Molecular-level protein semantic learning via structure-aware coarse-grained language modeling.

ABSTRACT:

Motivation

Protein language models (PLMs) have emerged as pivotal tools for protein representation, enabling significant advances in structure-function prediction and computational biology. However, current PLMs predominantly rely on fine-grained amino acid sequences as input, treating individual residues as tokens. While this approach facilitates semantic learning at the residue level, it struggles to capture molecular-level semantics, particularly for large proteins, where sequence truncation and inefficient local pattern extraction hinder holistic understanding. The spatial structure of a protein determines its function. Despite the critical role of protein function analysis, coarse-grained protein language frameworks that bridge sequence and structural semantics remain underdev

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12758601 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature