1
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Krivoshchapov NV, Medvedev MG. Accurate and Efficient Conformer Sampling of Cyclic Drug-Like Molecules with Inverse Kinematics. J Chem Inf Model 2024; 64:4542-4552. [PMID: 38776465 DOI: 10.1021/acs.jcim.3c02040] [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: 05/25/2024]
Abstract
Identification of all of the influential conformers of biomolecules is a crucial step in many tasks of computational biochemistry. Specifically, molecular docking, a key component of in silico drug development, requires a comprehensive set of conformations for potential candidates in order to generate the optimal ligand-receptor poses and, ultimately, find the best drug candidates. However, the presence of flexible cycles in a molecule complicates the initial search for conformers since exhaustive sampling algorithms via torsional random and systematic searches become very inefficient. The devised inverse-kinematics-based Monte Carlo with refinement (MCR) algorithm identifies independently rotatable dihedral angles in (poly)cyclic molecules and uses them to perform global conformational sampling, outperforming popular alternatives (MacroModel, CREST, and RDKit) in terms of speed and diversity of the resulting conformer ensembles. Moreover, MCR quickly and accurately recovers naturally occurring macrocycle conformations for most of the considered molecules.
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Affiliation(s)
- Nikolai V Krivoshchapov
- N. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russian Federation
| | - Michael G Medvedev
- N. D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russian Federation
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2
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Azmi MB, Nasir MF, Asif U, Kazi M, Uddin MN, Qureshi SA. Analyzing molecular signatures in preeclampsia and fetal growth restriction: Identifying key genes, pathways, and therapeutic targets for preterm birth. Front Mol Biosci 2024; 11:1384214. [PMID: 38712342 PMCID: PMC11070483 DOI: 10.3389/fmolb.2024.1384214] [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: 02/08/2024] [Accepted: 03/22/2024] [Indexed: 05/08/2024] Open
Abstract
Background Intrauterine growth restriction (IUGR) and preeclampsia (PE) are intricately linked with specific maternal health conditions, exhibit shared placental abnormalities, and play pivotal roles in precipitating preterm birth (PTB) incidences. However, the molecular mechanism underlying the association between PE and IUGR has not been determined. Therefore, we aimed to analyze the data of females with PE and those with PE + IUGR to identify the key gene(s), their molecular pathways, and potential therapeutic interactions. Methods In this study, a comprehensive relationship analysis of both PE and PE + IUGR was conducted using RNA sequence datasets. Using two datasets (GSE148241 and GSE114691), differential gene expression analysis via DESeq2 through R-programming was performed. Gene set enrichment analysis was performed using ClusterProfiler, protein‒protein interaction (PPI) networks were constructed, and cluster analyses were conducted using String and MCODE in Cytoscape. Functional enrichment analyses of the resulting subnetworks were performed using ClueGO software. The hub genes were identified under both conditions using the CytoHubba method. Finally, the most common hub protein was docked against a library of bioactive flavonoids and PTB drugs using the PyRx AutoDock tool, followed by molecular dynamic (MD) simulation analysis. Pharmacokinetic analysis was performed to determine the ADMET properties of the compounds using pkCSM. Results We identified eight hub genes highly expressed in the case of PE, namely, PTGS2, ENG, KIT, MME, CGA, GAPDH, GPX3, and P4HA1, and the network of the PE + IUGR gene set demonstrated that nine hub genes were overexpressed, namely, PTGS2, FGF7, FGF10, IL10, SPP1, MPO, THBS1, CYBB, and PF4. PTGS2 was the most common hub gene found under both conditions (PE and PEIUGR). Moreover, the greater (-9.1 kcal/mol) molecular binding of flavoxate to PTGS2 was found to have satisfactory pharmacokinetic properties compared with those of other compounds. The flavoxate-bound PTGS2 protein complex remained stable throughout the simulation; with a ligand fit to protein, i.e., a RMSD ranging from ∼2.0 to 4.0 Å and a RMSF ranging from ∼0.5 to 2.9 Å, was observed throughout the 100 ns analysis. Conclusion The findings of this study may be useful for treating PE and IUGR in the management of PTB.
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Affiliation(s)
- Muhammad Bilal Azmi
- Computational Biochemistry Research Laboratory, Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan
| | - Mushyeda Fatima Nasir
- Department of Biosciences, Faculty of Life Sciences, Mohammad Ali Jinnah University, Karachi, Pakistan
| | - Uzma Asif
- Department of Biochemistry, Medicine Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Mohsin Kazi
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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3
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Beck TL, Carloni P, Asthagiri DN. All-Atom Biomolecular Simulation in the Exascale Era. J Chem Theory Comput 2024; 20:1777-1782. [PMID: 38382017 DOI: 10.1021/acs.jctc.3c01276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.
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Affiliation(s)
- Thomas L Beck
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Paolo Carloni
- INM-9/IAS-5 Computational Biomedicine, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, D-54245 Jülich, Germany
- Department of Physics, RWTH Aachen University, D-52078 Aachen, Germany
| | - Dilipkumar N Asthagiri
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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4
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Yehorova D, Crean RM, Kasson PM, Kamerlin SCL. Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families. Protein Sci 2024; 33:e4911. [PMID: 38358258 PMCID: PMC10868456 DOI: 10.1002/pro.4911] [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: 11/03/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 02/16/2024]
Abstract
Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.
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Affiliation(s)
- Dariia Yehorova
- School of Chemistry and Biochemistry, Georgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Rory M. Crean
- Department of Chemistry—BMCUppsala UniversityUppsalaSweden
| | - Peter M. Kasson
- Department of Molecular PhysiologyUniversity of VirginiaCharlottesvilleVirginiaUSA
- Department Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginiaUSA
- Department of Cell and Molecular BiologyUppsala UniversityUppsalaSweden
| | - Shina C. L. Kamerlin
- School of Chemistry and Biochemistry, Georgia Institute of TechnologyAtlantaGeorgiaUSA
- Department of Chemistry—BMCUppsala UniversityUppsalaSweden
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5
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Oliveira AS, Rubio J, Noble CEM, Anderson JLR, Anders J, Mulholland AJ. Fluctuation Relations to Calculate Protein Redox Potentials from Molecular Dynamics Simulations. J Chem Theory Comput 2024; 20:385-395. [PMID: 38150288 PMCID: PMC10782445 DOI: 10.1021/acs.jctc.3c00785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 12/28/2023]
Abstract
The tunable design of protein redox potentials promises to open a range of applications in biotechnology and catalysis. Here, we introduce a method to calculate redox potential changes by combining fluctuation relations with molecular dynamics simulations. It involves the simulation of reduced and oxidized states, followed by the instantaneous conversion between them. Energy differences introduced by the perturbations are obtained using the Kubo-Onsager approach. Using a detailed fluctuation relation coupled with Bayesian inference, these are postprocessed into estimates for the redox potentials in an efficient manner. This new method, denoted MD + CB, is tested on a de novo four-helix bundle heme protein (the m4D2 "maquette") and five designed mutants, including some mutants characterized experimentally in this work. The MD + CB approach is found to perform reliably, giving redox potential shifts with reasonably good correlation (0.85) to the experimental values for the mutants. The MD + CB approach also compares well with redox potential shift predictions using a continuum electrostatic method. The estimation method employed within the MD + CB approach is straightforwardly transferable to standard equilibrium MD simulations and holds promise for redox protein engineering and design applications.
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Affiliation(s)
- A. S.
F. Oliveira
- Centre
for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, U.K.
- School
of Biochemistry, University of Bristol, Bristol BS8 1DT, U.K.
- BrisSynBio
Synthetic Biology Research Centre, University
of Bristol, Bristol BS8 1TQ, U.K.
| | - J. Rubio
- School
of Mathematics and Physics, University of
Surrey, Guildford GU2 7XH, U.K.
- Department
of Physics and Astronomy, University of
Exeter, Stocker Road, Exeter EX4
4QL, U.K.
| | - C. E. M. Noble
- School
of Biochemistry, University of Bristol, Bristol BS8 1DT, U.K.
- BrisSynBio
Synthetic Biology Research Centre, University
of Bristol, Bristol BS8 1TQ, U.K.
| | - J. L. R. Anderson
- School
of Biochemistry, University of Bristol, Bristol BS8 1DT, U.K.
- BrisSynBio
Synthetic Biology Research Centre, University
of Bristol, Bristol BS8 1TQ, U.K.
| | - J. Anders
- Department
of Physics and Astronomy, University of
Exeter, Stocker Road, Exeter EX4
4QL, U.K.
- Institute
of Physics and Astronomy, University of
Potsdam, Potsdam 14476, Germany
| | - A. J. Mulholland
- Centre
for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, U.K.
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6
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Beltrán D, Hospital A, Gelpí JL, Orozco M. A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort. Nucleic Acids Res 2024; 52:D393-D403. [PMID: 37953362 PMCID: PMC10767965 DOI: 10.1093/nar/gkad991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).
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Affiliation(s)
- Daniel Beltrán
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Josep Lluís Gelpí
- Department of Biochemistry and Biomedicine. University of Barcelona, Barcelona, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology, Barcelona, Spain
- Department of Biochemistry and Biomedicine. University of Barcelona, Barcelona, Spain
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7
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Wu J, Lv J, Zhao L, Zhao R, Gao T, Xu Q, Liu D, Yu Q, Ma F. Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:167028. [PMID: 37704131 DOI: 10.1016/j.scitotenv.2023.167028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/03/2023] [Accepted: 09/10/2023] [Indexed: 09/15/2023]
Abstract
Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.
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Affiliation(s)
- Jieting Wu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Jin Lv
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Lei Zhao
- State Key Laboratory of Urban Water Resources & Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Ruofan Zhao
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Tian Gao
- Key Laboratory of Integrated Regulation and Resource Development of Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Xikang Road #1, Nanjing 210098, China
| | - Qi Xu
- PetroChina Fushun Petrochemical Company, Fushun 113000, China
| | - Dongbo Liu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Qiqi Yu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Fang Ma
- State Key Laboratory of Urban Water Resources & Environment, Harbin Institute of Technology, Harbin 150090, China.
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8
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Kao CF, Tsai MH, Carillo KJ, Tzou DL, Chang W. Structural and functional analysis of vaccinia viral fusion complex component protein A28 through NMR and molecular dynamic simulations. PLoS Pathog 2023; 19:e1011500. [PMID: 37948471 PMCID: PMC10664964 DOI: 10.1371/journal.ppat.1011500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/22/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023] Open
Abstract
Host cell entry of vaccinia virus (a poxvirus) proceeds through multiple steps that involve many viral proteins to mediate cell infection. Upon binding to cells, vaccinia virus membrane fuses with host membranes via a viral entry fusion protein complex comprising 11 proteins: A16, A21, A28, F9, G3, G9, H2, J5, L1, L5 and O3. Despite vaccinia virus having two infectious forms, mature and enveloped, that have different membrane layers, both forms require an identical viral entry fusion complex for membrane fusion. Components of the poxvirus entry fusion complex that have been structurally assessed to date share no known homology with all other type I, II and III viral fusion proteins, and the large number of fusion protein components renders it a unique system to investigate poxvirus-mediated membrane fusion. Here, we determined the NMR structure of a truncated version of vaccinia A28 protein. We also expressed a soluble H2 protein and showed that A28 interacts with H2 protein at a 1:1 ratio in vitro. Furthermore, we performed extensive in vitro alanine mutagenesis to identify A28 protein residues that are critical for H2 binding, entry fusion complex formation, and virus-mediated membrane fusion. Finally, we used molecular dynamic simulations to model full-length A28-H2 subcomplex in membranes. In summary, we characterized vaccinia virus A28 protein and determined residues important in its interaction with H2 protein and membrane components. We also provide a structural model of the A28-H2 protein interaction to illustrate how it forms a 1:1 subcomplex on a modeled membrane.
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Affiliation(s)
- Chi-Fei Kao
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
| | - Min-Hsin Tsai
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | | | - Der-Lii Tzou
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| | - Wen Chang
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
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9
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Singh VK, Thakur DC, Rajak N, Mishra A, Kumar A, Giri R, Garg N. The multi-protein targeting potential of bioactive syringin in inflammatory diseases: using molecular modelling and in-silico analysis of regulatory elements. J Biomol Struct Dyn 2023:1-12. [PMID: 37882327 DOI: 10.1080/07391102.2023.2273440] [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: 08/02/2023] [Accepted: 10/14/2023] [Indexed: 10/27/2023]
Abstract
Inflammation plays a crucial role in the onset or progression of a variety of acute and chronic diseases. Non-steroidal anti-inflammatory drugs (NSAIDs) are the only available FDA-approved therapy. The therapeutic outcome of NSAIDs is still finite due to off-target effects and extreme side effects on other vital organs. Bioactive syringin has been manifested to hold anti-osteoporosis, cardiac hypertrophy, alter autophagy, anti-cancer, neuro-preventive effects, etc. However, its multi-protein targeting potential in inflammation mostly remains unexplored. In the present work, we have checked the multi-protein targeting potential of bioactive glycoside syringin in inflammatory diseases. Based on the binding score of protein-ligand complexes, glycoside syringin scored greater than -7 kcal/mol against 12 inflammatory proteins. Our molecular dynamic simulation study (200 ns) confirmed that bioactive syringin remained inside the binding cavity of inflammatory proteins (JAK1, TYK2, and COX1) in a stable conformation. Further, our co-expression analysis suggests that these genes play an essential role in multiple pathways and are regulated by multiple miRNAs. Our study demonstrates that bioactive glycoside syringin might be a multi-protein targeting potential against inflammatory diseases and could be further investigated utilizing different preclinical approaches.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Vipendra Kumar Singh
- School of Biosciences and Bioengineering, Indian Institute of Technology Mandi, Mandi, India
| | - D C Thakur
- School of Biosciences and Bioengineering, Indian Institute of Technology Mandi, Mandi, India
| | - Naina Rajak
- Faculty of Ayurveda, Department of Medicinal Chemistry, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
| | - Anand Mishra
- Molecular Plant Pathology Laboratory, CSIR-Institute of Himalayan Bioresource Technology, Palampur, India
| | - Ankur Kumar
- School of Biosciences and Bioengineering, Indian Institute of Technology Mandi, Mandi, India
| | - Rajanish Giri
- School of Biosciences and Bioengineering, Indian Institute of Technology Mandi, Mandi, India
| | - Neha Garg
- Faculty of Ayurveda, Department of Medicinal Chemistry, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
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10
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Buigues P, Gehrke S, Badaoui M, Dudas B, Mandana G, Qi T, Bottegoni G, Rosta E. Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor. J Chem Theory Comput 2023; 19:5260-5272. [PMID: 37458730 PMCID: PMC10413856 DOI: 10.1021/acs.jctc.3c00023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Indexed: 08/09/2023]
Abstract
Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3's activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization.
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Affiliation(s)
- Pedro
J. Buigues
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Sascha Gehrke
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Magd Badaoui
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Balint Dudas
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Gaurav Mandana
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Tianyun Qi
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
| | - Giovanni Bottegoni
- Dipartimento
di Scienze Biomolecolari (DISB), University
of Urbino, Urbino Piazza Rinascimento, 6, Urbino 61029, Italy
- Institute
of Clinical Sciences, University of Birmingham, Edgbaston, B15 2TT Birmingham, United Kingdom
| | - Edina Rosta
- Department
of Physics and Astronomy, University College
London, London WC1E 6BT, United
Kingdom
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11
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Van Speybroeck V. Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220239. [PMID: 37211031 DOI: 10.1098/rsta.2022.0239] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/23/2023] [Indexed: 05/23/2023]
Abstract
The question is addressed in how far current modelling strategies are capable of modelling dynamic phenomena in realistic nanostructured materials at operating conditions. Nanostructured materials used in applications are far from perfect; they possess a broad range of heterogeneities in space and time extending over several orders of magnitude. Spatial heterogeneities from the subnanometre to the micrometre scale in crystal particles with a finite size and specific morphology, impact the material's dynamics. Furthermore, the material's functional behaviour is largely determined by the operating conditions. Currently, there exists a huge length-time scale gap between attainable theoretical length-time scales and experimentally relevant scales. Within this perspective, three key challenges are highlighted within the molecular modelling chain to bridge this length-time scale gap. Methods are needed that enable (i) building structural models for realistic crystal particles having mesoscale dimensions with isolated defects, correlated nanoregions, mesoporosity, internal and external surfaces; (ii) the evaluation of interatomic forces with quantum mechanical accuracy albeit at much lower computational cost than the currently used density functional theory methods and (iii) derivation of the kinetics of phenomena taking place in a multi-length-time scale window to obtain an overall view of the dynamics of the process. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.
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12
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pour PM, Mahnam K, Taherzadeh M, Ahangarzadeh S, Alibakhshi A, Mohammadi E. The effect of mutation on neurotoxicity reduction of new chimeric reteplase, a computational study. Res Pharm Sci 2023; 18:404-412. [PMID: 37614611 PMCID: PMC10443662 DOI: 10.4103/1735-5362.378087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/23/2022] [Accepted: 05/27/2022] [Indexed: 08/25/2023] Open
Abstract
Background and purpose Excitotoxicity in nerve cells is a type of neurotoxicity in which excessive stimulation of receptors (such as N-methyl-d-aspartate glutamate receptors (NMDAR)) leads to the influx of high-level calcium ions into cells and finally cell damage or death. This complication can occur after taking some of the plasminogen activators like tissue plasminogen activator and reteplase. The interaction of the kringle2 domain in such plasminogen activator with the amino-terminal domain (ATD) of the NR1 subunit of NMDAR finally leads to excitotoxicity. In this study, we assessed the interaction of two new chimeric reteplase, mutated in the kringle2 domain, with ATD and compared the interaction of wild-type reteplase with ATD, computationally. Experimental approach Homology modeling, protein docking, molecular dynamic simulation, and molecular dynamics trajectory analysis were used for the assessment of this interaction. Findings/Results The results of the free energy analysis between reteplase and ATD (wild reteplase: -2127.516 ± 0.0, M1-chr: -1761.510 ± 0.0, M2-chr: -521.908 ± 0.0) showed lower interaction of this chimeric reteplase with ATD compared to the wild type. Conclusion and implications The decreased interaction between two chimeric reteplase and ATD of NR1 subunit in NMDAR which leads to lower neurotoxicity related to these drugs, can be the start of a way to conduct more tests and if the results confirm this feature, they can be considered potential drugs in acute ischemic stroke treatment.
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Affiliation(s)
- Pardis Mohammadi pour
- Phytochemistry Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Karim Mahnam
- Biology Department, Faculty of Science, Shahrekord University, Shahrekord, Iran
| | - Mahsa Taherzadeh
- Department of Anatomy and Cell Biology, McGill University, Montreal, H3A 0C7, QC, Canada
| | - Shahrzad Ahangarzadeh
- Infectious Diseases and Tropical Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Abbas Alibakhshi
- Molecular Medicine Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Elmira Mohammadi
- Core Research Facilities, Isfahan University of Medical Sciences, Isfahan, Iran
- Applied Physiology Research Center, Cardiovascular Research Institute, Department of Physiology, Isfahan University of Medical Sciences, Isfahan, Iran
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13
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Clark F, Robb G, Cole DJ, Michel J. Comparison of Receptor-Ligand Restraint Schemes for Alchemical Absolute Binding Free Energy Calculations. J Chem Theory Comput 2023; 19:3686-3704. [PMID: 37285579 PMCID: PMC10308817 DOI: 10.1021/acs.jctc.3c00139] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Indexed: 06/09/2023]
Abstract
Alchemical absolute binding free energy calculations are of increasing interest in drug discovery. These calculations require restraints between the receptor and ligand to restrict their relative positions and, optionally, orientations. Boresch restraints are commonly used, but they must be carefully selected in order to sufficiently restrain the ligand and to avoid inherent instabilities. Applying multiple distance restraints between anchor points in the receptor and ligand provides an alternative framework without inherent instabilities which may provide convergence benefits by more strongly restricting the relative movements of the receptor and ligand. However, there is no simple method to calculate the free energy of releasing these restraints due to the coupling of the internal and external degrees of freedom of the receptor and ligand. Here, a method to rigorously calculate free energies of binding with multiple distance restraints by imposing intramolecular restraints on the anchor points is proposed. Absolute binding free energies for the human macrophage migration inhibitory factor/MIF180, system obtained using a variety of Boresch restraints and rigorous and nonrigorous implementations of multiple distance restraints are compared. It is shown that several multiple distance restraint schemes produce estimates in good agreement with Boresch restraints. In contrast, calculations without orientational restraints produce erroneously favorable free energies of binding by up to approximately 4 kcal mol-1. These approaches offer new options for the deployment of alchemical absolute binding free energy calculations.
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Affiliation(s)
- Finlay Clark
- EaStCHEM
School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Graeme Robb
- Oncology
R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Daniel J. Cole
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, United Kingdom
| | - Julien Michel
- EaStCHEM
School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
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14
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Hardie A, Cossins BP, Lovera S, Michel J. Deconstructing allostery by computational assessment of the binding determinants of allosteric PTP1B modulators. Commun Chem 2023; 6:125. [PMID: 37322137 DOI: 10.1038/s42004-023-00926-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/08/2023] [Indexed: 06/17/2023] Open
Abstract
Fragment-based drug discovery is an established methodology for finding hit molecules that can be elaborated into lead compounds. However it is currently challenging to predict whether fragment hits that do not bind to an orthosteric site could be elaborated into allosteric modulators, as in these cases binding does not necessarily translate into a functional effect. We propose a workflow using Markov State Models (MSMs) with steered molecular dynamics (sMD) to assess the allosteric potential of known binders. sMD simulations are employed to sample protein conformational space inaccessible to routine equilibrium MD timescales. Protein conformations sampled by sMD provide starting points for seeded MD simulations, which are combined into MSMs. The methodology is demonstrated on a dataset of protein tyrosine phosphatase 1B ligands. Experimentally confirmed allosteric inhibitors are correctly classified as inhibitors, whereas the deconstructed analogues show reduced inhibitory activity. Analysis of the MSMs provide insights into preferred protein-ligand arrangements that correlate with functional outcomes. The present methodology may find applications for progressing fragments towards lead molecules in FBDD campaigns.
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Affiliation(s)
- Adele Hardie
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, EH9 3FJ, UK
| | - Benjamin P Cossins
- UCB Pharma, 216 Bath Road, Slough, UK
- Exscientia, The Schrödinger Building, Oxford Science Park, Oxford, UK
| | - Silvia Lovera
- UCB Pharma, Chemin du Foriest 1, 1420, Braine-l'Alleud, Belgium
| | - Julien Michel
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, EH9 3FJ, UK.
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15
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Morado J, Mortenson PN, Nissink JWM, Essex JW, Skylaris CK. Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins. J Chem Inf Model 2023; 63:2810-2827. [PMID: 37071825 PMCID: PMC10170518 DOI: 10.1021/acs.jcim.2c01510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. The results and discussion presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected hydrogen bonding and overstabilize global minima and shows problems related to inadequate description of dispersion interactions. Furthermore, while ANI-2x is a viable model for modeling in the gas phase, conventional force fields still play an important role, especially for condensed-phase simulations. Overall, this study highlights the strengths and weaknesses of each model, providing guidelines for the use and future development of force fields and machine learning potentials.
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Affiliation(s)
- João Morado
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Paul N Mortenson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - J Willem M Nissink
- Computational Chemistry, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Chris-Kriton Skylaris
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
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16
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Castro-Velázquez V, Díaz-Cervantes E, Rodríguez-González V, Cortés-García CJ. In-silico assay of a dosing vehicle based on chitosan-TiO 2 and modified benzofuran-isatin molecules against Pseudomonas aeruginosa. PEERJ PHYSICAL CHEMISTRY 2023. [DOI: 10.7717/peerj-pchem.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2023] Open
Abstract
A high priority of the World Health Organization (WHO) is the study of drugs against Pseudomonas aeruginosa, which has developed antibiotic resistance. In this order, recent research is analyzing biomaterials and metal oxide nanoparticles, such as chitosan (QT) and TiO2 (NT), which can transport molecules with biological activity against bacteria, to propose them as drug carrier candidates. In the present work, 10 modified benzofuran-isatin molecules were studied through computational simulation using density functional theory (DFT) and molecular docking assays against Hfq and LpxC (proteins of P. aeruginosa). The results show that the ligand efficiency of commercial drugs C-CP and C-AZI against Hfq is low compared with the best-designed molecule MOL-A. However, we highlight that the influence of NT promotes a better interaction of some molecules, where MOL-E generates a better interaction by 0.219 kcal/mol when NT is introduced in Hfq, forming the system Hfq-NT (Target-NT). Similar behavior is observed in the LpxC target, in which MOL-J is better at 0.072 kcal/mol. Finally, two pharmacophoric models for Hfq and LpxC implicate hydrophobic and aromatic-hydrophobic fragments.
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Affiliation(s)
- Verónica Castro-Velázquez
- División de Materiales Avanzados, Instituto Potosino de Investigación Científica y Tecnología, San Luis Potosí, San Luis Potosí, Mexico
- Departamento de Alimentos, Universidad de Guanajuato, Tierra Blanca, Guanajuato, Mexico
| | - Erik Díaz-Cervantes
- Departamento de Alimentos, Universidad de Guanajuato, Tierra Blanca, Guanajuato, Mexico
| | - Vicente Rodríguez-González
- División de Materiales Avanzados, Instituto Potosino de Investigación Científica y Tecnología, San Luis Potosí, San Luis Potosí, Mexico
| | - Carlos J. Cortés-García
- Laboratorio de Diseño Molecular/Instituto de Investigaciones Químico Biológicas, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, Mexico
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17
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Abstract
ConspectusThe quantum chemical cluster approach has been used for modeling enzyme active sites and reaction mechanisms for more than two decades. In this methodology, a relatively small part of the enzyme around the active site is selected as a model, and quantum chemical methods, typically density functional theory, are used to calculate energies and other properties. The surrounding enzyme is modeled using implicit solvation and atom fixing techniques. Over the years, a large number of enzyme mechanisms have been solved using this method. The models have gradually become larger as a result of the faster computers, and new kinds of questions have been addressed. In this Account, we review how the cluster approach can be utilized in the field of biocatalysis. Examples from our recent work are chosen to illustrate various aspects of the methodology. The use of the cluster model to explore substrate binding is discussed first. It is emphasized that a comprehensive search is necessary in order to identify the lowest-energy binding mode(s). It is also argued that the best binding mode might not be the productive one, and the full reactions for a number of enzyme-substrate complexes have therefore to be considered to find the lowest-energy reaction pathway. Next, examples are given of how the cluster approach can help in the elucidation of detailed reaction mechanisms of biocatalytically interesting enzymes, and how this knowledge can be exploited to develop enzymes with new functions or to understand the reasons for lack of activity toward non-natural substrates. The enzymes discussed in this context are phenolic acid decarboxylase and metal-dependent decarboxylases from the amidohydrolase superfamily. Next, the application of the cluster approach in the investigation of enzymatic enantioselectivity is discussed. The reaction of strictosidine synthase is selected as a case study, where the cluster calculations could reproduce and rationalize the selectivities of both the natural and non-natural substrates. Finally, we discuss how the cluster approach can be used to guide the rational design of enzyme variants with improved activity and selectivity. Acyl transferase from Mycobacterium smegmatis serves as an instructive example here, for which the calculations could pinpoint the factors controlling the reaction specificity and enantioselectivity. The cases discussed in this Account highlight thus the value of the cluster approach as a tool in biocatalysis. It complements experiments and other computational techniques in this field and provides insights that can be used to understand existing enzymes and to develop new variants with tailored properties.
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Affiliation(s)
- Xiang Sheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, PR China
| | - Fahmi Himo
- Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, SE-10691 Stockholm, Sweden
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18
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Dutta P, Sengupta N. Efficient Interrogation of the Kinetic Barriers Demarcating Catalytic States of a Tyrosine Kinase with Optimal Physical Descriptors and Mixture Models. Chemphyschem 2023; 24:e202200595. [PMID: 36394126 DOI: 10.1002/cphc.202200595] [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: 08/10/2022] [Revised: 11/16/2022] [Accepted: 11/16/2022] [Indexed: 11/18/2022]
Abstract
Computer simulations are increasingly used to access thermo-kinetic information underlying structural transformation of protein kinases. Such information are necessary to probe their roles in disease progression and interactions with drug targets. However, the investigations are frequently challenged by forbiddingly high computational expense, and by the lack of standard protocols for the design of low dimensional physical descriptors that encode system features important for transitions. Here, we consider the demarcating characteristics of the different states of Abelson tyrosine kinase associated with distinct catalytic activity to construct a set of physically meaningful, orthogonal collective variables that preserve the slow modes of the system. Independent sampling of each metastable state is followed by the estimation of global partition function along the appropriate physical descriptors using the modified Expectation Maximized Molecular Dynamics method. The resultant free energy barriers are in excellent agreement with experimentally known rate-limiting dynamics and activation energy computed with conventional enhanced sampling methods. We discuss possible directions for further development and applications.
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Affiliation(s)
- Pallab Dutta
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, 741246, India
| | - Neelanjana Sengupta
- Department of Biological Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur, 741246, India
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19
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Computational identification of drug-like marine natural products as potential RNA polymerase inhibitors against Nipah virus. Comput Biol Chem 2023; 104:107850. [PMID: 36907056 DOI: 10.1016/j.compbiolchem.2023.107850] [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: 12/15/2022] [Revised: 03/05/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023]
Abstract
Nipah virus (NiV) has been an alarming threat to human populations in southern Asia for more than a decade. It is one of the most deadly viruses in the Mononegavirales order. Despite its high mortality rate and virulence, no chemotherapeutic agent or vaccine is publicly available. Hence, this work was conducted to computationally screen marine natural products database for drug-like potential inhibitors for the viral RNA-dependent RNA polymerase (RdRp). The structural model was subjected to molecular dynamics (MD) simulation to obtain the native ensemble of the protein. The CMNPDB dataset of marine natural products was filtered to retain only compounds following Lipinski's five rules. The molecules were energy minimized and docked into different conformers of the RdRp using AutoDock Vina. The best 35 molecules were rescored by GNINA, a deep learning-based docking software. The resulting nine compounds were evaluated for their pharmacokinetic profiles and medicinal chemistry properties. The best five compounds were subjected to MD simulation for 100 ns, followed by binding free energy estimation via Molecular Mechanics/ Generalized Born Surface Area (MM/GBSA) calculations. The results showed remarkable behavior of five hits as inferred by stable binding pose and orientation to block the exit channel of RNA synthesis products in the RdRp cavity. These hits are promising starting materials for in vitro validation and structural modifications to enhance the pharmacokinetic and medicinal chemistry properties for developing antiviral lead compounds.
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20
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Marchetti L, Nifosì R, Martelli PL, Da Pozzo E, Cappello V, Banterle F, Trincavelli ML, Martini C, D’Elia M. Quantum computing algorithms: getting closer to critical problems in computational biology. Brief Bioinform 2022; 23:6758194. [PMID: 36220772 PMCID: PMC9677474 DOI: 10.1093/bib/bbac437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/15/2022] [Accepted: 09/08/2022] [Indexed: 12/14/2022] Open
Abstract
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.
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Affiliation(s)
| | | | - Pier Luigi Martelli
- Corresponding authors: Pier Luigi Martelli. Tel.: +39 0512094005; Fax: +39 0512094005; E-mail: ; Claudia Martini. Tel.: +39 0502219522; Fax: +39 050 2210680; E-mail:
| | - Eleonora Da Pozzo
- University of Pisa, Department of Pharmacy, via Bonanno 6, 56126 Pisa Italy
| | - Valentina Cappello
- Italian Institute of Technology, Center for Materials Interfaces, Viale Rinaldo Piaggio 34, 56025 Pontedera (PI), Italy
| | | | | | - Claudia Martini
- Corresponding authors: Pier Luigi Martelli. Tel.: +39 0512094005; Fax: +39 0512094005; E-mail: ; Claudia Martini. Tel.: +39 0502219522; Fax: +39 050 2210680; E-mail:
| | - Massimo D’Elia
- University of Pisa, Department of Physics, Largo Bruno Pontecorvo 3, 56127, Pisa Italy
- INFN, Sezione di Pisa, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy
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21
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Zlobin A, Golovin A. Between Protein Fold and Nucleophile Identity: Multiscale Modeling of the TEV Protease Enzyme-Substrate Complex. ACS OMEGA 2022; 7:40279-40292. [PMID: 36385818 PMCID: PMC9647873 DOI: 10.1021/acsomega.2c05201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
The cysteine protease from the tobacco etch virus (TEVp) is a well-known and widely utilized enzyme. TEVp's chymotrypsin-like fold is generally associated with serine catalytic triads that differ in terms of a reaction mechanism from the most well-studied papain-like cysteine proteases. The question of what dominates the TEVp mechanism, nucleophile identity, or structural composition has never been previously addressed. Here, we use enhanced sampling multiscale modeling to uncover that TEVp combines the features of two worlds in such a way that potentially hampers its activity. We show that TEVp cysteine is strictly in the anionic form in a free enzyme similar to papain. Peptide binding shifts the equilibrium toward the nucleophile's protonated form, characteristic of chymotrypsin-like proteases, although the cysteinyl anion form is still present and interconversion is rapid. This way cysteine protonation generates enzyme states that are a diversion from the most effective course of action, with only 13.2% of Michaelis complex sub-states able to initiate the reaction. As a result, we propose an updated view on the reaction mechanism catalyzed by TEVp. We also demonstrate that AlphaFold is able to construct protease-substrate complexes with high accuracy. We propose that our findings open a way for its industrious use in enzymological tasks. Unique features of TEVp discovered in this work open a discussion on the evolutionary history and trade-offs of optimizing serine triad-associated folds to cysteine as a nucleophile.
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Affiliation(s)
- Alexander Zlobin
- Belozersky
Institute of Physico-Chemical Biology, Lomonosov
Moscow State University, 119991 Moscow, Russia
- Shemyakin
and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
| | - Andrey Golovin
- Belozersky
Institute of Physico-Chemical Biology, Lomonosov
Moscow State University, 119991 Moscow, Russia
- Shemyakin
and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Sirius
University of Science and Technology, 354340 Sochi, Russia
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22
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Cordier BA, Sawaya NPD, Guerreschi GG, McWeeney SK. Biology and medicine in the landscape of quantum advantages. J R Soc Interface 2022; 19:20220541. [PMID: 36448288 PMCID: PMC9709576 DOI: 10.1098/rsif.2022.0541] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.
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Affiliation(s)
- Benjamin A. Cordier
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA
| | | | | | - Shannon K. McWeeney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA,Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97202, USA,Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97202, USA
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23
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Singh O, Venugopal PP, Mathur A, Chakraborty D. Exploring the multiple conformational states of RNA genome through interhelical dynamics and network analysis. J Mol Graph Model 2022; 116:108264. [PMID: 35820344 DOI: 10.1016/j.jmgm.2022.108264] [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: 04/09/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 12/14/2022]
Abstract
The structural variation of RNA is often very transient and can be easily missed in experiments. Molecular dynamics simulation studies along with network analysis can be an effective tool to identify prominent conformations of such dynamic biomolecular systems. Here we describe a method to effectively sample different RNA conformations at six different temperatures based on the changes in the interhelical orientations. This method gives the information about prominent states of the RNA as well as the probability of the existence of different conformations and their interconnections during the process of evolution. In the case of the SARS-CoV-2 genome, the change of prominent structures was found to be faster at 333 K as compared to higher temperatures due to the formation of the non-native base pairs. ΔΔG calculated between 288 K and 363 K are found to be 10.31 kcal/mol (88 nt) considering the contribution from the multiple states of the RNA which agrees well with the experimentally reported denaturation energy for E. coli α mRNA pseudoknot (∼16 kcal/mol, 112 nt) determined by calorimetry/UV hyperchromicity and human telomerase RNA telomerase (4.5-6.6 kcal/mol, 54 nt) determined by FRET analysis.
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Affiliation(s)
- Omkar Singh
- Biophysical and Computational Chemistry Laboratory, Department of Chemistry, National Institute of Technology, Karnataka, 575025, India
| | - Pushyaraga P Venugopal
- Biophysical and Computational Chemistry Laboratory, Department of Chemistry, National Institute of Technology, Karnataka, 575025, India
| | - Apoorva Mathur
- Biophysical and Computational Chemistry Laboratory, Department of Chemistry, National Institute of Technology, Karnataka, 575025, India
| | - Debashree Chakraborty
- Biophysical and Computational Chemistry Laboratory, Department of Chemistry, National Institute of Technology, Karnataka, 575025, India.
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24
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Lang EJM, Baker EG, Woolfson DN, Mulholland AJ. Generalized Born Implicit Solvent Models Do Not Reproduce Secondary Structures of De Novo Designed Glu/Lys Peptides. J Chem Theory Comput 2022; 18:4070-4076. [PMID: 35687842 PMCID: PMC9281390 DOI: 10.1021/acs.jctc.1c01172] [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] [Indexed: 11/28/2022]
Abstract
![]()
We test a range of
standard generalized Born (GB) models and protein
force fields for a set of five experimentally characterized, designed
peptides comprising alternating blocks of glutamate and lysine, which
have been shown to differ significantly in α-helical content.
Sixty-five combinations of force fields and GB models are evaluated
in >800 μs of molecular dynamics simulations. GB models generally
do not reproduce the experimentally observed α-helical content,
and none perform well for all five peptides. These results illustrate
that these models are not usefully predictive in this context. These
peptides provide a useful test set for simulation methods.
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Affiliation(s)
- Eric J M Lang
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, U.K.,School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, U.K.,BrisSynBio, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, U.K
| | - Emily G Baker
- School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, U.K.,BrisSynBio, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, U.K
| | - Derek N Woolfson
- School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, U.K.,BrisSynBio, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, U.K.,School of Biochemistry, University of Bristol, Medical Sciences Building, University Walk, Bristol BS8 1TD, U.K
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, U.K.,School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, U.K
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25
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Bayarri G, Andrio P, Hospital A, Orozco M, Gelpí JL. BioExcel Building Blocks Workflows (BioBB-Wfs), an integrated web-based platform for biomolecular simulations. Nucleic Acids Res 2022; 50:W99-W107. [PMID: 35639735 PMCID: PMC9252775 DOI: 10.1093/nar/gkac380] [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: 03/29/2022] [Revised: 04/19/2022] [Accepted: 05/02/2022] [Indexed: 11/15/2022] Open
Abstract
We present BioExcel Building Blocks Workflows, a web-based graphical user interface (GUI) offering access to a collection of transversal pre-configured biomolecular simulation workflows assembled with the BioExcel Building Blocks library. Available workflows include Molecular Dynamics setup, protein-ligand docking, trajectory analyses and small molecule parameterization. Workflows can be launched in the platform or downloaded to be run in the users’ own premises. Remote launching of long executions to user's available High-Performance computers is possible, only requiring configuration of the appropriate access credentials. The web-based graphical user interface offers a high level of interactivity, with integration with the NGL viewer to visualize and check 3D structures, MDsrv to visualize trajectories, and Plotly to explore 2D plots. The server requires no login but is recommended to store the users’ projects and manage sensitive information such as remote credentials. Private projects can be made public and shared with colleagues with a simple URL. The tool will help biomolecular simulation users with the most common and repetitive processes by means of a very intuitive and interactive graphical user interface. The server is accessible at https://mmb.irbbarcelona.org/biobb-wfs.
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Affiliation(s)
- Genís Bayarri
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology. Baldiri Reixac 10-12, 08028 Barcelona, Spain
| | - Pau Andrio
- Barcelona Supercomputing Center (BSC), Jordi Girona 29, 08034, Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology. Baldiri Reixac 10-12, 08028 Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology. Baldiri Reixac 10-12, 08028 Barcelona, Spain.,Department of Biochemistry and Molecular Biology, University of Barcelona, 08028 Barcelona, Spain
| | - Josep Lluís Gelpí
- Barcelona Supercomputing Center (BSC), Jordi Girona 29, 08034, Barcelona, Spain.,Department of Biochemistry and Molecular Biology, University of Barcelona, 08028 Barcelona, Spain
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26
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Suruzhon M, Bodnarchuk MS, Ciancetta A, Wall ID, Essex JW. Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo. J Chem Theory Comput 2022; 18:3894-3910. [PMID: 35588256 PMCID: PMC9202307 DOI: 10.1021/acs.jctc.1c01198] [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] [Indexed: 11/28/2022]
Abstract
The sampling problem is one of the most widely studied topics in computational chemistry. While various methods exist for sampling along a set of reaction coordinates, many require system-dependent hyperparameters to achieve maximum efficiency. In this work, we present an alchemical variation of adaptive sequential Monte Carlo (SMC), an irreversible importance resampling method that is part of a well-studied class of methods that have been used in various applications but have been underexplored in computational biophysics. Afterward, we apply alchemical SMC on a variety of test cases, including torsional rotations of solvated ligands (butene and a terphenyl derivative), translational and rotational movements of protein-bound ligands, and protein side chain rotation coupled to the ligand degrees of freedom (T4-lysozyme, protein tyrosine phosphatase 1B, and transforming growth factor β). We find that alchemical SMC is an efficient way to explore targeted degrees of freedom and can be applied to a variety of systems using the same hyperparameters to achieve a similar performance. Alchemical SMC is a promising tool for preparatory exploration of systems where long-timescale sampling of the entire system can be traded off against short-timescale sampling of a particular set of degrees of freedom over a population of conformers.
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Affiliation(s)
- Miroslav Suruzhon
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, U.K
| | | | - Antonella Ciancetta
- Sygnature Discovery, Bio City, Pennyfoot Street, Nottingham NG1 1GR, U.K.,Department of Chemical, Pharmaceutical and Agricultural Sciences─DOCPAS, University of Ferrara, Via Fossato di Mortara 17/19, 44121 Ferrara, Italy
| | - Ian D Wall
- GSK Medicines Research Centre, Gunnels Wood Road, Stevenage SG1 2NY, U.K
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, U.K
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27
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Cordelier S, Crouzet J, Gilliard G, Dorey S, Deleu M, Dhondt-Cordelier S. Deciphering the role of plant plasma membrane lipids in response to invasion patterns: how could biology and biophysics help? JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:2765-2784. [PMID: 35560208 DOI: 10.1093/jxb/erab517] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/25/2021] [Indexed: 06/15/2023]
Abstract
Plants have to constantly face pathogen attacks. To cope with diseases, they have to detect the invading pathogen as early as possible via the sensing of conserved motifs called invasion patterns. The first step of perception occurs at the plasma membrane. While many invasion patterns are perceived by specific proteinaceous immune receptors, several studies have highlighted the influence of the lipid composition and dynamics of the plasma membrane in the sensing of invasion patterns. In this review, we summarize current knowledge on how some microbial invasion patterns could interact with the lipids of the plasma membrane, leading to a plant immune response. Depending on the invasion pattern, different mechanisms are involved. This review outlines the potential of combining biological with biophysical approaches to decipher how plasma membrane lipids are involved in the perception of microbial invasion patterns.
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Affiliation(s)
- Sylvain Cordelier
- Université de Reims Champagne Ardenne, RIBP EA 4707, USC INRAE 1488, SFR Condorcet FR CNRS 3417, 51100 Reims, France
| | - Jérôme Crouzet
- Université de Reims Champagne Ardenne, RIBP EA 4707, USC INRAE 1488, SFR Condorcet FR CNRS 3417, 51100 Reims, France
| | - Guillaume Gilliard
- Laboratoire de Biophysique Moléculaire aux Interfaces, SFR Condorcet FR CNRS 3417, TERRA Research Center, Gembloux Agro-Bio Tech, Université de Liège, 2 Passage des Déportés, B-5030 Gembloux, Belgium
| | - Stéphan Dorey
- Université de Reims Champagne Ardenne, RIBP EA 4707, USC INRAE 1488, SFR Condorcet FR CNRS 3417, 51100 Reims, France
| | - Magali Deleu
- Laboratoire de Biophysique Moléculaire aux Interfaces, SFR Condorcet FR CNRS 3417, TERRA Research Center, Gembloux Agro-Bio Tech, Université de Liège, 2 Passage des Déportés, B-5030 Gembloux, Belgium
| | - Sandrine Dhondt-Cordelier
- Université de Reims Champagne Ardenne, RIBP EA 4707, USC INRAE 1488, SFR Condorcet FR CNRS 3417, 51100 Reims, France
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28
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Dong Y, Li T, Zhang S, Sanchis J, Yin H, Ren J, Sheng X, Li G, Reetz MT. Biocatalytic Baeyer–Villiger Reactions: Uncovering the Source of Regioselectivity at Each Evolutionary Stage of a Mutant with Scrutiny of Fleeting Chiral Intermediates. ACS Catal 2022. [DOI: 10.1021/acscatal.2c00415] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Yijie Dong
- State Key Laboratory for Biology of Plant Diseases and Insect Pests/Key Laboratory of Control of Biological Hazard Factors (Plant Origin) for Agri-product Quality and Safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
- Key Laboratory of Agricultural Microbiomics and Precision Application − Ministry of Agriculture and Rural Affairs, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Guangdong Microbial Culture Collection Center (GDMCC), Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, PR China
| | - Tang Li
- Liaoning Provincial Key Laboratory of Carbohydrates, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, PR China
| | - Shiqing Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P.R. China
| | - Joaquin Sanchis
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
| | - Heng Yin
- Liaoning Provincial Key Laboratory of Carbohydrates, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, PR China
| | - Jie Ren
- State Key Laboratory for Biology of Plant Diseases and Insect Pests/Key Laboratory of Control of Biological Hazard Factors (Plant Origin) for Agri-product Quality and Safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Xiang Sheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, P.R. China
| | - Guangyue Li
- State Key Laboratory for Biology of Plant Diseases and Insect Pests/Key Laboratory of Control of Biological Hazard Factors (Plant Origin) for Agri-product Quality and Safety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Manfred T. Reetz
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, Mülheim 45470, Germany
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29
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Modelling the transport mechanism of organic molecules into cell membranes: The role of organic solvents. Comput Biol Chem 2022; 98:107663. [DOI: 10.1016/j.compbiolchem.2022.107663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 03/03/2022] [Accepted: 03/03/2022] [Indexed: 11/19/2022]
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30
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Badaoui M, Buigues PJ, Berta D, Mandana GM, Gu H, Földes T, Dickson CJ, Hornak V, Kato M, Molteni C, Parsons S, Rosta E. Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics. J Chem Theory Comput 2022; 18:2543-2555. [PMID: 35195418 PMCID: PMC9097281 DOI: 10.1021/acs.jctc.1c00924] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
![]()
The
determination of drug residence times, which define the time
an inhibitor is in complex with its target, is a fundamental part
of the drug discovery process. Synthesis and experimental measurements
of kinetic rate constants are, however, expensive and time consuming.
In this work, we aimed to obtain drug residence times computationally.
Furthermore, we propose a novel algorithm to identify molecular design
objectives based on ligand unbinding kinetics. We designed an enhanced
sampling technique to accurately predict the free-energy profiles
of the ligand unbinding process, focusing on the free-energy barrier
for unbinding. Our method first identifies unbinding paths determining
a corresponding set of internal coordinates (ICs) that form contacts
between the protein and the ligand; it then iteratively updates these
interactions during a series of biased molecular dynamics (MD) simulations
to reveal the ICs that are important for the whole of the unbinding
process. Subsequently, we performed finite-temperature string simulations
to obtain the free-energy barrier for unbinding using the set of ICs
as a complex reaction coordinate. Importantly, we also aimed to enable
the further design of drugs focusing on improved residence times.
To this end, we developed a supervised machine learning (ML) approach
with inputs from unbiased “downhill” trajectories initiated
near the transition state (TS) ensemble of the string unbinding path.
We demonstrate that our ML method can identify key ligand–protein
interactions driving the system through the TS. Some of the most important
drugs for cancer treatment are kinase inhibitors. One of these kinase
targets is cyclin-dependent kinase 2 (CDK2), an appealing target for
anticancer drug development. Here, we tested our method using two
different CDK2 inhibitors for the potential further development of
these compounds. We compared the free-energy barriers obtained from
our calculations with those observed in available experimental data.
We highlighted important interactions at the distal ends of the ligands
that can be targeted for improved residence times. Our method provides
a new tool to determine unbinding rates and to identify key structural
features of the inhibitors that can be used as starting points for
novel design strategies in drug discovery.
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Affiliation(s)
- Magd Badaoui
- Department of Chemistry, King's College London, London SE1 1DB, United Kingdom.,Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Pedro J Buigues
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Dénes Berta
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Gaurav M Mandana
- Department of Chemistry, King's College London, London SE1 1DB, United Kingdom
| | - Hankang Gu
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Tamás Földes
- Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
| | - Callum J Dickson
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Viktor Hornak
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Mitsunori Kato
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Carla Molteni
- Department of Physics, King's College London, London WC2R 2LS, United Kingdom
| | - Simon Parsons
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, United Kingdom
| | - Edina Rosta
- Department of Chemistry, King's College London, London SE1 1DB, United Kingdom.,Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
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31
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Reid LM, Guzzetti I, Svensson T, Carlsson AC, Su W, Leek T, von Sydow L, Czechtizky W, Miljak M, Verma C, De Maria L, Essex JW. How well does molecular simulation reproduce environment-specific conformations of the intrinsically disordered peptides PLP, TP2 and ONEG? Chem Sci 2022; 13:1957-1971. [PMID: 35308859 PMCID: PMC8848758 DOI: 10.1039/d1sc03496k] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 01/03/2022] [Indexed: 12/31/2022] Open
Abstract
Understanding the conformational ensembles of intrinsically disordered proteins and peptides (IDPs) in their various biological environments is essential for understanding their mechanisms and functional roles in the proteome, leading to a greater knowledge of, and potential treatments for, a broad range of diseases. To determine whether molecular simulation is able to generate accurate conformational ensembles of IDPs, we explore the structural landscape of the PLP peptide (an intrinsically disordered region of the proteolipid membrane protein) in aqueous and membrane-mimicking solvents, using replica exchange with solute scaling (REST2), and examine the ability of four force fields (ff14SB, ff14IDPSFF, CHARMM36 and CHARMM36m) to reproduce literature circular dichroism (CD) data. Results from variable temperature (VT) 1H and Rotating frame Overhauser Effect SpectroscopY (ROESY) nuclear magnetic resonance (NMR) experiments are also presented and are consistent with the structural observations obtained from the simulations and CD. We also apply the optimum simulation protocol to TP2 and ONEG (a cell-penetrating peptide (CPP) and a negative control peptide, respectively) to gain insight into the structural differences that may account for the observed difference in their membrane-penetrating abilities. Of the tested force fields, we find that CHARMM36 and CHARMM36m are best suited to the study of IDPs, and accurately predict a disordered to helical conformational transition of the PLP peptide accompanying the change from aqueous to membrane-mimicking solvents. We also identify an α-helical structure of TP2 in the membrane-mimicking solvents and provide a discussion of the mechanistic implications of this observation with reference to the previous literature on the peptide. From these results, we recommend the use of CHARMM36m with the REST2 protocol for the study of environment-specific IDP conformations. We believe that the simulation protocol will allow the study of a broad range of IDPs that undergo conformational transitions in different biological environments.
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Affiliation(s)
- Lauren M Reid
- School of Chemistry, University of Southampton Highfield Southampton SO17 1BJ UK
- Bioinformatics Institute (ASTAR) 30 Biolpolis Street Matrix 138671 Singapore
- MedChemica Ltd Alderley Park Macclesfield Cheshire SK10 4TG UK
| | - Ileana Guzzetti
- Medical Chemistry, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Tor Svensson
- Medical Chemistry, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Anna-Carin Carlsson
- Early Chemical Development, Pharmaceutical Sciences, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Wu Su
- Medical Chemistry, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Tomas Leek
- Medical Chemistry, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Lena von Sydow
- Medical Chemistry, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Werngard Czechtizky
- Medical Chemistry, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Marija Miljak
- School of Chemistry, University of Southampton Highfield Southampton SO17 1BJ UK
| | - Chandra Verma
- Bioinformatics Institute (ASTAR) 30 Biolpolis Street Matrix 138671 Singapore
- Department of Biological Sciences, National University of Singapore 16 Science Drive 4 117558 Singapore
- School of Biological Sciences, Nanyang Technological University 60 Nanyang Dr 637551 Singapore
| | - Leonardo De Maria
- Medical Chemistry, Research and Early Development, Respiratory & Immunology, BioPharmaceuticals R&D AstraZeneca Gothenburg Sweden
| | - Jonathan W Essex
- School of Chemistry, University of Southampton Highfield Southampton SO17 1BJ UK
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32
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Gervasoni S, Spencer J, Hinchliffe P, Pedretti A, Vairoletti F, Mahler G, Mulholland AJ. A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors. Proteins 2022; 90:372-384. [PMID: 34455628 PMCID: PMC8944931 DOI: 10.1002/prot.26227] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/09/2021] [Accepted: 08/18/2021] [Indexed: 02/03/2023]
Abstract
Antibiotic resistance is a major threat to global public health. β-lactamases, which catalyze breakdown of β-lactam antibiotics, are a principal cause. Metallo β-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all β-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.
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Affiliation(s)
- Silvia Gervasoni
- Department of Pharmaceutical Sciences, University of Milan, Milan, Italy
| | - James Spencer
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | - Philip Hinchliffe
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK
| | | | - Franco Vairoletti
- Laboratorio de Química Farmacéutica, Departamento de Química Orgánica, Facultad de Química, Universidad de la República (UdelaR), Avda. General Flores 2124, Montevideo, Uruguay
| | - Graciela Mahler
- Laboratorio de Química Farmacéutica, Departamento de Química Orgánica, Facultad de Química, Universidad de la República (UdelaR), Avda. General Flores 2124, Montevideo, Uruguay
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33
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Shpiro B, Fabian MD, Rabani E, Baer R. Forces from Stochastic Density Functional Theory under Nonorthogonal Atom-Centered Basis Sets. J Chem Theory Comput 2022; 18:1458-1466. [PMID: 35099187 PMCID: PMC8908760 DOI: 10.1021/acs.jctc.1c00794] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
We
develop a formalism for calculating forces on the nuclei within
the linear-scaling stochastic density functional theory (sDFT) in
a nonorthogonal atom-centered basis set representation (Fabian et al. 2019, 9, e1412, 10.1002/wcms.1412) and apply it to the Tryptophan Zipper 2 (Trp-zip2) peptide
solvated in water. We use an embedded-fragment approach to reduce
the statistical errors (fluctuation and systematic bias), where the
entire peptide is the main fragment and the remaining 425 water molecules
are grouped into small fragments. We analyze the magnitude of the
statistical errors in the forces and find that the systematic bias
is of the order of 0.065 eV/Å (∼1.2 × 10–3Eh/a0) when 120 stochastic orbitals are used, independently
of system size. This magnitude of bias is sufficiently small to ensure
that the bond lengths estimated by stochastic DFT (within a Langevin
molecular dynamics simulation) will deviate by less than 1% from those
predicted by a deterministic calculation.
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Affiliation(s)
- Ben Shpiro
- Fritz Haber Center for Molecular Dynamics and Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Marcel David Fabian
- Fritz Haber Center for Molecular Dynamics and Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Eran Rabani
- Department of Chemistry, University of California, Berkeley, California 94720, United States.,Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,The Raymond and Beverly Sackler Center of Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv 69978, Israel
| | - Roi Baer
- Fritz Haber Center for Molecular Dynamics and Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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34
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Abstract
We use molecular dynamics simulations to study the thermodynamics and kinetics of alanine dipeptide isomerization at the air-water interface. Thermodynamically, we find an affinity of the dipeptide to the interface. This affinity arises from stabilizing intramolecular interactions that become unshielded as the dipeptide is desolvated. Kinetically, we consider the rate of transitions between the αL and β conformations of alanine dipeptide and evaluate it as a continuous function of the distance from the interface using a recent extension of transition path sampling, TPS+U. The rate of isomerization at the Gibbs dividing surface is suppressed relative to the bulk by a factor of 3. Examination of the ensemble of transition states elucidates the role of solvent degrees of freedom in mediating favorable intramolecular interactions along the reaction pathway of isomerization. Near the air-water interface, water is less effective at mediating these intramolecular interactions.
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Affiliation(s)
- Aditya N Singh
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - David T Limmer
- Department of Chemistry, University of California, Berkeley, California 94720, United States
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Kavli Energy Nanoscience Institute at Berkeley, Berkeley, California 94720, United States
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35
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Bím D, Navrátil M, Gutten O, Konvalinka J, Kutil Z, Culka M, Navrátil V, Alexandrova AN, Bařinka C, Rulíšek L. Predicting Effects of Site-Directed Mutagenesis on Enzyme Kinetics by QM/MM and QM Calculations: A Case of Glutamate Carboxypeptidase II. J Phys Chem B 2022; 126:132-143. [PMID: 34978450 DOI: 10.1021/acs.jpcb.1c09240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Quantum and molecular mechanics (QM/MM) and QM-only (cluster model) modeling techniques represent the two workhorses in mechanistic understanding of enzyme catalysis. One of the stringent tests for QM/MM and/or QM approaches is to provide quantitative answers to real-world biochemical questions, such as the effect of single-point mutations on enzyme kinetics. This translates into predicting the relative activation energies to 1-2 kcal·mol-1 accuracy; such predictions can be used for the rational design of novel enzyme variants with desired/improved characteristics. Herein, we employ glutamate carboxypeptidase II (GCPII), a dizinc metallopeptidase, also known as the prostate specific membrane antigen, as a model system. The structure and activity of this major cancer antigen have been thoroughly studied, both experimentally and computationally, which makes it an ideal model system for method development. Its reaction mechanism is quite well understood: the reaction coordinate comprises a "tetrahedral intermediate" and two transition states and experimental activation Gibbs free energy of ∼17.5 kcal·mol-1 can be inferred for the known kcat ≈ 1 s-1. We correlate experimental kinetic data (including the E424H variant, newly characterized in this work) for various GCPII mutants (kcat = 8.6 × 10-5 s-1 to 2.7 s-1) with the energy profiles calculated by QM/MM and QM-only (cluster model) approaches. We show that the near-quantitative agreement between the experimental values and the calculated activation energies (ΔH⧧) can be obtained and recommend the combination of the two protocols: QM/MM optimized structures and cluster model (QM) energetics. The trend in relative activation energies is mostly independent of the QM method (DFT functional) used. Last but not least, a satisfactory correlation between experimental and theoretical data allows us to provide qualitative and fairly simple explanations of the observed kinetic effects which are thus based on a rigorous footing.
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Affiliation(s)
- Daniel Bím
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10 Praha 6, Czech Republic.,Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095-1569, United States
| | - Michal Navrátil
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10 Praha 6, Czech Republic
| | - Ondrej Gutten
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10 Praha 6, Czech Republic
| | - Jan Konvalinka
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10 Praha 6, Czech Republic.,Department of Biochemistry, Faculty of Science, Charles University, Hlavova 2030, 2120 00 Prague, Czech Republic
| | - Zsófia Kutil
- Institute of Biotechnology of the Czech Academy of Sciences, Průmyslová 595, 252 50 Vestec, Czech Republic
| | - Martin Culka
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10 Praha 6, Czech Republic
| | - Václav Navrátil
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10 Praha 6, Czech Republic
| | - Anastassia N Alexandrova
- Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095-1569, United States
| | - Cyril Bařinka
- Institute of Biotechnology of the Czech Academy of Sciences, Průmyslová 595, 252 50 Vestec, Czech Republic
| | - Lubomír Rulíšek
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo náměstí 2, 166 10 Praha 6, Czech Republic
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36
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Gorai B, Vashisth H. Progress in Simulation Studies of Insulin Structure and Function. Front Endocrinol (Lausanne) 2022; 13:908724. [PMID: 35795141 PMCID: PMC9252437 DOI: 10.3389/fendo.2022.908724] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 04/28/2022] [Indexed: 01/02/2023] Open
Abstract
Insulin is a peptide hormone known for chiefly regulating glucose level in blood among several other metabolic processes. Insulin remains the most effective drug for treating diabetes mellitus. Insulin is synthesized in the pancreatic β-cells where it exists in a compact hexameric architecture although its biologically active form is monomeric. Insulin exhibits a sequence of conformational variations during the transition from the hexamer state to its biologically-active monomer state. The structural transitions and the mechanism of action of insulin have been investigated using several experimental and computational methods. This review primarily highlights the contributions of molecular dynamics (MD) simulations in elucidating the atomic-level details of conformational dynamics in insulin, where the structure of the hormone has been probed as a monomer, dimer, and hexamer. The effect of solvent, pH, temperature, and pressure have been probed at the microscopic scale. Given the focus of this review on the structure of the hormone, simulation studies involving interactions between the hormone and its receptor are only briefly highlighted, and studies on other related peptides (e.g., insulin-like growth factors) are not discussed. However, the review highlights conformational dynamics underlying the activities of reported insulin analogs and mimetics. The future prospects for computational methods in developing promising synthetic insulin analogs are also briefly highlighted.
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37
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Kameda T, Awazu A, Togashi Y. Molecular dynamics analysis of biomolecular systems including nucleic acids. Biophys Physicobiol 2022; 19:e190027. [DOI: 10.2142/biophysico.bppb-v19.0027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/18/2022] [Indexed: 12/01/2022] Open
Affiliation(s)
| | - Akinori Awazu
- Graduate School of Integrated Sciences for Life, Hiroshima University
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38
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Prejanò M, Romeo I, La Serra MA, Russo N, Marino T. Computational Study Reveals the Role of Water Molecules in the Inhibition Mechanism of LAT1 by 1,2,3-Dithiazoles. J Chem Inf Model 2021; 61:5883-5892. [PMID: 34788052 PMCID: PMC8715508 DOI: 10.1021/acs.jcim.1c01012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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The L-type amino
acid transporter LAT1, involved in many biological
processes including the overexpression of some tumors, is considered
a potential pharmacological target. The 1,2,3-Dithiazole scaffold
was predicted to inhibit LAT1 by the formation of an intermolecular
disulfide bond with the thiolate group of cysteine(s). As a result
of the identification of these irreversible covalent inhibitors, we
decided to deeply investigate the recognition stage and the covalent
interaction, characterizing the chemical structures of the selected
ligands. With the aim to provide new insights into the access of the
ligands to the binding pocket and to reveal the residues involved
in the inhibition, we performed docking, molecular dynamics simulations,
and density functional theory-based investigation of three 1,2,3-dithiazoles
against LAT1. Our computational analysis further highlighted the crucial
role played by water molecules in the inhibition mechanism. The results
here presented are consistent with experimental observations and provide
insights that can be helpful for the rational design of new-to-come
LAT1’s inhibitors.
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Affiliation(s)
- Mario Prejanò
- Department of Chemistry and Chemical Technologies, University of Calabria, 87036 Arcavacata di Rende, Cosenza, Italy
| | - Isabella Romeo
- Department of Chemistry and Chemical Technologies, University of Calabria, 87036 Arcavacata di Rende, Cosenza, Italy
| | - Maria Antonietta La Serra
- Department of Chemistry and Chemical Technologies, University of Calabria, 87036 Arcavacata di Rende, Cosenza, Italy
| | - Nino Russo
- Department of Chemistry and Chemical Technologies, University of Calabria, 87036 Arcavacata di Rende, Cosenza, Italy
| | - Tiziana Marino
- Department of Chemistry and Chemical Technologies, University of Calabria, 87036 Arcavacata di Rende, Cosenza, Italy
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39
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Sun L, Zhang C, Chen J, Zhao X, Bai F, Zhong S. Combining oligomer build-up with alanine scanning to determine the flocculation protein mutants for enhancing oligosaccharide binding. MOLECULAR SIMULATION 2021. [DOI: 10.1080/08927022.2021.2015068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Lu Sun
- School of Bioengineering, Dalian University of Technology, Liaoning, People’s Republic of China
| | - Chenhong Zhang
- School of Bioengineering, Dalian University of Technology, Liaoning, People’s Republic of China
| | - Jiemin Chen
- School of Bioengineering, Dalian University of Technology, Liaoning, People’s Republic of China
| | - Xinqing Zhao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Fengwu Bai
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
| | - Shijun Zhong
- School of Bioengineering, Dalian University of Technology, Liaoning, People’s Republic of China
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40
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Yang Z, Twidale RM, Gervasoni S, Suardíaz R, Colenso CK, Lang EJM, Spencer J, Mulholland AJ. Multiscale Workflow for Modeling Ligand Complexes of Zinc Metalloproteins. J Chem Inf Model 2021; 61:5658-5672. [PMID: 34748329 DOI: 10.1021/acs.jcim.1c01109] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Zinc metalloproteins are ubiquitous, with protein zinc centers of structural and functional importance, involved in interactions with ligands and substrates and often of pharmacological interest. Biomolecular simulations are increasingly prominent in investigations of protein structure, dynamics, ligand interactions, and catalysis, but zinc poses a particular challenge, in part because of its versatile, flexible coordination. A computational workflow generating reliable models of ligand complexes of biological zinc centers would find broad application. Here, we evaluate the ability of alternative treatments, using (nonbonded) molecular mechanics (MM) and quantum mechanics/molecular mechanics (QM/MM) at semiempirical (DFTB3) and density functional theory (DFT) levels of theory, to describe the zinc centers of ligand complexes of six metalloenzyme systems differing in coordination geometries, zinc stoichiometries (mono- and dinuclear), and the nature of interacting groups (specifically the presence of zinc-sulfur interactions). MM molecular dynamics (MD) simulations can overfavor octahedral geometries, introducing additional water molecules to the zinc coordination shell, but this can be rectified by subsequent semiempirical (DFTB3) QM/MM MD simulations. B3LYP/MM geometry optimization further improved the accuracy of the description of coordination distances, with the overall effectiveness of the approach depending upon factors, including the presence of zinc-sulfur interactions that are less well described by semiempirical methods. We describe a workflow comprising QM/MM MD using DFTB3 followed by QM/MM geometry optimization using DFT (e.g., B3LYP) that well describes our set of zinc metalloenzyme complexes and is likely to be suitable for creating accurate models of zinc protein complexes when structural information is more limited.
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Affiliation(s)
- Zongfan Yang
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K.,School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, U.K
| | - Rebecca M Twidale
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K
| | - Silvia Gervasoni
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K.,Department of Pharmaceutical Sciences, University of Milan, Via Mangiagalli, 25, I-20133 Milano, Italy
| | - Reynier Suardíaz
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K
| | - Charlotte K Colenso
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K.,School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, U.K
| | - Eric J M Lang
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K
| | - James Spencer
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, U.K
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TH, U.K
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41
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Morado J, Mortenson PN, Nissink JWM, Verdonk ML, Ward RA, Essex JW, Skylaris CK. Generation of Quantum Configurational Ensembles Using Approximate Potentials. J Chem Theory Comput 2021; 17:7021-7042. [PMID: 34644088 DOI: 10.1021/acs.jctc.1c00532] [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
Conformational analysis is of paramount importance in drug design: it is crucial to determine pharmacological properties, understand molecular recognition processes, and characterize the conformations of ligands when unbound. Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC) and molecular dynamics (MD), are usually employed to generate ensembles of structures due to their ability to extensively sample the conformational space of molecules. The accuracy of these MM-based schemes strongly depends on the functional form of the force field (FF) and its parametrization, components that often hinder their performance. High-level methods, such as ab initio MD, provide reliable structural information but are still too computationally expensive to allow for extensive sampling. Therefore, to overcome these limitations, we present a multilevel MC method that is capable of generating quantum configurational ensembles while keeping the computational cost at a minimum. We show that FF reparametrization is an efficient route to generate FFs that reproduce QM results more closely, which, in turn, can be used as low-cost models to achieve the gold standard QM accuracy. We demonstrate that the MC acceptance rate is strongly correlated with various phase space overlap measurements and that it constitutes a robust metric to evaluate the similarity between the MM and QM levels of theory. As a more advanced application, we present a self-parametrizing version of the algorithm, which combines sampling and FF parametrization in one scheme, and apply the methodology to generate the QM/MM distribution of a ligand in aqueous solution.
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Affiliation(s)
- João Morado
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Paul N Mortenson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - J Willem M Nissink
- Medicinal Chemistry, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Marcel L Verdonk
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Richard A Ward
- Medicinal Chemistry, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Chris-Kriton Skylaris
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
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42
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Edwards T, Foloppe N, Harris SA, Wells G. The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design. Acta Crystallogr D Struct Biol 2021; 77:1348-1356. [PMID: 34726163 PMCID: PMC8561735 DOI: 10.1107/s2059798321009712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 09/17/2021] [Indexed: 02/04/2023] Open
Abstract
The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.
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Affiliation(s)
- Tom Edwards
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
| | | | - Sarah Anne Harris
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, United Kingdom
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
| | - Geoff Wells
- School of Pharmacy, University College London, London, United Kingdom
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43
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Qiu Y, Smith DGA, Boothroyd S, Jang H, Hahn DF, Wagner J, Bannan CC, Gokey T, Lim VT, Stern CD, Rizzi A, Tjanaka B, Tresadern G, Lucas X, Shirts MR, Gilson MK, Chodera JD, Bayly CI, Mobley DL, Wang LP. Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field. J Chem Theory Comput 2021; 17:6262-6280. [PMID: 34551262 PMCID: PMC8511297 DOI: 10.1021/acs.jctc.1c00571] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.
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Affiliation(s)
- Yudong Qiu
- Chemistry Department, The University of California at Davis, Davis, California 95616, United States
| | - Daniel G A Smith
- The Molecular Sciences Software Institute (MolSSI), Blacksburg, Virginia 24060, United States
| | - Simon Boothroyd
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Hyesu Jang
- Chemistry Department, The University of California at Davis, Davis, California 95616, United States
| | - David F Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Jeffrey Wagner
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Caitlin C Bannan
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences, The University of California at San Diego, La Jolla, California 92093, United States
| | - Trevor Gokey
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Victoria T Lim
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Chaya D Stern
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Andrea Rizzi
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York 10065, United States
| | - Bryon Tjanaka
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Xavier Lucas
- F. Hoffmann-La Roche AG, Basel 4070, Switzerland
| | - Michael R Shirts
- Chemical & Biological Engineering Department, The University of Colorado at Boulder, Boulder, Colorado 80309, United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, The University of California at San Diego, La Jolla, California 92093, United States
| | - John D Chodera
- Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | | | - David L Mobley
- Chemistry Department, The University of California at Irvine, Irvine, California 92617, United States
| | - Lee-Ping Wang
- Chemistry Department, The University of California at Davis, Davis, California 95616, United States
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44
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Kang H, Zheng M. Influence of the quantum mechanical region size in QM/MM modelling: A case study of fluoroacetate dehalogenase catalyzed C F bond cleavage. COMPUT THEOR CHEM 2021. [DOI: 10.1016/j.comptc.2021.113399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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45
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Parise A, Romeo I, Russo N, Marino T. The Se-S Bond Formation in the Covalent Inhibition Mechanism of SARS-CoV-2 Main Protease by Ebselen-like Inhibitors: A Computational Study. Int J Mol Sci 2021; 22:9792. [PMID: 34575955 PMCID: PMC8467846 DOI: 10.3390/ijms22189792] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 12/20/2022] Open
Abstract
The inhibition mechanism of the main protease (Mpro) of SARS-CoV-2 by ebselen (EBS) and its analog with a hydroxyl group at position 2 of the benzisoselenazol-3(2H)-one ring (EBS-OH) was studied by using a density functional level of theory. Preliminary molecular dynamics simulations on the apo form of Mpro were performed taking into account both the hydrogen donor and acceptor natures of the Nδ and Nε of His41, a member of the catalytic dyad. The potential energy surfaces for the formation of the Se-S covalent bond mediated by EBS and EBS-OH on Mpro are discussed in detail. The EBS-OH shows a distinctive behavior with respect to EBS in the formation of the noncovalent complex. Due to the presence of canonical H-bonds and noncanonical ones involving less electronegative atoms, such as sulfur and selenium, the influence on the energy barriers and reaction energy of the Minnesota hybrid meta-GGA functionals M06, M06-2X and M08HX, and the more recent range-separated hybrid functional wB97X were also considered. The knowledge of the inhibition mechanism of Mpro by the small protease inhibitors EBS or EBS-OH can enlarge the possibilities for designing more potent and selective inhibitor-based drugs to be used in combination with other antiviral therapies.
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Affiliation(s)
- Angela Parise
- Dipartimento di Chimica e Tecnologie Chimiche, Università della Calabria, Via Pietro Bucci, 87036 Arcavacata di Rende, CS, Italy; (A.P.); (I.R.); (N.R.)
- Institut de Chimie Physique UMR8000, Université Paris-Saclay, CNRS, 91405 Orsay, France
| | - Isabella Romeo
- Dipartimento di Chimica e Tecnologie Chimiche, Università della Calabria, Via Pietro Bucci, 87036 Arcavacata di Rende, CS, Italy; (A.P.); (I.R.); (N.R.)
| | - Nino Russo
- Dipartimento di Chimica e Tecnologie Chimiche, Università della Calabria, Via Pietro Bucci, 87036 Arcavacata di Rende, CS, Italy; (A.P.); (I.R.); (N.R.)
| | - Tiziana Marino
- Dipartimento di Chimica e Tecnologie Chimiche, Università della Calabria, Via Pietro Bucci, 87036 Arcavacata di Rende, CS, Italy; (A.P.); (I.R.); (N.R.)
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46
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Charzewski Ł, Krzyśko KA, Lesyng B. Exploring Covalent Docking Mechanisms of Boron-Based Inhibitors to Class A, C and D β-Lactamases Using Time-dependent Hybrid QM/MM Simulations. Front Mol Biosci 2021; 8:633181. [PMID: 34434961 PMCID: PMC8380965 DOI: 10.3389/fmolb.2021.633181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Recently, molecular covalent docking has been extensively developed to design new classes of inhibitors that form chemical bonds with their biological targets. This strategy for the design of such inhibitors, in particular boron-based inhibitors, holds great promise for the vast family of β-lactamases produced, inter alia, by Gram-negative antibiotic-resistant bacteria. However, the description of covalent docking processes requires a quantum-mechanical approach, and so far, only a few studies of this type have been presented. This study accurately describes the covalent docking process between two model inhibitors - representing two large families of inhibitors based on boronic-acid and bicyclic boronate scaffolds, and three β-lactamases which belong to the A, C, and D classes. Molecular fragments containing boron can be converted from a neutral, trigonal, planar state with sp2 hybridization to the anionic, tetrahedral sp3 state in a process sometimes referred to as morphing. This study applies multi-scale modeling methods, in particular, the hybrid QM/MM approach which has predictive power reaching well beyond conventional molecular modeling. Time-dependent QM/MM simulations indicated several structural changes and geometric preferences, ultimately leading to covalent docking processes. With current computing technologies, this approach is not computationally expensive, can be used in standard molecular modeling and molecular design works, and can effectively support experimental research which should allow for a detailed understanding of complex processes important to molecular medicine. In particular, it can support the rational design of covalent boron-based inhibitors for β-lactamases as well as for many other enzyme systems of clinical relevance, including SARS-CoV-2 proteins.
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Affiliation(s)
| | | | - Bogdan Lesyng
- Department of Biophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland
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47
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Wu L, Qin L, Nie Y, Xu Y, Zhao YL. Computer-aided understanding and engineering of enzymatic selectivity. Biotechnol Adv 2021; 54:107793. [PMID: 34217814 DOI: 10.1016/j.biotechadv.2021.107793] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/26/2021] [Accepted: 06/28/2021] [Indexed: 12/26/2022]
Abstract
Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.
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Affiliation(s)
- Lunjie Wu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Lei Qin
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yao Nie
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Suqian Industrial Technology Research Institute of Jiangnan University, Suqian 223814, China.
| | - Yan Xu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
| | - Yi-Lei Zhao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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48
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Bolnykh V, Rossetti G, Rothlisberger U, Carloni P. Expanding the boundaries of ligand–target modeling by exascale calculations. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Viacheslav Bolnykh
- Laboratory of Computational Chemistry and Biochemistry École Polytechnique Fédérale de Lausanne Lausanne Switzerland
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM‐9)/Institute for Advanced Simulations (IAS‐5) Forschungszentrum Jülich Jülich Germany
| | - Giulia Rossetti
- Computational Biomedicine, Institute of Neuroscience and Medicine (INM‐9)/Institute for Advanced Simulations (IAS‐5) Forschungszentrum Jülich Jülich Germany
- Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany
- Department of Hematology, Oncology, Hemostaseology and Stem Cell Transplantation University Hospital Aachen RWTH Aachen University Aachen Germany
| | - Ursula Rothlisberger
- Laboratory of Computational Chemistry and Biochemistry École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Paolo Carloni
- Institute for Neuroscience and Medicine and Institute for Advanced Simulations (IAS‐5/INM‐9) “Computational Biomedicine” Forschungszentrum Jülich Jülich Germany
- JARA‐Institute INM‐11 “Molecular Neuroscience and Neuroimaging” Forschungszentrum Jülich Jülich Germany
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49
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Qiu Y, Shan W, Zhang H. Force Field Benchmark of Amino Acids. 3. Hydration with Scaled Lennard-Jones Interactions. J Chem Inf Model 2021; 61:3571-3582. [PMID: 34185520 DOI: 10.1021/acs.jcim.1c00339] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Classical protein force fields were reported with too weak protein-water interactions relative to protein-protein interactions, leading to more compact structures and artificial protein aggregation. Here we investigated the impacts of scaled Lennard-Jones (LJ) interactions on the hydration of amino acids and the simulation of folded and intrinsically disordered proteins (IDPs). The obtained optimal scaling parameters reproduce accurately hydration free energies of neutral amino acid side chain analogues and do not affect the compactness and structural stability of folded proteins significantly. The scaling leads to less compact IDPs and varies from case to case. Strengthening the interactions between protein and water oxygen or hydrogen atoms by increasing the interacting LJ well depth (ε) appears more effective than weakening protein-protein interactions by reducing the interacting dispersion coefficients (C6). We demonstrate that weakening water-water interactions is a solution as well to obtaining more favorable protein-water interactions in an indirect way, although modern force fields like Amber ff19SB and a99SB-disp tend to use water models with strong water-water interactions. This is likely a compromise between strong protein-protein interactions and strong water-water interactions. Independent optimization of protein force fields and water models is therefore needed to make both interactions more close to reality, leading to good accuracy without bias or scaling.
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Affiliation(s)
- Yejie Qiu
- Department of Biological Science and Engineering, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, 100083 Beijing, China
| | - Wenjie Shan
- Department of Biological Science and Engineering, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, 100083 Beijing, China
| | - Haiyang Zhang
- Department of Biological Science and Engineering, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, 100083 Beijing, China
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50
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Computational methods for exploring protein conformations. Biochem Soc Trans 2021; 48:1707-1724. [PMID: 32756904 PMCID: PMC7458412 DOI: 10.1042/bst20200193] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 07/07/2020] [Accepted: 07/09/2020] [Indexed: 12/13/2022]
Abstract
Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.
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