1
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Zhang Y, Wu K, Li Y, Wu S, Warshel A, Bai C. Predicting Mutational Effects on Ca 2+-Activated Chloride Conduction of TMEM16A Based on a Simulation Study. J Am Chem Soc 2024; 146:4665-4679. [PMID: 38319142 DOI: 10.1021/jacs.3c11940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
The dysfunction and defects of ion channels are associated with many human diseases, especially for loss-of-function mutations in ion channels such as cystic fibrosis transmembrane conductance regulator mutations in cystic fibrosis. Understanding ion channels is of great current importance for both medical and fundamental purposes. Such an understanding should include the ability to predict mutational effects and describe functional and mechanistic effects. In this work, we introduce an approach to predict mutational effects based on kinetic information (including reaction barriers and transition state locations) obtained by studying the working mechanism of target proteins. Specifically, we take the Ca2+-activated chloride channel TMEM16A as an example and utilize the computational biology model to predict the mutational effects of key residues. Encouragingly, we verified our predictions through electrophysiological experiments, demonstrating a 94% prediction accuracy regarding mutational directions. The mutational strength assessed by Pearson's correlation coefficient is -0.80 between our calculations and the experimental results. These findings suggest that the proposed methodology is reliable and can provide valuable guidance for revealing functional mechanisms and identifying key residues of the TMEM16A channel. The proposed approach can be extended to a broad scope of biophysical systems.
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Affiliation(s)
- Yue Zhang
- Warshel Institute for Computational Biology, School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Kang Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Yuqing Li
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, California 90089-1062, United States
| | - Chen Bai
- Warshel Institute for Computational Biology, School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China
- Chenzhu Biotechnology Co., Ltd., Hangzhou 310005, China
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2
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Hong T, Long L, Sang Y, Jiang Z, Ni H, Zheng M, Li L, Li Q, Zhu Y. Simultaneous enhancement of thermostability and catalytic activity of κ-carrageenase from Pseudoalteromonas tetraodonis by rational design. Enzyme Microb Technol 2023; 167:110241. [PMID: 37060759 DOI: 10.1016/j.enzmictec.2023.110241] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 04/09/2023] [Accepted: 04/10/2023] [Indexed: 04/17/2023]
Abstract
κ-Carrageenase provides an attractive enzymatic approach to preparation of κ-carrageenan oligosaccharides. Pseudoalteromonas tetraodonis κ-carrageenase is active at the alkaline conditions but displays low thermostability. To further improve its enzymatic performance, two mutants of Q42V and I51H exhibiting both improved thermostability and enzyme activity were screened by the PoPMuSiC algorithm. Compared with the wild-type κ-carrageenase (WT), Q42V and I51H increased the enzyme activity by 20.9% and 25.4%, respectively. After treatment at 50 ℃ for 40 min, Q42V and I51H enhanced the residual activity by 31.1% and 25.9%, respectively. The Tm values of Q42V, I51H, and WT determined by differential scanning calorimetry were 58.2 ℃, 54.8 ℃, and 51.2 ℃, respectively. Compared with untreated and HCl-treated κ-carrageenans, Q42V-treated κ-carrageenan exhibited higher pancreatic lipase inhibitory activity. Molecular docking analysis indicated that the additional pi-sigma force and hydrophobic interaction in the enzyme-substrate complex could account for the increased catalytic activity of Q42V and I51H, respectively. Molecular dynamics analysis indicated that the improved thermostability of mutants Q42V and I51H could be attributed to the less structural deviation and the flexible changes of enzyme conformation at high temperature. This study provides new insight into κ-carrageenase performance improvement and identifies good candidates for their industrial applications.
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Affiliation(s)
- Tao Hong
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, Xiamen 361021, China; Research Center of Food Biotechnology of Xiamen City, Xiamen 361021, China
| | - Liufei Long
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China
| | - Yuyan Sang
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China
| | - Zedong Jiang
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, Xiamen 361021, China; Research Center of Food Biotechnology of Xiamen City, Xiamen 361021, China
| | - Hui Ni
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, Xiamen 361021, China; Research Center of Food Biotechnology of Xiamen City, Xiamen 361021, China
| | - Mingjing Zheng
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, Xiamen 361021, China; Research Center of Food Biotechnology of Xiamen City, Xiamen 361021, China
| | - Lijun Li
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, Xiamen 361021, China; Research Center of Food Biotechnology of Xiamen City, Xiamen 361021, China
| | - Qingbiao Li
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, Xiamen 361021, China; Research Center of Food Biotechnology of Xiamen City, Xiamen 361021, China
| | - Yanbing Zhu
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, China; Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, Xiamen 361021, China; Research Center of Food Biotechnology of Xiamen City, Xiamen 361021, China.
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3
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Peccati F, Alunno-Rufini S, Jiménez-Osés G. Accurate Prediction of Enzyme Thermostabilization with Rosetta Using AlphaFold Ensembles. J Chem Inf Model 2023; 63:898-909. [PMID: 36647575 PMCID: PMC9930118 DOI: 10.1021/acs.jcim.2c01083] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Thermostability enhancement is a fundamental aspect of protein engineering as a biocatalyst's half-life is key for its industrial and biotechnological application, particularly at high temperatures and under harsh conditions. Thermostability changes upon mutation originate from modifications of the free energy of unfolding (ΔGu), making thermostabilization extremely challenging to predict with computational methods. In this contribution, we combine global conformational sampling with energy prediction using AlphaFold and Rosetta to develop a new computational protocol for the quantitative prediction of thermostability changes upon laboratory evolution of acyltransferase LovD and lipase LipA. We highlight how using an ensemble of protein conformations rather than a single three-dimensional model is mandatory for accurate thermostability predictions. By comparing our approaches with existing ones, we show that ensembles based on AlphaFold models provide more accurate and robust calculated thermostability trends than ensembles based solely on crystallographic structures as the latter introduce a strong distortion (scaffold bias) in computed thermostabilities. Eliminating this bias is critical for computer-guided enzyme design and evaluating the effect of multiple mutations on protein stability.
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Affiliation(s)
- Francesca Peccati
- Basque
Research and Technology Alliance (BRTA), Center for Cooperative Research in Biosciences (CIC bioGUNE), Bizkaia Technology Park, Building
800, 48160Derio, Spain,
| | - Sara Alunno-Rufini
- Basque
Research and Technology Alliance (BRTA), Center for Cooperative Research in Biosciences (CIC bioGUNE), Bizkaia Technology Park, Building
800, 48160Derio, Spain
| | - Gonzalo Jiménez-Osés
- Basque
Research and Technology Alliance (BRTA), Center for Cooperative Research in Biosciences (CIC bioGUNE), Bizkaia Technology Park, Building
800, 48160Derio, Spain,Ikerbasque, Basque
Foundation for Science, 48013Bilbao, Spain,
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4
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Baek KT, Kepp KP. Data set and fitting dependencies when estimating protein mutant stability: Toward simple, balanced, and interpretable models. J Comput Chem 2022; 43:504-518. [PMID: 35040492 DOI: 10.1002/jcc.26810] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/13/2021] [Accepted: 01/03/2022] [Indexed: 12/27/2022]
Abstract
Accurate prediction of protein stability changes upon mutation (ΔΔG) is increasingly important to evolution studies, protein engineering, and screening of disease-causing gene variants but is challenged by biases in training data. We investigated 45 linear regression models trained on data sets that account systematically for destabilization bias and mutation-type bias BM . The models were externally validated on three test data sets probing different pathologies and for internal consistency (symmetry and neutrality). Model structure and performance substantially depended on training data and even fitting method. We developed two final models: SimBa-IB for typical natural mutations and SimBa-SYM for situations where stabilizing and destabilizing mutations occur to a similar extent. SimBa-SYM, despite is simplicity, is essentially non-biased (vs. the Ssym data set) while still performing well for all data sets (R ~ 0.46-0.54, MAE = 1.16-1.24 kcal/mol). The simple models provide advantage in terms of interpretability, use and future improvement, and are freely available on GitHub.
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Affiliation(s)
| | - Kasper P Kepp
- DTU Chemistry, Technical University of Denmark, Lyngby, Denmark
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5
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Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation. J Mol Biol 2021; 434:167375. [PMID: 34826524 DOI: 10.1016/j.jmb.2021.167375] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/05/2021] [Accepted: 11/17/2021] [Indexed: 01/17/2023]
Abstract
This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R2 of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.
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6
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Louis BBV, Abriata LA. Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures. Mol Biotechnol 2021; 63:863-884. [PMID: 34101125 PMCID: PMC8443528 DOI: 10.1007/s12033-021-00349-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/01/2021] [Indexed: 11/26/2022]
Abstract
Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the limitations of prediction methods and of the datasets used to train them, which result in low reliability for actual applications despite globally capturing trends. Here, we review these and other issues by analyzing one of the most detailed, carefully curated datasets of melting temperature change (ΔTm) upon mutation for proteins with high-resolution structures. After examining the composition of this dataset to discuss imbalances and biases, we inspect several of its entries assisted by an online app for data navigation and structure display and aided by a neural network that predicts ΔTm with accuracy close to that of programs available to this end. We pose that the ΔTm predictions of our network, and also likely those of other programs, account only for a baseline-like general effect of each type of amino acid substitution which then requires substantial corrections to reproduce the actual stability changes. The corrections are very different for each specific case and arise from fine structural details which are not well represented in the dataset and which, despite appearing reasonable upon visual inspection of the structures, are hard to encode and parametrize. Based on these observations, additional analyses, and a review of recent literature, we propose recommendations for developers of stability prediction methods and for efforts aimed at improving the datasets used for training. We leave our interactive interface for analysis available online at http://lucianoabriata.altervista.org/papersdata/proteinstability2021/s1626navigation.html so that users can further explore the dataset and baseline predictions, possibly serving as a tool useful in the context of structural biology and protein biotechnology research and as material for education in protein biophysics.
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Affiliation(s)
- Benjamin B V Louis
- Master of Life Sciences Engineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland
| | - Luciano A Abriata
- Laboratory for Biomolecular Modeling, School of Life Sciences, École Polytechnique Fédérale de Lausanne, and Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland.
- Protein Production and Structure Core Facility, School of Life Sciences, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland.
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7
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Mazurenko S. Predicting protein stability and solubility changes upon mutations: data perspective. ChemCatChem 2020. [DOI: 10.1002/cctc.202000933] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Stanislav Mazurenko
- Loschmidt Laboratories Department of Experimental Biology and RECETOX Faculty of Science Masaryk University Zerotinovo nam. 617/9 601 77 Brno Czech Republic
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8
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Huang P, Chu SKS, Frizzo HN, Connolly MP, Caster RW, Siegel JB. Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset. ACS OMEGA 2020; 5:6487-6493. [PMID: 32258884 PMCID: PMC7114132 DOI: 10.1021/acsomega.9b04105] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 03/06/2020] [Indexed: 05/04/2023]
Abstract
Engineering proteins to enhance thermal stability is a widely utilized approach for creating industrially relevant biocatalysts. The development of new experimental datasets and computational tools to guide these engineering efforts remains an active area of research. Thus, to complement the previously reported measures of T 50 and kinetic constants, we are reporting an expansion of our previously published dataset of mutants for β-glucosidase to include both measures of T M and ΔΔG. For a set of 51 mutants, we found that T 50 and T M are moderately correlated, with a Pearson correlation coefficient and Spearman's rank coefficient of 0.58 and 0.47, respectively, indicating that the two methods capture different physical features. The performance of predicted stability using nine computational tools was also evaluated on the dataset of 51 mutants, none of which are found to be strong predictors of the observed changes in T 50, T M, or ΔΔG. Furthermore, the ability of the nine algorithms to predict the production of isolatable soluble protein was examined, which revealed that Rosetta ΔΔG, FoldX, DeepDDG, PoPMuSiC, and SDM were capable of predicting if a mutant could be produced and isolated as a soluble protein. These results further highlight the need for new algorithms for predicting modest, yet important, changes in thermal stability as well as a new utility for current algorithms for prescreening designs for the production of mutants that maintain fold and soluble production properties.
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Affiliation(s)
- Peishan Huang
- Biophysics
Graduate Group, University of California, Davis 95616, California, United States
| | - Simon K. S. Chu
- Biophysics
Graduate Group, University of California, Davis 95616, California, United States
| | - Henrique N. Frizzo
- Genome
Center, University of California, Davis 95616, California, United States
| | - Morgan P. Connolly
- Microbiology
Graduate Group, University of California, Davis 95616, California, United States
| | - Ryan W. Caster
- Genome
Center, University of California, Davis 95616, California, United States
| | - Justin B. Siegel
- Genome
Center, University of California, Davis 95616, California, United States
- Department
of Biochemistry & Molecular Medicine, University of California, Davis 95616, California, United States
- Department
of Chemistry, University of California, Davis 95616, California, United States
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9
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Fang J. A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation. Brief Bioinform 2019; 21:1285-1292. [PMID: 31273374 DOI: 10.1093/bib/bbz071] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 05/14/2019] [Accepted: 05/16/2019] [Indexed: 01/02/2023] Open
Abstract
A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative algorithms and found all of them suffer from the problem of overfitting. This approach is based on the fact that if a wild-type protein is more stable than a mutant protein, then the same mutant is less stable than the wild-type protein. We analyzed the underlying issues and suggest that the main causes of the overfitting problem include that the numbers of training cases were too small, and the features used in the models were not sufficiently informative for the task. We make recommendations on how to avoid overfitting in this important research area and improve the reliability and robustness of ML-based algorithms in general.
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Affiliation(s)
- Jianwen Fang
- Computational & Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA
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