Learning functional properties of proteins with language models

Unsal, Serbulent; Atas, Heval; ALBAYRAK, MUAMMER; TURHAN, KEMAL; Acar, AYBAR; DOĞAN, TUNCA

doi:10.1038/s42256-022-00457-9

Learning functional properties of proteins with language models

Unsal S., Atas H., ALBAYRAK M., TURHAN K., Acar A. C., DOĞAN T.

NATURE MACHINE INTELLIGENCE, vol.4, no.3, pp.227-245, 2022 (SCI-Expanded, Scopus)

Publication Type: Article / Article
Volume: 4 Issue: 3
Publication Date: 2022
Doi Number: 10.1038/s42256-022-00457-9
Journal Name: NATURE MACHINE INTELLIGENCE
Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Page Numbers: pp.227-245
Middle East Technical University Affiliated: Yes

Abstract

Deep learning methods have in recent years shown promising results in characterizing proteins and extracting complex sequence-structure-function relationships. This Analysis describes a benchmarking study to compare the performances and advantages of recent deep learning approaches in a range of protein prediction tasks.