Accurate prediction of protein function using statistics-informed graph networks.

ABSTRACT: Understanding protein function is pivotal in comprehending the intricate mechanisms that underlie many crucial biological activities, with far-reaching implications in the fields of medicine, biotechnology, and drug development. However, more than 200 million proteins remain uncharacterized, and computational efforts heavily rely on protein structural information to predict annotations of varying quality. Here, we present a method that utilizes statistics-informed graph networks to predict protein functions solely from its sequence. Our method inherently characterizes evolutionary signatures, allowing for a quantitative assessment of the significance of residues that carry out specific functions. PhiGnet not only demonstrates superior performance compared to alternative approaches but also

SUBMITTER: Jang YJ 

PROVIDER: S-EPMC11297950 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature