Open AccessArtificial intelligence for microbial biotechnology: beyond the hype
Author(s) -
Robinson Serina L.
Publication year - 2022
Publication title -
microbial biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.287
H-Index - 74
ISSN - 1751-7915
DOI - 10.1111/1751-7915.13943
Subject(s) - data science , globe , protein structure prediction , microbiology and biotechnology , artificial intelligence , computer science , biology , protein structure , biochemistry , neuroscience
Summary It has been a landmark year for artificial intelligence (AI) and biotechnology. Perhaps the most noteworthy of these advances was Google DeepMind’s AlphaFold2 algorithm which smashed records in protein structure prediction (Jumper et al ., 2021, Nature, 596, 583) complemented by progress made by other research groups around the globe (Baek et al ., 2021, Science , 373, 871; Zheng et al ., 2021, Proteins ). For the first time in history, AI achieved protein structure models rivalling the accuracy of experimentally determined structures. The power of accurate protein structure prediction at our fingertips has countless implications for drug discovery, de novo protein design and fundamental research in chemical biology. While acknowledging the significance of these breakthroughs, this perspective aims to cut through the hype and examine some key limitations using AlphaFold2 as a lens to consider the broader implications of AI for microbial biotechnology for the next 15 years and beyond.