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Diessner EM, Takahashi GR, Martin RW, Butts CT. Comparative Modeling and Analysis of Extremophilic D-Ala-D-Ala Carboxypeptidases. Biomolecules 2023; 13:biom13020328. [PMID: 36830697 PMCID: PMC9953012 DOI: 10.3390/biom13020328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/21/2023] [Accepted: 02/02/2023] [Indexed: 02/11/2023] Open
Abstract
Understanding the molecular adaptations of organisms to extreme environments requires a comparative analysis of protein structure, function, and dynamics across species found in different environmental conditions. Computational studies can be particularly useful in this pursuit, allowing exploratory studies of large numbers of proteins under different thermal and chemical conditions that would be infeasible to carry out experimentally. Here, we perform such a study of the MEROPS family S11, S12, and S13 proteases from psychophilic, mesophilic, and thermophilic bacteria. Using a combination of protein structure prediction, atomistic molecular dynamics, and trajectory analysis, we examine both conserved features and trends across thermal groups. Our findings suggest a number of hypotheses for experimental investigation.
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Affiliation(s)
| | - Gemma R. Takahashi
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA
| | - Rachel W. Martin
- Department of Chemistry, University of California, Irvine, CA 92697, USA
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA
- Correspondence: (R.W.M.); (C.T.B.)
| | - Carter T. Butts
- Departments of Sociology, Statistics, Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA
- Correspondence: (R.W.M.); (C.T.B.)
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2
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Diessner EM, Takahashi GR, Cross TJ, Martin RW, Butts CT. Mutation Effects on Structure and Dynamics: Adaptive Evolution of the SARS-CoV-2 Main Protease. Biochemistry 2023; 62:747-758. [PMID: 36656653 PMCID: PMC9888416 DOI: 10.1021/acs.biochem.2c00479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/29/2022] [Indexed: 01/20/2023]
Abstract
The main protease of SARS-CoV-2 (Mpro) plays a critical role in viral replication; although it is relatively conserved, Mpro has nevertheless evolved over the course of the COVID-19 pandemic. Here, we examine phenotypic changes in clinically observed variants of Mpro, relative to the originally reported wild-type enzyme. Using atomistic molecular dynamics simulations, we examine effects of mutation on protein structure and dynamics. In addition to basic structural properties such as variation in surface area and torsion angles, we use protein structure networks and active site networks to evaluate functionally relevant characters related to global cohesion and active site constraint. Substitution analysis shows a continuing trend toward more hydrophobic residues that are dependent on the location of the residue in primary, secondary, tertiary, and quaternary structures. Phylogenetic analysis provides additional evidence for the impact of selective pressure on mutation of Mpro. Overall, these analyses suggest evolutionary adaptation of Mpro toward more hydrophobicity and a less-constrained active site in response to the selective pressures of a novel host environment.
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Affiliation(s)
- Elizabeth M Diessner
- Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Gemma R Takahashi
- Department of Molecular Biology & Biochemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Thomas J Cross
- Department of Chemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Rachel W Martin
- Departments of Chemistry and Molecular Biology & Biochemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Carter T Butts
- Departments of Sociology, Statistics, Computer Science, and EECS, University of California, Irvine, Irvine, California 92697, United States
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3
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Marchetti A, Orlando M, Mangiagalli M, Lotti M. A cold‐active esterase enhances mesophilic properties through Mn
2+
binding. FEBS J 2022; 290:2394-2411. [PMID: 36266734 DOI: 10.1111/febs.16661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/20/2022] [Accepted: 10/19/2022] [Indexed: 12/12/2022]
Abstract
A key aspect of adaptation to cold environments is the production of cold-active enzymes by psychrophilic organisms. These enzymes not only have high activity at low temperatures, but also exhibit remarkable structural flexibility and thermolability. In this context, the role of metal ions has been little explored, and the few available studies seem to suggest that metal binding counteracts structural flexibility. This article reports an investigation into the role of the binding of manganese ion (Mn2+ ) in the thermal adaptation of an esterase (M-Est) of the GDSx family, identified in the genome of the Antarctic bacterium Marinomonas sp. ef1. M-Est is specific for esters containing acetate groups and turned out to be a highly thermolabile cold-active enzyme, with a catalysis optimum temperature of 5 °C and a melting temperature of 31.7 °C. A combination of biochemical and computational analyses, including molecular dynamics simulations, revealed that M-Est binds Mn2+ ions via a single binding site located on the surface of the enzyme, close to the active site. Although the interaction between M-Est and Mn2+ induces only local conformational changes involving the active site, quite surprisingly they trigger an improvement in both thermal stability and catalytic efficiency under mild temperature conditions. These results, together with the conservation of the Mn2+ binding site among psychrophilic and psychrotolerant homologues, suggest that Mn2+ binding may be a useful, albeit atypical, strategy to mitigate the detrimental effects of temperature on true cold-active enzymes.
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Affiliation(s)
| | - Marco Orlando
- Department of Biotechnology and Biosciences University of Milano‐Bicocca Italy
- Department of Biotechnology and Life Sciences University of Insubria Varese Italy
| | - Marco Mangiagalli
- Department of Biotechnology and Biosciences University of Milano‐Bicocca Italy
| | - Marina Lotti
- Department of Biotechnology and Biosciences University of Milano‐Bicocca Italy
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Khairul Anuar NFS, Huyop F, Ur-Rehman G, Abdullah F, Normi YM, Sabullah MK, Abdul Wahab R. An Overview into Polyethylene Terephthalate (PET) Hydrolases and Efforts in Tailoring Enzymes for Improved Plastic Degradation. Int J Mol Sci 2022; 23:12644. [PMID: 36293501 PMCID: PMC9603852 DOI: 10.3390/ijms232012644] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 11/18/2022] Open
Abstract
Plastic or microplastic pollution is a global threat affecting ecosystems, with the current generation reaching as much as 400 metric tons per/year. Soil ecosystems comprising agricultural lands act as microplastics sinks, though the impact could be unexpectedly more far-reaching. This is troubling as most plastic forms, such as polyethylene terephthalate (PET), formed from polymerized terephthalic acid (TPA) and ethylene glycol (EG) monomers, are non-biodegradable environmental pollutants. The current approach to use mechanical, thermal, and chemical-based treatments to reduce PET waste remains cost-prohibitive and could potentially produce toxic secondary pollutants. Thus, better remediation methods must be developed to deal with plastic pollutants in marine and terrestrial environments. Enzymatic treatments could be a plausible avenue to overcome plastic pollutants, given the near-ambient conditions under which enzymes function without the need for chemicals. The discovery of several PET hydrolases, along with further modification of the enzymes, has considerably aided efforts to improve their ability to degrade the ester bond of PET. Hence, this review emphasizes PET-degrading microbial hydrolases and their contribution to alleviating environmental microplastics. Information on the molecular and degradation mechanisms of PET is also highlighted in this review, which might be useful in the future rational engineering of PET-hydrolyzing enzymes.
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Affiliation(s)
- Nurul Fatin Syamimi Khairul Anuar
- Department of Biosciences, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
- Enzyme Technology and Green Synthesis Research Group, Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
| | - Fahrul Huyop
- Department of Biosciences, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
| | - Ghani Ur-Rehman
- Enzyme Technology and Green Synthesis Research Group, Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
- Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
| | - Faizuan Abdullah
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
| | - Yahaya M. Normi
- Enzyme and Microbial Technology Research Center, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Malaysia
| | - Mohd Khalizan Sabullah
- Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia
| | - Roswanira Abdul Wahab
- Enzyme Technology and Green Synthesis Research Group, Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
- Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
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5
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Duong VT, Diessner EM, Grazioli G, Martin RW, Butts CT. Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures. Biomolecules 2021; 11:biom11121788. [PMID: 34944432 PMCID: PMC8698800 DOI: 10.3390/biom11121788] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 11/11/2021] [Accepted: 11/19/2021] [Indexed: 01/01/2023] Open
Abstract
Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.
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Affiliation(s)
- Vy T. Duong
- Department of Chemistry, University of California, Irvine, CA 92697, USA; (V.T.D.); (E.M.D.)
| | - Elizabeth M. Diessner
- Department of Chemistry, University of California, Irvine, CA 92697, USA; (V.T.D.); (E.M.D.)
| | - Gianmarc Grazioli
- Department of Chemistry, San Jose State University, San Jose, CA 95192, USA;
| | - Rachel W. Martin
- Department of Chemistry, University of California, Irvine, CA 92697, USA; (V.T.D.); (E.M.D.)
- Department of Molecular Biology & Biochemistry, University of California, Irvine, CA 92697, USA
- Correspondence: (R.W.M.); (C.T.B.)
| | - Carter T. Butts
- Departments of Sociology, Statistics and Electrical Engineering & Computer Science, University of California, Irvine, CA 92697, USA
- Correspondence: (R.W.M.); (C.T.B.)
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6
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Cross TJ, Takahashi GR, Diessner EM, Crosby MG, Farahmand V, Zhuang S, Butts CT, Martin RW. Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease. Biochemistry 2020; 59:3741-3756. [PMID: 32931703 PMCID: PMC7518256 DOI: 10.1021/acs.biochem.0c00462] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 09/12/2020] [Indexed: 02/08/2023]
Abstract
The SARS-CoV-2 main protease (Mpro) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new Mpro mutations arising over time. Identification and structural characterization of Mpro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine Mpro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.
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Affiliation(s)
- Thomas J Cross
- Department of Chemistry, University of California, Irvine, California 92697-2025, United States
| | - Gemma R Takahashi
- Department of Molecular Biology and Biochemistry, University of California, Irvine, California 92697-3900, United States
| | - Elizabeth M Diessner
- Department of Chemistry, University of California, Irvine, California 92697-2025, United States
- California Institute for Telecommunications and Information Technology, University of California, Irvine, California 92697-3900, United States
| | - Marquise G Crosby
- Department of Molecular Biology and Biochemistry, University of California, Irvine, California 92697-3900, United States
| | - Vesta Farahmand
- Department of Chemistry, University of California, Irvine, California 92697-2025, United States
| | - Shannon Zhuang
- Department of Chemistry, University of California, Irvine, California 92697-2025, United States
| | - Carter T Butts
- California Institute for Telecommunications and Information Technology, University of California, Irvine, California 92697-3900, United States
- Departments of Sociology, Statistics, Computer Science, and Electrical Engineering and Computer Science, University of California, Irvine, California 92697-3900, United States
| | - Rachel W Martin
- Department of Chemistry, University of California, Irvine, California 92697-2025, United States
- Department of Molecular Biology and Biochemistry, University of California, Irvine, California 92697-3900, United States
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7
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The Droserasin 1 PSI: A Membrane-Interacting Antimicrobial Peptide from the Carnivorous Plant Drosera capensis. Biomolecules 2020; 10:biom10071069. [PMID: 32709016 PMCID: PMC7407137 DOI: 10.3390/biom10071069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 07/08/2020] [Accepted: 07/13/2020] [Indexed: 12/11/2022] Open
Abstract
The Droserasins, aspartic proteases from the carnivorous plant Drosera capensis, contain a 100-residue plant-specific insert (PSI) that is post-translationally cleaved and independently acts as an antimicrobial peptide. PSIs are of interest not only for their inhibition of microbial growth, but also because they modify the size of lipid vesicles and strongly interact with biological membranes. PSIs may therefore be useful for modulating lipid systems in NMR studies of membrane proteins. Here we present the expression and biophysical characterization of the Droserasin 1 PSI (D1 PSI.) This peptide is monomeric in solution and maintains its primarily α-helical secondary structure over a wide range of temperatures and pH values, even under conditions where its three disulfide bonds are reduced. Vesicle fusion assays indicate that the D1 PSI strongly interacts with bacterial and fungal lipids at pH 5 and lower, consistent with the physiological pH of D. capensis mucilage. It binds lipids with a variety of head groups, highlighting its versatility as a potential stabilizer for lipid nanodiscs. Solid-state NMR spectra collected at a field strength of 36 T, using a unique series-connected hybrid magnet, indicate that the peptide is folded and strongly bound to the membrane. Molecular dynamics simulations indicate that the peptide is stable as either a monomer or a dimer in a lipid bilayer. Both the monomer and the dimer allow the passage of water through the membrane, albeit at different rates.
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8
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Cross TJ, Takahashi GR, Diessner EM, Crosby MG, Farahmand V, Zhuang S, Butts CT, Martin RW. Sequence characterization and molecular modeling of clinically relevant variants of the SARS-CoV-2 main protease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.05.15.097493. [PMID: 32511408 PMCID: PMC7263555 DOI: 10.1101/2020.05.15.097493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
The SARS-CoV-2 main protease (M pro ) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant selection pressure, with new M pro mutations arising over time. Identification and structural characterization of M pro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine M pro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.
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9
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Grazioli G, Martin RW, Butts CT. Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods. Front Mol Biosci 2019; 6:42. [PMID: 31245383 PMCID: PMC6581705 DOI: 10.3389/fmolb.2019.00042] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/20/2019] [Indexed: 01/23/2023] Open
Abstract
Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (Aβ1−40) and its “Arctic” variant (E22G), systems that play a central role in the etiology of Alzheimer's disease. Our analyses demonstrate ways in which ML and related approaches can be used to elucidate subtle differences between these proteins, including transient structure that is poorly captured by conventional metrics.
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Affiliation(s)
- Gianmarc Grazioli
- California Institute for Telecommunications and Information Technology (Calit2), University of California, Irvine, Irvine, CA, United States.,Department of Chemistry, University of California, Irvine, Irvine, CA, United States
| | - Rachel W Martin
- Department of Chemistry, University of California, Irvine, Irvine, CA, United States.,Department of Molecular Biology and Biochemistry, University of California, Irvine, Irvine, CA, United States
| | - Carter T Butts
- California Institute for Telecommunications and Information Technology (Calit2), University of California, Irvine, Irvine, CA, United States.,Department of Computer Science, University of California, Irvine, Irvine, CA, United States.,Department of Sociology, Statistics, and Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
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