PLM-eXplain: divide and conquer the protein embedding space.

ABSTRACT:

Motivation

Protein language models (PLMs) have revolutionized computational biology through their ability to generate powerful sequence representations for diverse prediction tasks. However, their black-box nature limits biological interpretation and translation to actionable insights. Bridging this gap requires approaches that maintain predictive performance while providing interpretable explanations of model behaviour.

Results

We present PLM-eXplain (PLM-X), an explainable adapter layer that bridges this gap by factoring PLM embeddings into two complementary components: an interpretable subspace based on established biochemical features, and a residual subspace that retains predictive, non-interpretable information. Using embeddings from ESM2 and ProtBert, PLM-X incorporat

SUBMITTER: van Eck J 

PROVIDER: S-EPMC12790820 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature