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Jiang L, Tian Y, Zhang H, Liu S. Molecular-level insight into the effects of low moisture and trehalose on the thermostability of β-glucosidase. Food Chem 2024; 460:140607. [PMID: 39068804 DOI: 10.1016/j.foodchem.2024.140607] [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: 02/24/2024] [Revised: 07/12/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024]
Abstract
The high temperature induces conformational changes in β-glucosidase, making it inactive and limiting its application field. In this paper, the effect of trehalose on the thermostability of β-glucosidase from low-moisture Hevea brasiliensis seeds was investigated. The results showed that the residual enzyme activities of β-glucosidase supplemented with trehalose after high-temperature treatment were significantly higher than that of the control group. The improvement of thermostability could be explained by low-field nuclear magnetic resonance (LF-NMR) and molecular dynamics (MD) simulations at the molecular level. Moreover, adding trehalose increased the water activity and water content of β-glucosidase, leading to a more stable conformation. Trehalose replaced some water and formed a stable network of hydrogen bonds with protein and surrounding water. The glass formed by trehalose also reduced molecular movement, thus providing good protection for enzymes.
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Affiliation(s)
- Lian Jiang
- School of Food Science and Engineering, Hainan University, Haikou 570228, China; Engineering Research Center of Utilization of Tropical Polysaccharide Resources, the Ministry of Education, Haikou 570228, China; Key Laboratory of Food Nutrition and Functional Food of Hainan Province, Haikou 570228, China
| | - Yongli Tian
- School of Food Science and Engineering, Hainan University, Haikou 570228, China; Engineering Research Center of Utilization of Tropical Polysaccharide Resources, the Ministry of Education, Haikou 570228, China; Key Laboratory of Food Nutrition and Functional Food of Hainan Province, Haikou 570228, China
| | - Haide Zhang
- School of Food Science and Engineering, Hainan University, Haikou 570228, China; Engineering Research Center of Utilization of Tropical Polysaccharide Resources, the Ministry of Education, Haikou 570228, China; Key Laboratory of Food Nutrition and Functional Food of Hainan Province, Haikou 570228, China
| | - Shisheng Liu
- School of Food Science and Engineering, Hainan University, Haikou 570228, China; Engineering Research Center of Utilization of Tropical Polysaccharide Resources, the Ministry of Education, Haikou 570228, China; Key Laboratory of Food Nutrition and Functional Food of Hainan Province, Haikou 570228, China.
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2
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Aguilera-Puga MDC, Plisson F. Structure-aware machine learning strategies for antimicrobial peptide discovery. Sci Rep 2024; 14:11995. [PMID: 38796582 PMCID: PMC11127937 DOI: 10.1038/s41598-024-62419-y] [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: 02/08/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024] Open
Abstract
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico
| | - Fabien Plisson
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico.
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3
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Champion C, Lehner M, Smith AA, Ferrage F, Bolik-Coulon N, Riniker S. Unraveling motion in proteins by combining NMR relaxometry and molecular dynamics simulations: A case study on ubiquitin. J Chem Phys 2024; 160:104105. [PMID: 38465679 DOI: 10.1063/5.0188416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/20/2024] [Indexed: 03/12/2024] Open
Abstract
Nuclear magnetic resonance (NMR) relaxation experiments shine light onto the dynamics of molecular systems in the picosecond to millisecond timescales. As these methods cannot provide an atomically resolved view of the motion of atoms, functional groups, or domains giving rise to such signals, relaxation techniques have been combined with molecular dynamics (MD) simulations to obtain mechanistic descriptions and gain insights into the functional role of side chain or domain motion. In this work, we present a comparison of five computational methods that permit the joint analysis of MD simulations and NMR relaxation experiments. We discuss their relative strengths and areas of applicability and demonstrate how they may be utilized to interpret the dynamics in MD simulations with the small protein ubiquitin as a test system. We focus on the aliphatic side chains given the rigidity of the backbone of this protein. We find encouraging agreement between experiment, Markov state models built in the χ1/χ2 rotamer space of isoleucine residues, explicit rotamer jump models, and a decomposition of the motion using ROMANCE. These methods allow us to ascribe the dynamics to specific rotamer jumps. Simulations with eight different combinations of force field and water model highlight how the different metrics may be employed to pinpoint force field deficiencies. Furthermore, the presented comparison offers a perspective on the utility of NMR relaxation to serve as validation data for the prediction of kinetics by state-of-the-art biomolecular force fields.
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Affiliation(s)
- Candide Champion
- Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Marc Lehner
- Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Albert A Smith
- Institute for Medical Physics and Biophysics, Leipzig University, Härtelstrasse 16-18, 04107 Leipzig, Germany
| | - Fabien Ferrage
- Laboratoire des Biomolécules, LBM, Département de Chimie, École normale supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | - Nicolas Bolik-Coulon
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Sereina Riniker
- Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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4
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Kouba P, Kohout P, Haddadi F, Bushuiev A, Samusevich R, Sedlar J, Damborsky J, Pluskal T, Sivic J, Mazurenko S. Machine Learning-Guided Protein Engineering. ACS Catal 2023; 13:13863-13895. [PMID: 37942269 PMCID: PMC10629210 DOI: 10.1021/acscatal.3c02743] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/20/2023] [Indexed: 11/10/2023]
Abstract
Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.
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Affiliation(s)
- Petr Kouba
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Faculty of
Electrical Engineering, Czech Technical
University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic
| | - Pavel Kohout
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Faraneh Haddadi
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Anton Bushuiev
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Raman Samusevich
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Jiri Sedlar
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Jiri Damborsky
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Tomas Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Josef Sivic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Stanislav Mazurenko
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
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5
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Gu J, Xu Y, Nie Y. Role of distal sites in enzyme engineering. Biotechnol Adv 2023; 63:108094. [PMID: 36621725 DOI: 10.1016/j.biotechadv.2023.108094] [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: 08/02/2022] [Revised: 11/15/2022] [Accepted: 01/01/2023] [Indexed: 01/06/2023]
Abstract
The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.
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Affiliation(s)
- Jie Gu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yan Xu
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China; State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
| | - Yao Nie
- Lab of Brewing Microbiology and Applied Enzymology, School of Biotechnology and Key laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China; Suqian Industrial Technology Research Institute of Jiangnan University, Suqian 223814, China.
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6
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Kennedy EN, Foster CA, Barr SA, Bourret RB. General strategies for using amino acid sequence data to guide biochemical investigation of protein function. Biochem Soc Trans 2022; 50:1847-1858. [PMID: 36416676 PMCID: PMC10257402 DOI: 10.1042/bst20220849] [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: 08/18/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 11/24/2022]
Abstract
The rapid increase of '-omics' data warrants the reconsideration of experimental strategies to investigate general protein function. Studying individual members of a protein family is likely insufficient to provide a complete mechanistic understanding of family functions, especially for diverse families with thousands of known members. Strategies that exploit large amounts of available amino acid sequence data can inspire and guide biochemical experiments, generating broadly applicable insights into a given family. Here we review several methods that utilize abundant sequence data to focus experimental efforts and identify features truly representative of a protein family or domain. First, coevolutionary relationships between residues within primary sequences can be successfully exploited to identify structurally and/or functionally important positions for experimental investigation. Second, functionally important variable residue positions typically occupy a limited sequence space, a property useful for guiding biochemical characterization of the effects of the most physiologically and evolutionarily relevant amino acids. Third, amino acid sequence variation within domains shared between different protein families can be used to sort a particular domain into multiple subtypes, inspiring further experimental designs. Although generally applicable to any kind of protein domain because they depend solely on amino acid sequences, the second and third approaches are reviewed in detail because they appear to have been used infrequently and offer immediate opportunities for new advances. Finally, we speculate that future technologies capable of analyzing and manipulating conserved and variable aspects of the three-dimensional structures of a protein family could lead to broad insights not attainable by current methods.
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Affiliation(s)
- Emily N. Kennedy
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
| | - Clay A. Foster
- Department of Pediatrics, Section Hematology/Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Sarah A. Barr
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
| | - Robert B. Bourret
- Department of Microbiology & Immunology, University of North Carolina, Chapel Hill, NC, United States of America
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