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Integrative Computational Strategies for Drug Screening

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 32113

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Special Issue Editors

Dr. Pilar Cossio

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Guest Editor

Max Planck Tandem Group, University of Antioquia, Colombia and Max Planck Institute of Biophysics, Frankfurt am Main, Germany
Interests: molecular structure; computational and theoretical chemistry; molecular dynamics simulations, cryo-electron microscopy and single-molecule experiments

Dr. Claudio Cavasotto

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Guest Editor

Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina, and Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
Interests: computer-aided drug discovery; protein modelling; cheminformatics; machine learning in drug discovery

Dr. Mattia Sturlese

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Co-Guest Editor

Department of Pharmaceutical and Pharmacological Sciences, University of Padova (UNIPD), Padua, Italy
Interests: Molecular Dynamics simulations; computer-aided drug discovery; Cheminformatics; Drug Discovery by NMR

Special Issue Information

Dear Colleagues,

Computational methods for drug discovery have proven useful in the search for new therapeutic agents. There is a wide variety of strategies ranging from low cost pharmacophore-based methods to highly accurate techniques, for example, free energy perturbation or thermodynamic integration using molecular dynamics simulations. Recently, considerable progress has been made with protocols that combine different methods differing in computational costs and accuracy, allowing for an efficient compound screening. The purpose of this special edition “Integrative Computational Strategies for Drug Screening” is to present the state-of-the-art methodologies for in silico drug discovery, covering the integration of the many computational techniques -such as pharmacophore-based modeling, docking, molecular dynamics simulations, quantum mechanics-, and supplying the community with the most recent advances in the field.

Dr. Pilar Cossio
Dr. Claudio Cavasotto
Guest Editors

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Keywords

Drug discovery
Integrative methods
Funnel-like strategies
Pharmacophore
Docking
Molecular dynamics

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Published Papers (7 papers)

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Research

12 pages, 43874 KiB

Open AccessArticle

Computational Screening of Newly Designed Compounds against Coxsackievirus A16 and Enterovirus A71

by Amita Sripattaraphan, Kamonpan Sanachai, Warinthorn Chavasiri, Siwaporn Boonyasuppayakorn, Phornphimon Maitarad and Thanyada Rungrotmongkol

Molecules 2022, 27(6), 1908; https://doi.org/10.3390/molecules27061908 - 15 Mar 2022

Cited by 8 | Viewed by 3216

Abstract

Outbreaks of hand, foot, and mouth disease (HFMD) that occur worldwide are mainly caused by the Coxsackievirus-A16 (CV-A16) and Enterovirus-A71 (EV-A71). Unfortunately, neither an anti-HFMD drug nor a vaccine is currently available. Rupintrivir in phase II clinical trial candidate for rhinovirus showed highly potent antiviral activities against enteroviruses as an inhibitor for 3C protease (3Cpro). In the present study, we focused on designing 50 novel rupintrivir analogs against CV-A16 and EV-A71 3Cpro using computational tools. From their predicted binding affinities, the five compounds with functional group modifications at P1′, P2, P3, and P4 sites, namely P1′-1, P2-m3, P3-4, P4-5, and P4-19, could bind with both CV-A16 and EV-A71 3Cpro better than rupintrivir. Subsequently, these five analogs were studied by 500 ns molecular dynamics simulations. Among them, P2-m3, the derivative with meta-aminomethyl-benzyl group at the P2 site, showed the greatest potential to interact with the 3Cpro target by delivering the highest number of intermolecular hydrogen bonds and contact atoms. It formed the hydrogen bonds with L127 and K130 residues at the P2 site stronger than rupintrivir, supported by significantly lower MM/PB(GB)SA binding free energies. Elucidation of designed rupintrivir analogs in our study provides the basis for developing compounds that can be candidate compounds for further HFMD treatment. Full article

(This article belongs to the Special Issue Integrative Computational Strategies for Drug Screening)

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19 pages, 30320 KiB

Open AccessArticle

Risedronate and Methotrexate Are High-Affinity Inhibitors of New Delhi Metallo-β-Lactamase-1 (NDM-1): A Drug Repurposing Approach

by Ghazala Muteeb, Abdulrahman Alsultan, Mohd Farhan and Mohammad Aatif

Molecules 2022, 27(4), 1283; https://doi.org/10.3390/molecules27041283 - 14 Feb 2022

Cited by 13 | Viewed by 3067

Abstract

Bacteria expressing New Delhi metallo-β-lactamase-1 (NDM-1) can hydrolyze β-lactam antibiotics (penicillins, cephalosporins, and carbapenems) and, thus, mediate multidrug resistance. The worldwide dissemination of NDM-1 poses a serious threat to public health, imposing a huge economic burden in the development of new antibiotics. Thus, there is an urgent need for the identification of novel NDM-1 inhibitors from a pool of already-known drug molecules. Here, we screened a library of FDA-approved drugs to identify novel non-β-lactam ring-containing inhibitors of NDM-1 by applying computational as well as in vitro experimental approaches. Different steps of high-throughput virtual screening, molecular docking, molecular dynamics simulation, and enzyme kinetics were performed to identify risedronate and methotrexate as the inhibitors with the most potential. The molecular mechanics/generalized Born surface area (MM/GBSA) and molecular dynamics (MD) simulations showed that both of the compounds (risedronate and methotrexate) formed a stable complex with NDM-1. Furthermore, analyses of the binding pose revealed that risedronate formed two hydrogen bonds and three electrostatic interactions with the catalytic residues of NDM-1. Similarly, methotrexate formed four hydrogen bonds and one electrostatic interaction with NDM-1’s active site residues. The docking scores of risedronate and methotrexate for NDM-1 were –10.543 kcal mol⁻¹ and −10.189 kcal mol⁻¹, respectively. Steady-state enzyme kinetics in the presence of risedronate and methotrexate showed a decreased catalytic efficiency (i.e., kcat/Km) of NDM-1 on various antibiotics, owing to poor catalytic proficiency and affinity. The results were further validated by determining the MICs of imipenem and meropenem in the presence of risedronate and methotrexate. The IC₅₀ values of the identified inhibitors were in the micromolar range. The findings of this study should be helpful in further characterizing the potential of risedronate and methotrexate to treat bacterial infections. Full article

(This article belongs to the Special Issue Integrative Computational Strategies for Drug Screening)

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22 pages, 7033 KiB

Open AccessArticle

Hepatitis C Virus NS3/4A Inhibition and Host Immunomodulation by Tannins from Terminalia chebula: A Structural Perspective

by Vishal S. Patil, Darasaguppe R. Harish, Umashankar Vetrivel, Subarna Roy, Sanjay H. Deshpande and Harsha V. Hegde

Molecules 2022, 27(3), 1076; https://doi.org/10.3390/molecules27031076 - 5 Feb 2022

Cited by 17 | Viewed by 4071

Abstract

Terminalia chebula Retz. forms a key component of traditional folk medicine and is also reported to possess antihepatitis C virus (HCV) and immunomodulatory activities. However, information on the intermolecular interactions of phytochemicals from this plant with HCV and human proteins are yet to be established. Thus, by this current study, we investigated the HCV NS3/4A inhibitory and host immune-modulatory activity of phytocompounds from T. chebula through in silico strategies involving network pharmacology and structural bioinformatics techniques. To start with, the phytochemical dataset of T. chebula was curated from biological databases and the published literature. Further, the target ability of the phytocompounds was predicted using BindingDB for both HCV NS3/4A and other probable host targets involved in the immune system. Further, the identified targets were docked to the phytochemical dataset using AutoDock Vina executed through the POAP pipeline. The resultant docked complexes with significant binding energy were subjected to 50 ns molecular dynamics (MD) simulation in order to infer the stability of complex formation. During network pharmacology analysis, the gene set pathway enrichment of host targets was performed using the STRING and Reactome pathway databases. Further, the biological network among compounds, proteins, and pathways was constructed using Cytoscape 3.6.1. Furthermore, the druglikeness, side effects, and toxicity of the phytocompounds were also predicted using the MolSoft, ADVERpred, and PreADMET methods, respectively. Out of 41 selected compounds, 10 were predicted to target HCV NS3/4A and also to possess druglike and nontoxic properties. Among these 10 molecules, Chebulagic acid and 1,2,3,4,6-Pentagalloyl glucose exhibited potent HCV NS3/4A inhibitory activity, as these scored a lowest binding energy (BE) of −8.6 kcal/mol and −7.7 kcal/mol with 11 and 20 intermolecular interactions with active site residues, respectively. These findings are highly comparable with Asunaprevir (known inhibitor of HCV NS3/4A), which scored a BE of −7.4 kcal/mol with 20 key intermolecular interactions. MD studies also strongly suggest that chebulagic acid and 1,2,3,4,6-Pentagalloyl glucose as promising leads, as these molecules showed stable binding during 50 ns of production run. Further, the gene set enrichment and network analysis of 18 protein targets prioritized 10 compounds and were predicted to potentially modulate the host immune system, hemostasis, cytokine levels, interleukins signaling pathways, and platelet aggregation. On overall analysis, this present study predicts that tannins from T. chebula have a potential HCV NS3/4A inhibitory and host immune-modulatory activity. However, further experimental studies are required to confirm the efficacies. Full article

(This article belongs to the Special Issue Integrative Computational Strategies for Drug Screening)

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19 pages, 1161 KiB

Open AccessArticle

Analyzing Kinase Similarity in Small Molecule and Protein Structural Space to Explore the Limits of Multi-Target Screening

by Denis Schmidt, Magdalena M. Scharf, Dominique Sydow, Eva Aßmann, Maria Martí-Solano, Marina Keul, Andrea Volkamer and Peter Kolb

Molecules 2021, 26(3), 629; https://doi.org/10.3390/molecules26030629 - 26 Jan 2021

Cited by 8 | Viewed by 4716

Abstract

While selective inhibition is one of the key assets for a small molecule drug, many diseases can only be tackled by simultaneous inhibition of several proteins. An example where achieving selectivity is especially challenging are ligands targeting human kinases. This difficulty arises from the high structural conservation of the kinase ATP binding sites, the area targeted by most inhibitors. We investigated the possibility to identify novel small molecule ligands with pre-defined binding profiles for a series of kinase targets and anti-targets by in silico docking. The candidate ligands originating from these calculations were assayed to determine their experimental binding profiles. Compared to previous studies, the acquired hit rates were low in this specific setup, which aimed at not only selecting multi-target kinase ligands, but also designing out binding to anti-targets. Specifically, only a single profiled substance could be verified as a sub-micromolar, dual-specific EGFR/ErbB2 ligand that indeed avoided its selected anti-target BRAF. We subsequently re-analyzed our target choice and in silico strategy based on these findings, with a particular emphasis on the hit rates that can be expected from a given target combination. To that end, we supplemented the structure-based docking calculations with bioinformatic considerations of binding pocket sequence and structure similarity as well as ligand-centric comparisons of kinases. Taken together, our results provide a multi-faceted picture of how pocket space can determine the success of docking in multi-target drug discovery efforts. Full article

(This article belongs to the Special Issue Integrative Computational Strategies for Drug Screening)

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8 pages, 1925 KiB

Open AccessArticle

Molecular Modeling to Estimate the Diffusion Coefficients of Drugs and Other Small Molecules

by Shuichi Miyamoto and Kazumi Shimono

Molecules 2020, 25(22), 5340; https://doi.org/10.3390/molecules25225340 - 16 Nov 2020

Cited by 24 | Viewed by 5192

Abstract

Diffusion is a spontaneous process and one of the physicochemical phenomena responsible for molecular transport, the rate of which is governed mainly by the diffusion coefficient; however, few coefficients are available because the measurement of diffusion rates is not straightforward. The translational diffusion coefficient is related by the Stokes–Einstein equation to the approximate radius of the diffusing molecule. Therefore, the stable conformations of small molecules were first calculated by molecular modeling. A simple radius r_s and an effective radius r_e were then proposed and estimated using the stable conformers with the van der Waals radii of atoms. The diffusion coefficients were finally calculated with the Stokes–Einstein equation. The results showed that, for the molecules with strong hydration ability, the diffusion coefficients are best given by r_e and for other compounds, r_s provided the best coefficients, with a reasonably small deviation of ~0.3 × 10⁻⁶ cm²/s from the experimental data. This demonstrates the effectiveness of the theoretical estimation approach, suggesting that diffusion coefficients have potential use as an additional molecular property in drug screening. Full article

(This article belongs to the Special Issue Integrative Computational Strategies for Drug Screening)

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24 pages, 10693 KiB

Open AccessArticle

Computational Studies towards the Identification of Novel Rhodopsin-Binding Compounds as Chemical Chaperones for Misfolded Opsins

by Gaia Pasqualetto, Martin Schepelmann, Carmine Varricchio, Elisa Pileggi, Caroline Khogali, Siân R. Morgan, Ian Boostrom, Malgorzata Rozanowska, Andrea Brancale, Salvatore Ferla and Marcella Bassetto

Molecules 2020, 25(21), 4904; https://doi.org/10.3390/molecules25214904 - 23 Oct 2020

Cited by 12 | Viewed by 4626

Abstract

Accumulation of misfolded and mistrafficked rhodopsin on the endoplasmic reticulum of photoreceptor cells has a pivotal role in the pathogenesis of retinitis pigmentosa and a subset of Leber’s congenital amaurosis. One potential strategy to reduce rhodopsin misfolding and aggregation in these conditions is to use opsin-binding compounds as chemical chaperones for opsin. Such molecules have previously shown the ability to aid rhodopsin folding and proper trafficking to the outer cell membranes of photoreceptors. As means to identify novel chemical chaperones for rhodopsin, a structure-based virtual screening of commercially available drug-like compounds (300,000) was performed on the main binding site of the visual pigment chromophore, the 11-cis-retinal. The best 24 virtual hits were examined for their ability to compete for the chromophore-binding site of opsin. Among these, four small molecules demonstrated the ability to reduce the rate constant for the formation of the 9-cis-retinal-rhodopsin complex, while five molecules surprisingly enhanced the formation of this complex. Compound 7, 13, 20 and 23 showed a weak but detectable increase in the trafficking of the P23H mutant, widely used as a model for both retinitis pigmentosa and Leber’s congenital amaurosis, from the ER to the cell membrane. The compounds did not show any relevant cytotoxicity in two different human cell lines, with the only exception of 13. Based on the structures of these active compounds, a series of in silico studies gave important insights on the potential structural features required for a molecule to act either as chemical chaperone or as stabiliser of the 11-cis-retinal-rhodopsin complex. Thus, this study revealed a series of small molecules that represent a solid foundation for the future development of novel therapeutics against these severe inherited blinding diseases. Full article

(This article belongs to the Special Issue Integrative Computational Strategies for Drug Screening)

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13 pages, 1673 KiB

Open AccessArticle

Drug Repurposing for Candidate SARS-CoV-2 Main Protease Inhibitors by a Novel In Silico Method

by Milan Sencanski, Vladimir Perovic, Snezana B. Pajovic, Miroslav Adzic, Slobodan Paessler and Sanja Glisic

Molecules 2020, 25(17), 3830; https://doi.org/10.3390/molecules25173830 - 23 Aug 2020

Cited by 51 | Viewed by 6079

Abstract

The SARS-CoV-2 outbreak caused an unprecedented global public health threat, having a high transmission rate with currently no drugs or vaccines approved. An alternative powerful additional approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 main protease is essential for viral replication and an attractive drug target. In this study, we used the virtual screening protocol with both long-range and short-range interactions to select candidate SARS-CoV-2 main protease inhibitors. First, the Informational spectrum method applied for small molecules was used for searching the Drugbank database and further followed by molecular docking. After in silico screening of drug space, we identified 57 drugs as potential SARS-CoV-2 main protease inhibitors that we propose for further experimental testing. Full article

(This article belongs to the Special Issue Integrative Computational Strategies for Drug Screening)

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