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Bianco G, Holcomb M, Santos-Martins D, Tillack A, Hansel-Harris A, Forli S. Reactive Docking: A Computational Method for High-Throughput Virtual Screenings of Reactive Species. J Chem Inf Model 2023; 63:5631-5640. [PMID: 37639635 PMCID: PMC10756071 DOI: 10.1021/acs.jcim.3c00832] [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: 08/31/2023]
Abstract
We describe the formalization of the reactive docking protocol, a method developed to model and predict reactions between small molecules and biological macromolecules. The method has been successfully used in a number of applications already, including recapitulating large proteomics data sets, performing structure-reactivity target optimizations, and prospective virtual screenings. By modeling a near-attack conformation-like state, no QM calculations are required to model the ligand and receptor geometries. Here, we present its generalization using a large data set containing more than 400 ligand-target complexes, 8 nucleophilic modifiable residue types, and more than 30 warheads. The method correctly predicts the modified residue in ∼85% of complexes and shows enrichments comparable to standard focused virtual screenings in ranking ligands. This performance supports this approach for the docking and screening of reactive ligands in virtual chemoproteomics and drug design campaigns.
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Affiliation(s)
- Giulia Bianco
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, 10550 N. Torrey Pines, La Jolla, CA 92037-1000, USA
| | - Matthew Holcomb
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, 10550 N. Torrey Pines, La Jolla, CA 92037-1000, USA
| | - Diogo Santos-Martins
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, 10550 N. Torrey Pines, La Jolla, CA 92037-1000, USA
| | - Andreas Tillack
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, 10550 N. Torrey Pines, La Jolla, CA 92037-1000, USA
| | - Althea Hansel-Harris
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, 10550 N. Torrey Pines, La Jolla, CA 92037-1000, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research Institute, 10550 N. Torrey Pines, La Jolla, CA 92037-1000, USA
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2
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Huang X, Liu Y, Wang Q, Rehman HM, Horváth D, Zhou S, Fu R, Zhang L, Szöllősi AG, Li Z. Brief literature review and comprehensive bioinformatics analytics unravel the potential mechanism of curcumin in the treatment of periodontitis. BMC Oral Health 2023; 23:469. [PMID: 37422651 PMCID: PMC10329799 DOI: 10.1186/s12903-023-03181-x] [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: 01/26/2023] [Accepted: 06/28/2023] [Indexed: 07/10/2023] Open
Abstract
OBJECTIVE Periodontitis is a chronic oral disease prevalent worldwide, and natural products are recommended as adjunctive therapy due to their minor side effects. Curcumin, a widely used ancient compound, has been reported to possess therapeutic effects in periodontitis. However, the exact mechanism underlying its activity remains unclear. In this context, the present study aimed to conduct computational simulations to uncover the potential mechanism of action of Curcumin in the treatment of periodontitis. MATERIALS AND METHODS Single-cell analysis was conducted using a dataset (i.e., GSE164241) curated from the Gene Expression Omnibus (GEO) database through an R package "Seurat package." Bulk RNA sequencing data were curated from GSE10334 and GSE16134 and processed by R package "Limma." Then, the marker genes in the single-cell transcriptome and differentially expressed genes (DEGs) in the bulk transcriptome were integrated. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were also carried out to reveal their functionalities. Key targets were mined from their protein-protein interaction (PPI) network topologically. Afterward, molecular docking was performed. The top-ranked pose was subjected to molecular dynamics simulations to investigate the stability of the docking result. RESULTS FOS, CXCL1, CXCL8, and IL1B, were filtered after a series of selected processes. The results of molecular modeling suggested that except for IL1B, the Vena Scores of the rest exceeded -5 kcal/mol. Furthermore, the molecular dynamic simulation indicated that the binding of the CXCL8-Curcumin complex was stable over the entire 100 ns simulation. CONCLUSION The present study unlocked the binding modes of CXCL1, FOS, and CXCL8 with the Curcumin molecule, which were relatively stable, especially for CXCL8, hindering its promising potential to serve as the critical targets of Curcumin in periodontitis treatment.
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Affiliation(s)
- Xufeng Huang
- Faculty of Dentistry, University of Debrecen, Debrecen, Hungary
- Department of Immunology, University of Debrecen, Debrecen, Hungary
| | - Ying Liu
- Department of Cardiology, Sixth Medical Center, PLA General Hospital, Beijing, China
| | - Qi Wang
- Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, China
| | - Hafiz Muzzammel Rehman
- School of Biochemistry and Biotechnology, University of the Punjab, LahorePunjab, 54590 Pakistan
- Alnoorians Group of Institutes, 55-Elahi Bukhsh Park, Amir Road, Shad Bagh, Lahore, 54000 Pakistan
| | - Dorottya Horváth
- Department of Immunology, University of Debrecen, Debrecen, Hungary
| | - Shujing Zhou
- Department of Immunology, University of Debrecen, Debrecen, Hungary
| | - Rao Fu
- Department of Oral and Maxillofacial-Head and Neck Oncology, College of Stomatology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology and National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Ling Zhang
- Department of Oral and Maxillofacial-Head and Neck Oncology, College of Stomatology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology and National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | | | - Zhengrui Li
- Department of Oral and Maxillofacial-Head and Neck Oncology, College of Stomatology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology and National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
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3
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Jin S, Qian K, He L, Zhang Z. iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants. INSECTS 2023; 14:560. [PMID: 37367376 DOI: 10.3390/insects14060560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/28/2023] [Accepted: 06/09/2023] [Indexed: 06/28/2023]
Abstract
The use of insect-specific odorants to control the behavior of insects has always been a hot spot in research on "green" control strategies of insects. However, it is generally time-consuming and laborious to explore insect-specific odorants with traditional reverse chemical ecology methods. Here, an insect odorant receptor (OR) and ligand database website (iORandLigandDB) was developed for the specific exploration of insect-specific odorants by using deep learning algorithms. The website provides a range of specific odorants before molecular biology experiments as well as the properties of ORs in closely related insects. At present, the existing three-dimensional structures of ORs in insects and the docking data with related odorants can be retrieved from the database and further analyzed.
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Affiliation(s)
- Shuo Jin
- College of Plant Protection, Southwest University, Chongqing 400716, China
| | - Kun Qian
- College of Plant Protection, Southwest University, Chongqing 400716, China
| | - Lin He
- College of Plant Protection, Southwest University, Chongqing 400716, China
| | - Zan Zhang
- College of Plant Protection, Southwest University, Chongqing 400716, China
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Mészáros B, Park E, Malinverni D, Sejdiu BI, Immadisetty K, Sandhu M, Lang B, Babu MM. Recent breakthroughs in computational structural biology harnessing the power of sequences and structures. Curr Opin Struct Biol 2023; 80:102608. [PMID: 37182396 DOI: 10.1016/j.sbi.2023.102608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/16/2023]
Abstract
Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.
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Affiliation(s)
- Bálint Mészáros
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| | - Electa Park
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| | - Duccio Malinverni
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/DucMalinverni
| | - Besian I Sejdiu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/bisejdiu
| | - Kalyan Immadisetty
- Department of Bone Marrow Transplantation & Cellular Therapy, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/k_immadisetty
| | - Manbir Sandhu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/M5andhu
| | - Benjamin Lang
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/langbnj
| | - M Madan Babu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
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Tang S, Chen R, Lin M, Lin Q, Zhu Y, Ding J, Hu H, Ling M, Wu J. Accelerating AutoDock Vina with GPUs. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27093041. [PMID: 35566391 PMCID: PMC9103882 DOI: 10.3390/molecules27093041] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/01/2022] [Accepted: 05/02/2022] [Indexed: 11/23/2022]
Abstract
AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.
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Affiliation(s)
- Shidi Tang
- School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (S.T.); (J.D.)
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Ruiqi Chen
- VeriMake Research, Nanjing Renmian Integrated Circuit Technology Co., Ltd., Nanjing 210088, China; (R.C.); (M.L.); (Y.Z.)
| | - Mengru Lin
- VeriMake Research, Nanjing Renmian Integrated Circuit Technology Co., Ltd., Nanjing 210088, China; (R.C.); (M.L.); (Y.Z.)
| | - Qingde Lin
- National ASIC System Engineering Technology Research Center, Southeast University, Nanjing 210096, China; (Q.L.); (M.L.)
| | - Yanxiang Zhu
- VeriMake Research, Nanjing Renmian Integrated Circuit Technology Co., Ltd., Nanjing 210088, China; (R.C.); (M.L.); (Y.Z.)
| | - Ji Ding
- School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (S.T.); (J.D.)
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Haifeng Hu
- School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
| | - Ming Ling
- National ASIC System Engineering Technology Research Center, Southeast University, Nanjing 210096, China; (Q.L.); (M.L.)
| | - Jiansheng Wu
- School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (S.T.); (J.D.)
- Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- Correspondence:
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Solis-Vasquez L, Tillack AF, Santos-Martins D, Koch A, LeGrand S, Forli S. Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking. PARALLEL COMPUTING 2022; 109:102861. [PMID: 34898769 PMCID: PMC8654209 DOI: 10.1016/j.parco.2021.102861] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.
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Affiliation(s)
- Leonardo Solis-Vasquez
- Embedded Systems and Applications Group. Technical University of Darmstadt, Darmstadt, Germany
- Hochschulstr. 10, D-64289, Darmstadt, Germany
| | - Andreas F. Tillack
- Department of Integrative Structural and Computational Biology. The Scripps Research Institute, La Jolla, CA, United States
| | - Diogo Santos-Martins
- Department of Integrative Structural and Computational Biology. The Scripps Research Institute, La Jolla, CA, United States
| | - Andreas Koch
- Embedded Systems and Applications Group. Technical University of Darmstadt, Darmstadt, Germany
| | | | - Stefano Forli
- Department of Integrative Structural and Computational Biology. The Scripps Research Institute, La Jolla, CA, United States
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7
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Abstract
Abstract
Macrocycles represent an important class of ligands, both in natural products and designed drugs. In drug design, macrocyclizations can impart specific ligand conformations and contribute to passive permeation by encouraging intramolecular H-bonds. AutoDock-GPU and Vina can model macrocyclic ligands flexibly, without requiring the enumeration of macrocyclic conformers before docking. Here, we characterize the performance of the method for handling macrocyclic compounds, which is implemented and the default behaviour for ligand preparation with our ligand preparation pipeline, Meeko. A pseudoatom is used to encode bond geometry and produce an anisotropic closure force for macrocyclic rings. This method is evaluated on a diverse set of small molecule and peptide macrocycles, ranging from 7- to 33-membered rings, showing little accuracy loss compared to rigid redocking of the X-ray macrocycle conformers. This suggests that for conformationally flexible macrocycles with unknown binding modes, this method can be effectively used to predict the macrocycle conformation.
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8
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Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J Chem Inf Model 2021; 61:3891-3898. [PMID: 34278794 PMCID: PMC10683950 DOI: 10.1021/acs.jcim.1c00203] [Citation(s) in RCA: 1463] [Impact Index Per Article: 487.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.
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Affiliation(s)
- Jerome Eberhardt
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, 92037 California, United States
| | - Diogo Santos-Martins
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, 92037 California, United States
| | - Andreas F Tillack
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, 92037 California, United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, 92037 California, United States
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9
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Santos-Martins D, Solis-Vasquez L, Tillack AF, Sanner MF, Koch A, Forli S. Accelerating AutoDock4 with GPUs and Gradient-Based Local Search. J Chem Theory Comput 2021; 17:1060-1073. [PMID: 33403848 DOI: 10.1021/acs.jctc.0c01006] [Citation(s) in RCA: 114] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.
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Affiliation(s)
- Diogo Santos-Martins
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Leonardo Solis-Vasquez
- Embedded Systems and Applications Group, Technical University of Darmstadt, Darmstadt D-64289, Germany
| | - Andreas F Tillack
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Michel F Sanner
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Andreas Koch
- Embedded Systems and Applications Group, Technical University of Darmstadt, Darmstadt D-64289, Germany
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
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10
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Vermaas JV, Sedova A, Baker MB, Boehm S, Rogers DM, Larkin J, Glaser J, Smith MD, Hernandez O, Smith JC. Supercomputing Pipelines Search for Therapeutics Against COVID-19. Comput Sci Eng 2021; 23:7-16. [PMID: 35939280 PMCID: PMC9280802 DOI: 10.1109/mcse.2020.3036540] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/31/2020] [Accepted: 11/03/2020] [Indexed: 11/15/2022]
Abstract
The urgent search for drugs to combat SARS-CoV-2 has included the use of supercomputers. The use of general-purpose graphical processing units (GPUs), massive parallelism, and new software for high-performance computing (HPC) has allowed researchers to search the vast chemical space of potential drugs faster than ever before. We developed a new drug discovery pipeline using the Summit supercomputer at Oak Ridge National Laboratory to help pioneer this effort, with new platforms that incorporate GPU-accelerated simulation and allow for the virtual screening of billions of potential drug compounds in days compared to weeks or months for their ability to inhibit SARS-COV-2 proteins. This effort will accelerate the process of developing drugs to combat the current COVID-19 pandemic and other diseases.
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Affiliation(s)
| | - Ada Sedova
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | - Swen Boehm
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | | | - Jens Glaser
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
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11
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Goodsell DS, Sanner MF, Olson AJ, Forli S. The AutoDock suite at 30. Protein Sci 2020; 30:31-43. [PMID: 32808340 DOI: 10.1002/pro.3934] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/06/2020] [Accepted: 08/11/2020] [Indexed: 12/13/2022]
Abstract
The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.,Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Michel F Sanner
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
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