Academic Journal
Deep Learning-Based Prediction of Enzyme Optimal pH and Design of Point Mutations to Improve Acid Resistance.
| Τίτλος: | Deep Learning-Based Prediction of Enzyme Optimal pH and Design of Point Mutations to Improve Acid Resistance. |
|---|---|
| Συγγραφείς: | Qiu S; Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom., Wang NK; Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, PR China.; Yixing Institute of Food and Biotechnology Co. Ltd, Yixing 214200, PR China., Lu Y; Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom.; Oxford E-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford OX1 3QG, United Kingdom., Gong JS; Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, PR China.; Yixing Institute of Food and Biotechnology Co. Ltd, Yixing 214200, PR China., Shi JS; Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi 214122, PR China.; Yixing Institute of Food and Biotechnology Co. Ltd, Yixing 214200, PR China., Yang A; Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom. |
| Πηγή: | ACS synthetic biology [ACS Synth Biol] 2025 Dec 19; Vol. 14 (12), pp. 4897-4906. Date of Electronic Publication: 2025 Nov 21. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101575075 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2161-5063 (Electronic) Linking ISSN: 21615063 NLM ISO Abbreviation: ACS Synth Biol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Washington, D.C. : American Chemical Society, c2012- |
| Ιατρικοί όροι (MeSH): | Point Mutation*/genetics , Deep Learning*, Pyrococcus horikoshii/enzymology ; Pyrococcus horikoshii/genetics ; Hydrogen-Ion Concentration |
| Περίληψη: | An accurate deep learning predictor of enzyme optimal pH is essential to quantitatively describe how pH influences the enzyme catalytic activity. CatOpt, developed in this study, outperformed existing predictors of enzyme optimal pH (RMSE = 0.833 and R2 = 0.479), and could provide good interpretability with informative residue attention weights. The classification of acidophilic and alkaliphilic enzymes and prediction of enzyme optimal pH shifts caused by point mutations showcased the capability of CatOpt as an effective computational tool for identifying enzyme pH preferences. Furthermore, a single point mutation designed with the guidance of CatOpt successfully enhanced the activity of Pyrococcus horikoshii diacetylchitobiose deacetylase at low pH (pH = 4.5/5.5) by approximately 7%, suggesting that CatOpt is a promising in silico enzyme design tool for pH-dependent enzyme activities. |
| References: | J Mol Biol. 1993 May 20;231(2):459-74. (PMID: 8510157) PLoS Comput Biol. 2012;8(11):e1002761. (PMID: 23133364) Meat Sci. 2008 Oct;80(2):545-9. (PMID: 22063364) BMC Bioinformatics. 2020 Nov 10;21(1):512. (PMID: 33167861) PLoS One. 2013 Oct 09;8(10):e75726. (PMID: 24130738) ACS Synth Biol. 2024 Sep 20;13(9):3013-3021. (PMID: 39197156) Toxins (Basel). 2024 Dec 13;16(12):. (PMID: 39728798) Indian J Microbiol. 2022 Mar;62(1):96-102. (PMID: 35068609) J Biotechnol. 2017 Nov 10;261:194-206. (PMID: 28438579) Science. 2023 Mar 17;379(6637):1123-1130. (PMID: 36927031) Arch Biochem Biophys. 1999 Mar 15;363(2):349-55. (PMID: 10068458) Protein Sci. 2009 Mar;18(3):569-78. (PMID: 19241384) J Theor Biol. 2015 Jan 21;365:197-203. (PMID: 25452135) Bioresour Technol. 2021 May 1;334:125241. (PMID: 33964814) Nucleic Acids Res. 2023 Jan 6;51(D1):D523-D531. (PMID: 36408920) Nat Biotechnol. 2017 Nov;35(11):1026-1028. (PMID: 29035372) Curr Opin Biotechnol. 2002 Aug;13(4):345-51. (PMID: 12323357) Appl Environ Microbiol. 2019 Aug 14;85(17):. (PMID: 31253675) Bioinformatics. 2022 Mar 28;38(7):1995-2002. (PMID: 35043942) Biochim Biophys Acta. 1978 Aug 7;525(2):314-24. (PMID: 28774) Bioresour Bioprocess. 2022 May 16;9(1):54. (PMID: 38647756) Curr Opin Struct Biol. 2025 Apr;91:103004. (PMID: 39983412) Biotechnol Lett. 2022 Mar;44(3):473-483. (PMID: 35072843) Cell Syst. 2017 Dec 27;5(6):538-541. (PMID: 29284126) Brief Bioinform. 2025 Mar 04;26(2):. (PMID: 40079266) J Am Chem Soc. 2014 Jun 18;136(24):8496-9. (PMID: 24842060) Brief Bioinform. 2023 Nov 22;25(1):. (PMID: 38189538) Brief Bioinform. 2024 Jul 25;25(5):. (PMID: 39129365) Biotechnol Bioeng. 2022 Apr;119(4):1115-1128. (PMID: 35067915) Biophys Chem. 2021 Jul;274:106591. (PMID: 33895555) Nat Commun. 2023 Dec 11;14(1):8211. (PMID: 38081905) |
| Contributed Indexing: | Keywords: acid resistance; deep learning; enzyme engineering; enzyme optimal pH; self-attention; sequence-based prediction |
| Entry Date(s): | Date Created: 20251121 Date Completed: 20251219 Latest Revision: 20260513 |
| Update Code: | 20260513 |
| PubMed Central ID: | PMC12723737 |
| DOI: | 10.1021/acssynbio.5c00679 |
| PMID: | 41269167 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2161-5063 |
|---|---|
| DOI: | 10.1021/acssynbio.5c00679 |