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Sprenger HG, Mittenbühler MJ, Sun Y, Van Vranken JG, Schindler S, Jayaraj A, Khetarpal SA, Vargas-Castillo A, Puszynska AM, Spinelli JB, Armani A, Kunchok T, Ryback B, Seo HS, Song K, Sebastian L, O'Young C, Braithwaite C, Dhe-Paganon S, Burger N, Mills EL, Gygi SP, Arthanari H, Chouchani ET, Sabatini DM, Spiegelman BM. Ergothioneine boosts mitochondrial respiration and exercise performance via direct activation of MPST. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588849. [PMID: 38645260 PMCID: PMC11030429 DOI: 10.1101/2024.04.10.588849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Ergothioneine (EGT) is a diet-derived, atypical amino acid that accumulates to high levels in human tissues. Reduced EGT levels have been linked to age-related disorders, including neurodegenerative and cardiovascular diseases, while EGT supplementation is protective in a broad range of disease and aging models in mice. Despite these promising data, the direct and physiologically relevant molecular target of EGT has remained elusive. Here we use a systematic approach to identify how mitochondria remodel their metabolome in response to exercise training. From this data, we find that EGT accumulates in muscle mitochondria upon exercise training. Proteome-wide thermal stability studies identify 3-mercaptopyruvate sulfurtransferase (MPST) as a direct molecular target of EGT; EGT binds to and activates MPST, thereby boosting mitochondrial respiration and exercise training performance in mice. Together, these data identify the first physiologically relevant EGT target and establish the EGT-MPST axis as a molecular mechanism for regulating mitochondrial function and exercise performance.
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McGibbon M, Money-Kyrle S, Blay V, Houston DR. SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation. J Adv Res 2022; 46:135-147. [PMID: 35901959 PMCID: PMC10105235 DOI: 10.1016/j.jare.2022.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 07/08/2022] [Accepted: 07/09/2022] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION The discovery of a new drug is a costly and lengthy endeavour. The computational prediction of which small molecules can bind to a protein target can accelerate this process if the predictions are fast and accurate enough. Recent machine-learning scoring functions re-evaluate the output of molecular docking to achieve more accurate predictions. However, previous scoring functions were trained on crystalised protein-ligand complexes and datasets of decoys. The limited availability of crystal structures and biases in the decoy datasets can lower the performance of scoring functions. OBJECTIVES To address key limitations of previous scoring functions and thus improve the predictive performance of structure-based virtual screening. METHODS A novel machine-learning scoring function was created, named SCORCH (Scoring COnsensus for RMSD-based Classification of Hits). To develop SCORCH, training data is augmented by considering multiple ligand poses and labelling poses based on their RMSD from the native pose. Decoy bias is addressed by generating property-matched decoys for each ligand and using the same methodology for preparing and docking decoys and ligands. A consensus of 3 different machine learning approaches is also used to improve performance. RESULTS We find that multi-pose augmentation in SCORCH improves its docking power and screening power on independent benchmark datasets. SCORCH outperforms an equivalent scoring function trained on single poses, with a 1% enrichment factor (EF) of 13.78 vs. 10.86 on 18 DEKOIS 2.0 targets and a mean native pose rank of 5.9 vs 30.4 on CSAR 2014. Additionally, SCORCH outperforms widely used scoring functions in virtual screening and pose prediction on independent benchmark datasets. CONCLUSION By rationally addressing key limitations of previous scoring functions, SCORCH improves the performance of virtual screening. SCORCH also provides an estimate of its uncertainty, which can help reduce the cost and time required for drug discovery.
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Affiliation(s)
- Miles McGibbon
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK
| | - Sam Money-Kyrle
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK
| | - Vincent Blay
- Department of Microbiology and Environmental Toxicology, University of California at Santa Cruz, Santa Cruz, CA 95064, USA; Institute for Integrative Systems Biology (I(2)SysBio), Universitat de València and Spanish Research Council (CSIC), 46980 Valencia, Spain.
| | - Douglas R Houston
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK.
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Structure Prediction, Evaluation, and Validation of GPR18 Lipid Receptor Using Free Programs. Int J Mol Sci 2022; 23:ijms23147917. [PMID: 35887268 PMCID: PMC9319093 DOI: 10.3390/ijms23147917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/04/2022] [Accepted: 07/08/2022] [Indexed: 11/30/2022] Open
Abstract
The GPR18 receptor, often referred to as the N-arachidonylglycine receptor, although assigned (along with GPR55 and GPR119) to the new class A GPCR subfamily-lipid receptors, officially still has the status of a class A GPCR orphan. While its signaling pathways and biological significance have not yet been fully elucidated, increasing evidence points to the therapeutic potential of GPR18 in relation to immune, neurodegenerative, and cancer processes to name a few. Therefore, it is necessary to understand the interactions of potential ligands with the receptor and the influence of particular structural elements on their activity. Thus, given the lack of an experimentally solved structure, the goal of the present study was to obtain a homology model of the GPR18 receptor in the inactive state, meeting all requirements in terms of protein structure quality and recognition of active ligands. To increase the reliability and precision of the predictions, different contemporary protein structure prediction methods and software were used and compared herein. To test the usability of the resulting models, we optimized and compared the selected structures followed by the assessment of the ability to recognize known, active ligands. The stability of the predicted poses was then evaluated by means of molecular dynamics simulations. On the other hand, most of the best-ranking contemporary CADD software/platforms for its full usability require rather expensive licenses. To overcome this down-to-earth obstacle, the overarching goal of these studies was to test whether it is possible to perform the thorough CADD experiments with high scientific confidence while using only license-free/academic software and online platforms. The obtained results indicate that a wide range of freely available software and/or academic licenses allow us to carry out meaningful molecular modelling/docking studies.
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Li C, Li J, Sun J, Mao L, Palade V, Ahmad B. Parallel multi-swarm cooperative particle swarm optimization for protein-ligand docking and virtual screening. BMC Bioinformatics 2022; 23:201. [PMID: 35637537 PMCID: PMC9150318 DOI: 10.1186/s12859-022-04711-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 05/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A high-quality docking method tends to yield multifold gains with half pains for the new drug development. Over the past few decades, great efforts have been made for the development of novel docking programs with great efficiency and intriguing accuracy. AutoDock Vina (Vina) is one of these achievements with improved speed and accuracy compared to AutoDock4. Since it was proposed, some of its variants, such as PSOVina and GWOVina, have also been developed. However, for all these docking programs, there is still large room for performance improvement. RESULTS In this work, we propose a parallel multi-swarm cooperative particle swarm model, in which one master swarm and several slave swarms mutually cooperate and co-evolve. Our experiments show that multi-swarm programs possess better docking robustness than PSOVina. Moreover, the multi-swarm program based on random drift PSO can achieve the best highest accuracy of protein-ligand docking, an outstanding enrichment effect for drug-like activate compounds, and the second best AUC screening accuracy among all the compared docking programs, but with less computation consumption than most of the other docking programs. CONCLUSION The proposed multi-swarm cooperative model is a novel algorithmic modeling suitable for protein-ligand docking and virtual screening. Owing to the existing coevolution between the master and the slave swarms, this model in parallel generates remarkable docking performance. The source code can be freely downloaded from https://github.com/li-jin-xing/MPSOVina .
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Affiliation(s)
- Chao Li
- Department of Computer Science and Technology, Jiangnan University, No.1800, Lihu Avenue, Wuxi, Jiangsu, People's Republic of China
| | - Jinxing Li
- Department of Computer Science and Technology, Jiangnan University, No.1800, Lihu Avenue, Wuxi, Jiangsu, People's Republic of China
| | - Jun Sun
- Department of Computer Science and Technology, Jiangnan University, No.1800, Lihu Avenue, Wuxi, Jiangsu, People's Republic of China.
| | - Li Mao
- Department of Computer Science and Technology, Jiangnan University, No.1800, Lihu Avenue, Wuxi, Jiangsu, People's Republic of China
| | - Vasile Palade
- Centre for Computational Science and Mathematical Modelling, Coventry University, Priory Street, Coventry, CV1 5FB, UK
| | - Bilal Ahmad
- Department of Computer Science and Technology, Jiangnan University, No.1800, Lihu Avenue, Wuxi, Jiangsu, People's Republic of China
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Sasaki-Tanaka R, Nagulapalli Venkata KC, Okamoto H, Moriyama M, Kanda T. Evaluation of Potential Anti-Hepatitis A Virus 3C Protease Inhibitors Using Molecular Docking. Int J Mol Sci 2022; 23:6044. [PMID: 35682728 PMCID: PMC9181686 DOI: 10.3390/ijms23116044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 12/05/2022] Open
Abstract
Hepatitis A virus (HAV) infection is a major cause of acute hepatitis worldwide and occasionally causes acute liver failure and can lead to death in the absence of liver transplantation. Although HAV vaccination is available, the prevalence of HAV vaccination is not adequate in some countries. Additionally, the improvements in public health reduced our immunity to HAV infection. These situations motivated us to develop potentially new anti-HAV therapeutic options. We carried out the in silico screening of anti-HAV compounds targeting the 3C protease enzyme using the Schrodinger Modeling software from the antiviral library of 25,000 compounds to evaluate anti-HAV 3C protease inhibitors. Additionally, in vitro studies were introduced to examine the inhibitory effects of HAV subgenomic replicon replication and HAV HA11-1299 genotype IIIA replication in hepatoma cell lines using luciferase assays and real-time RT-PCR. In silico studies enabled us to identify five lead candidates with optimal binding interactions in the active site of the target HAV 3C protease using the Schrodinger Glide program. In vitro studies substantiated our hypothesis from in silico findings. One of our lead compounds, Z10325150, showed 47% inhibitory effects on HAV genotype IB subgenomic replicon replication and 36% inhibitory effects on HAV genotype IIIA HA11-1299 replication in human hepatoma cell lines, with no cytotoxic effects at concentrations of 100 μg/mL. The effects of the combination therapy of Z10325150 and RNA-dependent RNA polymerase inhibitor, favipiravir on HAV genotype IB HM175 subgenomic replicon replication and HAV genotype IIIA HA11-1299 replication showed 64% and 48% inhibitory effects of HAV subgenomic replicon and HAV replication, respectively. We identified the HAV 3C protease inhibitor Z10325150 through in silico screening and confirmed the HAV replication inhibitory activity in human hepatocytes. Z10325150 may offer the potential for a useful HAV inhibitor in severe hepatitis A.
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Affiliation(s)
- Reina Sasaki-Tanaka
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku, Tokyo 173-8610, Japan; (M.M.); (T.K.)
| | - Kalyan C. Nagulapalli Venkata
- Department of Pharmaceutical and Administrative Sciences, Saint Louis College of Pharmacy, University of Health Sciences and Pharmacy, St. Louis, MO 63010, USA;
| | - Hiroaki Okamoto
- Division of Virology, Department of Infection and Immunity, Jichi Medical University School of Medicine, 3311-1 Yakushiji, Shimotsuke-shi, Tochigi 329-0498, Japan;
| | - Mitsuhiko Moriyama
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku, Tokyo 173-8610, Japan; (M.M.); (T.K.)
| | - Tatsuo Kanda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchi-kamicho, Itabashi-ku, Tokyo 173-8610, Japan; (M.M.); (T.K.)
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Scafuri B, Verdino A, D'Arminio N, Marabotti A. Computational methods to assist in the discovery of pharmacological chaperones for rare diseases. Brief Bioinform 2022; 23:6590149. [PMID: 35595532 DOI: 10.1093/bib/bbac198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/13/2022] [Accepted: 04/28/2022] [Indexed: 12/21/2022] Open
Abstract
Pharmacological chaperones are chemical compounds able to bind proteins and stabilize them against denaturation and following degradation. Some pharmacological chaperones have been approved, or are under investigation, for the treatment of rare inborn errors of metabolism, caused by genetic mutations that often can destabilize the structure of the wild-type proteins expressed by that gene. Given that, for rare diseases, there is a general lack of pharmacological treatments, many expectations are poured out on this type of compounds. However, their discovery is not straightforward. In this review, we would like to focus on the computational methods that can assist and accelerate the search for these compounds, showing also examples in which these methods were successfully applied for the discovery of promising molecules belonging to this new category of pharmacologically active compounds.
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Affiliation(s)
- Bernardina Scafuri
- Department of Chemistry and Biology "A. Zambelli", University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
| | - Anna Verdino
- Department of Chemistry and Biology "A. Zambelli", University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
| | - Nancy D'Arminio
- Department of Chemistry and Biology "A. Zambelli", University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
| | - Anna Marabotti
- Department of Chemistry and Biology "A. Zambelli", University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy
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Li J, Li C, Sun J, Palade V. RDPSOVina: the random drift particle swarm optimization for protein–ligand docking. J Comput Aided Mol Des 2022; 36:415-425. [DOI: 10.1007/s10822-022-00455-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 04/20/2022] [Indexed: 11/25/2022]
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Li C, Sun J, Palade V. MSLDOCK: Multi-Swarm Optimization for Flexible Ligand Docking and Virtual Screening. J Chem Inf Model 2021; 61:1500-1515. [PMID: 33657798 DOI: 10.1021/acs.jcim.0c01358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Autodock and its various variants are widely utilized docking approaches, which adopt optimization methods as search algorithms for flexible ligand docking and virtual screening. However, many of them have their limitations, such as poor accuracy for dockings with highly flexible ligands and low docking efficiency. In this paper, a multi-swarm optimization algorithm integrated with Autodock environment is proposed to design a high-performance and high-efficiency docking program, namely, MSLDOCK. The search algorithm is a combination of the random drift particle swarm optimization with a novel multi-swarm strategy and the Solis and Wets local search method with a modified implementation. Due to the algorithm's structure, MSLDOCK also has a multithread mode. The experimental results reveal that MSLDOCK outperforms other two Autodock-based approaches in many aspects, such as self-docking, cross-docking, and virtual screening accuracies as well as docking efficiency. Moreover, compared with three non-Autodock-based docking programs, MSLDOCK can be a reliable choice for self-docking and virtual screening, especially for dealing with highly flexible ligand docking problems. The source code of MSLDOCK can be downloaded for free from https://github.com/lcmeteor/MSLDOCK.
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Affiliation(s)
- Chao Li
- Department of Computer Science and Technology, Jiangnan University, No.1800, Lihu Avenue, Wuxi, Jiangsu 214122, PR China
| | - Jun Sun
- Department of Computer Science and Technology, Jiangnan University, No.1800, Lihu Avenue, Wuxi, Jiangsu 214122, PR China
| | - Vasile Palade
- Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry CV1 5FB, U.K
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Gorgulla C, Padmanabha Das KM, Leigh KE, Cespugli M, Fischer PD, Wang ZF, Tesseyre G, Pandita S, Shnapir A, Calderaio A, Gechev M, Rose A, Lewis N, Hutcheson C, Yaffe E, Luxenburg R, Herce HD, Durmaz V, Halazonetis TD, Fackeldey K, Patten J, Chuprina A, Dziuba I, Plekhova A, Moroz Y, Radchenko D, Tarkhanova O, Yavnyuk I, Gruber C, Yust R, Payne D, Näär AM, Namchuk MN, Davey RA, Wagner G, Kinney J, Arthanari H. A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening. iScience 2021; 24:102021. [PMID: 33426509 PMCID: PMC7783459 DOI: 10.1016/j.isci.2020.102021] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/28/2020] [Accepted: 12/29/2020] [Indexed: 02/07/2023] Open
Abstract
The unparalleled global effort to combat the continuing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic over the last year has resulted in promising prophylactic measures. However, a need still exists for cheap, effective therapeutics, and targeting multiple points in the viral life cycle could help tackle the current, as well as future, coronaviruses. Here, we leverage our recently developed, ultra-large-scale in silico screening platform, VirtualFlow, to search for inhibitors that target SARS-CoV-2. In this unprecedented structure-based virtual campaign, we screened roughly 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets. In addition to targeting the active sites of viral enzymes, we also targeted critical auxiliary sites such as functionally important protein-protein interactions.
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Affiliation(s)
- 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 02215, USA
| | - Krishna M. Padmanabha Das
- 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 02215, USA
| | - Kendra E. Leigh
- Max Planck Institute of Biophysics, Frankfurt am Main, Hessen 60438, Germany
| | | | - Patrick D. Fischer
- 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 02215, USA
- Department of Pharmacy, Pharmaceutical and Medicinal Chemistry, Saarland University, Saarbrücken, Saarland 66123, Germany
| | - Zi-Fu Wang
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, Boston, MA 02115, USA
| | | | | | | | - Anthony Calderaio
- VirtualFlow Organization, https://virtual-flow.org/, Boston, MA 02115, USA
| | | | - Alexander Rose
- Mol∗ Consortium, https://molstar.org, San Diego, CA 92109, USA
| | | | | | | | | | - Henry D. Herce
- 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 02215, USA
| | | | | | - Konstantin Fackeldey
- Zuse Institute Berlin (ZIB), Berlin 14195, Germany
- Institute of Mathematics, Technical University Berlin, Berlin 10587, Germany
| | - J.J. Patten
- Department of Microbiology, Boston University Medical School, Boston University, Boston, MA 02118, USA
| | | | | | | | - Yurii Moroz
- Chemspace, Kyiv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine
| | - Dmytro Radchenko
- Enamine, Kyiv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine
| | | | | | - Christian Gruber
- Innophore GmbH, Graz 8010, Austria
- Institute of Molecular Biosciences, University of Graz, Graz 8010, Austria
| | - Ryan Yust
- Google, Mountain View, CA 94043, USA
| | | | - Anders M. Näär
- Department of Nutritional Sciences & Toxicology, University of California Berkeley, Berkeley, CA 94720, USA
| | - Mark N. Namchuk
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, Boston, MA 02115, USA
| | - Robert A. Davey
- Department of Microbiology, Boston University Medical School, Boston University, Boston, MA 02118, USA
| | - Gerhard Wagner
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, 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 02215, USA
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Gorgulla C, Fackeldey K, Wagner G, Arthanari H. Accounting of Receptor Flexibility in Ultra-Large Virtual Screens with VirtualFlow Using a Grey Wolf Optimization Method. SUPERCOMPUTING FRONTIERS AND INNOVATIONS 2020; 7:4-12. [PMID: 34693068 PMCID: PMC8530406 DOI: 10.14529/jsfi200301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Structure-based virtual screening approaches have the ability to dramatically reduce the time and costs associated to the discovery of new drug candidates. Studies have shown that the true hit rate of virtual screenings improves with the scale of the screened ligand libraries. Therefore, we have recently developed an open source drug discovery platform (VirtualFlow), which is able to routinely carry out ultra-large virtual screenings. One of the primary challenges of molecular docking is the circumstance when the protein is highly dynamic or when the structure of the protein cannot be captured by a static pose. To accommodate protein dynamics, we report the extension of VirtualFlow to allow the docking of ligands using a grey wolf optimization algorithm using the docking program GWOVina, which substantially improves the quality and efficiency of flexible receptor docking compared to AutoDock Vina. We demonstrate the linear scaling behavior of VirtualFlow utilizing GWOVina up to 128 000 CPUs. The newly supported docking method will be valuable for drug discovery projects in which protein dynamics and flexibility play a significant role.
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Affiliation(s)
- Christoph Gorgulla
- Department of Physics, Harvard University, Cambridge, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, USA
| | - Konstantin Fackeldey
- Institute of Mathematics, Technical University Berlin, Berlin, Germany
- Zuse Institute Berlin, Berlin, Germany
| | - Gerhard Wagner
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, USA
| | - Haribabu Arthanari
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, USA
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Wong KM, Tai HK, Siu SWI. GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking. Chem Biol Drug Des 2020; 97:97-110. [PMID: 32679606 PMCID: PMC7818481 DOI: 10.1111/cbdd.13764] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/18/2020] [Accepted: 07/05/2020] [Indexed: 12/19/2022]
Abstract
Protein–ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, gwovina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side‐chain sampling. The new method was validated for rigid and flexible‐receptor docking using four independent datasets. In rigid docking, gwovina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible‐receptor docking, gwovina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, gwovina can play a role in solving the complex flexible‐receptor docking cases and is suitable for virtual screening of compound libraries. gwovina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.
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Affiliation(s)
- Kin Meng Wong
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, China
| | - Hio Kuan Tai
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, China
| | - Shirley W I Siu
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau, China
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