation t(11;14)(q13:q32).

AIM

The present study aims to identify potential inhibition of MMP9, Proteasome, BTK, and TAK1 and determine the most suitable and effective protein target for the MCL.

METHODOLOGY

Nine known inhibitors for MMP9, 24 for proteasome, 15 for BTK and 14 for TAK1 were screened. SB-3CT (PubChem ID: 9883002), oprozomib (PubChem ID: 25067547), zanubrutinib (PubChem ID: 135565884) and TAK1 inhibitor (PubChem ID: 66760355) were recognized as drugs with high binding capacity with their respective protein receptors. 41, 72, 102 and 3 virtual screened compounds were obtained after the similarity search with compound (PubChem ID:102173753), PubChem compound SCHEMBL15569297 (PubChem ID:72374403), PubChem compound SCHEMBL17075298 (PubChem ID:136970120) and compound CID: 71814473 with best virtual screened compounds.

RESULT

MMP9 inhibitors show commendable affinity and good interaction profile of compound holding PubChem ID:102173753 over the most effective established inhibitor SB-3CT. The pharmacophore study of the best virtual screened compound reveals its high efficacy based on various interactions. The virtual screened compound's better affinity with the target MMP9 protein was deduced using toxicity and integration profile studies.

CONCLUSION

Based on the ADMET profile, the compound (PubChem ID: 102173753) could be a potent drug for MCL treatment. Similar to the established SB-3CT, the compound was non-toxic with LD50 values for both the compounds lying in the same range.

Exploration of stilbenoid trimers as potential inhibitors of sirtuin1 enzyme using a molecular docking and molecular dynamics simulation approach

A combination of molecular docking and molecular dynamics simulation (250 ns) has been carried out to study the interaction of stilbenoid trimer compounds with the SIRT1 enzyme as the target protein. SIRT1 expression regulates cellular stress responses that lead to the development of cancer. Redocking showed a good native ligand pose with an RMSD value of 1.40 Å at the receptor active site's coordinates. The molecular docking score uses a grid score functional (kcal mol-1), which shows results of 1NS: 79.56, TS1: -26.83, TS2: -87.77, and TS3: -83.67. The TS2 and TS3 candidates were chosen for further analysis because they had a lower grid score than the native ligand (1NS). Furthermore, prediction of binding free energy (kcal mol-1) using the Quantum Mechanics/generalized Born Surface Area (QM/MM-GBSA) method shows the results of 1NS: -31.52 ± 0.39, TS2: -58.99 ± 0.34, and TS3: -43.38 ± 0.35. These results indicate that the TS2 and TS3 compounds have good potential as inhibitors of the SIRT1 enzyme. Additionally, the amino acid residues were responsible for the inhibition mechanism through hydrogen bond interactions at the molecular level, including ASP22, PHE91, PRO11, ILE165, ASP166, and VAL230. The observations made in this study provide theoretical information for exploring the stilbenoid trimers as anticancer agents by targeting the SIRT1 enzyme.

A Bittersweet Computational Journey among Glycosaminoglycans

Glycosaminoglycans (GAGs) are linear polysaccharides. In proteoglycans (PGs), they are attached to a core protein. GAGs and PGs can be found as free molecules, associated with the extracellular matrix or expressed on the cell membrane. They play a role in the regulation of a wide array of physiological and pathological processes by binding to different proteins, thus modulating their structure and function, and their concentration and availability in the microenvironment. Unfortunately, the enormous structural diversity of GAGs/PGs has hampered the development of dedicated analytical technologies and experimental models. Similarly, computational approaches (in particular, molecular modeling, docking and dynamics simulations) have not been fully exploited in glycobiology, despite their potential to demystify the complexity of GAGs/PGs at a structural and functional level. Here, we review the state-of-the art of computational approaches to studying GAGs/PGs with the aim of pointing out the “bitter” and “sweet” aspects of this field of research. Furthermore, we attempt to bridge the gap between bioinformatics and glycobiology, which have so far been kept apart by conceptual and technical differences. For this purpose, we provide computational scientists and glycobiologists with the fundamentals of these two fields of research, with the aim of creating opportunities for their combined exploitation, and thereby contributing to a substantial improvement in scientific knowledge.

Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 Mpro by Using Supervised Molecular Dynamics (SuMD) Simulations

Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low‐throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high‐throughput supervised molecular dynamics simulations (HT‐SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS‐CoV‐2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD‐based posing approaches, a high number of incorrect binding modes still complicate HT‐SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in‐silico pipeline, allowing us to prioritize only the more reliable predictions.

Screening of potent phytochemical inhibitors against SARS-CoV-2 protease and its two Asian mutants

Background

COVID-19, declared a pandemic in March 2020 by the World Health Organization is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus has already killed more than 2.3 million people worldwide.

Object

The principal intent of this work was to investigate lead compounds by screening natural product library (NPASS) for possible treatment of COVID-19.

Methods

Pharmacophore features were used to screen a large database to get a small dataset for structure-based virtual screening of natural product compounds. In the structure-based screening, molecular docking was performed to find a potent inhibitor molecule against the main protease (M^pro) of SARS-CoV-2 (PDB ID: 6Y7M). The predicted lead compound was further subjected to Molecular Dynamics (MD) simulation to check the stability of the leads compound with the evolution of time.

Results

In pharmacophore-based virtual screening, 2,361 compounds were retained out of 30,927. In the structure-based screening, the lead compounds were filtered based on their docking scores. Among the 2,360 compounds, 12 lead compounds were selected based on their docking score. Kazinol T with NPASS ID: NPC474104 showed the highest docking score of −14.355 and passed criteria of Lipinski's drug-like parameters. Monitoring ADMET properties, Kazinol T showed its safety for consumption. Docking of Kazinol T with two Asian mutants (R60C and I152V) showed variations in binding and energy parameters. Normal mode analysis for ligand-bound and unbound form of protease along with its mutants, revealed displacement and correlation parameters for C-alpha atoms. MD simulation results showed that all ligand-protein complexes remained intact and stable in a dynamic environment with negative Gibbs free energy.

Conclusions

The natural product Kazinol T was a predicted lead compound against the main protease of SARS-CoV-2 and will be the possible treatment for COVID-19.

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block CD4-binding site of the viral envelope protein gp120 was developed. To do this, the following studies were carried out: (i) an autoencoder architecture was constructed; (ii) a virtual compound library of potential anti-HIV-1 agents for training the neural network was formed by the concept of click chemistry allowing one to generate a large number of drug candidates by their assembly from small modular units; (iii) molecular docking of all compounds from this library with gp120 was made and calculations of the values of binding free energy were performed; (iv) molecular fingerprints of chemical compounds from the training dataset were generated; (v) training of the developed autoencoder was implemented followed by the validation of this neural network using more than 21 million molecules from the ZINC15 database. As a result, three small drug-like compounds that exhibited the high-affinity binding to gp120 were identified. According to the data from molecular docking, machine learning, quantum chemical calculations, and molecular dynamics simulations, these compounds show the low values of binding free energy in the complexes with gp120 similar to those calculated using the same computational protocols for the HIV-1 entry inhibitors NBD-11021 and NBD-14010, highly potent and broad anti-HIV-1 agents presenting a new generation of the viral CD4 antagonists. The identified CD4-mimetic candidates are suggested to present good scaffolds for the design of novel antiviral drugs inhibiting the early stages of HIV-1 infection.Communicated by Ramaswamy H. Sarma.

Circadian clock modulating small molecules repurposing as inhibitors of SARS-CoV-2 Mpro for pharmacological interventions in COVID-19 pandemic

The COVID-19 pandemic caused by SARS-CoV-2 is a global health emergency warranting the development of targeted treatment. The main protease M^pro is considered as a key drug target in coronavirus infections because of its vital role in the proteolytic processing of two essential polyproteins required for the replication and transcription of viral RNA. Targeting and inhibiting the M^pro activity represents a valid approach to prevent the SARS-CoV-2 replication and spread. Based on the structure-assisted drug designing, here we report a circadian clock-modulating small molecule “SRT2183” as a potent inhibitor of M^pro to block the replication of SARS-CoV-2. The findings are expected to pave the way for the development of therapeutics for COVID-19.

Novel inhibitors of the main protease enzyme of SARS-CoV-2 identified via molecular dynamics simulation-guided in vitro assay

For the COVID-19 pandemic caused by SARS-CoV-2, there are currently no effective drugs or vaccines to treat this coronavirus infection. In this study, we focus on the main protease enzyme of SARS-CoV-2, 3CL^pro, which is critical for viral replication. We employ explicit solvent molecular dynamics simulations of about 150 compounds docked into 3CL^pro’s binding site and that had emerged as good main protease ligands from our previous in silico screening of over 1.2 million compounds. By incoporating protein dynamics and applying a range of structural descriptors, such as the ability to form specific contacts with the catalytic dyad residues of 3CL^pro and the structural fluctuations of the ligands in the binding site, we are able to further refine our compound selection. Fourteen compounds including estradiol shown to be the most promising based on our calculations were procured and screened against recombinant 3CL^pro in a fluorescence assay. Eight of these compounds have significant activity in inhibiting the SARS-CoV-2 main protease. Among these are corilagin, a gallotannin, and lurasidone, an antipsychotic drug, which emerged as the most promising natural product and drug, respectively, and might thus be candidates for drug repurposing for the treatment of COVID-19. In addition, we also tested the inhibitory activity of testosterone, and our results reveal testosterone as possessing moderate inhibitory potency against the 3CL^pro enzyme, which may thus provide an explanation why older men are more severely affected by COVID-19.

In silico screening of therapeutic potentials from Strychnos nux-vomica against the dimeric main protease (Mpro) structure of SARS-CoV-2

The novel coronavirus also referred to as SARS-CoV-2 causes COVID-19 and became global epidemic since its initial outbreak in Wuhan, China, in December 2019. Research efforts are still been endeavoured towards discovering/designing of potential drugs and vaccines against this virus. In the present studies, we have contributed to the development of a drug based on natural products to combat the newly emerged and life-threatening disease. The main protease (M^Pro) of SARS-CoV-2 is a homodimer and a key component involved in viral replication, and is considered as a prime target for anti-SARS-CoV-2 drug development. Literature survey revealed that the phytochemicals present in Strychnos nux-vomica possess several therapeutic activities. Initially, in the light of drug likeness laws, the ligand library of phytoconstituents was subjected to drug likeness analysis. The resulting compounds were taken to binding site-specific consensus-based molecular docking studies and the results were compared with the positive control drug, lopinavir, which is a main protease inhibitor. The top compounds were tested for ADME-Tox properties and antiviral activity. Further molecular dynamics simulations and MM-PBSA-based binding affinity estimation were carried out for top two lead compounds' complexes along with the apo form of main protease and positive control drug lopinavir complex, and the results were comparatively analysed. The results revealed that the two analogues of same scaffold, namely demethoxyguiaflavine and strychnoflavine, have potential against M^pro and can be validated through clinical studies.Communicated by Ramaswamy H. Sarma.

Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective

The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.

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.

The ecological roles of microbial lipopeptides: Where are we going?

Lipopeptides (LPs) are secondary metabolites produced by a diversity of bacteria and fungi. Their unique chemical structure comprises both a peptide and a lipid moiety. LPs are of major biotechnological interest owing to their emulsification, antitumor, immunomodulatory, and antimicrobial activities. To date, these versatile compounds have been applied across multiple industries, from pharmaceuticals through to food processing, cosmetics, agriculture, heavy metal, and hydrocarbon bioremediation. The variety of LP structures and the diversity of the environments from which LP-producing microorganisms have been isolated suggest important functions in their natural environment. However, our understanding of the ecological role of LPs is limited. In this review, the mode of action and the role of LPs in motility, antimicrobial activity, heavy metals removal and biofilm formation are addressed. We include discussion on the need to characterise LPs from a diversity of microorganisms, with a focus on taxa inhabiting 'extreme' environments. We introduce the use of computational target fishing and molecular dynamics simulations as powerful tools to investigate the process of interaction between LPs and cell membranes. Together, these advances will provide new understanding of the mechanism of action of novel LPs, providing greater insights into the roles of LPs in the natural environment.

Abiraterone and D4, 3-keto Abiraterone binding to CYP17A1, a structural comparison study by molecular dynamic simulation

The importance of computer-aided drug design and development is clear nowadays. These approaches smooth the way of designing some efficient candidates based on drugs in use. At this place, we studied the mechanism of D4-abiraterone (D4A), the active metabolite of Abiraterone (Abi), binding to CYP17A1 compared with Abi. The molecular dynamics simulation results reveal that the metabolite, which lacks the key 3β-OH group, has a varied H-bond forming pattern. The critical H-bond between 3β-OH of Abi with Asn_202 turns to 3 Keto-O of D4A with Arg_239 in the substrate-binding site. This interaction causes a remarkable distance of 0.63 nm between D4A nitrogen and Fe in heme, which reduces its 17,20 lyase selectivity. The D4A keto moiety presents an immense number of H-bond with surrounding solvent molecules compared with the Abi hydroxyl group. As a result, D4A develops a weaker H-bond network with the enzyme. Otherwise, the heterocyclic nature of inhibitors helps for noticeable van der Waals interaction formation with CYP17A1. However, Abi stabilized position in the binding site helps more van der Waals interactions deposition than D4A. These results convinced the importance of the conserved H-bond for acquiring the proper position by the substrate or inhibitor in the binding site.

Structural analogues of existing anti-viral drugs inhibit SARS-CoV-2 RNA dependent RNA polymerase: A computational hierarchical investigation

The Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became a pandemic, resulting in an exponentially increased mortality globally and scientists all over the world are struggling to find suitable solutions to combat it. Multiple repurposed drugs have already been in several clinical trials or recently completed. However, none of them shows any promising effect in combating COVID-19. Therefore, developing an effective drug is an unmet global need. RdRp (RNA dependent RNA polymerase) plays a pivotal role in viral replication. Therefore, it is considered as a prime target of drugs that may treat COVID-19. In this study, we have screened a library of compounds, containing approved RdRp inhibitor drugs that were or in use to treat other viruses (favipiravir, sofosbuvir, ribavirin, lopinavir, tenofovir, ritonavir, galidesivir and remdesivir) and their structural analogues, in order to identify potential inhibitors of SARS-CoV-2 RdRp. Extensive screening, molecular docking and molecular dynamics show that five structural analogues have notable inhibitory effects against RdRp of SARS-CoV-2. Importantly, comparative protein-antagonists interaction revealed that these compounds fit well in the pocket of RdRp. ADMET analysis of these compounds suggests their potency as drug candidates. Our identified compounds may serve as potential therapeutics for COVID-19.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Molecular modeling and phenoloxidase inhibitory activity of arbutin and arbutin undecylenic acid ester

Excessive melanin formation has been linked to various skin disorders such as hyperpigmentation and skin cancer. Tyrosinase is the most prominent target for inhibitors of melanin production. In this study, we investigated whether arbutin and its prodrug, arbutin undecylenic acid ester, might inhibit phenoloxidase (PO), a tyrosinase-like enzyme. Molecular docking simulation results suggested that arbutin and arbutin undecylenic acid ester can bind to the substrate-binding pocket of PO. Arbutin undecylenic acid ester with an IC₅₀ 6.34 mM was effective to inhibit PO compared to arbutin (IC₅₀ 29.42 mM). In addition, arbutin undecylenic acid ester showed low cytotoxicity in Drosophila S2 cells and the compound inhibited the melanization reaction. Therefore, the results of this study have demonstrated that arbutin undecylenic acid ester as a potential inhibitor of PO. We successfully designed a new platform utilizing Drosophila melanogaster and Bombyx mori as animal models propounding fast, cheap, and high effectiveness in method to screen tyrosinase inhibitors.

From genomes to molecular dynamics - A bottom up approach in extrication of SARS CoV-2 main protease inhibitors

The recent pandemic Coronavirus disease-19 outbreak had traumatized global countries since its origin in late December 2019. Though the virus originated in China, it has spread rapidly across the world due its firmly established community transmission. To successfully tackle the spread and further infection, there needs a clear multidimensional understanding of the molecular mechanisms. Henceforth, 942 viral genome sequences were analysed to predict the core genomes crucial in virus life cycle. Additionally, 35 small interfering RNA transcripts were predicted that can target specifically the viral core proteins and reduce pathogenesis. The crystal structure of Covid-19 main protease-6LU7 was chosen as an attractive target due to the factors that there were fewer mutations and whose structure had significant identity to the annotated protein sequence of the core genome. Drug repurposing of both recruiting and non recruiting drugs was carried out through molecular docking procedures to recognize bitolterol as a good inhibitor of Covid-19 protease. The study was extended further to screen antiviral phytocompounds through quantitative structure activity relationship and molecular docking to identify davidigenin, from licorice as the best novel lead with good interactions and binding energy. The docking of the best compounds in all three categories was validated with molecular dynamics simulations which implied stable binding of the drug and lead molecule. Though the studies need clinical evaluations, the results are suggestive of curbing the pandemic.

Microsecond MD Simulation and Multiple-Conformation Virtual Screening to Identify Potential Anti-COVID-19 Inhibitors Against SARS-CoV-2 Main Protease

The recent pandemic outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), raised global health and economic concerns. Phylogenetically, SARS-CoV-2 is closely related to SARS-CoV, and both encode the enzyme main protease (M^pro/3CL^pro), which can be a potential target inhibiting viral replication. Through this work, we have compiled the structural aspects of M^pro conformational changes, with molecular modeling and 1-μs MD simulations. Long-scale MD simulation resolves the mechanism role of crucial amino acids involved in protein stability, followed by ensemble docking which provides potential compounds from the Traditional Chinese Medicine (TCM) database. These lead compounds directly interact with active site residues (His41, Gly143, and Cys145) of M^pro, which plays a crucial role in the enzymatic activity. Through the binding mode analysis in the S1, S1′, S2, and S4 binding subsites, screened compounds may be functional for the distortion of the oxyanion hole in the reaction mechanism, and it may lead to the inhibition of M^pro in SARS-CoV-2. The hit compounds are naturally occurring compounds; they provide a sustainable and readily available option for medical treatment in humans infected by SARS-CoV-2. Henceforth, extensive analysis through molecular modeling approaches explained that the proposed molecules might be promising SARS-CoV-2 inhibitors for the inhibition of COVID-19, subjected to experimental validation.

Elucidation of the 3D structure of grape seed 7S globulin and its interaction with malvidin 3-glucoside: A molecular modeling approach

Plant proteins are biopolymers with interesting technological applications for the food industry due to their ability to interact with phenolic compounds such as anthocyanins. The 3D structure of the 7S globulin from grape seed was elucidated for the first time using a homology model. The constructed 3D model showed that grape seed 7S globulin is rich in α-helices and β-sheets stabilized by six disulfide bridges. The interaction with the major grape anthocyanin malvidin-3-glucoside was also assessed by Docking and Molecular Dynamic simulation. Theoretical results demonstrated that 7S globulin interacts with Mv3glc through hydrogen, alkyl and π-alkyl bonds and the flavylium cation is oriented towards a hydrophobic region of the protein, being protected from hydration. Results provide valuable insights for understanding the mechanisms involved in the molecular interaction of grape anthocyanins with grape seed proteins that could be relevant to use them as potential color protecting agents in food industry applications.

Current computer-aided drug design methodologies in discovery of novel drug candidates for neuropsychiatric and inflammatory diseases

Drug discovery and development is a very challenging, expensive and time-consuming process. Impressive technological advances in computer sciences and molecular biology have made it possible to use computer-aided drug design (CADD) methods in various stages of the drug discovery and development pipeline. Nowadays, CADD presents an efficacious and indispensable tool, widely used in medicinal chemistry, to lead rational drug design and synthesis of novel compounds. In this article, an overview of commonly used CADD approaches from hit identification to lead optimization was presented. Moreover, different aspects of design of multitarget ligands for neuropsychiatric and anti-inflammatory diseases were summarized. Apparently, designing multi-target directed ligands for treatment of various complex diseases may offer better efficacy, and fewer side effects. Antipsychotics that act through aminergic G protein-coupled receptors (GPCRs), especially Dopamine D2 and serotonin 5-HT2A receptors, are the best option for treatment of various symptoms associated with neuropsychiatric disorders. Furthermore, multi-target directed cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors are also a successful approach to aid the discovery of new anti-inflammatory drugs with fewer side effects. Overall, employing CADD approaches in the process of rational drug design provides a great opportunity for future development, allowing rapid identification of compounds with the optimal polypharmacological profile.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1

Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to expose the links between local dynamic patterns and different degrees of allosteric inhibition of the ATPase function in the molecular chaperone TRAP1. We focused on 11 novel allosteric modulators with similar affinities to the target but with inhibitory efficacy between the 26.3 and 76%. Using a set of experimentally related local descriptors, ML enabled us to connect the molecular dynamics (MD) accessible to ligand-bound (perturbed) and unbound (unperturbed) systems to the degree of ATPase allosteric inhibition. The ML analysis of the comparative perturbed ensembles revealed a redistribution of dynamic states in the inhibitor-bound versus inhibitor-free systems following allosteric binding. Linear regression models were built to quantify the percentage of experimental variance explained by the predicted inhibitor-bound TRAP1 states. Our strategy provides a comparative MD–ML framework to infer allosteric ligand functionality. Alleviating the time scale issues which prevent the routine use of MD, a combination of MD and ML represents a promising strategy to support in silico mechanistic studies and drug design.

Application of In Silico and HTS Approaches to Identify Nuclear Import Inhibitors for Venezuelan Equine Encephalitis Virus Capsid Protein: A Case Study

The development of new drugs is costly and time-consuming, with estimates of over $US1 billion and 15 years for a product to reach the market. As understanding of the molecular basis of disease improves, various approaches have been used to target specific molecular interactions in the search for effective drugs. These include high-throughput screening (HTS) for novel drug identification and computer-aided drug design (CADD) to assess the properties of putative drugs before experimental work begins. We have applied conventional HTS and CADD approaches to the problem of identifying antiviral compounds to limit infection by Venezuelan equine encephalitis virus (VEEV). Nuclear targeting of the VEEV capsid (CP) protein through interaction with the host nuclear import machinery has been shown to be essential for viral pathogenicity, with viruses incapable of this interaction being greatly attenuated. Our previous conventional HTS and in silico structure-based drug design (SBDD) screens were successful in identifying novel inhibitors of CP interaction with the host nuclear import machinery, thus providing a unique opportunity to assess the relative value of the two screening approaches directly. This focused review compares and contrasts the two screening approaches, together with the properties of the inhibitors identified, as a case study for parallel use of the two approaches to identify antivirals. The utility of SBDD screens, especially when used in parallel with traditional HTS, in identifying agents of interest to target the host-pathogen interface is highlighted.

The latest automated docking technologies for novel drug discovery

INTRODUCTION

Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence.

AREAS COVERED

In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking.

EXPERT OPINION

There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.

Drug Repurposing Approach against Novel Coronavirus Disease (COVID-19) through Virtual Screening Targeting SARS-CoV-2 Main Protease

Simple Summary

With the urgent necessity of potential treatment against novel coronavirus disease, we used several computational methods to search for active drugs from an extensive database. The results of our investigation suggested several established drugs that can be subjected to further analysis for the treatment of novel coronavirus disease. Various methods used in this study proved the effectiveness of the retrieved drugs. Therefore, our findings highly recommend the mentioned drugs to be scrutinized to discover drugs against novel coronavirus.

Abstract

Novel coronavirus disease (COVID-19) was identified from China in December 2019 and spread rapidly through human-to-human transmission, affecting so many people worldwide. Until now, there has been no specific treatment against the disease and repurposing of the drug. Our investigation aimed to screen potential inhibitors against coronavirus for the repurposing of drugs. Our study analyzed sequence comparison among SARS-CoV, SARS-CoV-2, and MERS-CoV to determine the identity matrix using discovery studio. SARS-CoV-2 M^pro was targeted to generate an E-pharmacophore hypothesis to screen drugs from the DrugBank database having similar features. Promising drugs were used for docking-based virtual screening at several precisions. Best hits from virtual screening were subjected to MM/GBSA analysis to evaluate binding free energy, followed by the analysis of binding interactions. Furthermore, the molecular dynamics simulation approaches were carried out to assess the docked complex’s conformational stability. A total of 33 drug classes were found from virtual screening based on their docking scores. Among them, seven potential drugs with several anticancer, antibiotic, and immunometabolic categories were screened and showed promising MM/GBSA scores. During interaction analysis, these drugs exhibited different types of hydrogen and hydrophobic interactions with amino acid residue. Besides, 17 experimental drugs selected from virtual screening might be crucial for drug discovery against COVID-19. The RMSD, RMSF, SASA, Rg, and MM/PBSA descriptors from molecular dynamics simulation confirmed the complex’s firm nature. Seven promising drugs for repurposing against SARS-CoV-2 main protease (M^pro), namely sapanisertib, ornidazole, napabucasin, lenalidomide, daniquidone, indoximod, and salicylamide, could be vital for the treatment of COVID-19. However, extensive in vivo and in vitro studies are required to evaluate the mentioned drug’s activity.

Molecular modeling, docking and simulation dynamics of β-glucosidase reveals high-efficiency, thermo-stable, glucose tolerant enzyme in Paenibacillus lautus BHU3 strain

The enzyme β-glucosidase mediates the rate limiting step of conversion of cellobiose to glucose and thus plays a vital role in the process of cellulose degradation. The present study deals with analysis of the effective novel strain of Paenibacillus lautus BHU3 for identifying high-efficiency thermostable, glucose tolerant β-glucosidases. Seven counterparts with elevated T_m values ranging from 64.6 to 75.8 °C with high thermo-stability, were revealed through this analysis. The blind molecular docking of the model enzymes structures with cellobiose and pNPG gave high negative interaction energies ranging from -11.33 to -13.29 and -6.43 to -9.054 (kcal mol^-1), respectively. The enzyme WP_096774744.1 effectively formed 5 hydrogen bonds with the highest interaction energy (-13.29 kcal mol^-1) with cellobiose at its catalytic site. Molecular dynamics simulation analysis performed for the WP_096774744.1-pNPG complex predicted Glu5, Arg7, Lue68, Gly69 and Phe325 as the major contributing residues for accomplishing hydrolysis of β-1-4-linkage. Further, the molecular docking of WP_096774744.1 enzyme with glucose revealed a distinct glucose-binding site distant from the substrate-binding site, thus confirming the deficient competitive inhibition by glucose. Hence, WP_096774744.1 β-glucosidase appears to be an efficient enzyme with enhanced activity to biodegrade the cellulosic materials and highly relevant for waste management and various industrial applications.

Interaction of indole-3-acetic acid with horseradish peroxidase as a potential anticancer agent: from docking to molecular dynamics simulation

The oxidation process, catalyzed by the peroxidase enzymes, occurs in all domains of life to detoxify the hydrogen peroxide toxicity. The most well-known, applicable and vastly studied member of the peroxidases family is horseradish peroxidase (HRP), especially the isoenzyme C (HRP C). HRP (primarily HRP C) is commercially available and applicable in biotechnology and diagnosis. Recently, a novel application of HRP has been introduced in cancer therapy as the combination of HRP with indole-3-acetic acid (IAA). The anticancer activity of HRP/IAA complex is through oxidation of IAA by HRP in hypoxic tumor condition, which leads to apoptosis and cancerous cell death. However, the molecular interaction of HRP/IAA has not been elucidated. Identifying the interaction of IAA with HRP would provide a better insight into its function and applications. In this study, molecular docking and molecular dynamics (MD) simulation were applied to determine the molecular interaction of the IAA/HRP complex. The docking study represented that IAA bound at the 'exposed' heme edge of the HRP enzyme, and the IAA entrance to the enzyme was situated at the carboxymethyl side-chain of the selected structure. Our computational results showed the HRP/IAA complex structure stability. While hydrogen bond formation with ARG38 and HIS42 stabilized the substrate, hydrophobic interactions with Phe68, Gly69, Leu138, Pro139, Pro141 and Phe179 contributed to IAA/HRP complex stability. The results can help to better understand peroxidase enzyme activity and would pave the way for future development of new therapeutics with improved anticancer efficacy.Communicated by Ramaswamy H. Sarma.

Comparing cefotaxime and ceftriaxone in combating meningitis through nose-to-brain delivery using bio/chemoinformatics tools

Bio/chemoinformatics tools can be deployed to compare antimicrobial agents aiming to select an efficient nose-to-brain formulation targeting the meningitis disease by utilizing the differences in the main structural, topological and electronic descriptors of the drugs. Cefotaxime and ceftriaxone were compared at the formulation level (by comparing the loading in gelatin and tripalmitin matrices as bases for the formation of nanoparticulate systems), at the biopharmaceutical level (through the interaction with mucin and the P-gp efflux pumps) and at the therapeutic level (through studying the interaction with S. pneumoniae bacterial receptors). GROMACS v4.6.5 software package was used to carry-out all-atom molecular dynamics simulations. Higher affinity of ceftriaxone was observed compared to cefotaxime on the investigated biopharmaceutical and therapeutic macromolecules. Both drugs showed successful docking on mucin, P-gp efflux pump and S. pneumoniae PBP1a and 2b; but ceftriaxone showed higher affinity to the P-gp efflux pump proteins and higher docking on mucin. Ceftriaxone showed less out-of-matrix diffusion and higher entrapment on the gelatin and the tripalmitin matrices. Accordingly, Ceftriaxone gelatin nanospheres or tripalmitin solid lipid nanoparticles may pose a more feasible and efficient nose-to-brain formulation targeting the meningitis disease compared to the cefotaxime counterparts.

Molecular Dynamics Approach for Capturing Calixarene-Protein Interactions: The Case of Cytochrome C

Functionalized supramolecular cages are of growing importance in biology and biochemistry. They have recently been proposed as efficient auxiliaries to obtain high-resolution cocrystallized proteins. Here, we propose a molecular dynamics investigation of the supramolecular association of sulfonated calix-[8]-arenes to cytochrome c starting from initially distant proteins and ligands. We characterize two main binding sites for the sulfonated calixarene on the cytochrome c surface which are in perfect agreement with the previous experiments with regard to the structure (comparison with the X-ray structure PDB 6GD8) and the binding free energies [comparison between the molecular mechanics Poisson-Boltzmann surface area analysis and the isothermal titration calorimetry measurements]. The per-residue decomposition of the interaction energies reveals the detailed picture of this electrostatically driven association and notably the role of arginine R13 as a bridging residue between the two main anchoring sites. In addition, the analysis of the residue behavior by means of a supervised machine learning protocol unveils the formation of a hydrogen bond network far from the binding sites, increasing the rigidity of the protein. This study paves the way toward an automated procedure to predict the supramolecular protein-cage association, with the possibility of a computational screening of new promising derivatives for controlled protein assembly and protein surface recognition processes.