In the twilight zone of protein sequence homology: do protein language models learn protein structure?

ABSTRACT:

Motivation

Protein language models based on the transformer architecture are increasingly improving performance on protein prediction tasks, including secondary structure, subcellular localization, and more. Despite being trained only on protein sequences, protein language models appear to implicitly learn protein structure. This paper investigates whether sequence representations learned by protein language models encode structural information and to what extent.

Results

We address this by evaluating protein language models on remote homology prediction, where identifying remote homologs from sequence information alone requires structural knowledge, especially in the "twilight zone" of very low sequence identity. Through rigorous testing at progressively lower sequence iden

SUBMITTER: Kabir A 

PROVIDER: S-EPMC11344590 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature