1
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Qamar F, Sharif Z, Idrees J, Wasim A, Haider S, Salman S. SARS-CoV-2-induced phosphorylation and its pharmacotherapy backed by artificial intelligence and machine learning. Future Sci OA 2024; 10:FSO917. [PMID: 38827795 PMCID: PMC11140666 DOI: 10.2144/fsoa-2023-0112] [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: 06/16/2023] [Accepted: 10/04/2023] [Indexed: 06/05/2024] Open
Abstract
Aims: To investigate the role of phosphorylation in SARS-CoV-2 infection, potential therapeutic targets and its harmful genetic sequences. Materials & Methods: Data mining techniques were employed to identify upregulated kinases responsible for proteomic changes induced by SARS-CoV-2. Spike and nucleocapsid proteins' sequences were analyzed using predictive tools, including SNAP2, MutPred2, PhD-SNP, SNPs&Go, MetaSNP, Predict-SNP and PolyPhen-2. Missense variants were identified using ensemble-based algorithms and homology/structure-based models like SIFT, PROVEAN, Predict-SNP and MutPred-2. Results: Eight missense variants were identified in viral sequences. Four damaging variants were found, with SNPs&Go and PolyPhen-2. Promising therapeutic candidates, including gilteritinib, pictilisib, sorafenib, RO5126766 and omipalisib, were identified. Conclusion: This research offers insights into SARS-CoV-2 pathogenicity, highlighting potential treatments and harmful variants in viral proteins.
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Affiliation(s)
- Fouzia Qamar
- Department of Biology, Lahore Garrison University, Lahore-54000, Punjab, Pakistan
| | - Zubair Sharif
- Faculty of Medical Laboratory Sciences, Superior University, Lahore-54000, Punjab, Pakistan
| | - Jawaria Idrees
- Khyber Pakhtunkhwa Education Monitoring Authority, Khyber-Pakhtunkhwa, Peshawar-25000, Pakistan
| | - Asif Wasim
- Department of Pharmacy, CECOS University of IT & Emerging Sciences, Peshawar-25000, Khyber Pakhtunkhwa, Peshawar, Pakistan
| | - Sana Haider
- Department of Pharmacy, CECOS University of IT & Emerging Sciences, Peshawar-25000, Khyber Pakhtunkhwa, Peshawar, Pakistan
| | - Saad Salman
- Department of Pharmacy, CECOS University of IT & Emerging Sciences, Peshawar-25000, Khyber Pakhtunkhwa, Peshawar, Pakistan
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2
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Grotsch K, Sadybekov AV, Hiller S, Zaidi S, Eremin D, Le A, Liu Y, Smith EC, Illiopoulis-Tsoutsouvas C, Thomas J, Aggarwal S, Pickett JE, Reyes C, Picazo E, Roth BL, Makriyannis A, Katritch V, Fokin VV. Virtual Screening of a Chemically Diverse "Superscaffold" Library Enables Ligand Discovery for a Key GPCR Target. ACS Chem Biol 2024; 19:866-874. [PMID: 38598723 DOI: 10.1021/acschembio.3c00602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
The advent of ultra-large libraries of drug-like compounds has significantly broadened the possibilities in structure-based virtual screening, accelerating the discovery and optimization of high-quality lead chemotypes for diverse clinical targets. Compared to traditional high-throughput screening, which is constrained to libraries of approximately one million compounds, the ultra-large virtual screening approach offers substantial advantages in both cost and time efficiency. By expanding the chemical space with compounds synthesized from easily accessible and reproducible reactions and utilizing a large, diverse set of building blocks, we can enhance both the diversity and quality of the discovered lead chemotypes. In this study, we explore new chemical spaces using reactions of sulfur(VI) fluorides to create a combinatorial library consisting of several hundred million compounds. We screened this virtual library for cannabinoid type II receptor (CB2) antagonists using the high-resolution structure in conjunction with a rationally designed antagonist, AM10257. The top-predicted compounds were then synthesized and tested in vitro for CB2 binding and functional antagonism, achieving an experimentally validated hit rate of 55%. Our findings demonstrate the effectiveness of reliable reactions, such as sulfur fluoride exchange, in diversifying ultra-large chemical spaces and facilitate the discovery of new lead compounds for important biological targets.
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Affiliation(s)
- Katharina Grotsch
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Sydney Hiller
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Saheem Zaidi
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Dmitry Eremin
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Austen Le
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Yongfeng Liu
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Evan Carlton Smith
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Christos Illiopoulis-Tsoutsouvas
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Joice Thomas
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Shubhangi Aggarwal
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Julie E Pickett
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Cesar Reyes
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Elias Picazo
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Bryan L Roth
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Alexandros Makriyannis
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Vsevolod Katritch
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Valery V Fokin
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
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3
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Trepte P, Secker C, Olivet J, Blavier J, Kostova S, Maseko SB, Minia I, Silva Ramos E, Cassonnet P, Golusik S, Zenkner M, Beetz S, Liebich MJ, Scharek N, Schütz A, Sperling M, Lisurek M, Wang Y, Spirohn K, Hao T, Calderwood MA, Hill DE, Landthaler M, Choi SG, Twizere JC, Vidal M, Wanker EE. AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor. Mol Syst Biol 2024; 20:428-457. [PMID: 38467836 PMCID: PMC10987651 DOI: 10.1038/s44320-024-00019-8] [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: 02/09/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/13/2024] Open
Abstract
Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
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Affiliation(s)
- Philipp Trepte
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
- Brain Development and Disease, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, 1030, Vienna, Austria.
| | - Christopher Secker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
- Zuse Institute Berlin, Berlin, Germany.
| | - Julien Olivet
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Structural Biology Unit, Laboratory of Virology and Chemotherapy, Rega Institute for Medical Research, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit Leuven, 3000, Leuven, Belgium
| | - Jeremy Blavier
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Simona Kostova
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Sibusiso B Maseko
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Igor Minia
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
| | - Eduardo Silva Ramos
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Patricia Cassonnet
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, Centre National de la Recherche Scientifique (CNRS), Université de Paris, Paris, France
| | - Sabrina Golusik
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Martina Zenkner
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Stephanie Beetz
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Mara J Liebich
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Nadine Scharek
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Anja Schütz
- Protein Production & Characterization, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Marcel Sperling
- Multifunctional Colloids and Coating, Fraunhofer Institute for Applied Polymer Research (IAP), 14476, Potsdam-Golm, Germany
| | - Michael Lisurek
- Structural Chemistry and Computational Biophysics, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Markus Landthaler
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
- Institute of Biology, Humboldt-Universität zu Berlin, 13125, Berlin, Germany
| | - Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030, Gembloux, Belgium.
- Laboratory of Algal Synthetic and Systems Biology, Division of Science and Math, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
| | - Erich E Wanker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
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4
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Cao Z, Sciabola S, Wang Y. Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening. J Chem Inf Model 2024; 64:1882-1891. [PMID: 38442000 DOI: 10.1021/acs.jcim.3c01938] [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: 03/07/2024]
Abstract
Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.
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Affiliation(s)
- Zhonglin Cao
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
| | - Simone Sciabola
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
| | - Ye Wang
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
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5
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Cheng C, Beroza P. Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces. J Chem Inf Model 2024; 64:1251-1260. [PMID: 38335044 DOI: 10.1021/acs.jcim.3c01865] [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: 02/12/2024]
Abstract
Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.
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Affiliation(s)
- Chen Cheng
- Discovery Chemistry, Genentech, South San Francisco, California 94080, United States
| | - Paul Beroza
- Discovery Chemistry, Genentech, South San Francisco, California 94080, United States
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6
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Knight IS, Mailhot O, Tang KG, Irwin JJ. DockOpt: A Tool for Automatic Optimization of Docking Models. J Chem Inf Model 2024; 64:1004-1016. [PMID: 38206771 PMCID: PMC10865354 DOI: 10.1021/acs.jcim.3c01406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/17/2023] [Accepted: 12/26/2023] [Indexed: 01/13/2024]
Abstract
Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.
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Affiliation(s)
- Ian S. Knight
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - Olivier Mailhot
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - Khanh G. Tang
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
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7
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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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8
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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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9
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Parigger L, Krassnigg A, Grabuschnig S, Gruber K, Steinkellner G, Gruber CC. AI-assisted structural consensus-proteome prediction of human monkeypox viruses isolated within a year after the 2022 multi-country outbreak. Microbiol Spectr 2023; 11:e0231523. [PMID: 37874150 PMCID: PMC10714838 DOI: 10.1128/spectrum.02315-23] [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: 06/16/2023] [Accepted: 09/09/2023] [Indexed: 10/25/2023] Open
Abstract
IMPORTANCE The 2022 outbreak of the monkeypox virus already involves, by April 2023, 110 countries with 86,956 confirmed cases and 119 deaths. Understanding an emerging disease on a molecular level is essential to study infection processes and eventually guide drug discovery at an early stage. To support this, we provide the so far most comprehensive structural proteome of the monkeypox virus, which includes 210 structural models, each computed with three state-of-the-art structure prediction methods. Instead of building on a single-genome sequence, we generated our models from a consensus of 3,713 high-quality genome sequences sampled from patients within 1 year of the outbreak. Therefore, we present an average structural proteome of the currently isolated viruses, including mutational analyses with a special focus on drug-binding sites. Continuing dynamic mutation monitoring within the structural proteome presented here is essential to timely predict possible physiological changes in the evolving virus.
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Affiliation(s)
- Lena Parigger
- Innophore, Graz, Austria
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
| | | | | | - Karl Gruber
- Innophore, Graz, Austria
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Field of Excellence BioHealth, University of Graz, Graz, Austria
| | - Georg Steinkellner
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- Field of Excellence BioHealth, University of Graz, Graz, Austria
- Innophore, San Francisco, California, USA
| | - Christian C. Gruber
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Field of Excellence BioHealth, University of Graz, Graz, Austria
- Innophore, San Francisco, California, USA
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10
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Mustali J, Yasuda I, Hirano Y, Yasuoka K, Gautieri A, Arai N. Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 M pro. RSC Adv 2023; 13:34249-34261. [PMID: 38019981 PMCID: PMC10663885 DOI: 10.1039/d3ra06375e] [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: 09/19/2023] [Accepted: 11/06/2023] [Indexed: 12/01/2023] Open
Abstract
Molecular dynamics (MD) simulations, which are central to drug discovery, offer detailed insights into protein-ligand interactions. However, analyzing large MD datasets remains a challenge. Current machine-learning solutions are predominantly supervised and have data labelling and standardisation issues. In this study, we adopted an unsupervised deep-learning framework, previously benchmarked for rigid proteins, to study the more flexible SARS-CoV-2 main protease (Mpro). We ran MD simulations of Mpro with various ligands and refined the data by focusing on binding-site residues and time frames in stable protein conformations. The optimal descriptor chosen was the distance between the residues and the center of the binding pocket. Using this approach, a local dynamic ensemble was generated and fed into our neural network to compute Wasserstein distances across system pairs, revealing ligand-induced conformational differences in Mpro. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.
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Affiliation(s)
- Jessica Mustali
- Department of Electronics, Information and Bioengineering, Politecnico di Milano Italy
| | - Ikki Yasuda
- Department of Mechanical Engineering, Keio University Japan
| | | | - Kenji Yasuoka
- Department of Mechanical Engineering, Keio University Japan
| | - Alfonso Gautieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano Italy
| | - Noriyoshi Arai
- Department of Mechanical Engineering, Keio University Japan
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11
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Tran-Nguyen VK, Junaid M, Simeon S, Ballester PJ. A practical guide to machine-learning scoring for structure-based virtual screening. Nat Protoc 2023; 18:3460-3511. [PMID: 37845361 DOI: 10.1038/s41596-023-00885-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/03/2023] [Indexed: 10/18/2023]
Abstract
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.
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Affiliation(s)
| | - Muhammad Junaid
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Saw Simeon
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
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12
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Mihalič F, Benz C, Kassa E, Lindqvist R, Simonetti L, Inturi R, Aronsson H, Andersson E, Chi CN, Davey NE, Överby AK, Jemth P, Ivarsson Y. Identification of motif-based interactions between SARS-CoV-2 protein domains and human peptide ligands pinpoint antiviral targets. Nat Commun 2023; 14:5636. [PMID: 37704626 PMCID: PMC10499821 DOI: 10.1038/s41467-023-41312-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
The virus life cycle depends on host-virus protein-protein interactions, which often involve a disordered protein region binding to a folded protein domain. Here, we used proteomic peptide phage display (ProP-PD) to identify peptides from the intrinsically disordered regions of the human proteome that bind to folded protein domains encoded by the SARS-CoV-2 genome. Eleven folded domains of SARS-CoV-2 proteins were found to bind 281 peptides from human proteins, and affinities of 31 interactions involving eight SARS-CoV-2 protein domains were determined (KD ∼ 7-300 μM). Key specificity residues of the peptides were established for six of the interactions. Two of the peptides, binding Nsp9 and Nsp16, respectively, inhibited viral replication. Our findings demonstrate how high-throughput peptide binding screens simultaneously identify potential host-virus interactions and peptides with antiviral properties. Furthermore, the high number of low-affinity interactions suggest that overexpression of viral proteins during infection may perturb multiple cellular pathways.
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Affiliation(s)
- Filip Mihalič
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Caroline Benz
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Eszter Kassa
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Richard Lindqvist
- Department of Clinical Microbiology, Umeå University, 90185, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90187, Umeå, Sweden
| | - Leandro Simonetti
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Raviteja Inturi
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Hanna Aronsson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Eva Andersson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Celestine N Chi
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Norman E Davey
- Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
| | - Anna K Överby
- Department of Clinical Microbiology, Umeå University, 90185, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90187, Umeå, Sweden
| | - Per Jemth
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden.
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden.
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13
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Trepte P, Secker C, Kostova S, Maseko SB, Choi SG, Blavier J, Minia I, Ramos ES, Cassonnet P, Golusik S, Zenkner M, Beetz S, Liebich MJ, Scharek N, Schütz A, Sperling M, Lisurek M, Wang Y, Spirohn K, Hao T, Calderwood MA, Hill DE, Landthaler M, Olivet J, Twizere JC, Vidal M, Wanker EE. AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.14.544560. [PMID: 37398436 PMCID: PMC10312674 DOI: 10.1101/2023.06.14.544560] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
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Affiliation(s)
- Philipp Trepte
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
- Brain Development and Disease, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, 1030, Vienna, Austria
| | - Christopher Secker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
- Zuse Institute Berlin, Berlin, Germany
| | - Simona Kostova
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Sibusiso B. Maseko
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Jeremy Blavier
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Igor Minia
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
| | - Eduardo Silva Ramos
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Patricia Cassonnet
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, Centre National de la Recherche Scientifique (CNRS), Université de Paris, Paris, France
| | - Sabrina Golusik
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Martina Zenkner
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Stephanie Beetz
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Mara J. Liebich
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Nadine Scharek
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Anja Schütz
- Protein Production & Characterization, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Marcel Sperling
- Multifunctional Colloids and Coating, Fraunhofer Institute for Applied Polymer Research (IAP), 14476, Potsdam-Golm, Germany
| | - Michael Lisurek
- Structural Chemistry and Computational Biophysics, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Michael A. Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - David E. Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Markus Landthaler
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
- Institute of Biology, Humboldt-Universität zu Berlin, 13125, Berlin, Germany
| | - Julien Olivet
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Structural Biology Unit, Laboratory of Virology and Chemotherapy, Rega Institute for Medical Research, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit Leuven, 3000, Leuven, Belgium
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030, Gembloux, Belgium
- Laboratory of Algal Synthetic and Systems Biology, Division of Science and Math, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Erich E. Wanker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
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14
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Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616:673-685. [PMID: 37100941 DOI: 10.1038/s41586-023-05905-z] [Citation(s) in RCA: 135] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 03/01/2023] [Indexed: 04/28/2023]
Abstract
Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.
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Affiliation(s)
- Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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15
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Rogers DM, Agarwal R, Vermaas JV, Smith MD, Rajeshwar RT, Cooper C, Sedova A, Boehm S, Baker M, Glaser J, Smith JC. SARS-CoV2 billion-compound docking. Sci Data 2023; 10:173. [PMID: 36977690 PMCID: PMC10044124 DOI: 10.1038/s41597-023-01984-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 01/24/2023] [Indexed: 03/30/2023] Open
Abstract
This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.
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Affiliation(s)
- David M Rogers
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
| | - Rupesh Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Josh V Vermaas
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA
| | - Micholas Dean Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Rajitha T Rajeshwar
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA
| | - Connor Cooper
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Biological Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Ada Sedova
- Biological Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Swen Boehm
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Matthew Baker
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jens Glaser
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, Knoxville, TN, 37996, USA.
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16
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Joshi RP, Schultz KJ, Wilson JW, Kruel A, Varikoti RA, Kombala CJ, Kneller DW, Galanie S, Phillips G, Zhang Q, Coates L, Parvathareddy J, Surendranathan S, Kong Y, Clyde A, Ramanathan A, Jonsson CB, Brandvold KR, Zhou M, Head MS, Kovalevsky A, Kumar N. AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2. J Chem Inf Model 2023; 63:1438-1453. [PMID: 36808989 PMCID: PMC9969887 DOI: 10.1021/acs.jcim.2c01377] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Indexed: 02/23/2023]
Abstract
Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.
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Affiliation(s)
- Rajendra P. Joshi
- Earth and Biological Sciences Directorate,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
| | - Katherine J. Schultz
- Earth and Biological Sciences Directorate,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
| | - Jesse William Wilson
- Earth and Biological Sciences Directorate,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
| | - Agustin Kruel
- Earth and Biological Sciences Directorate,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
| | - Rohith Anand Varikoti
- Earth and Biological Sciences Directorate,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
| | - Chathuri J. Kombala
- Elson S. Floyd College of Medicine, Department of
Nutrition and Exercise Physiology, Washington State University,
Spokane, Washington 99202, United States
| | - Daniel W. Kneller
- Neutron Scattering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
| | - Stephanie Galanie
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
- Biosciences Division, Oak Ridge National
Laboratory, Oak Ridge, Tennessee 37831, United
States
- Department of Process Research and Development,
Merck & Co., Inc., 126 E. Lincoln Avenue, Rahway, New
Jersey 07065, United States
| | - Gwyndalyn Phillips
- Neutron Scattering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
| | - Qiu Zhang
- Neutron Scattering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
| | - Leighton Coates
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
- Second Target Station, Oak Ridge National
Laboratory, Oak Ridge, Tennessee 37831, United
States
| | - Jyothi Parvathareddy
- Regional Biocontainment Laboratory, The
University of Tennessee Health Science Center, Memphis, Tennessee 38105,
United States
| | - Surekha Surendranathan
- Regional Biocontainment Laboratory, The
University of Tennessee Health Science Center, Memphis, Tennessee 38105,
United States
| | - Ying Kong
- Regional Biocontainment Laboratory, The
University of Tennessee Health Science Center, Memphis, Tennessee 38105,
United States
| | - Austin Clyde
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
| | - Arvind Ramanathan
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
| | - Colleen B. Jonsson
- Regional Biocontainment Laboratory, The
University of Tennessee Health Science Center, Memphis, Tennessee 38105,
United States
- Institute for the Study of Host-Pathogen Systems,
University of Tennessee Health Science Center, Memphis,
Tennessee 38103, United States
- Department of Microbiology, Immunology and
Biochemistry, University of Tennessee Health Science Center,
Memphis, Tennessee 38103, United States
| | - Kristoffer R. Brandvold
- Earth and Biological Sciences Directorate,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
- Elson S. Floyd College of Medicine, Department of
Nutrition and Exercise Physiology, Washington State University,
Spokane, Washington 99202, United States
| | - Mowei Zhou
- Earth and Biological Sciences Directorate,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
| | - Martha S. Head
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
- Joint Institute for Biological Sciences,
Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831,
United States
- Center for Research Acceleration by Digital
Innovation, Amgen Research, Thousand Oaks, California 91320,
United States
| | - Andrey Kovalevsky
- Neutron Scattering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
| | - Neeraj Kumar
- Earth and Biological Sciences Directorate,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
- National Virtual Biotechnology Laboratory,
US Department of Energy, Washington, District of Columbia
20585, United States
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17
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Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. Int J Mol Sci 2023; 24:ijms24054401. [PMID: 36901832 PMCID: PMC10003049 DOI: 10.3390/ijms24054401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.
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18
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Clyde A, Liu X, Brettin T, Yoo H, Partin A, Babuji Y, Blaiszik B, Mohd-Yusof J, Merzky A, Turilli M, Jha S, Ramanathan A, Stevens R. AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection. Sci Rep 2023; 13:2105. [PMID: 36747041 PMCID: PMC9901402 DOI: 10.1038/s41598-023-28785-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.
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Affiliation(s)
- Austin Clyde
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA.
- Department of Computer Science, University of Chicago, Chicago, 60637, USA.
| | - Xuefeng Liu
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
| | - Thomas Brettin
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
- Argonne National Laboratory, Computing, Environment, and Life Sciences Directorate, Lemont, 60439, USA
| | - Hyunseung Yoo
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Alexander Partin
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Yadu Babuji
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
| | - Ben Blaiszik
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
- University of Chicago, Globus, Chicago, 60637, USA
| | - Jamaludin Mohd-Yusof
- Los Alamos National Laboratory, Computer, Computational, and Statistical Sciences, Los Alamos, 87545, USA
| | - Andre Merzky
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Matteo Turilli
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Shantenu Jha
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, 08854, USA
- Brookhaven National Laboratory, Computational Sciences Initiative, Upton, 11973, USA
| | - Arvind Ramanathan
- Argonne National Laboratory, Data Science and Learning Division, Chicago, Lemont, 60439, USA
| | - Rick Stevens
- Department of Computer Science, University of Chicago, Chicago, 60637, USA
- Argonne National Laboratory, Computing, Environment, and Life Sciences Directorate, Lemont, 60439, USA
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19
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Agarwal R, Smith JC. Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina. Mol Inform 2023; 42:e2200188. [PMID: 36262028 DOI: 10.1002/minf.202200188] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022]
Abstract
Structure-based virtual high-throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the 'docking power'. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein-ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.
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Affiliation(s)
- Rupesh Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6309, USA.,Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, 14311 Cumberland Avenue, Knoxville, TN 37996-1939, USA
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6309, USA.,Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, 14311 Cumberland Avenue, Knoxville, TN 37996-1939, USA
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20
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Jahirul Islam M, Nawal Islam N, Siddik Alom M, Kabir M, Halim MA. A review on structural, non-structural, and accessory proteins of SARS-CoV-2: Highlighting drug target sites. Immunobiology 2023; 228:152302. [PMID: 36434912 PMCID: PMC9663145 DOI: 10.1016/j.imbio.2022.152302] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 10/30/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is a highly transmittable and pathogenic human coronavirus that first emerged in China in December 2019. The unprecedented outbreak of SARS-CoV-2 devastated human health within a short time leading to a global public health emergency. A detailed understanding of the viral proteins including their structural characteristics and virulence mechanism on human health is very crucial for developing vaccines and therapeutics. To date, over 1800 structures of non-structural, structural, and accessory proteins of SARS-CoV-2 are determined by cryo-electron microscopy, X-ray crystallography, and NMR spectroscopy. Designing therapeutics to target the viral proteins has several benefits since they could be highly specific against the virus while maintaining minimal detrimental effects on humans. However, for ongoing and future research on SARS-CoV-2, summarizing all the viral proteins and their detailed structural information is crucial. In this review, we compile comprehensive information on viral structural, non-structural, and accessory proteins structures with their binding and catalytic sites, different domain and motifs, and potential drug target sites to assist chemists, biologists, and clinicians finding necessary details for fundamental and therapeutic research.
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Affiliation(s)
- Md. Jahirul Islam
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, BICCB, 16 Tejkunipara, Tejgaon, Dhaka 1215, Bangladesh
| | - Nafisa Nawal Islam
- Department of Biotechnology and Genetic Engineering, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Md. Siddik Alom
- Ohio State Biochemistry Program, The Ohio State University, Columbus, OH 43210, USA
| | - Mahmuda Kabir
- Department of Genetic Engineering and Biotechnology, University of Dhaka, Dhaka 1000, Bangladesh
| | - Mohammad A. Halim
- Department of Chemistry and Biochemistry, Kennesaw State University, 370 Paulding Avenue NW, Kennesaw, GA 30144, USA,Corresponding author
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21
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Maschietto F, Qiu T, Wang J, Shi Y, Allen B, Lisi GP, Lolis E, Batista VS. Valproate-coenzyme A conjugate blocks opening of receptor binding domains in the spike trimer of SARS-CoV-2 through an allosteric mechanism. Comput Struct Biotechnol J 2023; 21:1066-1076. [PMID: 36688026 PMCID: PMC9841741 DOI: 10.1016/j.csbj.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/13/2023] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
The receptor-binding domains (RBDs) of the SARS-CoV-2 spike trimer exhibit "up" and "down" conformations often targeted by neutralizing antibodies. Only in the "up" configuration can RBDs bind to the ACE2 receptor of the host cell and initiate the process of viral multiplication. Here, we identify a lead compound (3-oxo-valproate-coenzyme A conjugate or Val-CoA) that stabilizes the spike trimer with RBDs in the down conformation. Val-CoA interacts with three R408 residues, one from each RBD, which significantly reduces the inter-subunit R408-R408 distance by ∼ 13 Å and closes the central pore formed by the three RBDs. Experimental evidence is presented that R408 is part of a triggering mechanism that controls the prefusion to postfusion state transition of the spike trimer. By stabilizing the RBDs in the down configuration, this and other related compounds can likely attenuate viral transmission. The reported findings for binding of Val-CoA to the spike trimer suggest a new approach for the design of allosteric antiviral drugs that do not have to compete for specific virus-receptor interactions but instead hinder the conformational motion of viral membrane proteins essential for interaction with the host cell. Here, we introduce an approach to target the spike protein by identifying lead compounds that stabilize the RBDs in the trimeric "down" configuration. When these compounds trimerize monomeric RBD immunogens as co-immunogens, they could also induce new types of non-ACE2 blocking antibodies that prevent local cell-to-cell transmission of the virus, providing a novel approach for inhibition of SARS-CoV-2.
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Affiliation(s)
| | - Tianyin Qiu
- Department of Chemistry, Yale University, New Haven, CT 06520-8449, USA
| | - Jimin Wang
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520-8114, USA
- Corresponding authors.
| | - Yuanjun Shi
- Department of Chemistry, Yale University, New Haven, CT 06520-8449, USA
| | - Brandon Allen
- Department of Chemistry, Yale University, New Haven, CT 06520-8449, USA
| | - George P. Lisi
- Department of Molecular and Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA
| | - Elias Lolis
- Department of Pharmacology, Yale University, New Haven, CT 06520-8066, USA
| | - Victor S. Batista
- Department of Chemistry, Yale University, New Haven, CT 06520-8449, USA
- Corresponding authors.
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22
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Parigger L, Krassnigg A, Schopper T, Singh A, Tappler K, Köchl K, Hetmann M, Gruber K, Steinkellner G, Gruber CC. Recent changes in the mutational dynamics of the SARS-CoV-2 main protease substantiate the danger of emerging resistance to antiviral drugs. Front Med (Lausanne) 2022; 9:1061142. [PMID: 36590977 PMCID: PMC9794616 DOI: 10.3389/fmed.2022.1061142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction The current coronavirus pandemic is being combated worldwide by nontherapeutic measures and massive vaccination programs. Nevertheless, therapeutic options such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main-protease (Mpro) inhibitors are essential due to the ongoing evolution toward escape from natural or induced immunity. While antiviral strategies are vulnerable to the effects of viral mutation, the relatively conserved Mpro makes an attractive drug target: Nirmatrelvir, an antiviral targeting its active site, has been authorized for conditional or emergency use in several countries since December 2021, and a number of other inhibitors are under clinical evaluation. We analyzed recent SARS-CoV-2 genomic data, since early detection of potential resistances supports a timely counteraction in drug development and deployment, and discovered accelerated mutational dynamics of Mpro since early December 2021. Methods We performed a comparative analysis of 10.5 million SARS-CoV-2 genome sequences available by June 2022 at GISAID to the NCBI reference genome sequence NC_045512.2. Amino-acid exchanges within high-quality regions in 69,878 unique Mpro sequences were identified and time- and in-depth sequence analyses including a structural representation of mutational dynamics were performed using in-house software. Results The analysis showed a significant recent event of mutational dynamics in Mpro. We report a remarkable increase in mutational variability in an eight-residue long consecutive region (R188-G195) near the active site since December 2021. Discussion The increased mutational variability in close proximity to an antiviral-drug binding site as described herein may suggest the onset of the development of antiviral resistance. This emerging diversity urgently needs to be further monitored and considered in ongoing drug development and lead optimization.
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Affiliation(s)
- Lena Parigger
- Innophore GmbH, Graz, Austria
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
| | | | | | - Amit Singh
- Innophore GmbH, Graz, Austria
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
| | - Katharina Tappler
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
| | | | - Michael Hetmann
- Innophore GmbH, Graz, Austria
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- Austrian Centre of Industrial Biotechnology, Graz, Austria
| | - Karl Gruber
- Innophore GmbH, Graz, Austria
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Field of Excellence BioHealth, University of Graz, Graz, Austria
| | - Georg Steinkellner
- Innophore GmbH, Graz, Austria
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- Field of Excellence BioHealth, University of Graz, Graz, Austria
| | - Christian C. Gruber
- Innophore GmbH, Graz, Austria
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
- Austrian Centre of Industrial Biotechnology, Graz, Austria
- Field of Excellence BioHealth, University of Graz, Graz, Austria
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23
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Halma MTJ, Wever MJA, Abeln S, Roche D, Wuite GJL. Therapeutic potential of compounds targeting SARS-CoV-2 helicase. Front Chem 2022; 10:1062352. [PMID: 36561139 PMCID: PMC9763700 DOI: 10.3389/fchem.2022.1062352] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
The economical and societal impact of COVID-19 has made the development of vaccines and drugs to combat SARS-CoV-2 infection a priority. While the SARS-CoV-2 spike protein has been widely explored as a drug target, the SARS-CoV-2 helicase (nsp13) does not have any approved medication. The helicase shares 99.8% similarity with its SARS-CoV-1 homolog and was shown to be essential for viral replication. This review summarizes and builds on existing research on inhibitors of SARS-CoV-1 and SARS-CoV-2 helicases. Our analysis on the toxicity and specificity of these compounds, set the road going forward for the repurposing of existing drugs and the development of new SARS-CoV-2 helicase inhibitors.
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Affiliation(s)
- Matthew T. J. Halma
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- LUMICKS B. V., Amsterdam, Netherlands
| | - Mark J. A. Wever
- DCM, University of Grenoble Alpes, Grenoble, France
- Edelris, Lyon, France
| | - Sanne Abeln
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | - Gijs J. L. Wuite
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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24
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Discovering new potential inhibitors to SARS-CoV-2 RNA dependent RNA polymerase (RdRp) using high throughput virtual screening and molecular dynamics simulations. Sci Rep 2022; 12:19986. [PMID: 36411383 PMCID: PMC9676757 DOI: 10.1038/s41598-022-24695-4] [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: 11/27/2021] [Accepted: 03/14/2022] [Indexed: 11/23/2022] Open
Abstract
RNA dependent RNA polymerase (RdRp), is an essential in the RNA replication within the life cycle of the severely acute respiratory coronavirus-2 (SARS-CoV-2), causing the deadly respiratory induced sickness COVID-19. Remdesivir is a prodrug that has seen some success in inhibiting this enzyme, however there is still the pressing need for effective alternatives. In this study, we present the discovery of four non-nucleoside small molecules that bind favorably to SARS-CoV-2 RdRp over the active form of the popular drug remdesivir (RTP) and adenosine triphosphate (ATP) by utilizing high-throughput virtual screening (HTVS) against the vast ZINC compound database coupled with extensive molecular dynamics (MD) simulations. After post-trajectory analysis, we found that the simulations of complexes containing both ATP and RTP remained stable for the duration of their trajectories. Additionally, it was revealed that the phosphate tail of RTP was stabilized by both the positive amino acid pocket and magnesium ions near the entry channel of RdRp which includes residues K551, R553, R555 and K621. It was also found that residues D623, D760, and N691 further stabilized the ribose portion of RTP with U10 on the template RNA strand forming hydrogen pairs with the adenosine motif. Using these models of RdRp, we employed them to screen the ZINC database of ~ 17 million molecules. Using docking and drug properties scoring, we narrowed down our selection to fourteen candidates. These were subjected to 200 ns simulations each underwent free energy calculations. We identified four hit compounds from the ZINC database that have similar binding poses to RTP while possessing lower overall binding free energies, with ZINC097971592 having a binding free energy two times lower than RTP.
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25
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Chemical space docking enables large-scale structure-based virtual screening to discover ROCK1 kinase inhibitors. Nat Commun 2022; 13:6447. [PMID: 36307407 PMCID: PMC9616902 DOI: 10.1038/s41467-022-33981-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/05/2022] [Indexed: 12/25/2022] Open
Abstract
With the ever-increasing number of synthesis-on-demand compounds for drug lead discovery, there is a great need for efficient search technologies. We present the successful application of a virtual screening method that combines two advances: (1) it avoids full library enumeration (2) products are evaluated by molecular docking, leveraging protein structural information. Crucially, these advances enable a structure-based technique that can efficiently explore libraries with billions of molecules and beyond. We apply this method to identify inhibitors of ROCK1 from almost one billion commercially available compounds. Out of 69 purchased compounds, 27 (39%) have Ki values < 10 µM. X-ray structures of two leads confirm their docked poses. This approach to docking scales roughly with the number of reagents that span a chemical space and is therefore multiple orders of magnitude faster than traditional docking.
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26
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Direct Interaction of Coronavirus Nonstructural Protein 3 with Melanoma Differentiation-Associated Gene 5 Modulates Type I Interferon Response during Coronavirus Infection. Int J Mol Sci 2022; 23:ijms231911692. [PMID: 36232993 PMCID: PMC9570369 DOI: 10.3390/ijms231911692] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022] Open
Abstract
Coronavirus nonstructural protein 3 (nsp3) is a multi-functional protein, playing a critical role in viral replication and in regulating host antiviral innate immunity. In this study, we demonstrate that nsp3 from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and avian coronavirus infectious bronchitis virus (IBV) directly interacts with melanoma differentiation-associated gene 5 (MDA5), rendering an inhibitory effect on the MDA5-mediated type I interferon (IFN) response. By the co-expression of MDA5 with wild-type and truncated nsp3 constructs, at least three interacting regions mapped to the papain-like protease (PLpro) domain and two other domains located at the N- and C-terminal regions were identified in SARS-CoV-2 nsp3. Furthermore, by introducing point mutations to the catalytic triad, the deubiquitylation activity of the PLpro domain from both SARS-CoV-2 and IBV nsp3 was shown to be responsible for the suppression of the MDA5-mediated type I IFN response. It was also demonstrated that both MDA5 and nsp3 were able to interact with ubiquitin and ubiquitinated proteins, contributing to the interaction between the two proteins. This study confirms the antagonistic role of nsp3 in the MDA5-mediated type I IFN signaling, highlighting the complex interaction between a multi-functional viral protein and the innate immune response.
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27
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High-resolution structures of the SARS-CoV-2 N7-methyltransferase inform therapeutic development. Nat Struct Mol Biol 2022; 29:850-853. [PMID: 36075969 PMCID: PMC10388636 DOI: 10.1038/s41594-022-00828-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 07/28/2022] [Indexed: 11/08/2022]
Abstract
Emergence of SARS-CoV-2 coronavirus has led to millions of deaths globally. We present three high-resolution crystal structures of the SARS-CoV-2 nsp14 N7-methyltransferase core bound to S-adenosylmethionine (1.62 Å), S-adenosylhomocysteine (1.55 Å) and sinefungin (1.41 Å). We identify features of the methyltransferase core that are crucial for the development of antivirals and show SAH as the best scaffold for the design of antivirals against SARS-CoV-2 and other pathogenic coronaviruses.
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28
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Durmaz V, Köchl K, Krassnigg A, Parigger L, Hetmann M, Singh A, Nutz D, Korsunsky A, Kahler U, König C, Chang L, Krebs M, Bassetto R, Pavkov-Keller T, Resch V, Gruber K, Steinkellner G, Gruber CC. Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference. Sci Rep 2022; 12:14534. [PMID: 36008461 PMCID: PMC9406262 DOI: 10.1038/s41598-022-18507-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 08/08/2022] [Indexed: 01/16/2023] Open
Abstract
To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring.
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Affiliation(s)
| | | | | | | | - Michael Hetmann
- Institute of Molecular Biosciences, University of Graz, 8010, Graz, Austria.,Austrian Centre of Industrial Biotechnology, 8010, Graz, Austria
| | - Amit Singh
- Innophore GmbH, 8010, Graz, Austria.,Institute of Molecular Biosciences, University of Graz, 8010, Graz, Austria
| | | | | | | | | | - Lee Chang
- AWS Diagnostic Development Initiative-Global Social Impact, Seattle, WA, 98109, USA
| | - Marius Krebs
- Amazon Web Services EMEA SARL, 80807, Muenchen, Germany
| | | | - Tea Pavkov-Keller
- Institute of Molecular Biosciences, University of Graz, 8010, Graz, Austria
| | | | - Karl Gruber
- Institute of Molecular Biosciences, University of Graz, 8010, Graz, Austria.,Field of Excellence BioHealth-University of Graz, 8010, Graz, Austria
| | - Georg Steinkellner
- Innophore GmbH, 8010, Graz, Austria. .,Institute of Molecular Biosciences, University of Graz, 8010, Graz, Austria.
| | - Christian C Gruber
- Innophore GmbH, 8010, Graz, Austria. .,Institute of Molecular Biosciences, University of Graz, 8010, Graz, Austria.
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29
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Cerón‐Carrasco JP. When Virtual Screening Yields Inactive Drugs: Dealing with False Theoretical Friends. ChemMedChem 2022; 17:e202200278. [PMID: 35726731 PMCID: PMC9542010 DOI: 10.1002/cmdc.202200278] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/16/2022] [Indexed: 11/23/2022]
Abstract
The search of antivirals against SARS-CoV-2 in available libraries of compounds was initiated as soon as WHO announced that the coronavirus outbreak became a pandemic. That pivotal task has been conducted by both experimental groups in wet-labs as well as by theoretical chemists in supercomputing centers. The combination of biochemical and clinical intuitions yields first to remdesivir, a broad-spectrum antiviral that remains as the standard solution for the treatment of severe cases, while paxlovid, molnupiravir and fluvoxamine have been recently proposed as oral alternatives. Unfortunately, the intensive publication of standard virtual screening (VS) simulations might be not the best strategy to increase that short list of antivirals. This contribution joins theory and biological assays to rescore massive VS. Our goal is to critically assess pros and cons of using molecular models for drug repurposing.
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Affiliation(s)
- José P. Cerón‐Carrasco
- Centro Universitario de la Defensa, Academia General del AireUniversidad Politécnica de CartagenaC/Coronel López Peña S/N Santiago de La Ribera30720MurciaSpain
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Vergoten G, Bailly C. Interaction of panduratin A and derivatives with the SARS-CoV-2 main protease (m pro): a molecular docking study. J Biomol Struct Dyn 2022:1-11. [PMID: 35975613 DOI: 10.1080/07391102.2022.2112618] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
Panduratin A (Pa-A) is a prenylated cyclohexenyl chalcone isolated from the rhizomes of the medicinal and culinary plant Boesenbergia rotunda (L.) Mansf., commonly called fingerroots. Both an ethanolic plant extract and Pa-A have shown a marked antiviral activity against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic disease. Pa-A functions as a protease inhibitor inhibiting infection of human cells by the virus. We have modeled the interaction of Pa-A, and 26 panduratin analogues with the main protease (Mpro) of SARS-CoV-2 using molecular docking. The natural product 4-hydroxypanduratin showed a higher Mpro binding capacity than Pa-A and isopanduratin A. The interaction with MPro of all known panduratin derivatives (Pa-A to Pa-Y) have been compared, together with more than 60 reference products. Three compounds emerged as potential robust MPro binders: Pa-R, Pa-V, Pa-S, with a binding capacity significantly higher than 4-OH-Pa-A and Pa-A. The empirical energy of interaction (ΔE) calculated with the best compound in the panduratin series, Pa-R bound to Mpro, surpassed that measured with the top reference protease inhibitors such a ruprintrivir, lufotrelvir, and glecaprevir. Structure-binding relationships are discussed. Compounds with a flavanone moiety (PA-R/S) are the best binders, better than those with a chromene unit (Pa-F/G). The extended molecules (such as Pa-V) exhibit good Mpro binding, but the dimeric compound Pa-Y is too long and protrudes outside the binding cavity. The work provides novel ideas to guide the design of new molecules interacting with Mpro.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Gérard Vergoten
- Inserm, INFINITE - U1286, Institut de Chimie Pharmaceutique Albert Lespagnol (ICPAL), Faculté de Pharmacie, University of Lille, France, Lille
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Yazdi AK, Pakarian P, Perveen S, Hajian T, Santhakumar V, Bolotokova A, Li F, Vedadi M. Kinetic Characterization of SARS-CoV-2 nsp13 ATPase Activity and Discovery of Small-Molecule Inhibitors. ACS Infect Dis 2022; 8:1533-1542. [PMID: 35822715 PMCID: PMC9305828 DOI: 10.1021/acsinfecdis.2c00165] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Indexed: 11/29/2022]
Abstract
SARS-CoV-2 non-structural protein 13 (nsp13) is a highly conserved helicase and RNA 5'-triphosphatase. It uses the energy derived from the hydrolysis of nucleoside triphosphates for directional movement along the nucleic acids and promotes the unwinding of double-stranded nucleic acids. Nsp13 is essential for replication and propagation of all human and non-human coronaviruses. Combined with its defined nucleotide binding site and druggability, nsp13 is one of the most promising candidates for the development of pan-coronavirus therapeutics. Here, we report the development and optimization of bioluminescence assays for kinetic characterization of nsp13 ATPase activity in the presence and absence of single-stranded DNA. Screening of a library of 5000 small molecules in the presence of single-stranded DNA resulted in the discovery of six nsp13 small-molecule inhibitors with IC50 values ranging from 6 ± 0.5 to 50 ± 6 μM. In addition to providing validated methods for high-throughput screening of nsp13 in drug discovery campaigns, the reproducible screening hits we present here could potentially be chemistry starting points toward the development of more potent and selective nsp13 inhibitors, enabling the discovery of antiviral therapeutics.
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Affiliation(s)
| | - Paknoosh Pakarian
- Structural Genomics Consortium,
University of Toronto, Toronto, Ontario M5G 1L7,
Canada
| | - Sumera Perveen
- Structural Genomics Consortium,
University of Toronto, Toronto, Ontario M5G 1L7,
Canada
| | - Taraneh Hajian
- Structural Genomics Consortium,
University of Toronto, Toronto, Ontario M5G 1L7,
Canada
| | | | - Albina Bolotokova
- Structural Genomics Consortium,
University of Toronto, Toronto, Ontario M5G 1L7,
Canada
| | - Fengling Li
- Structural Genomics Consortium,
University of Toronto, Toronto, Ontario M5G 1L7,
Canada
| | - Masoud Vedadi
- Structural Genomics Consortium,
University of Toronto, Toronto, Ontario M5G 1L7,
Canada
- Department of Pharmacology and Toxicology,
University of Toronto, Toronto, Ontario M5S 1A8,
Canada
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32
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Gorgulla C, Jayaraj A, Fackeldey K, Arthanari H. Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches. Curr Opin Chem Biol 2022; 69:102156. [PMID: 35576813 PMCID: PMC9990419 DOI: 10.1016/j.cbpa.2022.102156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/16/2022] [Accepted: 04/07/2022] [Indexed: 11/19/2022]
Abstract
Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.
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Affiliation(s)
- Christoph Gorgulla
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA
| | | | - Konstantin Fackeldey
- Institute of Mathematics, Technical University Berlin, Berlin, Germany; Zuse Institute Berlin, Berlin, Germany
| | - Haribabu Arthanari
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School (HMS), Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute (DFCI), Boston, MA, USA.
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33
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The Main Protease of SARS-CoV-2 as a Target for Phytochemicals against Coronavirus. PLANTS 2022; 11:plants11141862. [PMID: 35890496 PMCID: PMC9319234 DOI: 10.3390/plants11141862] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 11/23/2022]
Abstract
In late December 2019, the first cases of COVID-19 emerged as an outbreak in Wuhan, China that later spread vastly around the world, evolving into a pandemic and one of the worst global health crises in modern history. The causative agent was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although several vaccines were authorized for emergency use, constantly emerging new viral mutants and limited treatment options for COVID-19 drastically highlighted the need for developing an efficient treatment for this disease. One of the most important viral components to target for this purpose is the main protease of the coronavirus (Mpro). This enzyme is an excellent target for a potential drug, as it is essential for viral replication and has no closely related homologues in humans, making its inhibitors unlikely to be toxic. Our review describes a variety of approaches that could be applied in search of potential inhibitors among plant-derived compounds, including virtual in silico screening (a data-driven approach), which could be structure-based or fragment-guided, the classical approach of high-throughput screening, and antiviral activity cell-based assays. We will focus on several classes of compounds reported to be potential inhibitors of Mpro, including phenols and polyphenols, alkaloids, and terpenoids.
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34
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Pitsillou E, Liang J, Hung A, Karagiannis TC. The SARS-CoV-2 helicase as a target for antiviral therapy: Identification of potential small molecule inhibitors by in silico modelling. J Mol Graph Model 2022; 114:108193. [PMID: 35462185 PMCID: PMC9014761 DOI: 10.1016/j.jmgm.2022.108193] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 03/29/2022] [Accepted: 04/11/2022] [Indexed: 11/18/2022]
Abstract
Although vaccines that provide protection against severe illness from coronavirus disease (COVID-19) have been made available, emerging variant strains of severe acute respiratory syndrome 2 coronavirus 2 (SARS-CoV-2) are of concern. A different research direction involves investigation of antiviral therapeutics. In addition to structural proteins, the SARS-CoV-2 non-structural proteins are of interest and this includes the helicase (nsp13). In this study, an initial screen of 300 ligands was performed to identify potential inhibitors of the SARS-CoV-2 nsp13 examining the nucleoside triphosphatase site (NTPase activity) as the target region. The antiviral activity of polyphenols has been previously reported in the literature and as a result, the phenolic compounds and fatty acids from the OliveNet™ library were utilised. Synthetic compounds with antimicrobial and anti-inflammatory properties were also selected. The structures of the SARS-CoV and MERS-CoV helicases, as well as the human RECQ-like DNA helicase, DHX9 helicase, PcrA helicase, hepatitis C NS3 helicase, and mouse Dna2 nuclease-helicase were used for comparison. As expected, sequence and structural homology between the various species was evident. A number of broad-spectrum and well-known inhibitors interacted with the NTPase active site highlighting the need to potentially identify more specific inhibitors for SARS-CoV-2. Acetylcysteine, clavulanic acid and homovanillic acid were identified as potential lead compounds for the SARS-CoV-2 helicase. Molecular dynamics simulations were performed with the leads bound to the SARS-CoV-2 helicase for 200 ns in triplicate, with favourable binding free energies to the NTPase site. Given their availability, further exploration of their potential inhibitory activity could be considered.
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Affiliation(s)
- Eleni Pitsillou
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; School of Science, STEM College, RMIT University, VIC, 3001, Australia
| | - Julia Liang
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; School of Science, STEM College, RMIT University, VIC, 3001, Australia
| | - Andrew Hung
- School of Science, STEM College, RMIT University, VIC, 3001, Australia
| | - Tom C Karagiannis
- Epigenomic Medicine, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia; Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3052, Australia.
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35
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Antiviral Drug Discovery for the Treatment of COVID-19 Infections. Viruses 2022; 14:v14050961. [PMID: 35632703 PMCID: PMC9143071 DOI: 10.3390/v14050961] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 02/04/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a recently emerged human coronavirus. COVID-19 vaccines have proven to be successful in protecting the vaccinated from infection, reducing the severity of disease, and deterring the transmission of infection. However, COVID-19 vaccination faces many challenges, such as the decline in vaccine-induced immunity over time, and the decrease in potency against some SARS-CoV-2 variants including the recently emerged Omicron variant, resulting in breakthrough infections. The challenges that COVID-19 vaccination is facing highlight the importance of the discovery of antivirals to serve as another means to tackle the pandemic. To date, neutralizing antibodies that block viral entry by targeting the viral spike protein make up the largest class of antivirals that has received US FDA emergency use authorization (EUA) for COVID-19 treatment. In addition to the spike protein, other key targets for the discovery of direct-acting antivirals include viral enzymes that are essential for SARS-CoV-2 replication, such as RNA-dependent RNA polymerase and proteases, as judged by US FDA approval for remdesivir, and EUA for Paxlovid (nirmatrelvir + ritonavir) for treating COVID-19 infections. This review presents an overview of the current status and future direction of antiviral drug discovery for treating SARS-CoV-2 infections, covering important antiviral targets such as the viral spike protein, non-structural protein (nsp) 3 papain-like protease, nsp5 main protease, and the nsp12/nsp7/nsp8 RNA-dependent RNA polymerase complex.
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36
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Deval J, Gurard-Levin ZA. Opportunities and Challenges in Targeting the Proofreading Activity of SARS-CoV-2 Polymerase Complex. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27092918. [PMID: 35566268 PMCID: PMC9103157 DOI: 10.3390/molecules27092918] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 01/01/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the COVID-19 pandemic. While the development of vaccines and the emergence of antiviral therapeutics is promising, alternative strategies to combat COVID-19 (and potential future pandemics) remain an unmet need. Coronaviruses feature a unique mechanism that may present opportunities for therapeutic intervention: the RNA polymerase complex of coronaviruses is distinct in its ability to proofread and remove mismatched nucleotides during genome replication and transcription. The proofreading activity has been linked to the exonuclease (ExoN) activity of non-structural protein 14 (NSP14). Here, we review the role of NSP14, and other NSPs, in SARS-CoV-2 replication and describe the assays that have been developed to assess the ExoN function. We also review the nucleoside analogs and non-nucleoside inhibitors known to interfere with the proofreading activity of NSP14. Although not yet validated, the potential use of non-nucleoside proofreading inhibitors in combination with chain-terminating nucleosides may be a promising avenue for the development of anti-CoV agents.
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Affiliation(s)
- Jerome Deval
- Aligos Therapeutics, Inc., San Francisco, CA 94080, USA
- Correspondence:
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37
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Fischer TR, Meidner L, Schwickert M, Weber M, Zimmermann RA, Kersten C, Schirmeister T, Helm M. Chemical biology and medicinal chemistry of RNA methyltransferases. Nucleic Acids Res 2022; 50:4216-4245. [PMID: 35412633 PMCID: PMC9071492 DOI: 10.1093/nar/gkac224] [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: 11/24/2021] [Revised: 03/17/2022] [Accepted: 04/08/2022] [Indexed: 12/24/2022] Open
Abstract
RNA methyltransferases (MTases) are ubiquitous enzymes whose hitherto low profile in medicinal chemistry, contrasts with the surging interest in RNA methylation, the arguably most important aspect of the new field of epitranscriptomics. As MTases become validated as drug targets in all major fields of biomedicine, the development of small molecule compounds as tools and inhibitors is picking up considerable momentum, in academia as well as in biotech. Here we discuss the development of small molecules for two related aspects of chemical biology. Firstly, derivates of the ubiquitous cofactor S-adenosyl-l-methionine (SAM) are being developed as bioconjugation tools for targeted transfer of functional groups and labels to increasingly visible targets. Secondly, SAM-derived compounds are being investigated for their ability to act as inhibitors of RNA MTases. Drug development is moving from derivatives of cosubstrates towards higher generation compounds that may address allosteric sites in addition to the catalytic centre. Progress in assay development and screening techniques from medicinal chemistry have led to recent breakthroughs, e.g. in addressing human enzymes targeted for their role in cancer. Spurred by the current pandemic, new inhibitors against coronaviral MTases have emerged at a spectacular rate, including a repurposed drug which is now in clinical trial.
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Affiliation(s)
- Tim R Fischer
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128Mainz, Germany
| | - Laurenz Meidner
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128Mainz, Germany
| | - Marvin Schwickert
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128Mainz, Germany
| | - Marlies Weber
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128Mainz, Germany
| | - Robert A Zimmermann
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128Mainz, Germany
| | - Christian Kersten
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128Mainz, Germany
| | - Tanja Schirmeister
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128Mainz, Germany
| | - Mark Helm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, 55128Mainz, Germany
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38
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Nazarova AL, Katritch V. It all clicks together: In silico drug discovery becoming mainstream. Clin Transl Med 2022; 12:e766. [PMID: 35377970 PMCID: PMC8979333 DOI: 10.1002/ctm2.766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/01/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Antonina L Nazarova
- Department of Quantitative and Computational Biology, Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, California, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, California, USA
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Kutzner C, Kniep C, Cherian A, Nordstrom L, Grubmüller H, de Groot BL, Gapsys V. GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design. J Chem Inf Model 2022; 62:1691-1711. [PMID: 35353508 PMCID: PMC9006219 DOI: 10.1021/acs.jcim.2c00044] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
We assess costs and
efficiency of state-of-the-art high-performance
cloud computing and compare the results to traditional on-premises
compute clusters. Our use case is atomistic simulations carried out
with the GROMACS molecular dynamics (MD) toolkit with a particular
focus on alchemical protein–ligand binding free energy calculations.
We set up a compute cluster in the Amazon Web Services (AWS) cloud
that incorporates various different instances with Intel, AMD, and
ARM CPUs, some with GPU acceleration. Using representative biomolecular
simulation systems, we benchmark how GROMACS performs on individual
instances and across multiple instances. Thereby we assess which instances
deliver the highest performance and which are the most cost-efficient
ones for our use case. We find that, in terms of total costs, including
hardware, personnel, room, energy, and cooling, producing MD trajectories
in the cloud can be about as cost-efficient as an on-premises cluster
given that optimal cloud instances are chosen. Further, we find that
high-throughput ligand-screening can be accelerated dramatically by
using global cloud resources. For a ligand screening study consisting
of 19 872 independent simulations or ∼200 μs of
combined simulation trajectory, we made use of diverse hardware available
in the cloud at the time of the study. The computations scaled-up
to reach peak performance using more than 4 000 instances,
140 000 cores, and 3 000 GPUs simultaneously. Our simulation
ensemble finished in about 2 days in the cloud, while weeks would
be required to complete the task on a typical on-premises cluster
consisting of several hundred nodes.
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Affiliation(s)
- Carsten Kutzner
- Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Christian Kniep
- Amazon Development Center Germany, Amazon Web Services, Krausenstr. 38, 10117 Berlin, Germany
| | - Austin Cherian
- Amazon Web Services Singapore Pte Ltd, 23 Church St, #10-01, Singapore 049481
| | - Ludvig Nordstrom
- Amazon Web Services, 60 Holborn Viaduct, London EC1A 2FD, United Kingdom
| | - Helmut Grubmüller
- Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Am Fassberg 11, 37077 Göttingen, Germany
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Rossetti GG, Ossorio MA, Rempel S, Kratzel A, Dionellis VS, Barriot S, Tropia L, Gorgulla C, Arthanari H, Thiel V, Mohr P, Gamboni R, Halazonetis TD. Non-covalent SARS-CoV-2 M pro inhibitors developed from in silico screen hits. Sci Rep 2022; 12:2505. [PMID: 35169179 PMCID: PMC8847420 DOI: 10.1038/s41598-022-06306-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/24/2022] [Indexed: 01/03/2023] Open
Abstract
Mpro, the main protease of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is essential for the viral life cycle. Accordingly, several groups have performed in silico screens to identify Mpro inhibitors that might be used to treat SARS-CoV-2 infections. We selected more than five hundred compounds from the top-ranking hits of two very large in silico screens for on-demand synthesis. We then examined whether these compounds could bind to Mpro and inhibit its protease activity. Two interesting chemotypes were identified, which were further evaluated by characterizing an additional five hundred synthesis on-demand analogues. The compounds of the first chemotype denatured Mpro and were considered not useful for further development. The compounds of the second chemotype bound to and enhanced the melting temperature of Mpro. The most active compound from this chemotype inhibited Mpro in vitro with an IC50 value of 1 μM and suppressed replication of the SARS-CoV-2 virus in tissue culture cells. Its mode of binding to Mpro was determined by X-ray crystallography, revealing that it is a non-covalent inhibitor. We propose that the inhibitors described here could form the basis for medicinal chemistry efforts that could lead to the development of clinically relevant inhibitors.
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Affiliation(s)
- Giacomo G Rossetti
- Department of Molecular Biology, University of Geneva, 1205, Geneva, Switzerland.,FoRx Therapeutics AG, 4056, Basel, Switzerland
| | - Marianna A Ossorio
- Department of Molecular Biology, University of Geneva, 1205, Geneva, Switzerland
| | | | - Annika Kratzel
- Institute of Virology and Immunology, University of Bern, 3012, Bern, Switzerland.,Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3012, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Vasilis S Dionellis
- Department of Molecular Biology, University of Geneva, 1205, Geneva, Switzerland
| | - Samia Barriot
- Department of Molecular Biology, University of Geneva, 1205, Geneva, Switzerland
| | - Laurence Tropia
- Department of Molecular Biology, University of Geneva, 1205, Geneva, Switzerland
| | - Christoph Gorgulla
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, Boston, MA, 02115, USA.,Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Haribabu Arthanari
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Volker Thiel
- Institute of Virology and Immunology, University of Bern, 3012, Bern, Switzerland.,Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3012, Bern, Switzerland
| | - Peter Mohr
- NANDASI Pharma Advisors, 4123, Allschwil, Switzerland
| | - Remo Gamboni
- NANDASI Pharma Advisors, 4123, Allschwil, Switzerland
| | - Thanos D Halazonetis
- Department of Molecular Biology, University of Geneva, 1205, Geneva, Switzerland. .,FoRx Therapeutics AG, 4056, Basel, Switzerland.
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41
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Luttens A, Gullberg H, Abdurakhmanov E, Vo DD, Akaberi D, Talibov VO, Nekhotiaeva N, Vangeel L, De Jonghe S, Jochmans D, Krambrich J, Tas A, Lundgren B, Gravenfors Y, Craig AJ, Atilaw Y, Sandström A, Moodie LWK, Lundkvist Å, van Hemert MJ, Neyts J, Lennerstrand J, Kihlberg J, Sandberg K, Danielson UH, Carlsson J. Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses. J Am Chem Soc 2022; 144:2905-2920. [PMID: 35142215 PMCID: PMC8848513 DOI: 10.1021/jacs.1c08402] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Drugs targeting SARS-CoV-2 could have saved millions of lives during the COVID-19 pandemic, and it is now crucial to develop inhibitors of coronavirus replication in preparation for future outbreaks. We explored two virtual screening strategies to find inhibitors of the SARS-CoV-2 main protease in ultralarge chemical libraries. First, structure-based docking was used to screen a diverse library of 235 million virtual compounds against the active site. One hundred top-ranked compounds were tested in binding and enzymatic assays. Second, a fragment discovered by crystallographic screening was optimized guided by docking of millions of elaborated molecules and experimental testing of 93 compounds. Three inhibitors were identified in the first library screen, and five of the selected fragment elaborations showed inhibitory effects. Crystal structures of target-inhibitor complexes confirmed docking predictions and guided hit-to-lead optimization, resulting in a noncovalent main protease inhibitor with nanomolar affinity, a promising in vitro pharmacokinetic profile, and broad-spectrum antiviral effect in infected cells.
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Affiliation(s)
- Andreas Luttens
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden
| | - Hjalmar Gullberg
- Science for Life Laboratory, Biochemical and Cellular Assay Facility, Drug Discovery and Development Platform, Department of Biochemistry and Biophysics, Stockholm University, Solna, SE-17121 Stockholm, Sweden
| | - Eldar Abdurakhmanov
- Science for Life Laboratory, Department of Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Duy Duc Vo
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden
| | - Dario Akaberi
- Department of Medical Biochemistry and Microbiology, Zoonosis Science Center, Uppsala University, SE-75123 Uppsala, Sweden
| | | | - Natalia Nekhotiaeva
- Science for Life Laboratory, Biochemical and Cellular Assay Facility, Drug Discovery and Development Platform, Department of Biochemistry and Biophysics, Stockholm University, Solna, SE-17121 Stockholm, Sweden
| | - Laura Vangeel
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Virology and Chemotherapy, 3000 Leuven, Belgium.,Global Virus Network, Baltimore, Maryland 21201, United States
| | - Steven De Jonghe
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Virology and Chemotherapy, 3000 Leuven, Belgium.,Global Virus Network, Baltimore, Maryland 21201, United States
| | - Dirk Jochmans
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Virology and Chemotherapy, 3000 Leuven, Belgium.,Global Virus Network, Baltimore, Maryland 21201, United States
| | - Janina Krambrich
- Department of Medical Biochemistry and Microbiology, Zoonosis Science Center, Uppsala University, SE-75123 Uppsala, Sweden
| | - Ali Tas
- Department of Medical Microbiology, Leiden University Medical Center, 2333ZA Leiden, The Netherlands
| | - Bo Lundgren
- Science for Life Laboratory, Biochemical and Cellular Assay Facility, Drug Discovery and Development Platform, Department of Biochemistry and Biophysics, Stockholm University, Solna, SE-17121 Stockholm, Sweden
| | - Ylva Gravenfors
- Science for Life Laboratory, Drug Discovery & Development Platform, Department of Organic Chemistry, Stockholm University, Solna, SE-17121 Stockholm, Sweden
| | - Alexander J Craig
- Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden
| | - Yoseph Atilaw
- Department of Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Anja Sandström
- Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden
| | - Lindon W K Moodie
- Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden.,Uppsala Antibiotic Centre, Uppsala University, SE-75123 Uppsala, Sweden
| | - Åke Lundkvist
- Department of Medical Biochemistry and Microbiology, Zoonosis Science Center, Uppsala University, SE-75123 Uppsala, Sweden
| | - Martijn J van Hemert
- Department of Medical Microbiology, Leiden University Medical Center, 2333ZA Leiden, The Netherlands
| | - Johan Neyts
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Virology and Chemotherapy, 3000 Leuven, Belgium.,Global Virus Network, Baltimore, Maryland 21201, United States
| | - Johan Lennerstrand
- Department of Medical Sciences, Section of Clinical Microbiology, Uppsala University, SE-75185 Uppsala, Sweden
| | - Jan Kihlberg
- Department of Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Kristian Sandberg
- Department of Medicinal Chemistry, Uppsala University, SE-75123 Uppsala, Sweden.,Department of Physiology and Pharmacology, Karolinska Institutet, SE-17177 Stockholm, Sweden.,Science for Life Laboratory, Drug Discovery & Development Platform, Uppsala Biomedical Center, Uppsala University, SE-75123 Uppsala, Sweden
| | - U Helena Danielson
- Science for Life Laboratory, Department of Chemistry-BMC, Uppsala University, SE-75123 Uppsala, Sweden
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-75124 Uppsala, Sweden
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42
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Clyde A, Galanie S, Kneller DW, Ma H, Babuji Y, Blaiszik B, Brace A, Brettin T, Chard K, Chard R, Coates L, Foster I, Hauner D, Kertesz V, Kumar N, Lee H, Li Z, Merzky A, Schmidt JG, Tan L, Titov M, Trifan A, Turilli M, Van Dam H, Chennubhotla SC, Jha S, Kovalevsky A, Ramanathan A, Head MS, Stevens R. High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor. J Chem Inf Model 2022; 62:116-128. [PMID: 34793155 PMCID: PMC8610012 DOI: 10.1021/acs.jcim.1c00851] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Indexed: 12/27/2022]
Abstract
Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 μM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple μs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.
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Affiliation(s)
- Austin Clyde
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- Department of Computer Science,
University of Chicago, Chicago, Illinois 60615,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Stephanie Galanie
- Biosciences Division, Oak Ridge National
Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Daniel W. Kneller
- Neutron Scattering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Heng Ma
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Yadu Babuji
- Department of Computer Science,
University of Chicago, Chicago, Illinois 60615,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Ben Blaiszik
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Alexander Brace
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- Department of Computer Science,
University of Chicago, Chicago, Illinois 60615,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Thomas Brettin
- Computing Environment and Life Sciences Directorate,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Kyle Chard
- Department of Computer Science,
University of Chicago, Chicago, Illinois 60615,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Ryan Chard
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- Department of Computer Science,
University of Chicago, Chicago, Illinois 60615,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Leighton Coates
- Neutron Scattering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Ian Foster
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- Department of Computer Science,
University of Chicago, Chicago, Illinois 60615,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Darin Hauner
- Computational Biology Group, Biological Science Division,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Vlimos Kertesz
- Neutron Scattering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Neeraj Kumar
- Computational Biology Group, Biological Science Division,
Pacific Northwest National Laboratory, Richland, Washington
99352, United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Hyungro Lee
- Department of Electrical and Computer Engineering,
Rutgers University, Piscataway, New Jersey 08854,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Zhuozhao Li
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- Department of Computer Science,
University of Chicago, Chicago, Illinois 60615,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Andre Merzky
- Department of Electrical and Computer Engineering,
Rutgers University, Piscataway, New Jersey 08854,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Jurgen G. Schmidt
- Bioscience Division, Los Alamos National
Laboratory, Los Alamos, New Mexico 87545, United
States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Li Tan
- Department of Electrical and Computer Engineering,
Rutgers University, Piscataway, New Jersey 08854,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Mikhail Titov
- Department of Electrical and Computer Engineering,
Rutgers University, Piscataway, New Jersey 08854,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Anda Trifan
- University of Illinois at
Urbana-Champaign, Champaign, Illinois 61820, United
States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Matteo Turilli
- Department of Electrical and Computer Engineering,
Rutgers University, Piscataway, New Jersey 08854,
United States
- Computational Science Initiative,
Brookhaven National Laboratory, Upton, New York 11973,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Hubertus Van Dam
- Computational Science Initiative,
Brookhaven National Laboratory, Upton, New York 11973,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Srinivas C. Chennubhotla
- Department of Computational and Systems
Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
15260, United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Shantenu Jha
- Department of Electrical and Computer Engineering,
Rutgers University, Piscataway, New Jersey 08854,
United States
- Computational Science Initiative,
Brookhaven National Laboratory, Upton, New York 11973,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Andrey Kovalevsky
- Second Target Station, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831, United
States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Arvind Ramanathan
- Data Science and Learning Division,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Martha S. Head
- Joint Institute for Biological Sciences,
Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
| | - Rick Stevens
- Department of Computer Science,
University of Chicago, Chicago, Illinois 60615,
United States
- Computing Environment and Life Sciences Directorate,
Argonne National Laboratory, Lemont, Illinois 60439,
United States
- National Virtual Biotechnology
Laboratory, Washington, District of Columbia 20585, United
States
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43
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Abstract
RNA viruses cause many routine illnesses, such as the common cold and the flu. Recently, more deadly diseases have emerged from this family of viruses. The hepatitis C virus has had a devastating impact worldwide. Despite the cures developed in the U.S. and Europe, economically disadvantaged countries remain afflicted by HCV infection due to the high cost of these medications. More recently, COVID-19 has swept across the world, killing millions and disrupting economies and lifestyles; the virus responsible for this pandemic is a coronavirus. Our understanding of HCV and SARS CoV-2 replication is still in its infancy. Helicases play a critical role in the replication, transcription and translation of viruses. These key enzymes need extensive study not only as an essential player in the viral lifecycle, but also as targets for antiviral therapeutics. In this review, we highlight the current knowledge for RNA helicases of high importance to human health.
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Affiliation(s)
- John C Marecki
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Binyam Belachew
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Jun Gao
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Kevin D Raney
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States.
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44
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Mehyar N, Mashhour A, Islam I, Alhadrami HA, Tolah AM, Alghanem B, Alkhaldi S, Somaie BA, Al Ghobain M, Alobaida Y, Alaskar AS, Boudjelal M. Discovery of Zafirlukast as a novel SARS-CoV-2 helicase inhibitor using in silico modelling and a FRET-based assay. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:963-983. [PMID: 34818959 DOI: 10.1080/1062936x.2021.1993995] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 10/12/2021] [Indexed: 06/13/2023]
Abstract
The coronavirus helicase is an essential enzyme required for viral replication/transcription pathways. Structural studies revealed a sulphate moiety that interacts with key residues within the nucleotide-binding site of the helicase. Compounds with a sulphoxide or a sulphone moiety could interfere with these interactions and consequently inhibit the enzyme. The molecular operating environment (MOE) was used to dock 189 sulphoxide and sulphone-containing FDA-approved compounds to the nucleotide-binding site. Zafirlukast, a leukotriene receptor antagonist used to treat chronic asthma, achieved the lowest docking score at -8.75 kcals/mol. The inhibitory effect of the compounds on the SARS-CoV-2 helicase dsDNA unwinding activity was tested by a FRET-based assay. Zafirlukast was the only compound to inhibit the enzyme (IC50 = 16.3 µM). The treatment of Vero E6 cells with 25 µM zafirlukast prior to SARS-CoV-2 infection decreased the cytopathic effects of SARS-CoV-2 significantly. These results suggest that zafirlukast alleviates SARS-CoV-2 pathogenicity by inhibiting the viral helicase and impairing the viral replication/transcription pathway. Zafirlukast could be clinically developed as a new antiviral treatment for SARS-CoV-2 and other coronavirus diseases. This discovery is based on molecular modelling, in vitro inhibition of the SARS-CoV helicase activity and cell-based SARS-CoV-2 viral replication.
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Affiliation(s)
- N Mehyar
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - A Mashhour
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - I Islam
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - H A Alhadrami
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia
- Molecular Diagnostic Laboratory, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
- Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - A M Tolah
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia
- Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - B Alghanem
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - S Alkhaldi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - B A Somaie
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - M Al Ghobain
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - Y Alobaida
- Sudair Pharmaceutical Co, Riyadh, Saudi Arabia
| | - A S Alaskar
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - M Boudjelal
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
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45
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Papaj K, Spychalska P, Hopko K, Kapica P, Fisher A, Lill MA, Bagrowska W, Nowak J, Szleper K, Smieško M, Kasprzycka A, Góra A. Investigation of Thiocarbamates as Potential Inhibitors of the SARS-CoV-2 Mpro. Pharmaceuticals (Basel) 2021; 14:1153. [PMID: 34832935 PMCID: PMC8621115 DOI: 10.3390/ph14111153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/04/2021] [Accepted: 11/08/2021] [Indexed: 11/29/2022] Open
Abstract
In the present study we tested, using the microscale thermophoresis technique, a small library of thionocarbamates, thiolocarbamates, sulfide and disulfide as potential lead compounds for SARS-CoV-2 Mpro drug design. The successfully identified binder is a representative of the thionocarbamates group with a high potential for future modifications aiming for higher affinity and solubility. The experimental analysis was extended by computational studies that show insufficient accuracy of the simplest and widely applied approaches and underline the necessity of applying more advanced methods to properly evaluate the affinity of potential SARS-CoV-2 Mpro binders.
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Affiliation(s)
- Katarzyna Papaj
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland; (K.P.); (P.K.); (W.B.); (K.S.)
| | - Patrycja Spychalska
- Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland; (P.S.); (K.H.); (A.K.)
| | - Katarzyna Hopko
- Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland; (P.S.); (K.H.); (A.K.)
| | - Patryk Kapica
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland; (K.P.); (P.K.); (W.B.); (K.S.)
| | - Andre Fisher
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland; (A.F.); (M.A.L.); (M.S.)
| | - Markus A. Lill
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland; (A.F.); (M.A.L.); (M.S.)
| | - Weronika Bagrowska
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland; (K.P.); (P.K.); (W.B.); (K.S.)
| | - Jakub Nowak
- Department of Physical Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, 30-387 Krakow, Poland;
| | - Katarzyna Szleper
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland; (K.P.); (P.K.); (W.B.); (K.S.)
| | - Martin Smieško
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland; (A.F.); (M.A.L.); (M.S.)
| | - Anna Kasprzycka
- Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland; (P.S.); (K.H.); (A.K.)
- Department of Chemistry, Silesian University of Technology, M. Strzody 9, 44-100 Gliwice, Poland
| | - Artur Góra
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, Krzywoustego 8, 44-100 Gliwice, Poland; (K.P.); (P.K.); (W.B.); (K.S.)
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46
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Yang Y, Yao K, Repasky MP, Leswing K, Abel R, Shoichet BK, Jerome SV. Efficient Exploration of Chemical Space with Docking and Deep Learning. J Chem Theory Comput 2021; 17:7106-7119. [PMID: 34592101 DOI: 10.1021/acs.jctc.1c00810] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
With the advent of make-on-demand commercial libraries, the number of purchasable compounds available for virtual screening and assay has grown explosively in recent years, with several libraries eclipsing one billion compounds. Today's screening libraries are larger and more diverse, enabling the discovery of more-potent hit compounds and unlocking new areas of chemical space, represented by new core scaffolds. Applying physics-based in silico screening methods in an exhaustive manner, where every molecule in the library must be enumerated and evaluated independently, is increasingly cost-prohibitive. Here, we introduce a protocol for machine learning-enhanced molecular docking based on active learning to dramatically increase throughput over traditional docking. We leverage a novel selection protocol that strikes a balance between two objectives: (1) identifying the best scoring compounds and (2) exploring a large region of chemical space, demonstrating superior performance compared to a purely greedy approach. Together with automated redocking of the top compounds, this method captures almost all the high scoring scaffolds in the library found by exhaustive docking. This protocol is applied to our recent virtual screening campaigns against the D4 and AMPC targets that produced dozens of highly potent, novel inhibitors, and a blind test against the MT1 target. Our protocol recovers more than 80% of the experimentally confirmed hits with a 14-fold reduction in compute cost, and more than 90% of the hit scaffolds in the top 5% of model predictions, preserving the diversity of the experimentally confirmed hit compounds.
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Affiliation(s)
- Ying Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Kun Yao
- Schrödinger, Inc., 120 West 45th Street, 17th Floor, New York, New York 10036, United States
| | - Matthew P Repasky
- Schrödinger, Inc., 101 SW Main Street, #1300, Portland, Oregon 97239, United States
| | - Karl Leswing
- Schrödinger, Inc., 120 West 45th Street, 17th Floor, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 120 West 45th Street, 17th Floor, New York, New York 10036, United States
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Steven V Jerome
- Schrödinger, Inc., 10201 Wateridge Cir Suite 220, San Diego, California 92121, United States
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47
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Barabas ME, Chakrabarti R, Muzzio M. One common enemy, a pandemic, uniting interdisciplinary teams. iScience 2021; 24:102992. [PMID: 34485871 PMCID: PMC8407854 DOI: 10.1016/j.isci.2021.102992] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
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48
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Schlick T, Portillo-Ledesma S. Biomolecular modeling thrives in the age of technology. NATURE COMPUTATIONAL SCIENCE 2021; 1:321-331. [PMID: 34423314 PMCID: PMC8378674 DOI: 10.1038/s43588-021-00060-9] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/22/2021] [Indexed: 12/12/2022]
Abstract
The biomolecular modeling field has flourished since its early days in the 1970s due to the rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase in size and timespan of biomolecular simulations has outpaced Moore's law. Here, we discuss the role of knowledge-based versus physics-based methods and hardware versus software advances in propelling the field forward. This rapid adaptation and outreach suggests a bright future for modeling, where theory, experimentation and simulation define three pillars needed to address future scientific and biomedical challenges.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, NY, USA
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
- New York University–East China Normal University Center for Computational Chemistry at New York University Shanghai, Shanghai, China
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49
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Singh N, Villoutreix BO. Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises. Comput Struct Biotechnol J 2021; 19:2537-2548. [PMID: 33936562 PMCID: PMC8074526 DOI: 10.1016/j.csbj.2021.04.059] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 12/11/2022] Open
Abstract
There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.
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Affiliation(s)
- Natesh Singh
- Université de Paris, Inserm UMR 1141 NeuroDiderot, Robert-Debré Hospital, 75019 Paris, France
| | - Bruno O. Villoutreix
- Université de Paris, Inserm UMR 1141 NeuroDiderot, Robert-Debré Hospital, 75019 Paris, France
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Singh A, Steinkellner G, Köchl K, Gruber K, Gruber CC. Serine 477 plays a crucial role in the interaction of the SARS-CoV-2 spike protein with the human receptor ACE2. Sci Rep 2021; 11:4320. [PMID: 33619331 PMCID: PMC7900180 DOI: 10.1038/s41598-021-83761-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 02/01/2021] [Indexed: 02/07/2023] Open
Abstract
Since the worldwide outbreak of the infectious disease COVID-19, several studies have been published to understand the structural mechanism of the novel coronavirus SARS-CoV-2. During the infection process, the SARS-CoV-2 spike (S) protein plays a crucial role in the receptor recognition and cell membrane fusion process by interacting with the human angiotensin-converting enzyme 2 (hACE2) receptor. However, new variants of these spike proteins emerge as the virus passes through the disease reservoir. This poses a major challenge for designing a potent antigen for an effective immune response against the spike protein. Through a normal mode analysis (NMA) we identified the highly flexible region in the receptor binding domain (RBD) of SARS-CoV-2, starting from residue 475 up to residue 485. Structurally, the position S477 shows the highest flexibility among them. At the same time, S477 is hitherto the most frequently exchanged amino acid residue in the RBDs of SARS-CoV-2 mutants. Therefore, using MD simulations, we have investigated the role of S477 and its two frequent mutations (S477G and S477N) at the RBD during the binding to hACE2. We found that the amino acid exchanges S477G and S477N strengthen the binding of the SARS-COV-2 spike with the hACE2 receptor.
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Affiliation(s)
- Amit Singh
- Institute of Molecular Bioscience, University of Graz, 8010, Graz, Austria
| | - Georg Steinkellner
- Institute of Molecular Bioscience, University of Graz, 8010, Graz, Austria
- Innophore GmbH, 8010, Graz, Austria
| | | | - Karl Gruber
- Institute of Molecular Bioscience, University of Graz, 8010, Graz, Austria.
- Field of Excellence BioHealth - University of Graz, 8010, Graz, Austria.
- Austrian Centre of Industrial Biotechnology, 8010, Graz, Austria.
| | - Christian C Gruber
- Institute of Molecular Bioscience, University of Graz, 8010, Graz, Austria.
- Innophore GmbH, 8010, Graz, Austria.
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