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STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning

[Image: see text] Protein engineering has made significant contributions to industries such as agriculture, food, and pharmaceuticals. In recent years, directed evolution combined with artificial intelligence has emerged as a cutting-edge R&D approach. However, the application of machine learnin...

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Detalles Bibliográficos
Autores principales: Yang, Likun, Liang, Xiaoli, Zhang, Na, Lu, Lu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688154/
https://www.ncbi.nlm.nih.gov/pubmed/38046324
http://dx.doi.org/10.1021/acsomega.3c04832
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author Yang, Likun
Liang, Xiaoli
Zhang, Na
Lu, Lu
author_facet Yang, Likun
Liang, Xiaoli
Zhang, Na
Lu, Lu
author_sort Yang, Likun
collection PubMed
description [Image: see text] Protein engineering has made significant contributions to industries such as agriculture, food, and pharmaceuticals. In recent years, directed evolution combined with artificial intelligence has emerged as a cutting-edge R&D approach. However, the application of machine learning techniques can be challenging for those without relevant experience and coding skills. To address this issue, we have developed a web-based protein sequence recommendation system: STAR (Sequence recommendaTion via ARtificial intelligence). Our system utilizes Bayesian optimization as its backbone and includes a filtering step using a regression model to enhance the success rate of recommended sequences. Additionally, we have incorporated an in silico-directed evolution approach to expand the exploration of the protein space. The Web site can be accessed at https://www.FindProteinStar.com/.
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spelling pubmed-106881542023-12-01 STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning Yang, Likun Liang, Xiaoli Zhang, Na Lu, Lu ACS Omega [Image: see text] Protein engineering has made significant contributions to industries such as agriculture, food, and pharmaceuticals. In recent years, directed evolution combined with artificial intelligence has emerged as a cutting-edge R&D approach. However, the application of machine learning techniques can be challenging for those without relevant experience and coding skills. To address this issue, we have developed a web-based protein sequence recommendation system: STAR (Sequence recommendaTion via ARtificial intelligence). Our system utilizes Bayesian optimization as its backbone and includes a filtering step using a regression model to enhance the success rate of recommended sequences. Additionally, we have incorporated an in silico-directed evolution approach to expand the exploration of the protein space. The Web site can be accessed at https://www.FindProteinStar.com/. American Chemical Society 2023-11-14 /pmc/articles/PMC10688154/ /pubmed/38046324 http://dx.doi.org/10.1021/acsomega.3c04832 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Yang, Likun
Liang, Xiaoli
Zhang, Na
Lu, Lu
STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning
title STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning
title_full STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning
title_fullStr STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning
title_full_unstemmed STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning
title_short STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning
title_sort star: a web server for assisting directed protein evolution with machine learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688154/
https://www.ncbi.nlm.nih.gov/pubmed/38046324
http://dx.doi.org/10.1021/acsomega.3c04832
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