Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models

Abstract Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leve...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Briefings in bioinformatics Ročník 24; číslo 5
Hlavní autoři: Qiu, Yuchi, Wei, Guo-Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 20.09.2023
Oxford Publishing Limited (England)
Témata:
Antibodies
Artificial Intelligence
Biotechnology
Data Analysis
Drug development
Food security
Machine learning
Natural Language Processing
Protein Engineering
Protein structure
Proteins
Review
Topology
protein engineering
deep learning and machine learning
protein language models
topological data analysis
ISSN:1467-5463, 1477-4054, 1477-4054
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Abstract Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbad289