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Moyano-Gómez P, Lehtonen JV, Pentikäinen OT, Postila PA. Building shape-focused pharmacophore models for effective docking screening. J Cheminform 2024; 16:97. [PMID: 39123240 PMCID: PMC11312248 DOI: 10.1186/s13321-024-00857-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 05/12/2024] [Indexed: 08/12/2024] Open
Abstract
The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.
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Affiliation(s)
- Paola Moyano-Gómez
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, 20014, Turku, Finland
- InFLAMES Research Flagship, University of Turku, 20014, Turku, Finland
| | - Jukka V Lehtonen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, 20500, Turku, Finland
- InFLAMES Research Flagship, Åbo Akademi University, 20500, Turku, Finland
| | - Olli T Pentikäinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, 20014, Turku, Finland
- InFLAMES Research Flagship, University of Turku, 20014, Turku, Finland
- Aurlide Ltd, Lemminkäisenkatu 14A, 20520, Turku, Finland
| | - Pekka A Postila
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, 20014, Turku, Finland.
- InFLAMES Research Flagship, University of Turku, 20014, Turku, Finland.
- Aurlide Ltd, Lemminkäisenkatu 14A, 20520, Turku, Finland.
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Urra G, Valdés-Muñoz E, Suardiaz R, Hernández-Rodríguez EW, Palma JM, Ríos-Rozas SE, Flores-Morales CA, Alegría-Arcos M, Yáñez O, Morales-Quintana L, D’Afonseca V, Bustos D. From Proteome to Potential Drugs: Integration of Subtractive Proteomics and Ensemble Docking for Drug Repurposing against Pseudomonas aeruginosa RND Superfamily Proteins. Int J Mol Sci 2024; 25:8027. [PMID: 39125594 PMCID: PMC11312079 DOI: 10.3390/ijms25158027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/08/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024] Open
Abstract
Pseudomonas aeruginosa (P. aeruginosa) poses a significant threat as a nosocomial pathogen due to its robust resistance mechanisms and virulence factors. This study integrates subtractive proteomics and ensemble docking to identify and characterize essential proteins in P. aeruginosa, aiming to discover therapeutic targets and repurpose commercial existing drugs. Using subtractive proteomics, we refined the dataset to discard redundant proteins and minimize potential cross-interactions with human proteins and the microbiome proteins. We identified 12 key proteins, including a histidine kinase and members of the RND efflux pump family, known for their roles in antibiotic resistance, virulence, and antigenicity. Predictive modeling of the three-dimensional structures of these RND proteins and subsequent molecular ensemble-docking simulations led to the identification of MK-3207, R-428, and Suramin as promising inhibitor candidates. These compounds demonstrated high binding affinities and effective inhibition across multiple metrics. Further refinement using non-covalent interaction index methods provided deeper insights into the electronic effects in protein-ligand interactions, with Suramin exhibiting superior binding energies, suggesting its broad-spectrum inhibitory potential. Our findings confirm the critical role of RND efflux pumps in antibiotic resistance and suggest that MK-3207, R-428, and Suramin could be effectively repurposed to target these proteins. This approach highlights the potential of drug repurposing as a viable strategy to combat P. aeruginosa infections.
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Affiliation(s)
- Gabriela Urra
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile; (G.U.); (E.W.H.-R.); (S.E.R.-R.)
| | - Elizabeth Valdés-Muñoz
- Doctorado en Biotecnología Traslacional, Facultad de Ciencias Agrarias y Forestales, Universidad Católica del Maule, Talca 3480094, Chile;
| | - Reynier Suardiaz
- Departamento de Química Física, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain;
| | - Erix W. Hernández-Rodríguez
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile; (G.U.); (E.W.H.-R.); (S.E.R.-R.)
- Unidad de Bioinformática Clínica, Centro Oncológico, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile
| | - Jonathan M. Palma
- Facultad de Ingeniería, Universidad de Talca, Curicó 3344158, Chile;
| | - Sofía E. Ríos-Rozas
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile; (G.U.); (E.W.H.-R.); (S.E.R.-R.)
| | | | - Melissa Alegría-Arcos
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago 7500000, Chile; (M.A.-A.); (O.Y.)
| | - Osvaldo Yáñez
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago 7500000, Chile; (M.A.-A.); (O.Y.)
| | - Luis Morales-Quintana
- Multidisciplinary Agroindustry Research Laboratory, Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Cinco Pte. N° 1670, Talca 3467987, Chile;
| | - Vívian D’Afonseca
- Departamento de Ciencias Preclínicas, Facultad de Medicina, Universidad Católica del Maule, Ave. San Miguel 3605, Talca 3466706, Chile
| | - Daniel Bustos
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca 3480094, Chile; (G.U.); (E.W.H.-R.); (S.E.R.-R.)
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3
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Liu H, Hu B, Chen P, Wang X, Wang H, Wang S, Wang J, Lin B, Cheng M. Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets. J Chem Inf Model 2024; 64:5413-5426. [PMID: 38958413 DOI: 10.1021/acs.jcim.4c00072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuracies in virtual screening and target predictions. We introduce the "Docking Score ML", developed from an analysis of over 200,000 docked complexes from 155 known targets for cancer treatments. The scoring functions used are founded on bioactivity data sourced from ChEMBL and have been fine-tuned using both supervised machine learning and deep learning techniques. We validated our approach extensively using multiple data sets such as validation of selectivity mechanism, the DUDE, DUD-AD, and LIT-PCBA data sets, and performed a multitarget analysis on drugs like sunitinib. To enhance prediction accuracy, feature fusion techniques were explored. By merging the capabilities of the Graph Convolutional Network (GCN) with multiple docking functions, our results indicated a clear superiority of our methodologies over conventional approaches. These advantages demonstrate that Docking Score ML is an efficient and accurate tool for virtual screening and reverse docking.
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Affiliation(s)
- Haihan Liu
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Baichun Hu
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Peiying Chen
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Xiao Wang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Hanxun Wang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Shizun Wang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Jian Wang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Bin Lin
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Maosheng Cheng
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
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Mslati H, Gentile F, Pandey M, Ban F, Cherkasov A. PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs. J Chem Inf Model 2024; 64:3034-3046. [PMID: 38504115 DOI: 10.1021/acs.jcim.3c01878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.
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Affiliation(s)
- Hazem Mslati
- Vancouver Prostate Centre, The University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
| | - Francesco Gentile
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Ottawa Institute of Systems Biology, Ottawa, Ontario K1N 6N5, Canada
| | - Mohit Pandey
- Vancouver Prostate Centre, The University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
| | - Fuqiang Ban
- Vancouver Prostate Centre, The University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre, The University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
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Zengin IN, Koca MS, Tayfuroglu O, Yildiz M, Kocak A. Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 M pro. J Comput Aided Mol Des 2024; 38:15. [PMID: 38532176 DOI: 10.1007/s10822-024-00554-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/26/2024] [Indexed: 03/28/2024]
Abstract
Here, we introduce the use of ANI-ML potentials as a rescoring function in the host-guest interaction in molecular docking. Our results show that the "docking power" of ANI potentials can compete with the current scoring functions at the same level of computational cost. Benchmarking studies on CASF-2016 dataset showed that ANI is ranked in the top 5 scoring functions among the other 34 tested. In particular, the ANI predicted interaction energies when used in conjunction with GOLD-PLP scoring function can boost the top ranked solution to be the closest to the x-ray structure. Rapid and accurate calculation of interaction energies between ligand and protein also enables screening of millions of drug candidates/docking poses. Using a unique protocol in which docking by GOLD-PLP, rescoring by ANI-ML potentials and extensive MD simulations along with end state free energy methods are combined, we have screened FDA approved drugs against the SARS-CoV-2 main protease (Mpro). The top six drug molecules suggested by the consensus of these free energy methods have already been in clinical trials or proposed as potential drug molecules in previous theoretical and experimental studies, approving the validity and the power of accuracy in our screening method.
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Affiliation(s)
- Irem N Zengin
- Department of Chemistry, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - M Serdar Koca
- Department of Molecular Biology and Genetics, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
- Pfizer - Universidad de Granada - Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), 18016, Granada, Spain
| | - Omer Tayfuroglu
- Department of Chemistry, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Muslum Yildiz
- Department of Molecular Biology and Genetics, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Abdulkadir Kocak
- Department of Chemistry, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey.
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6
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Nelen J, Carmena-Bargueño M, Martínez-Cortés C, Rodríguez-Martínez A, Villalgordo-Soto JM, Pérez-Sánchez H. ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery. J Chem Inf Model 2024; 64:1605-1614. [PMID: 38416513 DOI: 10.1021/acs.jcim.3c01982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement the HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose; however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches that combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking. In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening while also being designed for use in high-performance computing (HPC) environments.
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Affiliation(s)
- Jochem Nelen
- Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), HiTech Innovation Hub, UCAM Universidad Católica de Murcia, Murcia 30107, Spain
- Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Campus de los Jerónimos n°135, Guadalupe, Murcia 30107, Spain
| | - Miguel Carmena-Bargueño
- Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), HiTech Innovation Hub, UCAM Universidad Católica de Murcia, Murcia 30107, Spain
- Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Campus de los Jerónimos n°135, Guadalupe, Murcia 30107, Spain
| | - Carlos Martínez-Cortés
- Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), HiTech Innovation Hub, UCAM Universidad Católica de Murcia, Murcia 30107, Spain
| | - Alejandro Rodríguez-Martínez
- Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), HiTech Innovation Hub, UCAM Universidad Católica de Murcia, Murcia 30107, Spain
- Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Campus de los Jerónimos n°135, Guadalupe, Murcia 30107, Spain
| | | | - Horacio Pérez-Sánchez
- Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), HiTech Innovation Hub, UCAM Universidad Católica de Murcia, Murcia 30107, Spain
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Governa P, Biagi M, Manetti F, Forli S. Consensus screening for a challenging target: the quest for P-glycoprotein inhibitors. RSC Med Chem 2024; 15:720-732. [PMID: 38389870 PMCID: PMC10880898 DOI: 10.1039/d3md00649b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/18/2024] [Indexed: 02/24/2024] Open
Abstract
ATP-binding cassette (ABC) transporters are a large family of proteins involved in membrane transport of a wide variety of substrates. Among them, ABCB1, also known as MDR-1 or P-glycoprotein (P-gp), is the most characterized. By exporting xenobiotics out of the cell, P-gp activity can affect the ADME properties of several drugs. Moreover, P-gp has been found to mediate multidrug resistance in cancer cells. Thus, the inhibition of P-gp activity may lead to increased absorption and/or intracellular accumulation of co-administered drugs, enhancing their effectiveness. Using the human-mouse chimeric cryoEM 3D structure of the P-gp in the inhibitor-bound intermediate form (PDBID: 6qee), approximately 200 000 commercially available natural compounds from the ZINC database were virtually screened. To build a model able to discriminate between substrate and inhibitors, two datasets of compounds with known activity, including P-gp inhibitors, substrates, and inactive molecules were also docked. The best docking pose of selected substrates and inhibitors were used to generate 3D common feature pharmacophoric models that were combined with the Autodock Vina binding energy values to prioritize compounds for visual inspection. With this consensus approach, 13 potential candidates were identified and then tested for their ability to inhibit P-gp, using zosuquidar, a third generation P-gp inhibitor, as a reference drug. Eight compounds were found to be active with 6 of them having an IC50 lower than 5 μM in a membrane-based ATPase activity assay. Moreover, the P-gp inhibitory activity was also confirmed by two different cell-based in vitro methods. Both retrospective and prospective results demonstrate the ability of the combined structure-based pharmacophore modeling and docking-based virtual screening approach to predict novel hit compounds with inhibitory activity toward P-gp. The resulting chemical scaffolds could serve as inspiration for the optimization of novel and more potent P-gp inhibitors.
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Affiliation(s)
- Paolo Governa
- Department of Integrative Structural and Computational Biology, Scripps Research Institute La Jolla CA 92037 USA
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena 53100 Siena Italy
| | - Marco Biagi
- Department of Food and Drug, University of Parma 43121 Parma Italy
| | - Fabrizio Manetti
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena 53100 Siena Italy
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research Institute La Jolla CA 92037 USA
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Rincón RA, Rodríguez D, Coy-Barrera E. Susceptibility of Tetranychus urticae to the Alkaloidal Extract of Zanthoxylum schreberi Bark: Phenotypic and Biochemical Insights for Biotechnological Exploitation. BIOTECH 2024; 13:5. [PMID: 38390908 PMCID: PMC10885115 DOI: 10.3390/biotech13010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/18/2024] [Accepted: 02/18/2024] [Indexed: 02/24/2024] Open
Abstract
Tetranychus urticae Koch, a phytophagous mite, is one of the most significant crop pests globally. The primary method employed for controlling T. urticae involves chemical means, utilizing synthesized products, posing the risk of developing resistance. The urgency for novel strategies integrated into pest management programs to combat this mite is becoming increasingly imperative. Botanical pesticides emerge as a promising tool to forestall arthropod resistance. Among these, extracts from Rutaceae plants, abundant in bioactive specialized metabolites, have demonstrated potential as insecticides and miticides. In this study, various concentrations of alkaloidal extracts sourced from the bark of Zanthoxylum schreberi J.F.Gmel. (Rutaceae) were evaluated against T. urticae adult females. Furthermore, the extract's combination with three distinct commercial acaricides (i.e., chlorfenapyr, cyflumetofen, and abamectin) was also assessed for this mite. Chemical characterization of the extract via LC-MS allowed for the annotation of various compounds related to ten benzylisoquinoline-derived alkaloids. The extract, both alone and in combination with commercial insecticides, yielded varying responses, inducing over 40% mortality at 2% w/w, demonstrating a 90% repellency rate at the same concentration, and exerting a moderate impact on fecundity. These treatments extended beyond phenotypic responses, delving into the biochemical effects on treated T. urticae females through an exploration of the impact on four enzymes, i.e., acetylcholinesterase (AChE), glutathione S-transferase (GST), esterases (GE), and P450-like monooxygenases (PMO). Employing consensus docking studies and in vitro enzymatic evaluations, it was discovered that the Z. schreberi-derived extract and its constituents significantly affected two key enzymes, AChE and GST (IC50 < 6 µM), which were associated with the phenotypic observations of T. urticae females. The evaluation of alkaloid-rich botanicals showcases promising potential as a relevant biotechnological strategy in addressing mite-related concerns, offering a pathway toward innovative and sustainable pest management solutions.
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Affiliation(s)
- Ricardo A Rincón
- Biological Control Laboratory, Universidad Militar Nueva Granada, Cajicá 250247, Colombia
| | - Daniel Rodríguez
- Biological Control Laboratory, Universidad Militar Nueva Granada, Cajicá 250247, Colombia
| | - Ericsson Coy-Barrera
- Bioorganic Chemistry Laboratory, Universidad Militar Nueva Granada, Cajicá 250247, Colombia
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9
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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10
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Debnath A, Sharma S, Mazumder R, Mazumder A, Singh R, Kumar A, Dua A, Singhal P, Kumar A, Singh G. In Search of Novel SGLT2 Inhibitors by High-throughput Virtual Screening. Curr Drug Discov Technol 2024; 21:20-31. [PMID: 38047361 DOI: 10.2174/0115701638267615231123160650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND Type 2 diabetes mellitus constitutes approximately 90% of all reported forms of diabetes mellitus. Insulin resistance characterizes this manifestation of diabetes. The prevalence of this condition is commonly observed in patients aged 45 and above; however, there is an emerging pattern of younger cohorts receiving diagnoses primarily attributed to lifestyle-related variables, including obesity, sedentary behavior, and poor dietary choices. The enzyme SGLT2 exerts a negative regulatory effect on insulin signaling pathways, resulting in the development of insulin resistance and subsequent elevation of blood glucose levels. The maintenance of glucose homeostasis relies on the proper functioning of insulin signaling pathways, while disruptions in insulin signaling can contribute to the development of type 2 diabetes. OBJECTIVE Our study aimed to identify novel SGLT2 inhibitors by high-throughput virtual Screening. METHODS We screened the May bridge Hit Discover database to identify potent hits followed by druglikeness, synthetic accessibility, PAINS alert, toxicity estimation, ADME assessment, and consensus molecular docking. RESULTS The screening process led to the identification of three molecules that demonstrated significant binding affinity, favorable drug-like properties, effective ADME, and minimal toxicity. CONCLUSION The identified molecules could manage T2DM effectively by inhibiting SGLT2, providing a promising avenue for future therapeutic strategies.
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Affiliation(s)
- Abhijit Debnath
- Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area, Greater Noida, 201306, Uttar Pradesh, India
| | - Shalini Sharma
- Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area, Greater Noida, 201306, Uttar Pradesh, India
| | - Rupa Mazumder
- Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area, Greater Noida, 201306, Uttar Pradesh, India
| | - Avijit Mazumder
- Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area, Greater Noida, 201306, Uttar Pradesh, India
| | - Rajesh Singh
- Department of Dravyaguna, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
| | - Ankit Kumar
- Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area, Greater Noida, 201306, Uttar Pradesh, India
| | - Arpita Dua
- Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area, Greater Noida, 201306, Uttar Pradesh, India
| | - Priya Singhal
- Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area, Greater Noida, 201306, Uttar Pradesh, India
| | - Arvind Kumar
- Department of Biotechnology, Noida Institute of Engineering and Technology, 19 Knowledge Park-II, Institutional Area, Greater Noida, 201306, Uttar Pradesh, India
| | - Gurvinder Singh
- Department of Medicinal Chemistry, Lovely Professional University, Phagwara, 144001, Punjab, India
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11
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Morales-Salazar I, Garduño-Albino CE, Montes-Enríquez FP, Nava-Tapia DA, Navarro-Tito N, Herrera-Zúñiga LD, González-Zamora E, Islas-Jácome A. Synthesis of Pyrrolo[3,4- b]pyridin-5-ones via Ugi-Zhu Reaction and In Vitro-In Silico Studies against Breast Carcinoma. Pharmaceuticals (Basel) 2023; 16:1562. [PMID: 38004428 PMCID: PMC10674953 DOI: 10.3390/ph16111562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
An Ugi-Zhu three-component reaction (UZ-3CR) coupled in a one-pot manner to a cascade process (N-acylation/aza Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of pyrrolo[3,4-b]pyridin-5-ones in 20% to 92% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against breast cancer cell lines MDA-MB-231 and MCF-7, finding that compound 1f, at a concentration of 6.25 μM, exhibited a potential cytotoxic effect. Then, to understand the interactions between synthesized compounds and the main proteins related to the cancer cell lines, docking studies were performed on the serine/threonine kinase 1 (AKT1) and Orexetine type 2 receptor (Ox2R), finding moderate to strong binding energies, which matched accurately with the in vitro results. Additionally, molecular dynamics were performed between proteins related to the studied cell lines and the three best ligands.
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Affiliation(s)
- Ivette Morales-Salazar
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Carlos E. Garduño-Albino
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Flora P. Montes-Enríquez
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Dania A. Nava-Tapia
- Laboratorio de Biología Celular del Cáncer, Universidad Autónoma de Guerrero, Chilpancingo de los Bravo 39086, Mexico;
| | - Napoleón Navarro-Tito
- Laboratorio de Biología Celular del Cáncer, Universidad Autónoma de Guerrero, Chilpancingo de los Bravo 39086, Mexico;
| | - Leonardo David Herrera-Zúñiga
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Eduardo González-Zamora
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
| | - Alejandro Islas-Jácome
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, San Rafael Atlixco 186, Col. Vicentina, Iztapalapa, Ciudad de México 09340, Mexico; (I.M.-S.); (C.E.G.-A.); (F.P.M.-E.); (E.G.-Z.)
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12
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Monari L, Galentino K, Cecchini M. ChemFlow_py: a flexible toolkit for docking and rescoring. J Comput Aided Mol Des 2023; 37:565-572. [PMID: 37620503 DOI: 10.1007/s10822-023-00527-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
The design of accurate virtual screening tools is an open challenge in drug discovery. Several structure-based methods have been developed at different levels of approximation. Among them, molecular docking is an established technique with high efficiency, but typically low accuracy. Moreover, docking performances are known to be target-dependent, which makes the choice of the docking program and corresponding scoring function critical when approaching a new protein target. To compare the performances of different docking protocols, we developed ChemFlow_py, an automated tool to perform docking and rescoring. Using four protein systems extracted from DUD-E with 100 known active compounds and 3000 decoys per target, we compared the performances of several rescoring strategies including consensus scoring. We found that the average docking results can be improved by consensus ranking, which emphasizes the relevance of consensus scoring when little or no chemical information is available for a given target. ChemFlow_py is a free toolkit to optimize the performances of virtual high-throughput screening (vHTS). The software is publicly available at https://github.com/IFMlab/ChemFlow_py .
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Affiliation(s)
- Luca Monari
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Katia Galentino
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France
| | - Marco Cecchini
- Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67083, Strasbourg, Cedex, France.
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13
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Jiao F, Andrianov AM, Wang L, Furs KV, Gonchar AV, Wang Q, Xu W, Lu L, Xia S, Tuzikov AV, Jiang S. Repurposing Navitoclax to block SARS-CoV-2 fusion and entry by targeting heptapeptide repeat sequence 1 in S2 protein. J Med Virol 2023; 95:e29145. [PMID: 37804480 DOI: 10.1002/jmv.29145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/28/2023] [Accepted: 09/10/2023] [Indexed: 10/09/2023]
Abstract
Along with the long pandemic of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has come the dilemma of emerging viral variants of concern (VOC), particularly Omicron and its subvariants, able to deftly escape immune surveillance and the otherwise protective effect of current vaccines and antibody drugs. We previously identified a peptide-based pan-CoV fusion inhibitor, termed as EK1, able to bind the HR1 region in viral spike (S) protein S2 subunit. This effectively blocked formation of the six-helix bundle (6-HB) fusion core and, thus, showed efficacy against all human coronaviruses (HCoVs). EK1 is now in phase 3 clinical trials. However, the peptide drug generally lacks oral availability. Therefore, we herein performed a structure-based virtual screening of the libraries of biologically active molecules and identified nine candidate compounds. One is Navitoclax, an orally active anticancer drug by inhibition of Bcl-2. Like EK1 peptide, it could bind HR1 and block 6-HB formation, efficiently inhibiting fusion and infection of all SARS-CoV-2 variants tested, as well as SARS-CoV and MERS-CoV, with IC50 values ranging from 0.5 to 3.7 μM. These findings suggest that Navitoclax is a promising repurposed drug candidate for development as a safe and orally available broad-spectrum antiviral drug to combat the current SARS-CoV-2 and its variants, as well as other HCoVs.
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Affiliation(s)
- Fanke Jiao
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Frontiers Science Center of Pathogenic Microbes and Infection, Fudan University, Shanghai, China
| | - Alexander M Andrianov
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, Minsk, Belarus
| | - Lijue Wang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Frontiers Science Center of Pathogenic Microbes and Infection, Fudan University, Shanghai, China
| | - Konstantin V Furs
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, Minsk, Belarus
| | - Anna V Gonchar
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, Minsk, Belarus
| | - Qian Wang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Frontiers Science Center of Pathogenic Microbes and Infection, Fudan University, Shanghai, China
| | - Wei Xu
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Frontiers Science Center of Pathogenic Microbes and Infection, Fudan University, Shanghai, China
| | - Lu Lu
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Frontiers Science Center of Pathogenic Microbes and Infection, Fudan University, Shanghai, China
| | - Shuai Xia
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Frontiers Science Center of Pathogenic Microbes and Infection, Fudan University, Shanghai, China
| | - Alexander V Tuzikov
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, Minsk, Belarus
| | - Shibo Jiang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Frontiers Science Center of Pathogenic Microbes and Infection, Fudan University, Shanghai, China
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14
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Debroy R, Ramaiah S. Consolidated knowledge-guided computational pipeline for therapeutic intervention against bacterial biofilms - a review. BIOFOULING 2023; 39:928-947. [PMID: 38108207 DOI: 10.1080/08927014.2023.2294763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
Biofilm-associated bacterial infections attributed to multifactorial antimicrobial resistance have caused worldwide challenges in formulating successful treatment strategies. In search of accelerated yet cost-effective therapeutics, several researchers have opted for bioinformatics-based protocols to systemize targeted therapies against biofilm-producing strains. The present review investigated the up-to-date computational databases and servers dedicated to anti-biofilm research to design/screen novel biofilm inhibitors (antimicrobial peptides/phytocompounds/synthetic compounds) and predict their biofilm-inhibition efficacy. Scrutinizing the contemporary in silico methods, a consolidated approach has been highlighted, referred to as a knowledge-guided computational pipeline for biofilm-targeted therapy. The proposed pipeline has amalgamated prominently employed methodologies in genomics, transcriptomics, interactomics and proteomics to identify potential target proteins and their complementary anti-biofilm compounds for effective functional inhibition of biofilm-linked pathways. This review can pave the way for new portals to formulate successful therapeutic interventions against biofilm-producing pathogens.
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Affiliation(s)
- Reetika Debroy
- Medical and Biological Computing Laboratory, School of Bio-Sciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
- Department of Bio-Medical Sciences, School of Bio-Sciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
| | - Sudha Ramaiah
- Medical and Biological Computing Laboratory, School of Bio-Sciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
- Department of Bio-Sciences, School of Bio-Sciences and Technology (SBST), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India
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15
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Radaeva M, Morin H, Pandey M, Ban F, Guo M, LeBlanc E, Lallous N, Cherkasov A. Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening. Mol Inform 2023; 42:e2300026. [PMID: 37193651 DOI: 10.1002/minf.202300026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 05/18/2023]
Abstract
Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.
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Affiliation(s)
- Mariia Radaeva
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Helene Morin
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Mohit Pandey
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Fuqiang Ban
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Maria Guo
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Eric LeBlanc
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Nada Lallous
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, British Columbia, Canada, V6H 3Z6
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16
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Andrianov AM, Shuldau MA, Furs KV, Yushkevich AM, Tuzikov AV. AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease. Int J Mol Sci 2023; 24:ijms24098083. [PMID: 37175788 PMCID: PMC10178971 DOI: 10.3390/ijms24098083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/21/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.
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Affiliation(s)
- Alexander M Andrianov
- Institute of Bioorganic Chemistry, National Academy of Sciences of Belarus, 220141 Minsk, Belarus
| | - Mikita A Shuldau
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Konstantin V Furs
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Artsemi M Yushkevich
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
| | - Alexander V Tuzikov
- United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220012 Minsk, Belarus
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17
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Kersten C, Clower S, Barthels F. Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold. J Chem Inf Model 2023; 63:2218-2225. [PMID: 36884022 DOI: 10.1021/acs.jcim.2c01400] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
AlphaFold2 and RoseTTAfold impress with their high accuracy in protein structure prediction. However, for structure-based virtual screenings, not only the overall structure but especially the binding sites need to be accurately predicted. In this work, the docking performance for 66 targets with known ligands but without experimental structures available in the protein data bank was elucidated. The results suggest that using an experimental surrogate-ligand complex is often superior over homology models, and only at low sequence identity to the closest homologue AlphaFold2 structures show an equal performance. The generally high fluctuation of receiver operating characteristic area under the curve values obtained for different homology models suggests that multiple combinations of docking programs and homology models should be tested prior to prospective virtual screenings, and in some cases post-processing of crude models might be necessary.
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Affiliation(s)
- Christian Kersten
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128 Mainz, Germany
| | - Steven Clower
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128 Mainz, Germany
| | - Fabian Barthels
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128 Mainz, Germany
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18
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Potlitz F, Link A, Schulig L. Advances in the discovery of new chemotypes through ultra-large library docking. Expert Opin Drug Discov 2023; 18:303-313. [PMID: 36714919 DOI: 10.1080/17460441.2023.2171984] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (Mpro). AREAS COVERED This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel Mpro inhibitors targeting the SARS-CoV-2 virus. EXPERT OPINION With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.
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Affiliation(s)
- Felix Potlitz
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Andreas Link
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Lukas Schulig
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
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19
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Scardino V, Di Filippo JI, Cavasotto CN. How good are AlphaFold models for docking-based virtual screening? iScience 2023; 26:105920. [PMID: 36686396 PMCID: PMC9852548 DOI: 10.1016/j.isci.2022.105920] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/12/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.
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Affiliation(s)
- Valeria Scardino
- Meton AI, Inc, Wilmington, DE 19801, USA
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
| | - Juan I. Di Filippo
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET, Pilar, Buenos Aires, Argentina
| | - Claudio N. Cavasotto
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
- Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET, Pilar, Buenos Aires, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina
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20
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Heider J, Kilian J, Garifulina A, Hering S, Langer T, Seidel T. Apo2ph4: A Versatile Workflow for the Generation of Receptor-based Pharmacophore Models for Virtual Screening. J Chem Inf Model 2023; 63:101-110. [PMID: 36526584 PMCID: PMC9832483 DOI: 10.1021/acs.jcim.2c00814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Pharmacophore models are widely used as efficient virtual screening (VS) filters for the target-directed enrichment of large compound libraries. However, the generation of pharmacophore models that have the power to discriminate between active and inactive molecules traditionally requires structural information about ligand-target complexes or at the very least knowledge of one active ligand. The fact that the discovery of the first known active ligand of a newly investigated target represents a major hurdle at the beginning of every drug discovery project underscores the need for methods that are able to derive high-quality pharmacophore models even without the prior knowledge of any active ligand structures. In this work, we introduce a novel workflow, called apo2ph4, that enables the rapid derivation of pharmacophore models solely from the three-dimensional structure of the target receptor. The utility of this workflow is demonstrated retrospectively for the generation of a pharmacophore model for the M2 muscarinic acetylcholine receptor. Furthermore, in order to show the general applicability of apo2ph4, the workflow was employed for all 15 targets of the recently published LIT-PCBA dataset. Pharmacophore-based VS runs using the apo2ph4-derived models achieved a significant enrichment of actives for 13 targets. In the last presented example, a pharmacophore model derived from the etomidate site of the α1β2γ2 GABAA receptor was used in VS campaigns. Subsequent in vitro testing of selected hits revealed that 19 out of 20 (95%) tested compounds were able to significantly enhance GABA currents, which impressively demonstrates the applicability of apo2ph4 for real-world drug design projects.
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Affiliation(s)
- Jörg Heider
- Department
of Pharmaceutical Sciences, University of
Vienna, Josef-Holaubek-Platz
2, 1090Vienna, Austria,Vienna
Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090Vienna, Austria
| | - Jonas Kilian
- Vienna
Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090Vienna, Austria,Department
of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear
Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090Vienna, Austria
| | - Aleksandra Garifulina
- Division
of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090Vienna, Austria
| | - Steffen Hering
- Division
of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090Vienna, Austria
| | - Thierry Langer
- Department
of Pharmaceutical Sciences, University of
Vienna, Josef-Holaubek-Platz
2, 1090Vienna, Austria
| | - Thomas Seidel
- Department
of Pharmaceutical Sciences, University of
Vienna, Josef-Holaubek-Platz
2, 1090Vienna, Austria,
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21
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Chia S, Faidon Brotzakis Z, Horne RI, Possenti A, Mannini B, Cataldi R, Nowinska M, Staats R, Linse S, Knowles TPJ, Habchi J, Vendruscolo M. Structure-Based Discovery of Small-Molecule Inhibitors of the Autocatalytic Proliferation of α-Synuclein Aggregates. Mol Pharm 2023; 20:183-193. [PMID: 36374974 PMCID: PMC9811465 DOI: 10.1021/acs.molpharmaceut.2c00548] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The presence of amyloid fibrils of α-synuclein is closely associated with Parkinson's disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of α-synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a range of kinetic and thermodynamic assays for their ability to bind α-synuclein fibrils and prevent the further generation of α-synuclein oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of α-synuclein aggregates.
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Affiliation(s)
- Sean Chia
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Z. Faidon Brotzakis
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Robert I. Horne
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Andrea Possenti
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Benedetta Mannini
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Rodrigo Cataldi
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Magdalena Nowinska
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Roxine Staats
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Sara Linse
- Department
of Biochemistry & Structural Biology, Center for Molecular Protein
Science, Lund University, 221 00Lund, Sweden
| | - Tuomas P. J. Knowles
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.,Department
of Physics, Cavendish Laboratory, CambridgeCB3 0HE, U.K.
| | - Johnny Habchi
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.
| | - Michele Vendruscolo
- Centre
for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, CambridgeCB2 1EW, U.K.,
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22
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Nhat Phuong D, Flower DR, Chattopadhyay S, Chattopadhyay AK. Towards Effective Consensus Scoring in Structure-Based Virtual Screening. Interdiscip Sci 2023; 15:131-145. [PMID: 36550341 PMCID: PMC9941253 DOI: 10.1007/s12539-022-00546-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.
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Affiliation(s)
- Do Nhat Phuong
- grid.7273.10000 0004 0376 4727Department of Mathematics, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET UK
| | - Darren R. Flower
- grid.7273.10000 0004 0376 4727Life and Health Sciences, Aston University, Birmingham, B4 7ET UK
| | | | - Amit K. Chattopadhyay
- grid.7273.10000 0004 0376 4727Department of Mathematics, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET UK
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23
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Grasso G, Di Gregorio A, Mavkov B, Piga D, Labate GFD, Danani A, Deriu MA. Fragmented blind docking: a novel protein-ligand binding prediction protocol. J Biomol Struct Dyn 2022; 40:13472-13481. [PMID: 34641761 DOI: 10.1080/07391102.2021.1988709] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Arianna Di Gregorio
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland.,PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy
| | - Bojan Mavkov
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Dario Piga
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | | | - Andrea Danani
- Dalle Molle Institute for Artificial Intelligence, IDSIA - USI/SUPSI, Lugano-Viganello, Switzerland
| | - Marco A Deriu
- PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy
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24
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Blanes-Mira C, Fernández-Aguado P, de Andrés-López J, Fernández-Carvajal A, Ferrer-Montiel A, Fernández-Ballester G. Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening. Molecules 2022; 28:molecules28010175. [PMID: 36615367 PMCID: PMC9821981 DOI: 10.3390/molecules28010175] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.
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25
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Budipramana K, Sangande F. Molecular docking-based virtual screening: Challenges in hits identification for Anti-SARS-Cov-2 activity. PHARMACIA 2022. [DOI: 10.3897/pharmacia.69.e89812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) requires finding new drugs or repurposing drugs for clinical use. Molecular docking belongs to structure-based drug design providing a fast method for identifying the hit compounds with antiviral activity against SARS-Cov-2. However, the weakness of the docking method is compounded by the limited crystallographic information and comparison drugs due to the novelty of this virus can present challenges in identifying hits of anti-SARS-Cov-2. In the current review, we highlighted several aspects, especially those related to the target structure, docking validation, and virtual hit selection, that need to be considered to obtain reliable docking results. Here, we discussed several cases pertaining to the issue highlighted and approaches that could be used to solve them.
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26
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Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors. ARAB J CHEM 2022; 15:104334. [PMID: 36246784 PMCID: PMC9554199 DOI: 10.1016/j.arabjc.2022.104334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 10/03/2022] [Indexed: 11/24/2022] Open
Abstract
Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.
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Key Words
- CMNPD, comprehensive marine natural product database
- Consensus scoring
- DCCM, dynamic cross-correlation matrix
- H, hydrophobic
- HBA, hydrogen bond acceptor
- HBD, hydrogen bond donor
- MD, molecular dynamics
- MMGBSA, molecular mechanics generalized Born and surface area continuum solvation
- MW, molecular weight
- Marine natural products
- Molecular docking
- Molecular dynamics
- PCA, principal component analysis
- PI, positive ionization
- PLpro, SARS-CoV-2 papain-like protease
- Pharmacophore model
- SARS-CoV-2 PLpro
- TG, Total gain
- ns, nanoseconds
- ps, picoseconds
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27
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Morris CJ, Stern JA, Stark B, Christopherson M, Della Corte D. MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery. J Chem Inf Model 2022; 62:5342-5350. [PMID: 36342217 DOI: 10.1021/acs.jcim.2c00705] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To enable more accurate ranking of active over inactive ligands in virtual screening, we created a machine learning consensus docking tool, MILCDock, that uses predictions from five traditional molecular docking tools to predict the probability a ligand binds to a protein. MILCDock was trained and tested on data from both the DUD-E and LIT-PCBA docking datasets and shows improved performance over traditional molecular docking tools and other consensus docking methods on the DUD-E dataset. LIT-PCBA targets proved to be difficult for all methods tested. We also find that DUD-E data, although biased, can be effective in training machine learning tools if care is taken to avoid DUD-E's biases during training.
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Affiliation(s)
- Connor J Morris
- Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States
| | - Jacob A Stern
- Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States.,Department of Computer Science, Brigham Young University, Provo, Utah84602, United States
| | - Brenden Stark
- Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States
| | - Max Christopherson
- Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States
| | - Dennis Della Corte
- Department of Physics and Astronomy, Brigham Young University, Provo, Utah84602, United States
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28
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Rosignoli S, Paiardini A. Boosting the Full Potential of PyMOL with Structural Biology Plugins. Biomolecules 2022; 12:biom12121764. [PMID: 36551192 PMCID: PMC9775141 DOI: 10.3390/biom12121764] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.
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29
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Hernández-Silva D, Alcaraz-Pérez F, Pérez-Sánchez H, Cayuela ML. Virtual screening and zebrafish models in tandem, for drug discovery and development. Expert Opin Drug Discov 2022:1-13. [DOI: 10.1080/17460441.2022.2147503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- David Hernández-Silva
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Structural Bioinformatics and High-Performance Computing Research Group (BIOHPC), Computer Engineering Department, Universidad Católica de Murcia (UCAM), Guadalupe, 30107 Murcia, Spain
| | - Francisca Alcaraz-Pérez
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Horacio Pérez-Sánchez
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Maria Luisa Cayuela
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
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30
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Xu M, Shen C, Yang J, Wang Q, Huang N. Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening. ACS OMEGA 2022; 7:39417-39428. [PMID: 36340123 PMCID: PMC9632257 DOI: 10.1021/acsomega.2c05826] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.
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Affiliation(s)
- Min Xu
- College
of Life Sciences, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
| | - Cheng Shen
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
- Graduate
School of Peking Union Medical College, Chinese Academy of Medical Sciences, No. 9, Dongdan Santiao, Dongcheng District, Beijing 100730, China
| | - Jincai Yang
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
| | - Qing Wang
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
- School
of Pharmaceutical Science and Technology, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China
| | - Niu Huang
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
- Tsinghua
Institute of Multidisciplinary Biomedical Research, Tsinghua University, 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
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31
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García-Chacón J, Tello E, Coy-Barrera E, Peterson DG, Osorio C. Mono- n-butyl Malate-Derived Compounds from Camu-camu ( Myrciaria dubia) Malic Acid: The Alkyl-Dependent Antihyperglycemic-Related Activity. ACS OMEGA 2022; 7:39335-39346. [PMID: 36340106 PMCID: PMC9631754 DOI: 10.1021/acsomega.2c05551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
Malic acid derivatives from camu-camu (Myrciaria dubia) fruit exhibited a strong in vitro inhibitory activity toward pancreatic α-amylase and α-glucosidase enzymes. During a bioguided chromatographic fractionation process of the whole fruit (pulp and peelings) polar extract, isomers (S)-4-butoxy-2-hydroxy-4-oxobutanoic acid (1) and (S)-4-butoxy-3-hydroxy-4-oxobutanoic acid (2) (84:16) were isolated and identified as a potent inhibitor of α-amylase (IC50= 11.69 ± 1.75 μg/mL) and α-glucosidase (IC50 = 102.69 ± 4.16 μg/mL). The chemical structures were confirmed by HPLC-ESIMS and 1H and 13C NMR (one- and two-dimensional) analyses. The structure-based virtual screening demonstrated that the aliphatic moiety plays a significant role in the binding mode of the test alkyl malate esters. Compound 1 exhibited the best interaction profile to bind both enzymes, having key structural features to form relevant contacts by involving adequate enzyme-ligand complex stabilization and compactness over time.
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Affiliation(s)
| | - Edisson Tello
- Department
of Food Science and Technology, Parker Food Science & Technology
Building, The Ohio State University, 2015 Fyffe Rd., The Ohio State University, Columbus, Ohio43210, United States
| | - Ericsson Coy-Barrera
- Bioorganic
Chemistry Laboratory, Facultad de Ciencias Básicas y Aplicadas, Universidad Militar Nueva Granada, Campus Nueva Granada, Cajicá250247, Colombia
| | - Devin G. Peterson
- Department
of Food Science and Technology, Parker Food Science & Technology
Building, The Ohio State University, 2015 Fyffe Rd., The Ohio State University, Columbus, Ohio43210, United States
| | - Coralia Osorio
- Departamento
de Química, Universidad Nacional
de Colombia, AA 14490Bogotá, Colombia
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32
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Chen Z, Wang G, Xie X, Liu H, Liao J, Shi H, Chen M, Lai S, Wang Z, Wu X. Ginsenoside Rg5 allosterically interacts with P2RY12 and ameliorates deep venous thrombosis by counteracting neutrophil NETosis and inflammatory response. Front Immunol 2022; 13:918476. [PMID: 36032109 PMCID: PMC9411522 DOI: 10.3389/fimmu.2022.918476] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/21/2022] [Indexed: 11/26/2022] Open
Abstract
Background Deep venous thrombosis (DVT) highly occurs in patients with severe COVID-19 and probably accounted for their high mortality. DVT formation is a time-dependent inflammatory process in which NETosis plays an important role. However, whether ginsenoside Rg5 from species of Panax genus could alleviate DVT and its underlying mechanism has not been elucidated. Methods The interaction between Rg5 and P2RY12 was studied by molecular docking, molecular dynamics, surface plasmon resonance (SPR), and molecular biology assays. The preventive effect of Rg5 on DVT was evaluated in inferior vena cava stasis–induced mice, and immunocytochemistry, Western blot, and calcium flux assay were performed in neutrophils from bone marrow to explore the mechanism of Rg5 in NETosis via P2RY12. Results Rg5 allosterically interacted with P2RY12, formed stable complex, and antagonized its activity via residue E188 and R265. Rg5 ameliorated the formation of thrombus in DVT mice; accompanied by decreased release of Interleukin (IL)-6, IL-1β, and tumor necrosis factor-α in plasma; and suppressed neutrophil infiltration and neutrophil extracellular trap (NET) release. In lipopolysaccharide- and platelet-activating factor–induced neutrophils, Rg5 reduced inflammatory responses via inhibiting the activation of ERK/NF-κB signaling pathway while decreasing cellular Ca2+ concentration, thus reducing the activity and expression of peptidyl arginine deiminase 4 to prevent NETosis. The inhibitory effect on neutrophil activity was dependent on P2RY12. Conclusions Rg5 could attenuate experimental DVT by counteracting NETosis and inflammatory response in neutrophils via P2RY12, which may pave the road for its clinical application in the prevention of DVT-related disorders.
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Affiliation(s)
- Ziyu Chen
- Shanghai Key Laboratory of Compound Chinese Medicines, The Ministry of Education (MOE) Key Laboratory for Standardization of Chinese Medicines, The State Administration of TCM (SATCM) Key Laboratory for New Resources and Quality Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Gaorui Wang
- Shanghai Key Laboratory of Compound Chinese Medicines, The Ministry of Education (MOE) Key Laboratory for Standardization of Chinese Medicines, The State Administration of TCM (SATCM) Key Laboratory for New Resources and Quality Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xueqing Xie
- Shanghai Key Laboratory of Compound Chinese Medicines, The Ministry of Education (MOE) Key Laboratory for Standardization of Chinese Medicines, The State Administration of TCM (SATCM) Key Laboratory for New Resources and Quality Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Heng Liu
- School of Life Science and Technology, Shanghai Tech University, Shanghai, China
| | - Jun Liao
- School of Life Science and Technology, Shanghai Tech University, Shanghai, China
| | - Hailian Shi
- Shanghai Key Laboratory of Compound Chinese Medicines, The Ministry of Education (MOE) Key Laboratory for Standardization of Chinese Medicines, The State Administration of TCM (SATCM) Key Laboratory for New Resources and Quality Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Min Chen
- Guangxi Key Laboratory of Comprehensive Utilization Technology of Pseudo-Ginseng, Wuzhou, China
| | - Shusheng Lai
- Guangxi Key Laboratory of Comprehensive Utilization Technology of Pseudo-Ginseng, Wuzhou, China
| | - Zhengtao Wang
- Shanghai Key Laboratory of Compound Chinese Medicines, The Ministry of Education (MOE) Key Laboratory for Standardization of Chinese Medicines, The State Administration of TCM (SATCM) Key Laboratory for New Resources and Quality Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Xiaojun Wu, ; Zhengtao Wang,
| | - Xiaojun Wu
- Shanghai Key Laboratory of Compound Chinese Medicines, The Ministry of Education (MOE) Key Laboratory for Standardization of Chinese Medicines, The State Administration of TCM (SATCM) Key Laboratory for New Resources and Quality Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Xiaojun Wu, ; Zhengtao Wang,
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33
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Conev A, Devaurs D, Rigo MM, Antunes DA, Kavraki LE. 3pHLA-score improves structure-based peptide-HLA binding affinity prediction. Sci Rep 2022; 12:10749. [PMID: 35750701 PMCID: PMC9232595 DOI: 10.1038/s41598-022-14526-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/08/2022] [Indexed: 12/30/2022] Open
Abstract
Binding of peptides to Human Leukocyte Antigen (HLA) receptors is a prerequisite for triggering immune response. Estimating peptide-HLA (pHLA) binding is crucial for peptide vaccine target identification and epitope discovery pipelines. Computational methods for binding affinity prediction can accelerate these pipelines. Currently, most of those computational methods rely exclusively on sequence-based data, which leads to inherent limitations. Recent studies have shown that structure-based data can address some of these limitations. In this work we propose a novel machine learning (ML) structure-based protocol to predict binding affinity of peptides to HLA receptors. For that, we engineer the input features for ML models by decoupling energy contributions at different residue positions in peptides, which leads to our novel per-peptide-position protocol. Using Rosetta's ref2015 scoring function as a baseline we use this protocol to develop 3pHLA-score. Our per-peptide-position protocol outperforms the standard training protocol and leads to an increase from 0.82 to 0.99 of the area under the precision-recall curve. 3pHLA-score outperforms widely used scoring functions (AutoDock4, Vina, Dope, Vinardo, FoldX, GradDock) in a structural virtual screening task. Overall, this work brings structure-based methods one step closer to epitope discovery pipelines and could help advance the development of cancer and viral vaccines.
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Affiliation(s)
- Anja Conev
- grid.21940.3e0000 0004 1936 8278Department of Computer Science, Rice University, Houston, 77005 USA
| | - Didier Devaurs
- grid.4305.20000 0004 1936 7988MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU UK
| | - Mauricio Menegatti Rigo
- grid.21940.3e0000 0004 1936 8278Department of Computer Science, Rice University, Houston, 77005 USA
| | | | - Lydia E. Kavraki
- grid.21940.3e0000 0004 1936 8278Department of Computer Science, Rice University, Houston, 77005 USA
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Yau MQ, Loo JSE. Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA. J Comput Aided Mol Des 2022; 36:427-441. [PMID: 35581483 DOI: 10.1007/s10822-022-00456-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 04/28/2022] [Indexed: 01/09/2023]
Abstract
The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.
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Affiliation(s)
- Mei Qian Yau
- Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylor's, 47500, Subang Jaya, Selangor, Malaysia.,School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia
| | - Jason S E Loo
- Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylor's, 47500, Subang Jaya, Selangor, Malaysia. .,School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
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35
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Vázquez-Mendoza LH, Mendoza-Figueroa HL, García-Vázquez JB, Correa-Basurto J, García-Machorro J. In Silico Drug Repositioning to Target the SARS-CoV-2 Main Protease as Covalent Inhibitors Employing a Combined Structure-Based Virtual Screening Strategy of Pharmacophore Models and Covalent Docking. Int J Mol Sci 2022; 23:ijms23073987. [PMID: 35409348 PMCID: PMC8999907 DOI: 10.3390/ijms23073987] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
The epidemic caused by the SARS-CoV-2 coronavirus, which has spread rapidly throughout the world, requires urgent and effective treatments considering that the appearance of viral variants limits the efficacy of vaccines. The main protease of SARS-CoV-2 (Mpro) is a highly conserved cysteine proteinase, fundamental for the replication of the coronavirus and with a specific cleavage mechanism that positions it as an attractive therapeutic target for the proposal of irreversible inhibitors. A structure-based strategy combining 3D pharmacophoric modeling, virtual screening, and covalent docking was employed to identify the interactions required for molecular recognition, as well as the spatial orientation of the electrophilic warhead, of various drugs, to achieve a covalent interaction with Cys145 of Mpro. The virtual screening on the structure-based pharmacophoric map of the SARS-CoV-2 Mpro in complex with an inhibitor N3 (reference compound) provided high efficiency by identifying 53 drugs (FDA and DrugBank databases) with probabilities of covalent binding, including N3 (Michael acceptor) and others with a variety of electrophilic warheads. Adding the energy contributions of affinity for non-covalent and covalent docking, 16 promising drugs were obtained. Our findings suggest that the FDA-approved drugs Vaborbactam, Cimetidine, Ixazomib, Scopolamine, and Bicalutamide, as well as the other investigational peptide-like drugs (DB04234, DB03456, DB07224, DB7252, and CMX-2043) are potential covalent inhibitors of SARS-CoV-2 Mpro.
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Affiliation(s)
- Luis Heriberto Vázquez-Mendoza
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Posgrado en Farmacología de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico; (L.H.V.-M.); (J.C.-B.)
| | - Humberto L. Mendoza-Figueroa
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Posgrado en Farmacología de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico; (L.H.V.-M.); (J.C.-B.)
- Correspondence: (H.L.M.-F.); (J.B.G.-V.)
| | - Juan Benjamín García-Vázquez
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Posgrado en Farmacología de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico; (L.H.V.-M.); (J.C.-B.)
- Cátedras CONACyT-Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico
- Correspondence: (H.L.M.-F.); (J.B.G.-V.)
| | - José Correa-Basurto
- Laboratorio de Diseño y Desarrollo de Nuevos Fármacos e Innovación Biotecnológica, Posgrado en Farmacología de la Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico; (L.H.V.-M.); (J.C.-B.)
| | - Jazmín García-Machorro
- Laboratorio de Medicina de la Conservación, Escuela Superior de Medicina del Instituto Politécnico Nacional, Plan de San Luis y Salvador Díaz Mirón s/n, Casco de Santo Tomás, Ciudad de Mexico 11340, Mexico;
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36
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Timmons JA, Anighoro A, Brogan RJ, Stahl J, Wahlestedt C, Farquhar DG, Taylor-King J, Volmar CH, Kraus WE, Phillips SM. A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease. eLife 2022; 11:68832. [PMID: 35037854 PMCID: PMC8763401 DOI: 10.7554/elife.68832] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 11/26/2021] [Indexed: 12/22/2022] Open
Abstract
Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.
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Affiliation(s)
- James A Timmons
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.,Augur Precision Medicine LTD, Stirling, United Kingdom
| | | | | | - Jack Stahl
- Center for Therapeutic Innovation, Miller School of Medicine, University of Miami, Miami, United States
| | - Claes Wahlestedt
- Center for Therapeutic Innovation, Miller School of Medicine, University of Miami, Miami, United States
| | | | | | - Claude-Henry Volmar
- Center for Therapeutic Innovation, Miller School of Medicine, University of Miami, Miami, United States
| | | | - Stuart M Phillips
- Faculty of Science, Kinesiology, McMaster University, Hamilton, Canada
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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38
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Consensus combining outcomes of multiple ensemble dockings: examples using dDAT crystalized complexes. MethodsX 2022; 9:101788. [PMID: 35935527 PMCID: PMC9352961 DOI: 10.1016/j.mex.2022.101788] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 07/13/2022] [Indexed: 11/23/2022] Open
Abstract
Docking using different programs provides more reliable information about the interaction of molecules than data obtained in a single program. An exponential consensus ranking (ECR) was developed to combine scoring functions across docking programs differing in efficiencies and scales of measurements. The ECR method was adapted to merge results of re- and cross-dockings (i.e., ensemble docking) made in multiple docking programs. Adapted ECR consisted of four consecutive steps: 1- determination of scoring functions for a ligand with a series of macromolecules in multiple docking programs; 2- ranking of the scoring functions per macromolecule in each program; 3- combining the ranking across the programs creating a ranking per macromolecule; 4- averaging the ranking per macromolecule creating a final ranking. This last step incorporated the heterogeneity of the macromolecule conformations in the consensual score. The final ranking based on the adapted ECR represents relative affinity of a series of ligands to a macromolecule on average. As an example, a ranking of the average affinity of antidepressants and other ligands to the Drosophila melanogaster dopamine transporter (dDAT) was presented. Adapted ECR generated a ranking similar to that based on the affinity constant of each ligand obtained from the literature. • A final ranking of the average relative affinity of different ligands to the dDAT. • A consensus method combining multiple ensemble dockings. • A complete protocol to make re-docking and cross-docking using Autodock Vina, Gold and DockThor.
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39
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Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds. Biomedicines 2021; 9:biomedicines9121796. [PMID: 34944612 PMCID: PMC8698941 DOI: 10.3390/biomedicines9121796] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/27/2021] [Accepted: 11/05/2021] [Indexed: 12/13/2022] Open
Abstract
Ebola virus (EBOV) is one of the most lethal pathogens that can infect humans. The Ebola viral protein VP35 (EBOV VP35) inhibits host IFN-α/β production by interfering with host immune responses to viral invasion and is thus considered as a plausible drug target. The aim of this study was to identify potential novel lead compounds against EBOV VP35 using computational techniques in drug discovery. The 3D structure of the EBOV VP35 with PDB ID: 3FKE was used for molecular docking studies. An integrated library of 7675 African natural product was pre-filtered using ADMET risk, with a threshold of 7 and, as a result, 1470 ligands were obtained for the downstream molecular docking using AutoDock Vina, after an energy minimization of the protein via GROMACS. Five known inhibitors, namely, amodiaquine, chloroquine, gossypetin, taxifolin and EGCG were used as standard control compounds for this study. The area under the curve (AUC) value, evaluating the docking protocol obtained from the receiver operating characteristic (ROC) curve, generated was 0.72, which was considered to be acceptable. The four identified potential lead compounds of NANPDB4048, NANPDB2412, ZINC000095486250 and NANPDB2476 had binding affinities of −8.2, −8.2, −8.1 and −8.0 kcal/mol, respectively, and were predicted to possess desirable antiviral activity including the inhibition of RNA synthesis and membrane permeability, with the probable activity (Pa) being greater than the probable inactivity (Pi) values. The predicted anti-EBOV inhibition efficiency values (IC50), found using a random forest classifier, ranged from 3.35 to 11.99 μM, while the Ki values ranged from 0.97 to 1.37 μM. The compounds NANPDB4048 and NANPDB2412 had the lowest binding energy of −8.2 kcal/mol, implying a higher binding affinity to EBOV VP35 which was greater than those of the known inhibitors. The compounds were predicted to possess a low toxicity risk and to possess reasonably good pharmacological profiles. Molecular dynamics (MD) simulations of the protein–ligand complexes, lasting 50 ns, and molecular mechanisms Poisson-Boltzmann surface area (MM-PBSA) calculations corroborated the binding affinities of the identified compounds and identified novel critical interacting residues. The antiviral potential of the molecules could be confirmed experimentally, while the scaffolds could be optimized for the design of future novel anti-EBOV chemotherapeutics.
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Ribone SR, Paz SA, Abrams CF, Villarreal MA. Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking. J Comput Aided Mol Des 2021; 36:25-37. [PMID: 34825285 PMCID: PMC8616721 DOI: 10.1007/s10822-021-00432-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/08/2021] [Indexed: 12/15/2022]
Abstract
Screening already approved drugs for activity against a novel pathogen can be an important part of global rapid-response strategies in pandemics. Such high-throughput repurposing screens have already identified several existing drugs with potential to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifically against this pathogen requires unambiguous identification of their corresponding targets, something the high-throughput screens are not typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom protein structures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plurality of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method with known drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentially suitable for repurposing to the inverse docking procedure. The most common preferential targets were the human enzymes TMPRSS2 and PIKfyve, followed by the viral enzymes Helicase and PLpro. All compounds that selected TMPRSS2 are known serine protease inhibitors, and those that selected PIKfyve are known tyrosine kinase inhibitors. Detailed structural analysis of the docking poses revealed important insights into why these selections arose, and could potentially lead to more rational design of new drugs against these targets.
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Affiliation(s)
- Sergio R Ribone
- Departamento de Ciencias Farmacéuticas, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, X5000HUA, Córdoba, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Unidad de Investigación y Desarrollo en Tecnología Farmacéutica (UNITEFA), X5000HUA, Córdoba, Argentina
| | - S Alexis Paz
- Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba , X5000HUA, Córdoba, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Instituto de Fisicoquímica de Córdoba (INFIQC), X5000HUA, Córdoba, Argentina
| | - Cameron F Abrams
- Department of Chemical and Biological Engineering, Drexel University, Philadelphia, PA, 19104, USA
| | - Marcos A Villarreal
- Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba , X5000HUA, Córdoba, Argentina.
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Instituto de Fisicoquímica de Córdoba (INFIQC), X5000HUA, Córdoba, Argentina.
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41
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Liao Q, Chen Z, Tao Y, Zhang B, Wu X, Yang L, Wang Q, Wang Z. An integrated method for optimized identification of effective natural inhibitors against SARS-CoV-2 3CLpro. Sci Rep 2021; 11:22796. [PMID: 34815498 PMCID: PMC8611036 DOI: 10.1038/s41598-021-02266-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 11/12/2021] [Indexed: 02/06/2023] Open
Abstract
The current severe situation of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been reversed and posed great threats to global health. Therefore, there is an urgent need to find out effective antiviral drugs. The 3-chymotrypsin-like protease (3CLpro) in SARS-CoV-2 serve as a promising anti-virus target due to its essential role in the regulation of virus reproduction. Here, we report an improved integrated approach to identify effective 3CLpro inhibitors from effective Chinese herbal formulas. With this approach, we identified the 5 natural products (NPs) including narcissoside, kaempferol-3-O-gentiobioside, rutin, vicenin-2 and isoschaftoside as potential anti-SARS-CoV-2 candidates. Subsequent molecular dynamics simulation additionally revealed that these molecules can be tightly bound to 3CLpro and confirmed effectiveness against COVID-19. Moreover, kaempferol-3-o-gentiobioside, vicenin-2 and isoschaftoside were first reported to have SARS-CoV-2 3CLpro inhibitory activity. In summary, this optimized integrated strategy for drug screening can be utilized in the discovery of antiviral drugs to achieve rapid acquisition of drugs with specific effects on antiviral targets.
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Affiliation(s)
- Qi Liao
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziyu Chen
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yanlin Tao
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Beibei Zhang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiaojun Wu
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Li Yang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Qingzhong Wang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Zhengtao Wang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Ricci-Lopez J, Aguila SA, Gilson MK, Brizuela CA. Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning. J Chem Inf Model 2021; 61:5362-5376. [PMID: 34652141 DOI: 10.1021/acs.jcim.1c00511] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.
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Affiliation(s)
- Joel Ricci-Lopez
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California C.P. 22860, Mexico.,Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México (UNAM), Ensenada, Baja California C.P. 22860, Mexico
| | - Sergio A Aguila
- Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México (UNAM), Ensenada, Baja California C.P. 22860, Mexico
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, California 92093, United States
| | - Carlos A Brizuela
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California C.P. 22860, Mexico
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Scardino V, Bollini M, Cavasotto CN. Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds. RSC Adv 2021; 11:35383-35391. [PMID: 35424265 PMCID: PMC8965822 DOI: 10.1039/d1ra05785e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/26/2021] [Indexed: 11/24/2022] Open
Abstract
The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results. The new methodology named Pose/Ranking Consensus (PRC) combines both pose and ranking consensus strategies. It displays an enhanced performance in terms of enrichment factor and hit rate, ensuring the recovery of a suitable number of ligands.![]()
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Affiliation(s)
- Valeria Scardino
- Meton AI, Inc. Wilmington DE 19801 USA.,Austral Institute for Applied Artificial Intelligence, Universidad Austral Pilar Buenos Aires Argentina
| | - Mariela Bollini
- Centro de Investigaciones en BioNanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Ciudad de Buenos Aires Argentina
| | - Claudio N Cavasotto
- Austral Institute for Applied Artificial Intelligence, Universidad Austral Pilar Buenos Aires Argentina.,Computational Drug Design and Biomedical Informatics Laboratory, Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral-CONICET Pilar Buenos Aires Argentina.,Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral Pilar Buenos Aires Argentina
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44
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Zhao F, Tang X, Liu M, Qin Z, Li JQ, Xiao Y. Synthesis and insecticidal activity of novel 1,2,4-triazole derivatives containing trifluoroacetyl moieties. Mol Divers 2021; 26:2149-2158. [PMID: 34585322 DOI: 10.1007/s11030-021-10321-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/17/2021] [Indexed: 10/20/2022]
Abstract
A series of compounds containing trifluoroacetyl groups were synthesized, and their insecticidal activity against Nilaparvata lugens and Aphis craccivora was evaluated. The compound structure was identified by NMR, HRMS, and single-crystal diffraction. The bioassay results indicated that compound 4-1 (R1 is chloropyridine, R2 is H), 4-2 (R1 is chlorothiazole, R2 is H) and 4-19 (R1 is benzyl, R2 is isopropyl) had the best activity against Nilaparvata lugens (93.5%, 94.1% and 95.5%) at 100 mg/L concentration. The effect of different substituents of R1 or R2 on the activity was studied through the structure-activity relationship. Molecular docking of compounds 4-1 and 4-2 was discussed.
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Affiliation(s)
- Fenghai Zhao
- Department of Chemistry, Innovation Center of Pesticide Research, College of Science, China Agricultural University, Beijing, 100193, China
| | - Xianjun Tang
- Department of Chemistry, Innovation Center of Pesticide Research, College of Science, China Agricultural University, Beijing, 100193, China
| | - Min Liu
- Department of Chemistry, Innovation Center of Pesticide Research, College of Science, China Agricultural University, Beijing, 100193, China
| | - Zhaohai Qin
- Department of Chemistry, Innovation Center of Pesticide Research, College of Science, China Agricultural University, Beijing, 100193, China
| | - Jia-Qi Li
- Department of Chemistry, Innovation Center of Pesticide Research, College of Science, China Agricultural University, Beijing, 100193, China
| | - Yumei Xiao
- Department of Chemistry, Innovation Center of Pesticide Research, College of Science, China Agricultural University, Beijing, 100193, China
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dockECR: Open consensus docking and ranking protocol for virtual screening of small molecules. J Mol Graph Model 2021; 109:108023. [PMID: 34555725 PMCID: PMC8442548 DOI: 10.1016/j.jmgm.2021.108023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/26/2021] [Accepted: 09/02/2021] [Indexed: 12/17/2022]
Abstract
The development of open computational pipelines to accelerate the discovery of treatments for emerging diseases allows finding novel solutions in shorter periods of time. Consensus molecular docking is one of these approaches, and its main purpose is to increase the detection of real actives within virtual screening campaigns. Here we present dockECR, an open consensus docking and ranking protocol that implements the exponential consensus ranking method to prioritize molecular candidates. The protocol uses four open source molecular docking programs: AutoDock Vina, Smina, LeDock and rDock, to rank the molecules. In addition, we introduce a scoring strategy based on the average RMSD obtained from comparing the best poses from each single program to complement the consensus ranking with information about the predicted poses. The protocol was benchmarked using 15 relevant protein targets with known actives and decoys, and applied using the main protease of the SARS-CoV-2 virus. For the application, different crystal structures of the protease, and frames obtained from molecular dynamics simulations were used to dock a library of 79 molecules derived from previously co-crystallized fragments. The ranking obtained with dockECR was used to prioritize eight candidates, which were evaluated in terms of the interactions generated with key residues from the protease. The protocol can be implemented in any virtual screening campaign involving proteins as molecular targets. The dockECR code is publicly available at: https://github.com/rochoa85/dockECR.
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Llanos MA, Gantner ME, Rodriguez S, Alberca LN, Bellera CL, Talevi A, Gavernet L. Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: the Main Protease (Mpro) Case Study. J Chem Inf Model 2021; 61:3758-3770. [PMID: 34313128 DOI: 10.1021/acs.jcim.1c00404] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Manuel A Llanos
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Melisa E Gantner
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Santiago Rodriguez
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Lucas N Alberca
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Carolina L Bellera
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
| | - Luciana Gavernet
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata (B1900ADU), Buenos Aires, Argentina
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Orjuela A, Lakey-Beitia J, Mojica-Flores R, Hegde ML, Lans I, Alí-Torres J, Rao KS. Computational Evaluation of Interaction Between Curcumin Derivatives and Amyloid-β Monomers and Fibrils: Relevance to Alzheimer's Disease. J Alzheimers Dis 2021; 82:S321-S333. [PMID: 33337368 DOI: 10.3233/jad-200941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2022]
Abstract
BACKGROUND The most important hallmark in the neuropathology of Alzheimer's disease (AD) is the formation of amyloid-β (Aβ) fibrils due to the misfolding/aggregation of the Aβ peptide. Preventing or reverting the aggregation process has been an active area of research. Naturally occurring products are a potential source of molecules that may be able to inhibit Aβ42 peptide aggregation. Recently, we and others reported the anti-aggregating properties of curcumin and some of its derivatives in vitro, presenting an important therapeutic avenue by enhancing these properties. OBJECTIVE To computationally assess the interaction between Aβ peptide and a set of curcumin derivatives previously explored in experimental assays. METHODS The interactions of ten ligands with Aβ monomers were studied by combining molecular dynamics and molecular docking simulations. We present the in silico evaluation of the interaction between these derivatives and the Aβ42 peptide, both in the monomeric and fibril forms. RESULTS The results show that a single substitution in curcumin could significantly enhance the interaction between the derivatives and the Aβ42 monomers when compared to a double substitution. In addition, the molecular docking simulations showed that the interaction between the curcumin derivatives and the Aβ42 monomers occur in a region critical for peptide aggregation. CONCLUSION Results showed that a single substitution in curcumin improved the interaction of the ligands with the Aβ monomer more so than a double substitution. Our molecular docking studies thus provide important insights for further developing/validating novel curcumin-derived molecules with high therapeutic potential for AD.
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Affiliation(s)
- Adrian Orjuela
- Departamento de Química, Universidad Nacional de Colombia, Bogotá DC, Colombia
| | - Johant Lakey-Beitia
- Centre for Biodiversity and Drug Discovery, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Clayton, City of Knowledge, Panama
| | - Randy Mojica-Flores
- Centre for Biodiversity and Drug Discovery, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Clayton, City of Knowledge, Panama
| | - Muralidhar L Hegde
- Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Research Institute, Houston, TX, USA.,Weill Medical College of Cornell University, New York, NY, USA
| | - Isaias Lans
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia
| | - Jorge Alí-Torres
- Departamento de Química, Universidad Nacional de Colombia, Bogotá DC, Colombia
| | - K S Rao
- Centre for Neuroscience, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Clayton, City of Knowledge, Panama
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Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
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Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
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Battini L, Fidalgo DM, Álvarez DE, Bollini M. Discovery of a Potent and Selective Chikungunya Virus Envelope Protein Inhibitor through Computer-Aided Drug Design. ACS Infect Dis 2021; 7:1503-1518. [PMID: 34048233 DOI: 10.1021/acsinfecdis.0c00915] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The worldwide expansion of chikungunya virus (CHIKV) into tropical and subtropical areas in the last 15 years has posed a currently unmet need for vaccines and therapeutics. The E2-E1 envelope glycoprotein complex binds receptors on the host cell and promotes membrane fusion during CHIKV entry, thus constituting an attractive target for the development of antiviral drugs. In order to identify CHIKV antivirals acting through inhibition of the envelope glycoprotein complex function, our first approach was to search for amenable druggable sites within the E2-E1 heterodimer. We identified a pocket located in the interface between E2 and E1 around the fusion loop. Then, via a structure-based virtual screening approach and in vitro assay of antiviral activity, we identified compound 7 as a specific inhibitor of CHIKV. Through a lead optimization process, we obtained compound 11 that demonstrated increased antiviral activity and low cytotoxicity (EC50 1.6 μM, CC50 56.0 μM). Molecular dynamics simulations were carried out and described a possible interaction pattern of compound 11 and the E1-E2 dimer that could be useful for further optimization. As expected from target site selection, compound 11 inhibited virus internalization during CHIKV entry. In addition, virus populations resistant to compound 11 included mutation E2-P173S, which mapped to the proposed binding pocket, and second site mutation E1-Y24H. Construction of recombinant viruses showed that these mutations conferred antiviral resistance in the parental background. Finally, compound 11 presents acceptable solubility values and is chemically and enzymatically stable in different media. Altogether, these findings uncover a suitable pocket for the design of CHIKV entry inhibitors with promising antiviral activity and pharmacological profiles.
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Affiliation(s)
- Leandro Battini
- Laboratorio de Química Medicinal, Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires C1425FQD, Argentina
- Instituto de Investigaciones Biotecnológicas, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de San Martín, San Martín B1650, Argentina
| | - Daniela M. Fidalgo
- Laboratorio de Química Medicinal, Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires C1425FQD, Argentina
| | - Diego E. Álvarez
- Instituto de Investigaciones Biotecnológicas, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de San Martín, San Martín B1650, Argentina
| | - Mariela Bollini
- Laboratorio de Química Medicinal, Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires C1425FQD, Argentina
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50
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Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors. Sci Rep 2021; 11:11417. [PMID: 34075175 PMCID: PMC8169699 DOI: 10.1038/s41598-021-91069-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
The inconsistencies in the performance of the virtual screening (VS) process, depending on the used software and structural conformation of the protein, is a challenging issue in the drug design and discovery field. Varying performance, especially in terms of early recognition of the potential hit compounds, negatively affects the whole process and leads to unnecessary waste of the time and resources. Appropriate application of the ensemble docking and consensus-scoring approaches can significantly increase reliability of the VS results. Dihydroorotate dehydrogenase (DHODH) is a key enzyme in the pyrimidine biosynthesis pathway. It is considered as a valuable therapeutic target in cancer, autoimmune and viral diseases. Based on the conducted benchmark study and analysis of the effect of different combinations of the applied methods and approaches, here we suggested a structure-based virtual screening (SBVS) workflow that can be used to increase the reliability of VS.
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