Protein language models can capture protein quaternary state.

ABSTRACT:

Background

Determining a protein's quaternary state, i.e. the number of monomers in a functional unit, is a critical step in protein characterization. Many proteins form multimers for their activity, and over 50% are estimated to naturally form homomultimers. Experimental quaternary state determination can be challenging and require extensive work. To complement these efforts, a number of computational tools have been developed for quaternary state prediction, often utilizing experimentally validated structural information. Recently, dramatic advances have been made in the field of deep learning for predicting protein structure and other characteristics. Protein language models, such as ESM-2, that apply computational natural-language models to proteins successfully capture seconda

SUBMITTER: Avraham O 

PROVIDER: S-EPMC10647083 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature