Data and AI-driven synthetic binding protein discovery.

Trends Pharmacol Sci

School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China; Western (Chongqing) Collaborative Innovation Center for Intelligent Diagnostics and Digital Medicine, Chongqing National Biomedicine Industry Park, Chongqing 401329, China. Electronic address:

Published: January 2025

Synthetic binding proteins (SBPs) are a class of protein binders that are artificially created and do not exist naturally. Their broad applications in tackling challenges of research, diagnostics, and therapeutics have garnered significant interest. Traditional protein engineering is pivotal to the discovery of SBPs. Recently, this discovery has been significantly accelerated by computational approaches, such as molecular modeling and artificial intelligence (AI). Furthermore, while numerous bioinformatics databases offer a wealth of resources that fuel SBP discovery, the full potential of these data has not yet been fully exploited. In this review, we present a comprehensive overview of SBP data ecosystem and methodologies in SBP discovery, highlighting the critical role of high-quality data and AI technologies in accelerating the discovery of innovative SBPs with promising applications in pharmacological sciences.

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http://dx.doi.org/10.1016/j.tips.2024.12.002DOI Listing

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