1
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Gutkin E, Gusev F, Gentile F, Ban F, Koby SB, Narangoda C, Isayev O, Cherkasov A, Kurnikova MG. In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations. Chem Sci 2024; 15:8800-8812. [PMID: 38873063 PMCID: PMC11168082 DOI: 10.1039/d3sc06880c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/10/2024] [Indexed: 06/15/2024] Open
Abstract
The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each of which is followed by experimental validation of the computational predictions. For the CACHE Challenge #1, the Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 Repeat (WDR) domain was selected as the target for in silico hit-finding and optimization. Mutations in LRRK2 are the most common genetic cause of the familial form of Parkinson's disease. The LRRK2 WDR domain is an understudied drug target with no known molecular inhibitors. Herein we detail the first phase of our winning submission to the CACHE Challenge #1. We developed a framework for the high-throughput structure-based virtual screening of a chemically diverse small molecule space. Hit identification was performed using the large-scale Deep Docking (DD) protocol followed by absolute binding free energy (ABFE) simulations. ABFEs were computed using an automated molecular dynamics (MD)-based thermodynamic integration (TI) approach. 4.1 billion ligands from Enamine REAL were screened with DD followed by ABFEs computed by MD TI for 793 ligands. 76 ligands were prioritized for experimental validation, with 59 compounds successfully synthesized and 5 compounds identified as hits, yielding a 8.5% hit rate. Our results demonstrate the efficacy of the combined DD and ABFE approaches for hit identification for a target with no previously known hits. This approach is widely applicable for the efficient screening of ultra-large chemical libraries as well as rigorous protein-ligand binding affinity estimation leveraging modern computational resources.
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Affiliation(s)
- Evgeny Gutkin
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Filipp Gusev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University Pittsburgh PA 15213 USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Francesco Gentile
- Department of Chemistry and Biomolecular Sciences, University of Ottawa Ottawa ON Canada
- Ottawa Institute of Systems Biology Ottawa ON Canada
| | - Fuqiang Ban
- Vancouver Prostate Centre, The University of British Columbia Vancouver BC Canada
| | - S Benjamin Koby
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Chamali Narangoda
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Olexandr Isayev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University Pittsburgh PA 15213 USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University Pittsburgh PA 15213 USA
| | - Artem Cherkasov
- Vancouver Prostate Centre, The University of British Columbia Vancouver BC Canada
| | - Maria G Kurnikova
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University Pittsburgh PA 15213 USA
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2
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Carlsson J, Luttens A. Structure-based virtual screening of vast chemical space as a starting point for drug discovery. Curr Opin Struct Biol 2024; 87:102829. [PMID: 38848655 DOI: 10.1016/j.sbi.2024.102829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 06/09/2024]
Abstract
Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.
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Affiliation(s)
- Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-751 24 Uppsala, Sweden.
| | - Andreas Luttens
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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3
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Bedart C, Simoben CV, Schapira M. Emerging structure-based computational methods to screen the exploding accessible chemical space. Curr Opin Struct Biol 2024; 86:102812. [PMID: 38603987 DOI: 10.1016/j.sbi.2024.102812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 04/13/2024]
Abstract
Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hitting high-quality compounds during virtual screening exercises but also poses new challenges as the number of chemically accessible molecules grows faster than the computing power necessary to screen them. We review here two novel approaches rapidly gaining in popularity to address this problem: machine learning-accelerated and synthon-based library screening. We summarize the results from seminal proof-of-concept studies, highlight the latest developments, and discuss limitations and future directions.
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Affiliation(s)
- Corentin Bedart
- Univ. Lille, Inserm, CHU Lille, U1286 - INFINITE - Institute for Translational Research in Inflammation, F-59000, Lille, France
| | - Conrad Veranso Simoben
- Structural Genomics Consortium, University of Toronto, 101 College Street, MaRS South Tower, Suite 700, Toronto, Ontario M5G 1L7, Canada
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto, 101 College Street, MaRS South Tower, Suite 700, Toronto, Ontario M5G 1L7, Canada; Department of Pharmacology and Toxicology, University of Toronto, 1 King's College Circle, Toronto, Ontario M5S 1A8, Canada.
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4
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Matúška J, Bucinsky L, Gall M, Pitoňák M, Štekláč M. SchNetPack Hyperparameter Optimization for a More Reliable Top Docking Scores Prediction. J Phys Chem B 2024; 128:4943-4951. [PMID: 38733335 DOI: 10.1021/acs.jpcb.4c00296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
Options to improve the extrapolation power of the neural network designed using the SchNetPack package with respect to top docking scores prediction are presented. It is shown that hyperparameter tuning of the atomistic model representation (in the schnetpack.representation) improves the prediction of the top scoring compounds, which have characteristically a low incidence in randomized data sets for training of machine learning models. The prediction robustness is evaluated according to the mean square error (MSE) and the entropy of the average loss landscape decrease. Admittedly, the improvement of the top scoring compounds' prediction accuracy comes with the penalty of worsening the overall prediction power. It is revealed that the most impactful hyperparameter is the cutoff (5 Å is reported as the optimal choice). Other parameters (e.g., number of radial basis functions, number of interaction layers of the neural network, feature vector size or its batch size) are found to not affect the prediction robustness of the top scoring compounds in any comparable way relative to the cutoff. The MSE of the best docking score prediction (below -13 kcal/mol) improves from ca. 3.5 to 0.9 kcal/mol, while the prediction of less potent compounds (-13 to -11 kcal/mol) shows a lesser improvement, i.e., a decrease of MSE from 1.6 to 1.3 kcal/mol. Additionally, oversampling and undersampling of the training set with respect to the top scoring compounds' abundance is presented. The results indicate that the cutoff choice performs better than over- or undersampling of the training set, with undersampling performing better than oversampling.
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Affiliation(s)
- Ján Matúška
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
| | - Lukas Bucinsky
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
| | - Marián Gall
- Institute of Information Engineering, Automation and Mathematics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
- National SuperComputing Center, Dúbravská cesta č. 9, SK-84104 Bratislava, Slovak Republic
| | - Michal Pitoňák
- National SuperComputing Center, Dúbravská cesta č. 9, SK-84104 Bratislava, Slovak Republic
- Department of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská dolina Ilkovičova 6, SK-84215 Bratislava, Slovak Republic
| | - Marek Štekláč
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, SK-81237 Bratislava, Slovak Republic
- Computing Centre, Centre of Operations of the Slovak Academy of Sciences, Dúbravská cesta č. 9, SK-84535 Bratislava, Slovak Republic
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5
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Nguyen TH, Thai QM, Pham MQ, Minh PTH, Phung HTT. Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds. Mol Divers 2024; 28:553-561. [PMID: 36823394 PMCID: PMC9950021 DOI: 10.1007/s11030-023-10601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/04/2023] [Indexed: 02/25/2023]
Abstract
To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.
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Affiliation(s)
- Trung Hai Nguyen
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Quynh Mai Thai
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Minh Quan Pham
- Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Pham Thi Hong Minh
- Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Huong Thi Thu Phung
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
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6
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Sindt F, Seyller A, Eguida M, Rognan D. Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces. ACS CENTRAL SCIENCE 2024; 10:615-627. [PMID: 38559302 PMCID: PMC10979501 DOI: 10.1021/acscentsci.3c01521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 04/04/2024]
Abstract
Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.
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Affiliation(s)
- François Sindt
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | - Anthony Seyller
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | | | - Didier Rognan
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
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7
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Zajaček D, Dunárová A, Bucinsky L, Štekláč M. Compromise in Docking Power of Liganded Crystal Structures of M pro SARS-CoV-2 Surpasses 90% Success Rate. J Chem Inf Model 2024; 64:1628-1643. [PMID: 38408033 DOI: 10.1021/acs.jcim.3c01552] [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: 02/28/2024]
Abstract
Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.6% success rate in the active site), while the most relevant results for covalently bound inhibitors are produced by PLANTS (93.0%). AutoDock shows acceptable performance for both types of ligands, 81.1 and 76.4% for noncovalent and covalent complexes, respectively. All three programs manifest worse performance when reproducing surface-bound ligands. Comparison with other works illustrates the importance of crystal structure processing (12% of noncovalent and 26% of covalent ligands had to be manually corrected), proper sampling protocol settings, and inclusion of root-mean-square deviation (RMSD)/scoring function error compensations in crystal structure pose identification. Results are analyzed with respect to a clustering scheme of the noncovalently bound ligands and the chemical reaction type of the covalent ligand bound to the Cys145 residue. A comparison of screening power based on the docking scores of noncovalent ligands from the crystal structures with a "Directory of Useful Decoys, Enhanced" set of known decoys (6562 compounds) and ZINC15 in vivo subset (60,394 compounds) is provided. Ligand and protein input files are provided for future benchmarking purposes.
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Affiliation(s)
- Dávid Zajaček
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, SK-81237 Bratislava, Slovakia
| | - Adriána Dunárová
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, SK-81237 Bratislava, Slovakia
| | - Lukas Bucinsky
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, SK-81237 Bratislava, Slovakia
| | - Marek Štekláč
- Institute of Physical Chemistry and Chemical Physics, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, SK-81237 Bratislava, Slovakia
- Computing Center, Centre of Operations of the Slovak Academy of Sciences, Dúbravská cesta č. 9, SK-84535 Bratislava, Slovakia
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8
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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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9
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Sulimov AV, Ilin IS, Tashchilova AS, Kondakova OA, Kutov DC, Sulimov VB. Docking and other computing tools in drug design against SARS-CoV-2. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:91-136. [PMID: 38353209 DOI: 10.1080/1062936x.2024.2306336] [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: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 02/16/2024]
Abstract
The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.
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Affiliation(s)
- A V Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - I S Ilin
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - A S Tashchilova
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - O A Kondakova
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - D C Kutov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - V B Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
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10
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Janin YL. On the origins of SARS-CoV-2 main protease inhibitors. RSC Med Chem 2024; 15:81-118. [PMID: 38283212 PMCID: PMC10809347 DOI: 10.1039/d3md00493g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 10/13/2023] [Indexed: 01/30/2024] Open
Abstract
In order to address the world-wide health challenge caused by the COVID-19 pandemic, the 3CL protease/SARS-CoV-2 main protease (SARS-CoV-2-Mpro) coded by its nsp5 gene became one of the biochemical targets for the design of antiviral drugs. In less than 3 years of research, 4 inhibitors of SARS-CoV-2-Mpro have actually been authorized for COVID-19 treatment (nirmatrelvir, ensitrelvir, leritrelvir and simnotrelvir) and more such as EDP-235, FB-2001 and STI-1558/Olgotrelvir or five undisclosed compounds (CDI-988, ASC11, ALG-097558, QLS1128 and H-10517) are undergoing clinical trials. This review is an attempt to picture this quite unprecedented medicinal chemistry feat and provide insights on how these cysteine protease inhibitors were discovered. Since many series of covalent SARS-CoV-2-Mpro inhibitors owe some of their origins to previous work on other proteases, we first provided a description of various inhibitors of cysteine-bearing human caspase-1 or cathepsin K, as well as inhibitors of serine proteases such as human dipeptidyl peptidase-4 or the hepatitis C protein complex NS3/4A. This is then followed by a description of the results of the approaches adopted (repurposing, structure-based and high throughput screening) to discover coronavirus main protease inhibitors.
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Affiliation(s)
- Yves L Janin
- Structure et Instabilité des Génomes (StrInG), Muséum National d'Histoire Naturelle, INSERM, CNRS, Alliance Sorbonne Université 75005 Paris France
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11
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Popov KI, Wellnitz J, Maxfield T, Tropsha A. HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries. Mol Inform 2024; 43:e202300207. [PMID: 37802967 PMCID: PMC11156482 DOI: 10.1002/minf.202300207] [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: 08/16/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 10/08/2023]
Abstract
Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.
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Affiliation(s)
- Konstantin I. Popov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - James Wellnitz
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - Travis Maxfield
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
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12
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Song L, Gao S, Ye B, Yang M, Cheng Y, Kang D, Yi F, Sun JP, Menéndez-Arias L, Neyts J, Liu X, Zhan P. Medicinal chemistry strategies towards the development of non-covalent SARS-CoV-2 M pro inhibitors. Acta Pharm Sin B 2024; 14:87-109. [PMID: 38239241 PMCID: PMC10792984 DOI: 10.1016/j.apsb.2023.08.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/10/2023] [Accepted: 08/02/2023] [Indexed: 01/22/2024] Open
Abstract
The main protease (Mpro) of SARS-CoV-2 is an attractive target in anti-COVID-19 therapy for its high conservation and major role in the virus life cycle. The covalent Mpro inhibitor nirmatrelvir (in combination with ritonavir, a pharmacokinetic enhancer) and the non-covalent inhibitor ensitrelvir have shown efficacy in clinical trials and have been approved for therapeutic use. Effective antiviral drugs are needed to fight the pandemic, while non-covalent Mpro inhibitors could be promising alternatives due to their high selectivity and favorable druggability. Numerous non-covalent Mpro inhibitors with desirable properties have been developed based on available crystal structures of Mpro. In this article, we describe medicinal chemistry strategies applied for the discovery and optimization of non-covalent Mpro inhibitors, followed by a general overview and critical analysis of the available information. Prospective viewpoints and insights into current strategies for the development of non-covalent Mpro inhibitors are also discussed.
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Affiliation(s)
- Letian Song
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Shenghua Gao
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- Shenzhen Research Institute of Shandong University, Shenzhen 518057, China
| | - Bing Ye
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Mianling Yang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Yusen Cheng
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Dongwei Kang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Fan Yi
- The Key Laboratory of Infection and Immunity of Shandong Province, Department of Pharmacology, School of Basic Medical Sciences, Shandong University, Jinan 250012, China
| | - Jin-Peng Sun
- Key Laboratory Experimental Teratology of the Ministry of Education, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Luis Menéndez-Arias
- Centro de Biología Molecular “Severo Ochoa” (Consejo Superior de Investigaciones Científicas & Autonomous University of Madrid), Madrid 28049, Spain
| | - Johan Neyts
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Virology and Chemotherapy, Leuven 3000, Belgium
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
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13
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Kotev M, Diaz Gonzalez C. Molecular Dynamics and Other HPC Simulations for Drug Discovery. Methods Mol Biol 2024; 2716:265-291. [PMID: 37702944 DOI: 10.1007/978-1-0716-3449-3_12] [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] [Indexed: 09/14/2023]
Abstract
High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.
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Affiliation(s)
- Martin Kotev
- Evotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France
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14
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Ivanova YO, Skvortsov VS. The prediction of main protease SARS-CoV-2 inhibition based on models of enzyme-inhibitor complexes. BIOMEDITSINSKAIA KHIMIIA 2023; 69:322-327. [PMID: 37937435 DOI: 10.18097/pbmc20236905322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
A set of linear regression equations predicting the IC50 values for SARS-CoV-2 main protease inhibitors was analyzed. For 180 competitive inhibitors, we have simulated the molecular dynamics of enzyme-inhibitor complexes with known structures or modeled using molecular docking. In the docking procedure, the selection of final poses was restricted by similarity to known structural analogs. The values of the energy contributions obtained by means of calculation of the free energy change of the enzyme-inhibitor complex performed by two variants of the MMPBSA (MMGBSA) method and a number of physicochemical characteristics of the inhibitors were used as independent variables. During the learning process, indicator variables were used for inhibitor subsets obtained from various literature sources to compensate the existing systematic deviations from the target value. A leave one out and leave 20% out cross validation procedures were used to evaluate the prediction quality. For the total logarithmic range width of 3.71, the mean error in predicting the lg(IC50) value was 0.45 log units. The stability of the prediction depending on the variability of the complex in molecular dynamics was investigated.
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Affiliation(s)
- Ya O Ivanova
- Institute of Biomedical Chemistry, Moscow, Russia
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15
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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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16
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Schake P, Dishnica K, Kaiser F, Leberecht C, Haupt VJ, Schroeder M. An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds. Sci Rep 2023; 13:9204. [PMID: 37280244 DOI: 10.1038/s41598-023-35671-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 05/19/2023] [Indexed: 06/08/2023] Open
Abstract
The recent outbreak of the COVID-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has shown the necessity for fast and broad drug discovery methods to enable us to react quickly to novel and highly infectious diseases. A well-known SARS-CoV-2 target is the viral main 3-chymotrypsin-like cysteine protease (Mpro), known to control coronavirus replication, which is essential for the viral life cycle. Here, we applied an interaction-based drug repositioning algorithm on all protein-compound complexes available in the protein database (PDB) to identify Mpro inhibitors and potential novel compound scaffolds against SARS-CoV-2. The screen revealed a heterogeneous set of 692 potential Mpro inhibitors containing known ones such as Dasatinib, Amodiaquine, and Flavin mononucleotide, as well as so far untested chemical scaffolds. In a follow-up evaluation, we used publicly available data published almost two years after the screen to validate our results. In total, we are able to validate 17% of the top 100 predictions with publicly available data and can furthermore show that predicted compounds do cover scaffolds that are yet not associated with Mpro. Finally, we detected a potentially important binding pattern consisting of 3 hydrogen bonds with hydrogen donors of an oxyanion hole within the active side of Mpro. Overall, these results give hope that we will be better prepared for future pandemics and that drug development will become more efficient in the upcoming years.
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Affiliation(s)
- Philipp Schake
- Bioinformatics, Biotechnology Center (BIOTEC), Technische Universität Dresden, Dresden, Saxony, Germany.
| | | | | | | | | | - Michael Schroeder
- Bioinformatics, Biotechnology Center (BIOTEC), Technische Universität Dresden, Dresden, Saxony, Germany
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17
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Garland O, Ton AT, Moradi S, Smith JR, Kovacic S, Ng K, Pandey M, Ban F, Lee J, Vuckovic M, Worrall LJ, Young RN, Pantophlet R, Strynadka NCJ, Cherkasov A. Large-Scale Virtual Screening for the Discovery of SARS-CoV-2 Papain-like Protease (PLpro) Non-covalent Inhibitors. J Chem Inf Model 2023; 63:2158-2169. [PMID: 36930801 DOI: 10.1021/acs.jcim.2c01641] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
Abstract
The rapid global spread of the SARS-CoV-2 virus facilitated the development of novel direct-acting antiviral agents (DAAs). The papain-like protease (PLpro) has been proposed as one of the major SARS-CoV-2 targets for DAAs due to its dual role in processing viral proteins and facilitating the host's immune suppression. This dual role makes identifying small molecules that can effectively neutralize SARS-CoV-2 PLpro activity a high-priority task. However, PLpro drug discovery faces a significant challenge due to the high mobility and induced-fit effects in the protease's active site. Herein, we virtually screened the ZINC20 database with Deep Docking (DD) to identify prospective noncovalent PLpro binders and combined ultra-large consensus docking with two pharmacophore (ph4)-filtering strategies. The analysis of active compounds revealed their somewhat-limited diversity, likely attributed to the induced-fit nature of PLpro's active site in the crystal structures, and therefore, the use of rigid docking protocols poses inherited limitations. The top hits were assessed against recombinant viral proteins and live viruses, demonstrating desirable inhibitory activities. The best compound VPC-300195 (IC50: 15 μM) ranks among the top noncovalent PLpro inhibitors discovered through in silico methodologies. In the search for novel SARS-CoV-2 PLpro-specific chemotypes, the identified inhibitors could serve as diverse templates for the development of effective noncovalent PLpro inhibitors.
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Affiliation(s)
- Olivia Garland
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
| | - Anh-Tien Ton
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
| | - Shoeib Moradi
- Department of Biochemistry and Molecular Biology and Centre for Blood Research, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Jason R Smith
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada.,Department of Chemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Suzana Kovacic
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Kurtis Ng
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Mohit Pandey
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
| | - Fuqiang Ban
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
| | - Jaeyong Lee
- Department of Biochemistry and Molecular Biology and Centre for Blood Research, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Marija Vuckovic
- Department of Biochemistry and Molecular Biology and Centre for Blood Research, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Liam J Worrall
- Department of Biochemistry and Molecular Biology and Centre for Blood Research, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Robert N Young
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Ralph Pantophlet
- Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada.,Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Natalie C J Strynadka
- Department of Biochemistry and Molecular Biology and Centre for Blood Research, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada
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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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Theoharides TC, Kempuraj D. Role of SARS-CoV-2 Spike-Protein-Induced Activation of Microglia and Mast Cells in the Pathogenesis of Neuro-COVID. Cells 2023; 12:688. [PMID: 36899824 PMCID: PMC10001285 DOI: 10.3390/cells12050688] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/07/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). About 45% of COVID-19 patients experience several symptoms a few months after the initial infection and develop post-acute sequelae of SARS-CoV-2 (PASC), referred to as "Long-COVID," characterized by persistent physical and mental fatigue. However, the exact pathogenetic mechanisms affecting the brain are still not well-understood. There is increasing evidence of neurovascular inflammation in the brain. However, the precise role of the neuroinflammatory response that contributes to the disease severity of COVID-19 and long COVID pathogenesis is not clearly understood. Here, we review the reports that the SARS-CoV-2 spike protein can cause blood-brain barrier (BBB) dysfunction and damage neurons either directly, or via activation of brain mast cells and microglia and the release of various neuroinflammatory molecules. Moreover, we provide recent evidence that the novel flavanol eriodictyol is particularly suited for development as an effective treatment alone or together with oleuropein and sulforaphane (ViralProtek®), all of which have potent anti-viral and anti-inflammatory actions.
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Affiliation(s)
- Theoharis C. Theoharides
- Institute for Neuro-Immune Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL 33328, USA
- Laboratory of Molecular Immunopharmacology and Drug Discovery, Department of Immunology, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Duraisamy Kempuraj
- Institute for Neuro-Immune Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL 33328, USA
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20
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Abstract
In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.
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Affiliation(s)
- F Gentile
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, ON, Canada
| | - T I Oprea
- Roivant Sciences Inc, 451 D Street, Boston, MA, USA
| | - A Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - A Cherkasov
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.
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21
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Preto J, Gentile F. Development of Optimal Virtual Screening Strategies to Identify Novel Toll-Like Receptor Ligands Using the DockBox Suite. Methods Mol Biol 2023; 2700:39-56. [PMID: 37603173 DOI: 10.1007/978-1-0716-3366-3_2] [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] [Indexed: 08/22/2023]
Abstract
Toll-like receptors (TLRs) represent attractive targets for developing modulators for the treatment of many pathologies, including inflammation, cancer, and autoimmune diseases. Here, we describe a protocol based on the DockBox package that enables to set up and perform structure-based virtual screening in order to increase the chance of identifying novel TLR ligands from chemical libraries.
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Affiliation(s)
- Jordane Preto
- Centre de Recherche en Cancérologie de Lyon, Université Claude Bernard Lyon 1, Lyon, France.
| | - Francesco Gentile
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Canada.
- Ottawa Institute of Systems Biology, Ottawa, Canada.
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22
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Ngo ST, Nguyen TH, Tung NT, Vu VV, Pham MQ, Mai BK. Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches. Phys Chem Chem Phys 2022; 24:29266-29278. [PMID: 36449268 DOI: 10.1039/d2cp04476e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.
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Affiliation(s)
- Son Tung Ngo
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Trung Hai Nguyen
- Laboratory of Theoretical and Computational Biophysics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam. .,Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Nguyen Thanh Tung
- Institute of Materials Science, Vietnam Academy of Science and Technology, Hanoi, Vietnam. .,Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Van V Vu
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
| | - Minh Quan Pham
- Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam.,Institute of Natural Products Chemistry, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Binh Khanh Mai
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
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23
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Ivanova YO, Voronina AI, Skvortsov VS. [The prediction of SARS-CoV-2 main protease inhibition with filtering by position of ligand]. BIOMEDITSINSKAIA KHIMIIA 2022; 68:444-458. [PMID: 36573412 DOI: 10.18097/pbmc20226806444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The paper analyzes a set of equations that adequately predict the IC50 value for SARS-CoV-2 main protease inhibitors. The training set was obtained using filtering by criteria independent of prediction of target value. It included 76 compounds, and the test set included nine compounds. We used the values of energy contributions obtained in the calculation of the change of the free energy of complex by MMGBSA method and a number of characteristics of the physical and chemical properties of the inhibitors as independent variables. It is sufficient to use only seven independent variables without loss of prediction quality (Q² = 0.79; R²prediction = 0.89). The maximum error in this case does not exceed 0.92 lg(IC50) units with a full range of observed values from 1.26 to 4.95.
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Affiliation(s)
- Ya O Ivanova
- Institute of Biomedical Chemistry, Moscow, Russia
| | - A I Voronina
- Institute of Biomedical Chemistry, Moscow, Russia
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24
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Sun Y, Jiao Y, Shi C, Zhang Y. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5014-5027. [PMID: 36091720 PMCID: PMC9448712 DOI: 10.1016/j.csbj.2022.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/03/2022] [Accepted: 09/03/2022] [Indexed: 11/26/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.
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Affiliation(s)
- Yao Sun
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yanqi Jiao
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Chengcheng Shi
- State Key Lab of Urban Water Resource and Environment, School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
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25
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Pandey M, Radaeva M, Mslati H, Garland O, Fernandez M, Ester M, Cherkasov A. Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks. Molecules 2022; 27:molecules27165114. [PMID: 36014351 PMCID: PMC9416537 DOI: 10.3390/molecules27165114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/03/2022] [Accepted: 08/09/2022] [Indexed: 11/25/2022] Open
Abstract
Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus.
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Affiliation(s)
- Mohit Pandey
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Mariia Radaeva
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Hazem Mslati
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Olivia Garland
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Michael Fernandez
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
- Correspondence:
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26
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Liu XH, Cheng T, Liu BY, Chi J, Shu T, Wang T. Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development. Front Pharmacol 2022; 13:955648. [PMID: 36016554 PMCID: PMC9395726 DOI: 10.3389/fphar.2022.955648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/13/2022] [Indexed: 12/14/2022] Open
Abstract
COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.
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27
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Graff DE, Aldeghi M, Morrone JA, Jordan KE, Pyzer-Knapp EO, Coley CW. Self-Focusing Virtual Screening with Active Design Space Pruning. J Chem Inf Model 2022; 62:3854-3862. [DOI: 10.1021/acs.jcim.2c00554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- David E. Graff
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
| | - Matteo Aldeghi
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
| | - Joseph A. Morrone
- Computational Biology Center, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10594, United States
| | - Kirk E. Jordan
- IBM Thomas J. Watson Research Center, Cambridge, Massachusetts 02142, United States
| | | | - Connor W. Coley
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02142, United States
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts 02142, United States
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Ton AT, Pandey M, Smith JR, Ban F, Fernandez M, Cherkasov A. Targeting SARS-CoV-2 Papain-Like Protease in the Post-Vaccine Era. Trends Pharmacol Sci 2022; 43:906-919. [PMID: 36114026 PMCID: PMC9399131 DOI: 10.1016/j.tips.2022.08.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/10/2022] [Accepted: 08/19/2022] [Indexed: 11/29/2022]
Abstract
While vaccines remain at the forefront of global healthcare responses, pioneering therapeutics against SARS-CoV-2 are expected to fill the gaps for waning immunity. Rapid development and approval of orally available direct-acting antivirals targeting crucial SARS-CoV-2 proteins marked the beginning of the era of small-molecule drugs for COVID-19. In that regard, the papain-like protease (PLpro) can be considered a major SARS-CoV-2 therapeutic target due to its dual biological role in suppressing host innate immune responses and in ensuring viral replication. Here, we summarize the challenges of targeting PLpro and innovative early-stage PLpro-specific small molecules. We propose that state-of-the-art computer-aided drug design (CADD) methodologies will play a critical role in the discovery of PLpro compounds as a novel class of COVID-19 drugs.
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Affiliation(s)
- Anh-Tien Ton
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Mohit Pandey
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Jason R Smith
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada; Department of Chemistry, Simon Fraser University, Burnaby, Canada
| | - Fuqiang Ban
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Michael Fernandez
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada.
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Bajusz D, Keserű GM. Maximizing the integration of virtual and experimental screening in hit discovery. Expert Opin Drug Discov 2022; 17:629-640. [PMID: 35671403 DOI: 10.1080/17460441.2022.2085685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Experimental and virtual screening contributes to the discovery of more than 50% of clinical candidates. Considering the similar concept and goals, early-phase drug discovery would benefit from the effective integration of these approaches. AREAS COVERED After reviewing the recent trends in both experimental and virtual screening, the authors discuss different integration strategies from parallel, focused, sequential, and iterative screening. Strategic considerations are demonstrated in a number of real-life case studies. EXPERT OPINION Experimental and virtual screening are complementary approaches that should be integrated in lead discovery settings. Virtual screening can access extremely large synthetically feasible chemical space that can be effectively searched on GPU clusters or cloud architectures. Experimental screening provides reliable datasets by quantitative HTS applications, and DNA-encoded libraries (DEL) have enlarged the chemical space covered by these technologies. These developments, together with the use of artificial intelligence methods, represent new options for their efficient integration. The case studies discussed here demonstrate the benefits of complementary strategies, such as focused and iterative screening.
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Affiliation(s)
- Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
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Kondratov IS, Moroz YS, Grygorenko OO, Tolmachev AA. The Ukrainian Factor in Early-Stage Drug Discovery in the Context of Russian Invasion: The Case of Enamine Ltd. ACS Med Chem Lett 2022; 13:992-996. [DOI: 10.1021/acsmedchemlett.2c00211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Affiliation(s)
- Ivan S. Kondratov
- Enamine Ltd (www.enamine.net), Chervonotkatska Street 78, Kyïv 02094, Ukraine
- V.P. Kukhar Institute of Bioorganic Chemistry & Petrochemistry, National Academy of Sciences of Ukraine, Murmanska Street 1, Kyïv 02660, Ukraine
| | - Yurii S. Moroz
- Chemspace (www.chem-space.com), Chervonotkatska Street 85, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyïv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Oleksandr O. Grygorenko
- Enamine Ltd (www.enamine.net), Chervonotkatska Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyïv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Andrey A. Tolmachev
- Enamine Ltd (www.enamine.net), Chervonotkatska Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyïv, Volodymyrska Street 60, Kyïv 01601, Ukraine
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Venkatraman V, Colligan TH, Lesica GT, Olson DR, Gaiser J, Copeland CJ, Wheeler TJ, Roy A. Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets. Front Pharmacol 2022; 13:874746. [PMID: 35559261 PMCID: PMC9086895 DOI: 10.3389/fphar.2022.874746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.
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Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas H. Colligan
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - George T. Lesica
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Daniel R. Olson
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Jeremiah Gaiser
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Conner J. Copeland
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Travis J. Wheeler
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Amitava Roy
- Department of Computer Science, University of Montana, Missoula, MT, United States
- Rocky Mountain Laboratories, Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
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Prasetyo WE, Purnomo H, Sadrini M, Wibowo FR, Firdaus M, Kusumaningsih T. Identification of potential bioactive natural compounds from Indonesian medicinal plants against 3-chymotrypsin-like protease (3CL pro) of SARS-CoV-2: molecular docking, ADME/T, molecular dynamic simulations, and DFT analysis. J Biomol Struct Dyn 2022:1-18. [DOI: 10.1080/07391102.2022.2068071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Wahyu Eko Prasetyo
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Heri Purnomo
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Miracle Sadrini
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Fajar Rakhman Wibowo
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Maulidan Firdaus
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
| | - Triana Kusumaningsih
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Sebelas Maret University, Surakarta, Indonesia
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