Accurate prediction of protein function using statistics-informed graph networks

Yaan J. Jang (), Qi-Qi Qin, Si-Yu Huang, Arun T. John Peter, Xue-Ming Ding and Benoît Kornmann ()
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Yaan J. Jang: University of Oxford
Qi-Qi Qin: AmoAi Technologies
Si-Yu Huang: AmoAi Technologies
Arun T. John Peter: ETH Zürich
Xue-Ming Ding: University of Shanghai for Science and Technology
Benoît Kornmann: University of Oxford

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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 narrows the sequence-function gap, even in the absence of structural information. Our findings indicate that applying deep learning to evolutionary data can highlight functional sites at the residue level, providing valuable support for interpreting both existing properties and new functionalities of proteins in research and biomedicine.

Date: 2024
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DOI: 10.1038/s41467-024-50955-0

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