Protein language models meet reduced amino acid alphabets.

ABSTRACT:

Motivation

Protein language models (PLMs), which borrowed ideas for modelling and inference from natural language processing, have demonstrated the ability to extract meaningful representations in an unsupervised way. This led to significant performance improvement in several downstream tasks. Clustering amino acids based on their physical-chemical properties to achieve reduced alphabets has been of interest in past research, but their application to PLMs or folding models is unexplored.

Results

Here, we investigate the efficacy of PLMs trained on reduced amino acid alphabets in capturing evolutionary information, and we explore how the loss of protein sequence information impacts learned representations and downstream task performance. Our empirical work shows that PLMs tra

SUBMITTER: Ieremie I 

PROVIDER: S-EPMC10872054 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature