Pretrained protein language models choose between sequence novelty and structural completeness.

ABSTRACT: Protein language models (PLMs) have gained increasing acceptance in tasks ranging from variant effect prediction in disease to optimization and de novo design of proteins with improved stability, target-binding affinity, and catalytic performance. Despite encouraging performance in such applications, little is understood as far as the degree to which PLM-generated sequences - putative novel protein outputs - recapitulate the broad biophysical rules and diversity of sequence, structure, and function that defines natural protein-space, vital knowledge for boosting the design capacity of PLMs in ever-more-complex systems. Towards this end, we computationally profile and characterize the sequence and structure statistics and properties of hundreds of thousands of potential small protein

SUBMITTER: Subramanian AM 

PROVIDER: S-EPMC12621866 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature