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Pritam M, Dutta S, Medicherla KM, Kumar R, Singh SP. Computational analysis of spike protein of SARS-CoV-2 (Omicron variant) for development of peptide-based therapeutics and diagnostics. J Biomol Struct Dyn 2024; 42:7321-7339. [PMID: 37498146 DOI: 10.1080/07391102.2023.2239932] [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: 06/08/2022] [Accepted: 07/17/2023] [Indexed: 07/28/2023]
Abstract
In the last few years, the worldwide population has suffered from the SARS-CoV-2 pandemic. The WHO dashboard indicated that around 504,079,039 people were infected and 6,204,155 died from COVID-19 caused by different variants of SARS-CoV-2. Recently, a new variant of SARS-CoV-2 (B.1.1.529) was reported by South Africa known as Omicron. The high transmissibility rate and resistance towards available anti-SARS-CoV-2 drugs/vaccines/monoclonal antibodies, make Omicron a variant of concern. Because of various mutations in spike protein, available diagnostic and therapeutic treatments are not reliable. Therefore, the present study explored the development of some therapeutic peptides that can inhibit the SARS-CoV-2 virus interaction with host ACE2 receptors and can also be used for diagnostic purposes. The screened linear B cell epitopes derived from receptor-binding domain of spike protein of Omicron variant were evaluated as peptide inhibitor/vaccine candidates through different bioinformatics tools including molecular docking and simulation to analyze the interaction between Omicron peptide and human ACE2 receptor. Overall, in-silico studies revealed that Omicron peptides OP1-P12, OP14, OP20, OP23, OP24, OP25, OP26, OP27, OP28, OP29, and OP30 have the potential to inhibit Omicron interaction with ACE2 receptor. Moreover, Omicron peptides OP20, OP22, OP23, OP24, OP25, OP26, OP27, and OP30 have shown potential antigenic and immunogenic properties that can be used in design and development vaccines against Omicron. Although the in-silico validation was performed by comparative analysis with the control peptide inhibitor, further validation through wet lab experimentation is required before its use as therapeutic peptides.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Manisha Pritam
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Somenath Dutta
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
- Department of Bioinformatics, Pondicherry Central University, Puducherry, India
| | - Krishna Mohan Medicherla
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, Missouri, USA
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Min Y, Wei Y, Wang P, Wang X, Li H, Wu N, Bauer S, Zheng S, Shi Y, Wang Y, Wu J, Zhao D, Zeng J. From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2405404. [PMID: 39206846 DOI: 10.1002/advs.202405404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/29/2024] [Indexed: 09/04/2024]
Abstract
Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process.
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Affiliation(s)
- Yaosen Min
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Ye Wei
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Peizhuo Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
- School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Xiaoting Wang
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Nian Wu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Stefan Bauer
- Department of Intelligent Systems, KTH, Stockholm, 10044, Sweden
| | | | - Yu Shi
- Microsoft Research Asia, Beijing, 100080, China
| | - Yingheng Wang
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
| | - Ji Wu
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Jianyang Zeng
- School of Engineering, Westlake University, Hangzhou, 310030, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, 310030, China
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Linzer JT, Aminov E, Abdullah AS, Kirkup CE, Diaz Ventura RI, Bijoor VR, Jung J, Huang S, Tse CG, Álvarez Toucet E, Onghai HP, Ghosh AP, Grodzki AC, Haines ER, Iyer AS, Khalil MK, Leong AP, Neuhaus MA, Park J, Shahid A, Xie M, Ziembicki JM, Simmerling C, Nagan MC. Accurately Modeling RNA Stem-Loops in an Implicit Solvent Environment. J Chem Inf Model 2024; 64:6092-6104. [PMID: 39002142 DOI: 10.1021/acs.jcim.4c00756] [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: 07/15/2024]
Abstract
Ribonucleic acid (RNA) molecules can adopt a variety of secondary and tertiary structures in solution, with stem-loops being one of the more common motifs. Here, we present a systematic analysis of 15 RNA stem-loop sequences simulated with molecular dynamics simulations in an implicit solvent environment. Analysis of RNA cluster ensembles showed that the stem-loop structures can generally adopt the A-form RNA in the stem region. Loop structures are more sensitive, and experimental structures could only be reproduced with modification of CH···O interactions in the force field, combined with an implicit solvent nonpolar correction to better model base stacking interactions. Accurately modeling RNA with current atomistic physics-based models remains challenging, but the RNA systems studied herein may provide a useful benchmark set for testing other RNA modeling methods in the future.
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Affiliation(s)
- Jason T Linzer
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Ethan Aminov
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Aalim S Abdullah
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Colleen E Kirkup
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Rebeca I Diaz Ventura
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Vinay R Bijoor
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Jiyun Jung
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Sophie Huang
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Chi Gee Tse
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Emily Álvarez Toucet
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Hugo P Onghai
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Arghya P Ghosh
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Alex C Grodzki
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Emilee R Haines
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Aditya S Iyer
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Mark K Khalil
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Alexander P Leong
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Michael A Neuhaus
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Joseph Park
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Asir Shahid
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Matthew Xie
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Jan M Ziembicki
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Maria C Nagan
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
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Cao X, Hummel MH, Wang Y, Simmerling C, Coutsias EA. Exact Analytical Algorithm for the Solvent-Accessible Surface Area and Derivatives in Implicit Solvent Molecular Simulations on GPUs. J Chem Theory Comput 2024; 20:4456-4468. [PMID: 38780181 DOI: 10.1021/acs.jctc.3c01366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
In this paper, we present differentiable solvent-accessible surface area (dSASA), an exact geometric method to calculate SASA analytically along with atomic derivatives on GPUs. The atoms in a molecule are first assigned to tetrahedra in groups of four atoms by Delaunay tetrahedralization adapted for efficient GPU implementation, and the SASA values for atoms and molecules are calculated based on the tetrahedralization information and inclusion-exclusion method. The SASA values from the numerical icosahedral-based method can be reproduced with >98% accuracy for both proteins and RNAs. Having been implemented on GPUs and incorporated into AMBER, we can apply dSASA to implicit solvent molecular dynamics simulations with the inclusion of this nonpolar term. The current GPU version of GB/SA simulations has been accelerated up to nearly 20-fold compared to the CPU version, outperforming LCPO, a commonly used, fast algorithm for calculating SASA, as the system size increases. While we focus on the accuracy of the SASA calculations for proteins and nucleic acids, we also demonstrate stable GB/SA MD mini-protein simulations.
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Affiliation(s)
- Xin Cao
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Michelle H Hummel
- Sandia National Laboratories, Albuquerque, New Mexico 87123, United States
| | - Yuzhang Wang
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Evangelos A Coutsias
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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Cao X, Hummel MH, Wang Y, Simmerling C, Coutsias EA. Exact analytical algorithm for solvent accessible surface area and derivatives in implicit solvent molecular simulations on GPUs. ARXIV 2024:arXiv:2401.10462v2. [PMID: 38313200 PMCID: PMC10836080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
In this paper, we present dSASA (differentiable SASA), an exact geometric method to calculate solvent accessible surface area (SASA) analytically along with atomic derivatives on GPUs. The atoms in a molecule are first assigned to tetrahedra in groups of four atoms by Delaunay tetrahedrization adapted for efficient GPU implementation and the SASA values for atoms and molecules are calculated based on the tetrahedrization information and inclusion-exclusion method. The SASA values from the numerical icosahedral-based method can be reproduced with more than 98% accuracy for both proteins and RNAs. Having been implemented on GPUs and incorporated into the software Amber, we can apply dSASA to implicit solvent molecular dynamics simulations with inclusion of this nonpolar term. The current GPU version of GB/SA simulations has been accelerated up to nearly 20-fold compared to the CPU version, outperforming LCPO, a commonly used, fast algorithm for calculating SASA, as the system size increases. While we focus on the accuracy of the SASA calculations for proteins and nucleic acids, we also demonstrate stable GB/SA MD mini-protein simulations.
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Affiliation(s)
- Xin Cao
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, United States
| | | | - Yuzhang Wang
- Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, United States
| | - Evangelos A Coutsias
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, United States
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Nesabi A, Kalayan J, Al-Rawashdeh S, Ghattas MA, Bryce RA. Molecular dynamics simulations as a guide for modulating small molecule aggregation. J Comput Aided Mol Des 2024; 38:11. [PMID: 38470532 PMCID: PMC10933209 DOI: 10.1007/s10822-024-00557-1] [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: 02/02/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024]
Abstract
Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity.
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Affiliation(s)
- Azam Nesabi
- Division of Pharmacy and Optometry, School of Health Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Jas Kalayan
- Daresbury Laboratory, Science and Technologies Facilities Council (STFC), Keckwick Lane, Daresbury, Warrington, WA4 4AD, UK
| | - Sara Al-Rawashdeh
- Division of Pharmacy and Optometry, School of Health Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | | | - Richard A Bryce
- Division of Pharmacy and Optometry, School of Health Sciences, Manchester Academic Health Sciences Centre, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
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Aulifa DL, Al Shofwan AA, Megantara S, Fakih TM, Budiman A. Elucidation of Molecular Interactions Between Drug-Polymer in Amorphous Solid Dispersion by a Computational Approach Using Molecular Dynamics Simulations. Adv Appl Bioinform Chem 2024; 17:1-19. [PMID: 38282640 PMCID: PMC10821732 DOI: 10.2147/aabc.s441628] [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: 09/22/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024] Open
Abstract
Introduction Amorphous drug dispersion is frequently used to enhance the solubility and dissolution of poorly water-soluble drugs, thereby improving their oral bioavailability. The dispersion of these drugs into polymer matrix can inhibit their recrystallization. The inter-molecular interactions between drug and polymer plays a role in the improvement of the dissolution rate, solubility, and physical stability of drug. Aim This study aims to investigate the formation and interactions of ritonavir (RTV)/poloxamer (PLX) amorphous formulation using a computational approach via molecular dynamics (MD) simulations, which mimicked solvent evaporation and melt-quenching method. Methods TheRoot Mean Square Deviation (RMSD) value, Root Mean Square Fluctuation (RMSF), Radial Distribution Function (RDF), Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA), and hydrogen bond interactions were analyzed to determine interaction mechanisms between RTV and PLX in amorphous solid dispersion. Results The pi-alkyl bonds between RTV and PLX were formed after simulations of solvent evaporation, while the hydrogen bond interactions of RTV-PLX was observed during melt method simulations. These results indicate the successful formulation of amorphous solid dispersion (ASD) from RTV and PLX. The RMSD values obtained from the solvent evaporation, melt-cooling-A, melt-cooling-B, and melt-cooling-C methods were 3.33 Å, 1.97 Å, 1.30 Å, and 1.29 Å, respectively, while the average RMSF values were 2.65 Å, 1.04 Å, 1.05 Å, and 1.07 Å, respectively. This indicates that the suppression of translational motion of RTV from the melt method can be stronger than solvent evaporation caused by the intermolecular interactions of RTV-PLX. Conclusion MD simulations helped in understanding the formation and interaction mechanisms of ASD formulations that were difficult to detect by experimental approaches.
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Affiliation(s)
- Diah Lia Aulifa
- Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
| | - Adnan Aly Al Shofwan
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
| | - Sandra Megantara
- Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
| | - Taufik Muhammad Fakih
- Department of Pharmacy, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung, Bandung, Indonesia
| | - Arif Budiman
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
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Setiadi J, Boothroyd S, Slochower DR, Dotson DL, Thompson MW, Wagner JR, Wang LP, Gilson MK. Tuning Potential Functions to Host-Guest Binding Data. J Chem Theory Comput 2024; 20:239-252. [PMID: 38147689 PMCID: PMC10838530 DOI: 10.1021/acs.jctc.3c01050] [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: 12/28/2023]
Abstract
Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.
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Affiliation(s)
- Jeffry Setiadi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States
| | - Simon Boothroyd
- Boothroyd Scientific Consulting Ltd., London WC2H 9JQ, U.K
- Psivant Therapeutics, Boston, Massachusetts 02210, United States
| | | | - David L Dotson
- Datryllic LLC, Phoenix, Arizona 85003, United States
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Matthew W Thompson
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Jeffrey R Wagner
- The Open Force Field Consortium, Open Molecular Software Foundation, Davis, California 95616, United States
| | - Lee-Ping Wang
- Chemistry Department, University of California Davis, Davis, California 95616, United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States
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Pretti E, Shell MS. Mapping the configurational landscape and aggregation phase behavior of the tau protein fragment PHF6. Proc Natl Acad Sci U S A 2023; 120:e2309995120. [PMID: 37983502 PMCID: PMC10691331 DOI: 10.1073/pnas.2309995120] [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: 06/13/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
The PHF6 (Val-Gln-Ile-Val-Tyr-Lys) motif, found in all isoforms of the microtubule-associated protein tau, forms an integral part of ordered cores of amyloid fibrils formed in tauopathies and is thought to play a fundamental role in tau aggregation. Because PHF6 as an isolated hexapeptide assembles into ordered fibrils on its own, it is investigated as a minimal model for insight into the initial stages of aggregation of larger tau fragments. Even for this small peptide, however, the large length and time scales associated with fibrillization pose challenges for simulation studies of its dynamic assembly, equilibrium configurational landscape, and phase behavior. Here, we develop an accurate, bottom-up coarse-grained model of PHF6 for large-scale simulations of its aggregation, which we use to uncover molecular interactions and thermodynamic driving forces governing its assembly. The model, not trained on any explicit information about fibrillar structure, predicts coexistence of formed fibrils with monomers in solution, and we calculate a putative equilibrium phase diagram in concentration-temperature space. We also characterize the configurational and free energetic landscape of PHF6 oligomers. Importantly, we demonstrate with a model of heparin that this widely studied cofactor enhances the aggregation propensity of PHF6 by ordering monomers during nucleation and remaining associated with growing fibrils, consistent with experimentally characterized heparin-tau interactions. Overall, this effort provides detailed molecular insight into PHF6 aggregation thermodynamics and pathways and, furthermore, demonstrates the potential of modern multiscale modeling techniques to produce predictive models of amyloidogenic peptides simultaneously capturing sequence-specific effects and emergent aggregate structures.
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Affiliation(s)
- Evan Pretti
- Department of Chemical Engineering, University of California, Santa Barbara, CA93106-5080
| | - M. Scott Shell
- Department of Chemical Engineering, University of California, Santa Barbara, CA93106-5080
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Liao J, Shu Z, Gao J, Wu M, Chen C. SurfPB: A GPU-Accelerated Electrostatic Calculation and Visualization Tool for Biomolecules. J Chem Inf Model 2023; 63:4490-4496. [PMID: 37500509 DOI: 10.1021/acs.jcim.3c00745] [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: 07/29/2023]
Abstract
In this work, we present SurfPB as a useful tool for the study of biomolecules. It can do many typical calculations, including the molecular surface, electrostatic potential, solvation free energy, entropy, and binding free energy. Among all of the calculations, the entropy calculation is the most time-consuming one. In SurfPB, the calculation can be performed in a vacuum or implicit solvent and accelerated on GPU. The Poisson-Boltzmann equation solver is accelerated on GPU as well. Moreover, we developed a graphical user interface for SurfPB. It allows users to input the parameters and complete the whole calculation in a visual way. The calculated electrostatic potentials are shown on the molecular surface in a three-dimensional scene.
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Affiliation(s)
- Jun Liao
- Biomolecular Physics and Modeling Group, School of Physics Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Zirui Shu
- Biomolecular Physics and Modeling Group, School of Physics Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Junyong Gao
- Biomolecular Physics and Modeling Group, School of Physics Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Mincong Wu
- Biomolecular Physics and Modeling Group, School of Physics Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Changjun Chen
- Biomolecular Physics and Modeling Group, School of Physics Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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Kasavajhala K, Simmerling C. Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations. J Chem Theory Comput 2023; 19:1931-1944. [PMID: 36861842 PMCID: PMC10658647 DOI: 10.1021/acs.jctc.3c00005] [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] [Indexed: 03/03/2023]
Abstract
Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.
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Affiliation(s)
- Koushik Kasavajhala
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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12
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Sieradzan AK, Sans-Duñó J, Lubecka EA, Czaplewski C, Lipska AG, Leszczyński H, Ocetkiewicz KM, Proficz J, Czarnul P, Krawczyk H, Liwo A. Optimization of parallel implementation of UNRES package for coarse-grained simulations to treat large proteins. J Comput Chem 2023; 44:602-625. [PMID: 36378078 DOI: 10.1002/jcc.27026] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/19/2022] [Accepted: 10/17/2022] [Indexed: 11/16/2022]
Abstract
We report major algorithmic improvements of the UNRES package for physics-based coarse-grained simulations of proteins. These include (i) introduction of interaction lists to optimize computations, (ii) transforming the inertia matrix to a pentadiagonal form to reduce computing and memory requirements, (iii) removing explicit angles and dihedral angles from energy expressions and recoding the most time-consuming energy/force terms to minimize the number of operations and to improve numerical stability, (iv) using OpenMP to parallelize those sections of the code for which distributed-memory parallelization involves unfavorable computing/communication time ratio, and (v) careful memory management to minimize simultaneous access of distant memory sections. The new code enables us to run molecular dynamics simulations of protein systems with size exceeding 100,000 amino-acid residues, reaching over 1 ns/day (1 μs/day in all-atom timescale) with 24 cores for proteins of this size. Parallel performance of the code and comparison of its performance with that of AMBER, GROMACS and MARTINI 3 is presented.
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Affiliation(s)
- Adam K Sieradzan
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Jordi Sans-Duñó
- Department of Chemistry, University of Lleida, Lleida, Spain
| | - Emilia A Lubecka
- Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Agnieszka G Lipska
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Henryk Leszczyński
- Faculty of Mathematics, Physics and Informatics, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Krzysztof M Ocetkiewicz
- Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Jerzy Proficz
- Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Paweł Czarnul
- Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Henryk Krawczyk
- Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland.,Centre of Informatics Tri-city Academic Supercomputer and Network (CI TASK), Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
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13
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Meng Y, Liu R, Wang L, Li F, Tian Y, Lu H. Binding affinity and conformational change predictions for a series of inhibitors with RuBisCO in a carbon dioxide gas and water environment by multiple computational methods. J Mol Liq 2023. [DOI: 10.1016/j.molliq.2023.121478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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14
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Wang KW, Lee J, Zhang H, Suh D, Im W. CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs. J Phys Chem B 2022; 126:7354-7364. [PMID: 36117287 DOI: 10.1021/acs.jpcb.2c05294] [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
Implicit solvent models are widely used because they are advantageous to speed up simulations by drastically decreasing the number of solvent degrees of freedom, which allows one to achieve long simulation time scales for large system sizes. CHARMM-GUI, a web-based platform, has been developed to support the setup of complex multicomponent molecular systems and prepare input files. This study describes an Implicit Solvent Modeler (ISM) in CHARMM-GUI for various generalized Born (GB) implicit solvent simulations in different molecular dynamics programs such as AMBER, CHARMM, GENESIS, NAMD, OpenMM, and Tinker. The GB models available in ISM include GB-HCT, GB-OBC, GB-neck, GBMV, and GBSW with the CHARMM and Amber force fields for protein, DNA, RNA, glycan, and ligand systems. Using the system and input files generated by ISM, implicit solvent simulations of protein, DNA, and RNA systems produce similar results for different simulation packages with the same input information. Protein-ligand systems are also considered to further validate the systems and input files generated by ISM. Simple ligand root-mean-square deviation (RMSD) and molecular mechanics generalized Born surface area (MM/GBSA) calculations show that the performance of implicit simulations is better than docking and can be used for early stage ligand screening. These reasonable results indicate that ISM is a useful and reliable tool to provide various implicit solvent simulation applications.
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Affiliation(s)
- Kye Won Wang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Jumin Lee
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Han Zhang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Donghyuk Suh
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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15
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Yamazaki M, Ikeda K, Kameda T, Nakao H, Nakano M. Kinetic Mechanism of Amyloid-β-(16-22) Peptide Fibrillation. J Phys Chem Lett 2022; 13:6031-6036. [PMID: 35748616 DOI: 10.1021/acs.jpclett.2c01065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The kinetic mechanism of amyloid fibril formation by a peptide fragment containing seven residues of the amyloid-β protein Aβ-(16-22) was investigated. We found that the N- and C-terminal unprotected Aβ-(16-22), containing no aggregation nuclei, showed rapid fibrillation within seconds to minutes in a neutral aqueous buffer solution. The fibrillation kinetics were well described by the nucleation-elongation model, suggesting that primary nucleation was the rate-limiting step. On the basis of both experimental and theoretical analyses, the aggregated nucleus was estimated to be composed of 6-7 peptide molecules, wherein the two β-sheets were associated with their hydrophobic surfaces. Thin fibers with widths of 10-20 nm were formed, which increased their length and thickness, attaining a width of >20 nm over several tens of minutes, probably owing to the lateral association of the fibers. Electrostatic and hydrophobic interactions play important roles in aggregation. These results provide a basis for understanding the fibrillation of short peptides.
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Affiliation(s)
- Moe Yamazaki
- Department of Biointerface Chemistry, Faculty of Pharmaceutical Sciences, University of Toyama, Sugitani 2630, Toyama 930-0194, Japan
| | - Keisuke Ikeda
- Department of Biointerface Chemistry, Faculty of Pharmaceutical Sciences, University of Toyama, Sugitani 2630, Toyama 930-0194, Japan
| | - Tomoshi Kameda
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
| | - Hiroyuki Nakao
- Department of Biointerface Chemistry, Faculty of Pharmaceutical Sciences, University of Toyama, Sugitani 2630, Toyama 930-0194, Japan
| | - Minoru Nakano
- Department of Biointerface Chemistry, Faculty of Pharmaceutical Sciences, University of Toyama, Sugitani 2630, Toyama 930-0194, Japan
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16
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Lam K, Kasavajhala K, Gunasekera S, Simmerling C. Accelerating the Ensemble Convergence of RNA Hairpin Simulations with a Replica Exchange Structure Reservoir. J Chem Theory Comput 2022; 18:3930-3947. [PMID: 35502992 PMCID: PMC10658646 DOI: 10.1021/acs.jctc.2c00065] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
RNA is a key participant in many biological processes, but studies of RNA using computer simulations lag behind those of proteins, largely due to less-developed force fields and the slow dynamics of RNA. Generating converged RNA ensembles for force field development and other studies remains a challenge. In this study, we explore the ability of replica exchange molecular dynamics to obtain well-converged conformational ensembles for two RNA hairpin systems in an implicit solvent. Even for these small model systems, standard REMD remains computationally costly, but coupling to a pre-generated structure library using the reservoir REMD approach provides a dramatic acceleration of ensemble convergence for both model systems. Such precise ensembles could facilitate RNA force field development and validation and applications of simulation to more complex RNA systems. The advantages and remaining challenges of applying R-REMD to RNA are investigated in detail.
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Affiliation(s)
- Kenneth Lam
- Molecular and Cellular Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Koushik Kasavajhala
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Sarah Gunasekera
- Program in Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Molecular and Cellular Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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17
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Sen D, Debnath B, Debnath P, Debnath S, Zaki MEA, Masand VH. Identification of potential edible mushroom as SARS-CoV-2 main protease inhibitor using rational drug designing approach. Sci Rep 2022; 12:1503. [PMID: 35087077 PMCID: PMC8795408 DOI: 10.1038/s41598-022-05349-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/03/2022] [Indexed: 12/13/2022] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is highly pathogenic to humans and has created health care threats worldwide. This urgent situation has focused the researchers worldwide towards the development of novel vaccine or small molecule therapeutics for SARS-CoV-2. Although several vaccines have already been discovered and are in use for the masses, no therapeutic medication has yet been approved by FDA for the treatment of COVID-19. Keeping this in view, in the present study, we have identified promising hits against the main protease (Mpro) of SARS-CoV-2 from edible mushrooms. Structure-based virtual screening (VS) of 2433 compounds derived from mushrooms was performed with Mpro protein (6LU7). Four promising hits, namely, Kynapcin-12 (M_78), Kynapcin-28 (M_82), Kynapcin-24 (M_83), and Neonambiterphenyls-A (M_366) were identified based on the result of docking, Lipinski's rule, 100 ns molecular dynamics (MD) simulation and MM/PBSA binding free energy calculations. Finally, the inhibitory properties of these hits were compared with three known inhibitors, baicalein (1), baicalin (2), and biflavonoid (3). Data indicated that M_78, M_82 and M_83 compounds present in edible mushroom Polyozellus multiplex were potent inhibitors of Mproprotein (6LU7). It could be concluded that edible mushroom Polyozellus multiplex has potential activity against SARS-CoV-2 infection and identified molecules could be further explored as therapeutic inhibitors against SARS-CoV-2.
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Affiliation(s)
- Debanjan Sen
- BCDA College of Pharmacy & Technology, Jessore Road South, Hridaypur, Kolkata, West Bengal, 700127, India
| | - Bimal Debnath
- Department of Forestry and Biodiversity, Tripura University, Suryamaninagar, Tripura, 799022, India
| | - Pradip Debnath
- Department of Chemistry, Majaraja Bir Bikram College, Agartala, Tripura, 799004, India
| | - Sudhan Debnath
- Department of Chemistry, Netaji Subhash Mahavidyalaya, Udaipur, Tripura, 799114, India.
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, 13318, Saudi Arabia.
| | - Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, 444 602, India
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18
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Halder SK, Mim MM, Alif MMH, Shathi JF, Alam N, Shil A, Himel MK. Oxa-376 and Oxa-530 variants of β-lactamase: computational study uncovers potential therapeutic targets of Acinetobacter baumannii. RSC Adv 2022; 12:24319-24338. [PMID: 36128545 PMCID: PMC9412156 DOI: 10.1039/d2ra02939a] [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: 05/11/2022] [Accepted: 08/17/2022] [Indexed: 11/21/2022] Open
Abstract
Antimicrobial resistance is a major global health crisis, resulting in thousands of deaths each year. Antibiotics' effectiveness against microorganisms deteriorates over time as multidrug resistance (MDR) develops, which is exacerbated by irregular antibiotic use, poor disease management, and the evasive nature of bacteria. The World Health Organization has recognized multidrug resistance as a critical public health concern, and Acinetobacter baumannii has been at the center of attention due to its ability to develop multidrug resistance (MDR). It generally produces carbapenem-hydrolyzing oxacillinase, which has been identified as the primary source of beta-lactam resistance in MDR bacteria. Recently, point mutations in A. baumannii have been identified as a key factor of multidrug resistance, making them a prime concern for researchers. The goal of the current work was to establish a unique way of finding multidrug-resistant variants and identify the most damaging mutations in the existing databases. We characterized the deleterious variants of oxacillinases using several computational tools. Following a thorough analysis, Oxa-376 and Oxa-530 were found to be more damaging when compared with the wild-type Oxa-51. The mutants' 3D structures were then prepared and refined with RaptorX, GalaxyRefine, and SAVES servers. Our research incorporates seven antimicrobial agents to illustrate the resistance capability of the variants of oxacillinase by evaluating binding affinity in Autodock-vina and Schrodinger software. RMSD, RMSF, Radius of gyration analysis, the solvent-accessible surface area (SASA), hydrogen bonding analysis and MM-GBSA from Molecular Dynamics Simulation revealed the dynamic nature and stability of wild-type and Oxa-376 and Oxa-530 variants. Our findings will benefit researchers looking for the deleterious mutations of Acinetobacter baumannii and new therapeutics to combat those variants. However, further studies are necessary to evaluate the mechanism of hydrolyzing activity and antibiotic resistance of these variants. Determining novel therapeutic targets of Acinetobacter baumannii. Deleterious variants, causing antibiotic resistance, were identified by molecular docking and molecular dynamics simulation suggesting new therapeutic targets Oxa-376 and Oxa-530.![]()
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Affiliation(s)
- Sajal Kumar Halder
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
- Research Assistant at Padma Bioresearch, Dhaka, Bangladesh
| | - Maria Mulla Mim
- Department of Pharmacy, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Md. Meharab Hassan Alif
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Jannatul Fardous Shathi
- Department of Biochemistry and Molecular Biology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Nuhu Alam
- Department of Botany, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
| | - Aparna Shil
- Department of Botany, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh
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19
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Shu Z, Wu M, Liao J, Chen C. FSATOOL 2.0: An integrated molecular dynamics simulation and trajectory data analysis program. J Comput Chem 2021; 43:215-224. [PMID: 34751974 DOI: 10.1002/jcc.26772] [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: 08/09/2021] [Revised: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 11/08/2022]
Abstract
Molecular dynamics simulation is important in the computational study of the biomolecules. In this paper, we upgrade our previous FSATOOL to version 2.0. It is no longer a plugin as before. Besides the existed enhanced sampling and Markov state model analysis module, FSATOOL 2.0 has three new features now. First, it contains a molecular dynamics simulation engine on both CPU and GPU device. The engine works with an embedded enhanced sampling module. Second, it can do the free energy calculation by various practical methods, including the weighted histogram analysis method and Gaussian mixture model. Third, it has many subroutines to process the trajectory data, such as principal component analysis, time-structure based independent component analysis, contact analysis, and Φ-value analysis. Most importantly, all these calculations are integrated into one package. The trajectory data format is compatible with all the modules. With a proper input parameter file, users can do the molecular dynamics simulation and data analysis work by only a few simplified commands. The capabilities and theoretical backgrounds of FSATOOL 2.0 are introduced in the paper.
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Affiliation(s)
- Zirui Shu
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mincong Wu
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jun Liao
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Changjun Chen
- Biomolecular Physics and Modeling Group, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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20
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Chatterjee S, Kim CM, Kim DM. Potential efficacy of existing drug molecules against severe fever with thrombocytopenia syndrome virus: an in silico study. Sci Rep 2021; 11:20857. [PMID: 34675254 PMCID: PMC8531283 DOI: 10.1038/s41598-021-00294-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 09/23/2021] [Indexed: 11/09/2022] Open
Abstract
Severe fever with thrombocytopenia syndrome (SFTS) is a zoonotic disease caused by the SFTS virus (SFTSV). SFTS can be considered a life-threatening notifiable infectious disease. The unavailability of specific therapeutics encourages the investigation of potential efficacy of existing drugs against this infection. Drug repurposing was done by performing virtual screening of already established drug molecules followed by 100 ns molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area-based binding-energy calculation by targeting the SFTS L protein. On the basis of binding energy and protein-ligand interactions, top 10 promising hits were identified, showing stable binding with SFTS L protein. Further 100 ns atomistic MD simulation refined the hits from top 10 to top 4 with docking-based binding energy lesser than -8.0 kcal/mol toward the SFTS L protein and engaged in π-π interactions with pivotal amino acid residues. Various parameters and binding affinity of top 4 ligands towards L protein was computed. Ligand zaltoprofen exhibited best binding energy -220.095 kJ/mol. The present work is the first in silico study to assess bromfenac, cinchophen, elliptinium, and zaltoprofen; four promising hits against SFTS. Nonetheless, further proper biological evaluation is necessary to determine their efficacy against SFTS.
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Affiliation(s)
- Shilpa Chatterjee
- Department of Biomedical Science, College of Medicine, Chosun University, Gwangju, Republic of Korea
| | - Choon-Mee Kim
- Premedical Science, College of Medicine, Chosun University, Gwangju, Republic of Korea
| | - Dong-Min Kim
- Department of Internal Medicine, College of Medicine, Chosun University, 588 Seosuk-dong, Dong-gu, Gwangju, 501-717, Republic of Korea.
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21
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Li HX, Yang WY, Li LP, Zhou H, Li WY, Ma Y, Wang RL. Molecular dynamics study of CDC25B R492L mutant causing the activity decrease of CDC25B. J Mol Graph Model 2021; 109:108030. [PMID: 34509094 DOI: 10.1016/j.jmgm.2021.108030] [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: 07/09/2021] [Revised: 09/01/2021] [Accepted: 09/05/2021] [Indexed: 11/25/2022]
Abstract
Cell division cycle 25B (CDC25B) was responsible for regulating the various stages of cell division in the cell cycle. R492L was one of the common types of CDC25B mutants. Researches showed that compared to CDC25BWT, CDC25BR492L mutant had a ∼100-fold reduction in the rate constant for forming phosphatase intermediate (k2). However, the molecular basis of how the CDC25BR492L mutant influenced the process of binding between CDC25B and CDK2/CyclinA was not yet known. Therefore, the optimizations of three-dimensional structure of the CDC25BWT-CDK2/CyclinA system and the CDC25BR492L-CDK2/CyclinA system were constructed by ZDOCK and RDOCK, and five methods were employed to verify the reasonability of the docking structure. Then the molecular dynamics simulations on the two systems were performed to explore the reason why CDC25BR492L mutant caused the weak interactions between CDC25BR492L and CDK2/CyclinA, respectively. The remote docking site (Arg488-Tyr497) and the second active site (Lys538-Arg544) of CDC25B were observed to have high fluctuations in the CDC25BR492L-CDK2/CyclinA system with post-analysis, where the high fluctuation of these two regions resulted in weak interactions between CD25B and CDK2. In addition, Asp38-Glu42 and Asp206-Asp210 of CDK2 showed the slightly descending fluctuation, and CDK2 revealed an enhanced the self-interaction, which made CDK2 keep a relatively stable state in the CDC25BR492L-CDK2/CyclinA system. Finally, Leu492 of CDC25B was speculated to be the key residue, which had great effects on the binding between CDC25BR492L and CDK2 in the CDC25BR492L-CDK2/CyclinA system. Consequently, overall analyses appeared in this study ultimately offered a helpful understanding of the weak interactions between CDC25BR492L and CDK2.
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Affiliation(s)
- Hao-Xin Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Wen-Yu Yang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, People's Republic of China
| | - Li-Peng Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Hui Zhou
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Wei-Ya Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Ying Ma
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China.
| | - Run-Ling Wang
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China.
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22
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Dong T, Gong T, Li W. Accurate Estimation of Solvent Accessible Surface Area for Coarse-Grained Biomolecular Structures with Deep Learning. J Phys Chem B 2021; 125:9490-9498. [PMID: 34383495 DOI: 10.1021/acs.jpcb.1c05203] [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
Coarse-grained (CG) models of biomolecules have been widely used in protein/ribonucleic acid (RNA) three-dimensional structure prediction, docking, drug design, and molecular simulations due to their superiority in computational efficiency. Most of these applications strongly depend on the reasonable estimation of solvation free energy, which requires the accurate calculation of solvent accessible surface area (SASA). Although algorithms for SASA calculations with all-atom protein and RNA structures have been well-established, accurately estimating the SASA based on CG structures is extremely challenging. In this work, we developed a deep learning-based SASA estimator (DeepCGSA), which can provide almost perfect SASA estimation based on CG structures of protein and RNA molecules. Extensive testing analysis showed that for three types of widely used CG protein models, including the Cα-based, Cα-Cβ, and Martini models, the correlation coefficients between the predicted values and the reference values can be as high as 0.95-0.99, which perform dramatically better than available methods. In addition, the new method can be used for CG RNA structures and unfolded protein structures with much improved accuracy. We anticipate that DeepCGSA will be highly useful in the protein/RNA structure prediction, drug design, and other applications, in which accurate estimations of SASA for CG biomolecular structures are critically important.
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Affiliation(s)
- Tiejun Dong
- National Laboratory of Solid State Microstructure, Department of Physics, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325000, China.,Oujiang Laboratory, Wenzhou, Zhejiang 325000, China.,Institute of Drug R&D, Nanjing University, Nanjing 210093, China
| | - Tong Gong
- Institute of Drug R&D, Nanjing University, Nanjing 210093, China
| | - Wenfei Li
- National Laboratory of Solid State Microstructure, Department of Physics, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.,Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325000, China.,Oujiang Laboratory, Wenzhou, Zhejiang 325000, China
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23
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Chitranshi N, Kumar A, Sheriff S, Gupta V, Godinez A, Saks D, Sarkar S, Shen T, Mirzaei M, Basavarajappa D, Abyadeh M, Singh SK, Dua K, Zhang KYJ, Graham SL, Gupta V. Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer's Disease: Computational Refining and Biochemical Evaluation. Cells 2021; 10:cells10081946. [PMID: 34440715 PMCID: PMC8391575 DOI: 10.3390/cells10081946] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 07/23/2021] [Accepted: 07/27/2021] [Indexed: 02/07/2023] Open
Abstract
Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.
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Affiliation(s)
- Nitin Chitranshi
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
- Correspondence: (N.C.); (V.G.); Tel.: +61-(02)-9850-2804 (N.C.)
| | - Ashutosh Kumar
- Center for Biosystems Dynamics Research, Laboratory for Structural Bioinformatics, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama 230-0045, Kanagawa, Japan; (A.K.); (K.Y.J.Z.)
| | - Samran Sheriff
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
| | - Veer Gupta
- School of Medicine, Faculty of Health, Deakin University, Geelong, VIC 3220, Australia;
| | - Angela Godinez
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
| | - Danit Saks
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
| | - Soumalya Sarkar
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
| | - Ting Shen
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
| | - Mehdi Mirzaei
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
| | - Devaraj Basavarajappa
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
| | - Morteza Abyadeh
- Cell Science Research Center, Department of Molecular Systems Biology, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran 1665659911, Iran;
| | - Sachin K. Singh
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara 144411, Punjab, India;
| | - Kamal Dua
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Ultimo, NSW 2007, Australia;
- Australian Research Centre in Complementary and Integrative Medicine, Faculty of Health, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Kam Y. J. Zhang
- Center for Biosystems Dynamics Research, Laboratory for Structural Bioinformatics, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama 230-0045, Kanagawa, Japan; (A.K.); (K.Y.J.Z.)
| | - Stuart L. Graham
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
| | - Vivek Gupta
- Faculty of Medicine, Health and Human Sciences, Macquarie University, F10A, 2 Technology Place, North Ryde, NSW 2109, Australia; (S.S.); (A.G.); (D.S.); (S.S.); (T.S.); (M.M.); (D.B.); (S.L.G.)
- Correspondence: (N.C.); (V.G.); Tel.: +61-(02)-9850-2804 (N.C.)
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24
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Spoel D, Zhang J, Zhang H. Quantitative predictions from molecular simulations using explicit or implicit interactions. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1560] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- David Spoel
- Uppsala Center for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology Uppsala University Uppsala Sweden
| | - Jin Zhang
- Department of Chemistry Southern University of Science and Technology Shenzhen China
| | - Haiyang Zhang
- Department of Biological Science and Engineering, School of Chemistry and Biological Engineering University of Science and Technology Beijing Beijing China
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25
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Kasavajhala K, Lam K, Simmerling C. Exploring Protocols to Build Reservoirs to Accelerate Temperature Replica Exchange MD Simulations. J Chem Theory Comput 2020; 16:7776-7799. [PMID: 33142060 DOI: 10.1021/acs.jctc.0c00513] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Temperature replica exchange molecular dynamics (REMD) is a widely used enhanced sampling method for accelerating biomolecular simulations. During the past 2 decades, several variants of REMD have been developed to further improve the rate of conformational sampling of REMD. One such variant, reservoir REMD (RREMD), was shown to improve the rate of conformational sampling by around 5-20×. Despite the significant increase in the sampling speed, RREMD methods have not been widely used because of the difficulties in building the reservoir and also because of the code not being available on the graphics processing units (GPUs). In this work, we ported the Amber RREMD code onto GPUs making it 20× faster than the central processing unit code. Then, we explored protocols for building Boltzmann-weighted reservoirs as well as non-Boltzmann reservoirs and tested how each choice affects the accuracy of the resulting RREMD simulations. We show that, using the recommended protocols outlined here, RREMD simulations can accurately reproduce Boltzmann-weighted ensembles obtained by much more expensive conventional temperature-based REMD simulations, with at least 15× faster convergence rates even for larger proteins (>50 amino acids) compared to conventional REMD.
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Affiliation(s)
- Koushik Kasavajhala
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Kenneth Lam
- Molecular and Cellular Biology, Stony Brook University, Stony Brook, New York 11794, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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26
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Horvath D, Marcou G, Varnek A. "Big Data" Fast Chemoinformatics Model to Predict Generalized Born Radius and Solvent Accessibility as a Function of Geometry. J Chem Inf Model 2020; 60:2951-2965. [PMID: 32374171 DOI: 10.1021/acs.jcim.9b01172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The Generalized Born (GB) solvent model is offering the best accuracy/computing effort ratio yet requires drastic simplifications to estimate of the Effective Born Radii (EBR) in bypassing a too expensive volume integration step. EBRs are a measure of the degree of burial of an atom and not very sensitive to small changes of geometry: in molecular dynamics, the costly EBR update procedure is not mandatory at every step. This work however aims at implementing a GB model into the Sampler for Multiple Protein-Ligand Entities (S4MPLE) evolutionary algorithm with mandatory EBR updates at each step triggering arbitrarily large geometric changes. Therefore, a quantitative structure-property relationship has been developed in order to express the EBRs as a linear function of both the topological neighborhood and geometric occupancy of the space around atoms. A training set of 810 molecular systems, starting from fragment-like to drug-like compounds, proteins, host-guest systems, and ligand-protein complexes, has been compiled. For each species, S4MPLE generated several hundreds of random conformers. For each atom in each geometry of each species, its "standard" EBR was calculated by numeric integration and associated to topological and geometric descriptors of the atom neighborhood. This training set (EBR, atom descriptors) involving >5 M entries was subjected to a boot-strapping multilinear regression process with descriptor selection. In parallel, the strategy was repurposed to also learn atomic solvent-accessible areas (SA) based on the same descriptors. Resulting linear equations were challenged to predict EBR and SA values for a similarly compiled external set of >2000 new molecular systems. Solvation energies calculated with estimated EBR and SA match "standard" energies within the typical error of a force-field-based approach (a few kilocalories per mole). Given the extreme diversity of molecular systems covered by the model, this simple EBR/SA estimator covers a vast applicability domain.
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Affiliation(s)
- Dragos Horvath
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Gilles Marcou
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
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27
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Gong X, Chiricotto M, Liu X, Nordquist E, Feig M, Brooks CL, Chen J. Accelerating the Generalized Born with Molecular Volume and Solvent Accessible Surface Area Implicit Solvent Model Using Graphics Processing Units. J Comput Chem 2019; 41:830-838. [PMID: 31875339 DOI: 10.1002/jcc.26133] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 11/21/2019] [Accepted: 12/07/2019] [Indexed: 12/15/2022]
Abstract
The generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) implicit solvent model provides an accurate description of molecular volume and has the potential to accurately describe the conformational equilibria of structured and disordered proteins. However, its broader application has been limited by the computational cost and poor scaling in parallel computing. Here, we report an efficient implementation of both the electrostatic and nonpolar components of GBMV2/SA on graphics processing unit (GPU) within the CHARMM/OpenMM module. The GPU-GBMV2/SA is numerically equivalent to the original CPU-GBMV2/SA. The GPU acceleration offers ~60- to 70-fold speedup on a single NVIDIA TITAN X (Pascal) graphics card for molecular dynamic simulations of both folded and unstructured proteins of various sizes. The current implementation can be further optimized to achieve even greater acceleration with minimal reduction on the numerical accuracy. The successful development of GPU-GBMV2/SA greatly facilitates its application to biomolecular simulations and paves the way for further development of the implicit solvent methodology. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiping Gong
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
| | - Mara Chiricotto
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
| | - Xiaorong Liu
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
| | - Erik Nordquist
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, 48824
| | - Charles L Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan, 48109
| | - Jianhan Chen
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
- Department of Biochemistry and Molecular Biology, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003
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28
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Machado MR, Zeida A, Darré L, Pantano S. From quantum to subcellular scales: multi-scale simulation approaches and the SIRAH force field. Interface Focus 2019; 9:20180085. [PMID: 31065347 PMCID: PMC6501346 DOI: 10.1098/rsfs.2018.0085] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/11/2019] [Indexed: 12/11/2022] Open
Abstract
Modern molecular and cellular biology profits from astonishing resolution structural methods, currently even reaching the whole cell level. This is encompassed by the development of computational methods providing a deep view into the structure and dynamics of molecular processes happening at very different scales in time and space. Linking such scales is of paramount importance when aiming at far-reaching biological questions. Computational methods at the interface between classical and coarse-grained resolutions are gaining momentum with several research groups dedicating important efforts to their development and tuning. An overview of such methods is addressed herein, with special emphasis on the SIRAH force field for coarse-grained and multi-scale simulations. Moreover, we provide proof of concept calculations on the implementation of a multi-scale simulation scheme including quantum calculations on a classical fine-grained/coarse-grained representation of double-stranded DNA. This opens the possibility to include the effect of large conformational fluctuations in chromatin segments on, for instance, the reactivity of particular base pairs within the same simulation framework.
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Affiliation(s)
- Matías R. Machado
- Institut Pasteur de Montevideo, Group of Biomolecular Simulations, Mataojo 2020, CP 11400 Montevideo, Uruguay
| | - Ari Zeida
- Departamento de Bioquímica and Center for Free Radical and Biomedical Research, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Leonardo Darré
- Institut Pasteur de Montevideo, Group of Biomolecular Simulations, Mataojo 2020, CP 11400 Montevideo, Uruguay
- Institut Pasteur de Montevideo, Functional Genomics Unit, Mataojo 2020, CP 11400 Montevideo, Uruguay
| | - Sergio Pantano
- Institut Pasteur de Montevideo, Group of Biomolecular Simulations, Mataojo 2020, CP 11400 Montevideo, Uruguay
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29
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Nussinov R, Tsai CJ, Shehu A, Jang H. Computational Structural Biology: Successes, Future Directions, and Challenges. Molecules 2019; 24:molecules24030637. [PMID: 30759724 PMCID: PMC6384756 DOI: 10.3390/molecules24030637] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/05/2019] [Accepted: 02/10/2019] [Indexed: 02/06/2023] Open
Abstract
Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
| | - Amarda Shehu
- Departments of Computer Science, Department of Bioengineering, and School of Systems Biology, George Mason University, Fairfax, VA 22030, USA.
| | - Hyunbum Jang
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
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