1
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Panecka-Hofman J, Poehner I. Structure and dynamics of pteridine reductase 1: the key phenomena relevant to enzyme function and drug design. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2023; 52:521-532. [PMID: 37608196 PMCID: PMC10618315 DOI: 10.1007/s00249-023-01677-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 07/08/2023] [Accepted: 07/24/2023] [Indexed: 08/24/2023]
Abstract
Pteridine reductase 1 (PTR1) is a folate and pterin pathway enzyme unique for pathogenic trypanosomatids. As a validated drug target, PTR1 has been the focus of recent research efforts aimed at finding more effective treatments against human parasitic diseases such as leishmaniasis or sleeping sickness. Previous PTR1-centered structural studies highlighted the enzyme characteristics, such as flexible regions around the active site, highly conserved structural waters, and species-specific differences in pocket properties and dynamics, which likely impacts the binding of natural substrates and inhibitors. Furthermore, several aspects of the PTR1 function, such as the substrate inhibition phenomenon and the level of ligand binding cooperativity in the enzyme homotetramer, likely related to the global enzyme dynamics, are poorly known at the molecular level. We postulate that future drug design efforts could greatly benefit from a better understanding of these phenomena through studying both the local and global PTR1 dynamics. This review highlights the key aspects of the PTR1 structure and dynamics relevant to structure-based drug design that could be effectively investigated by modeling approaches. Particular emphasis is given to the perspective of molecular dynamics, what has been accomplished in this area to date, and how modeling could impact the PTR1-targeted drug design in the future.
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Affiliation(s)
- Joanna Panecka-Hofman
- Division of Biophysics, Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Pasteura 5, 02-093, Warsaw, Poland.
| | - Ina Poehner
- School of Pharmacy, University of Eastern Finland, Yliopistonranta 1 C, 70211, Kuopio, Finland
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2
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Cao JF, Gong Y, Wu M, Xiong L, Chen S, Huang H, Zhou X, Peng YC, Shen XF, Qu J, Wang YL, Zhang X. Molecular docking and molecular dynamics study Lianhua Qingwen granules (LHQW) treats COVID-19 by inhibiting inflammatory response and regulating cell survival. Front Cell Infect Microbiol 2022; 12:1044770. [PMID: 36506032 PMCID: PMC9729774 DOI: 10.3389/fcimb.2022.1044770] [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: 09/15/2022] [Accepted: 11/08/2022] [Indexed: 11/25/2022] Open
Abstract
Purpose 2019 Coronavirus disease (COVID-19) is endangering health of populations worldwide. Latest research has proved that Lianhua Qingwen granules (LHQW) can reduce tissue damage caused by inflammatory reactions and relieve patients' clinical symptoms. However, the mechanism of LHQW treats COVID-19 is currently lacking. Therefore, we employed computer simulations to investigate the mechanism of LHQW treats COVID-19 by modulating inflammatory response. Methods We employed bioinformatics to screen active ingredients in LHQW and intersection gene targets. PPI, GO and KEGG was used to analyze relationship of intersection gene targets. Molecular dynamics simulations validated the binding stability of active ingredients and target proteins. Binding free energy, radius of gyration and the solvent accessible surface area were analyzed by supercomputer platform. Results COVID-19 had 4628 gene targets, LHQW had 1409 gene targets, intersection gene targets were 415. Bioinformatics analysis showed that intersection targets were closely related to inflammation and immunomodulatory. Molecular docking suggested that active ingredients (including: licopyranocoumarin, Glycyrol and 3-3-Oxopropanoic acid) in LHQW played a role in treating COVID-19 by acting on CSF2, CXCL8, CCR5, NLRP3, IFNG and TNF. Molecular dynamics was used to prove the binding stability of active ingredients and protein targets. Conclusion The mechanism of active ingredients in LHQW treats COVID-19 was investigated by computer simulations. We found that active ingredients in LHQW not only reduce cell damage and tissue destruction by inhibiting the inflammatory response through CSF2, CXCL8, CCR5 and IFNG, but also regulate cell survival and growth through NLRP3 and TNF thereby reducing apoptosis.
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Affiliation(s)
- Jun-Feng Cao
- Chengdu Medical College, Chengdu, China,Chengdu Medical College of Basic Medical Sciences, Chengdu, China
| | | | - Mei Wu
- Chengdu Medical College, Chengdu, China
| | - Li Xiong
- Chengdu Medical College, Chengdu, China
| | | | | | | | - Ying-chun Peng
- Chengdu Medical College, Chengdu, China,The First Affifiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Xue-fang Shen
- Chengdu Medical College, Chengdu, China,The First Affifiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Jinyu Qu
- Chengdu Medical College, Chengdu, China,The First Affifiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Yi-li Wang
- Chengdu Medical College, Chengdu, China,The First Affifiliated Hospital of Chengdu Medical College, Chengdu, China,*Correspondence: Yi-li Wang, ; Xiao Zhang,
| | - Xiao Zhang
- Chengdu Medical College, Chengdu, China,Chengdu Medical College of Basic Medical Sciences, Chengdu, China,*Correspondence: Yi-li Wang, ; Xiao Zhang,
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3
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Siragusa L, Menna G, Buratta F, Baroni M, Desantis J, Cruciani G, Goracci L. CROMATIC: Cross-Relationship Map of Cavi ties from Coronaviruses. J Chem Inf Model 2022; 62:2901-2908. [PMID: 35695374 PMCID: PMC9211041 DOI: 10.1021/acs.jcim.2c00169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Indexed: 12/22/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of COVID-19 disease, has rapidly imposed an urgent need to identify effective drug candidates. In this context, the high resolution and non-redundant beta-Coronavirus protein cavities database is pivotal to help virtual screening protocols. Furthermore, the cross-relationship among cavities can lead to highlighting multitarget therapy chances. Here, we first collect all protein cavities on SARS-CoV-2, SARS-CoV, and MERS-CoV X-ray structures, and then, we compute a similarity map by using molecular interaction fields (MIFs). All the results come together in CROMATIC (CROss-relationship MAp of CaviTIes from Coronaviruses). CROMATIC encloses both a comprehensive and a non-redundant version of the cavities collection and a similarity map revealing, on the one hand, cavities that are conserved among the three Coronaviruses and, on the other hand, unexpected similarities among cavities that can represent a key starting point for multitarget therapy strategies. Similarity analysis was also performed for the available structures of SARS-CoV-2 spike variants, linking sequence mutations to three-dimensional interaction alterations. The CROMATIC repository is freely available to the scientific community at https://github.com/moldiscovery/sars-cromatic.
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Affiliation(s)
- Lydia Siragusa
- Molecular
Horizon srl, Bettona 06084, Italy
- Molecular
Discovery, Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom
| | - Gabriele Menna
- Molecular
Discovery, Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom
| | - Fabrizio Buratta
- Molecular
Discovery, Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom
| | - Massimo Baroni
- Molecular
Discovery, Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom
| | - Jenny Desantis
- Laboratory
for Chemometrics and Molecular Modeling, Department of Chemistry,
Biology, and Biotechnology, University of
Perugia, via Elce di Sotto, 8, 06123 Perugia (PG), Italy
| | - Gabriele Cruciani
- Laboratory
for Chemometrics and Molecular Modeling, Department of Chemistry,
Biology, and Biotechnology, University of
Perugia, via Elce di Sotto, 8, 06123 Perugia (PG), Italy
| | - Laura Goracci
- Laboratory
for Chemometrics and Molecular Modeling, Department of Chemistry,
Biology, and Biotechnology, University of
Perugia, via Elce di Sotto, 8, 06123 Perugia (PG), Italy
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4
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Wang J, Arantes PR, Bhattarai A, Hsu RV, Pawnikar S, Huang YMM, Palermo G, Miao Y. Gaussian accelerated molecular dynamics (GaMD): principles and applications. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2021; 11:e1521. [PMID: 34899998 PMCID: PMC8658739 DOI: 10.1002/wcms.1521] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 01/28/2021] [Indexed: 12/20/2022]
Abstract
Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, KS, 66047, United States
| | - Pablo R Arantes
- Department of Bioengineering, University of California Riverside, 900 University Avenue, Riverside, CA 92512, United States
| | - Apurba Bhattarai
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr, Lawrence, KS, 66047, United States
| | - Rohaine V Hsu
- Department of Bioengineering, University of California Riverside, 900 University Avenue, Riverside, CA 92512, United States
| | - Shristi Pawnikar
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, KS, 66047, United States
| | - Yu-Ming M Huang
- Department of Physics & Astronomy, Wayne State University, 666 W Hancock St, Detroit, MI 48207, USA
| | - Giulia Palermo
- Department of Bioengineering and Department of Chemistry, University of California Riverside, 900 University Avenue, Riverside, CA 92512, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, 2030 Becker Dr., Lawrence, Kansas 66047, United States
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5
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Cho E, Rosa M, Anjum R, Mehmood S, Soban M, Mujtaba M, Bux K, Moin ST, Tanweer M, Dantu S, Pandini A, Yin J, Ma H, Ramanathan A, Islam B, Mey ASJ, Bhowmik D, Haider S. Dynamic Profiling of β-Coronavirus 3CL M pro Protease Ligand-Binding Sites. J Chem Inf Model 2021; 61:3058-3073. [PMID: 34124899 PMCID: PMC8230960 DOI: 10.1021/acs.jcim.1c00449] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Indexed: 01/11/2023]
Abstract
β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.
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Affiliation(s)
- Eunice Cho
- UCL
School of Pharmacy, London WC1N 1AX, U.K.
| | | | - Ruhi Anjum
- Department
of Biochemistry, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India
| | - Saman Mehmood
- Department
of Zoology, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India
| | - Mariya Soban
- Department
of Biochemistry, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India
| | - Moniza Mujtaba
- Herricks
High School, New Hyde
Park, New York 11040 United States
| | - Khair Bux
- Third
World Center for Science and Technology, H.E.J. Research Institute
of Chemistry, International Centre of Chemical and Biological Sciences, University of Karachi, Karachi 75270 Pakistan
| | - Syed T. Moin
- Third
World Center for Science and Technology, H.E.J. Research Institute
of Chemistry, International Centre of Chemical and Biological Sciences, University of Karachi, Karachi 75270 Pakistan
| | | | - Sarath Dantu
- Department
of Computer Science, Brunel University, Uxbridge UB8 3PH, U.K.
| | - Alessandro Pandini
- Department
of Computer Science, Brunel University, Uxbridge UB8 3PH, U.K.
| | - Junqi Yin
- Center
for Computational Sciences, Oak Ridge National
Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Heng Ma
- Data
Science and Learning Division, Argonne National
Laboratory, Lemont, Illinois 60439, United States
| | - Arvind Ramanathan
- Data
Science and Learning Division, Argonne National
Laboratory, Lemont, Illinois 60439, United States
- Consortium
for Advanced Science and Engineering, University
of Chicago, Chicago, Illinois 60637, United
States
| | - Barira Islam
- Department
of Bioscience, University of Huddersfield, Huddersfield HD1 3DH, U.K.
| | - Antonia S. J.
S. Mey
- EaStCHEM
School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, U.K.
| | - Debsindhu Bhowmik
- Computer
Sciences and Engineering Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37830, United States
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6
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Evans DJ, Yovanno RA, Rahman S, Cao DW, Beckett MQ, Patel MH, Bandak AF, Lau AY. Finding Druggable Sites in Proteins Using TACTICS. J Chem Inf Model 2021; 61:2897-2910. [PMID: 34096704 DOI: 10.1021/acs.jcim.1c00204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.
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Affiliation(s)
- Daniel J Evans
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Remy A Yovanno
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Sanim Rahman
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - David W Cao
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Morgan Q Beckett
- Department of Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Milan H Patel
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Afif F Bandak
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Albert Y Lau
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
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7
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Krumm ZA, Lloyd GM, Francis CP, Nasif LH, Mitchell DA, Golde TE, Giasson BI, Xia Y. Precision therapeutic targets for COVID-19. Virol J 2021; 18:66. [PMID: 33781287 PMCID: PMC8006140 DOI: 10.1186/s12985-021-01526-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/04/2021] [Indexed: 01/18/2023] Open
Abstract
Beginning in late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a novel pathogen that causes coronavirus disease 2019 (COVID-19). SARS-CoV-2 has infected more than 111 million people worldwide and caused over 2.47 million deaths. Individuals infected with SARS-CoV-2 show symptoms of fever, cough, dyspnea, and fatigue with severe cases that can develop into pneumonia, myocarditis, acute respiratory distress syndrome, hypercoagulability, and even multi-organ failure. Current clinical management consists largely of supportive care as commonly administered treatments, including convalescent plasma, remdesivir, and high-dose glucocorticoids. These have demonstrated modest benefits in a small subset of hospitalized patients, with only dexamethasone showing demonstrable efficacy in reducing mortality and length of hospitalization. At this time, no SARS-CoV-2-specific antiviral drugs are available, although several vaccines have been approved for use in recent months. In this review, we will evaluate the efficacy of preclinical and clinical drugs that precisely target three different, essential steps of the SARS-CoV-2 replication cycle: the spike protein during entry, main protease (MPro) during proteolytic activation, and RNA-dependent RNA polymerase (RdRp) during transcription. We will assess the advantages and limitations of drugs that precisely target evolutionarily well-conserved domains, which are less likely to mutate, and therefore less likely to escape the effects of these drugs. We propose that a multi-drug cocktail targeting precise proteins, critical to the viral replication cycle, such as spike protein, MPro, and RdRp, will be the most effective strategy of inhibiting SARS-CoV-2 replication and limiting its spread in the general population.
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Affiliation(s)
- Zachary A Krumm
- Department of Neuroscience, College of Medicine, University of Florida, 1275 Center Drive, Gainesville, FL, 32610, USA
- Center for Translational Research in Neurodegenerative Disease, College of Medicine, University of Florida, Gainesville, FL, 32610, USA
| | - Grace M Lloyd
- Department of Neuroscience, College of Medicine, University of Florida, 1275 Center Drive, Gainesville, FL, 32610, USA
- Center for Translational Research in Neurodegenerative Disease, College of Medicine, University of Florida, Gainesville, FL, 32610, USA
| | - Connor P Francis
- College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, 32610, USA
- Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, FL, 32610, USA
- UF Clinical and Translational Science Institute, University of Florida, Gainesville, FL, 32610, USA
| | - Lith H Nasif
- Department of Neuroscience, College of Medicine, University of Florida, 1275 Center Drive, Gainesville, FL, 32610, USA
- Center for Translational Research in Neurodegenerative Disease, College of Medicine, University of Florida, Gainesville, FL, 32610, USA
| | - Duane A Mitchell
- College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, 32610, USA
- Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, FL, 32610, USA
- UF Clinical and Translational Science Institute, University of Florida, Gainesville, FL, 32610, USA
| | - Todd E Golde
- Department of Neuroscience, College of Medicine, University of Florida, 1275 Center Drive, Gainesville, FL, 32610, USA
- Center for Translational Research in Neurodegenerative Disease, College of Medicine, University of Florida, Gainesville, FL, 32610, USA
- College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, 32610, USA
| | - Benoit I Giasson
- Department of Neuroscience, College of Medicine, University of Florida, 1275 Center Drive, Gainesville, FL, 32610, USA.
- Center for Translational Research in Neurodegenerative Disease, College of Medicine, University of Florida, Gainesville, FL, 32610, USA.
- College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, 32610, USA.
| | - Yuxing Xia
- Department of Neuroscience, College of Medicine, University of Florida, 1275 Center Drive, Gainesville, FL, 32610, USA.
- Center for Translational Research in Neurodegenerative Disease, College of Medicine, University of Florida, Gainesville, FL, 32610, USA.
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8
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Jaffrelot Inizan T, Célerse F, Adjoua O, El Ahdab D, Jolly LH, Liu C, Ren P, Montes M, Lagarde N, Lagardère L, Monmarché P, Piquemal JP. High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling. Chem Sci 2021; 12:4889-4907. [PMID: 34168762 PMCID: PMC8179654 DOI: 10.1039/d1sc00145k] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 01/27/2021] [Indexed: 01/03/2023] Open
Abstract
We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs). The global exploration problem is decomposed into a set of separate MD trajectories that can be restarted within a selective process to achieve sufficient phase-space sampling. Accurate statistical properties can be obtained through reweighting. Within this highly parallel setup, the Tinker-HP package can be powered by an arbitrary large number of GPUs on supercomputers, reducing exploration time from years to days. This approach is used to tackle the urgent modeling problem of the SARS-CoV-2 Main Protease (Mpro) producing more than 38 μs of all-atom simulations of its apo (ligand-free) dimer using the high-resolution AMOEBA PFF. The first 15.14 μs simulation (physiological pH) is compared to available non-PFF long-timescale simulation data. A detailed clustering analysis exhibits striking differences between FFs, with AMOEBA showing a richer conformational space. Focusing on key structural markers related to the oxyanion hole stability, we observe an asymmetry between protomers. One of them appears less structured resembling the experimentally inactive monomer for which a 6 μs simulation was performed as a basis for comparison. Results highlight the plasticity of the Mpro active site. The C-terminal end of its less structured protomer is shown to oscillate between several states, being able to interact with the other protomer, potentially modulating its activity. Active and distal site volumes are found to be larger in the most active protomer within our AMOEBA simulations compared to non-PFFs as additional cryptic pockets are uncovered. A second 17 μs AMOEBA simulation is performed with protonated His172 residues mimicking lower pH. Data show the protonation impact on the destructuring of the oxyanion loop. We finally analyze the solvation patterns around key histidine residues. The confined AMOEBA polarizable water molecules are able to explore a wide range of dipole moments, going beyond bulk values, leading to a water molecule count consistent with experimental data. Results suggest that the use of PFFs could be critical in drug discovery to accurately model the complexity of the molecular interactions structuring Mpro.
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Affiliation(s)
| | - Frédéric Célerse
- Sorbonne Université, LCT, UMR 7616 CNRS Paris France
- Sorbonne Université, IPCM, UMR 8232 CNRS Paris France
| | | | - Dina El Ahdab
- Sorbonne Université, LCT, UMR 7616 CNRS Paris France
- Université Saint-Joseph de Beyrouth, UR-EGP Faculté des Sciences Lebanon
| | | | - Chengwen Liu
- University of Texas at Austin, Department of Biomedical Engineering Texas USA
| | - Pengyu Ren
- University of Texas at Austin, Department of Biomedical Engineering Texas USA
| | - Matthieu Montes
- Laboratoire GBCM, EA 7528, CNAM, Hésam Université Paris France
| | | | - Louis Lagardère
- Sorbonne Université, LCT, UMR 7616 CNRS Paris France
- Sorbonne Université, IP2CT, FR 2622 CNRS Paris France
| | - Pierre Monmarché
- Sorbonne Université, LCT, UMR 7616 CNRS Paris France
- Sorbonne Université, LJLL, UMR 7598 CNRS Paris France
| | - Jean-Philip Piquemal
- Sorbonne Université, LCT, UMR 7616 CNRS Paris France
- University of Texas at Austin, Department of Biomedical Engineering Texas USA
- Institut Universitaire de France Paris France
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9
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Carli M, Sormani G, Rodriguez A, Laio A. Candidate Binding Sites for Allosteric Inhibition of the SARS-CoV-2 Main Protease from the Analysis of Large-Scale Molecular Dynamics Simulations. J Phys Chem Lett 2021; 12:65-72. [PMID: 33306377 PMCID: PMC7755075 DOI: 10.1021/acs.jpclett.0c03182] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/08/2020] [Indexed: 05/20/2023]
Abstract
We analyzed a 100 μs MD trajectory of the SARS-CoV-2 main protease by a non-parametric data analysis approach which allows characterizing a free energy landscape as a simultaneous function of hundreds of variables. We identified several conformations that, when visited by the dynamics, are stable for several hundred nanoseconds. We explicitly characterize and describe these metastable states. In some of these configurations, the catalytic dyad is less accessible. Stabilizing them by a suitable binder could lead to an inhibition of the enzymatic activity. In our analysis we keep track of relevant contacts between residues which are selectively broken or formed in the states. Some of these contacts are formed by residues which are far from the catalytic dyad and are accessible to the solvent. Based on this analysis we propose some relevant contact patterns and three possible binding sites which could be targeted to achieve allosteric inhibition.
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Affiliation(s)
- Matteo Carli
- Scuola
Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
| | - Giulia Sormani
- Scuola
Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
| | - Alex Rodriguez
- The
Abdus Salam International Centre for Theoretical Physics (ICTP), Str. Costiera, 11, 34151 Trieste, Italy
| | - Alessandro Laio
- Scuola
Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
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10
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Yu A, Pak AJ, He P, Monje-Galvan V, Casalino L, Gaieb Z, Dommer AC, Amaro RE, Voth GA. A Multiscale Coarse-grained Model of the SARS-CoV-2 Virion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 33024966 DOI: 10.1101/2020.10.02.323915] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations, however, are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and on-going development of a largely "bottom-up" coarse-grained (CG) model of the SARS-CoV-2 virion. Structural data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data becomes publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion. Significance Statement This study reports the construction of a molecular model for the SARS-CoV-2 virion and details our multiscale approach towards model refinement. The resulting model and methods can be applied to and enable the simulation of SARS-CoV-2 virions.
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