Generative power of a protein language model trained on multiple sequence alignments.

ABSTRACT: Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating novel sequences belonging to protein families. Protein language models trained on multiple sequence alignments, such as MSA Transformer, are highly attractive candidates to this end. We propose and test an iterative method that directly employs the masked language modeling objective to generate sequences using MSA Transformer. We demonstrate that the resulting sequences score as well as natural sequences, for homology, coevolution, and structure-based measures. For large protein families, our synthetic sequences have similar or better prope

SUBMITTER: Sgarbossa D 

PROVIDER: S-EPMC10038667 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature