Year: 2025
Type: article
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 incorporates well-established properties, including secondary structure and hydropathy, while maintaining high predictive performance. We demonstrate the effectiveness of our approach across three biologically relevant classification tasks: extracellular vesicle association, transmembrane helix prediction, and aggregation propensity prediction. PLM-X enables biological interpretation of model decisions without sacrificing accuracy, offering a generalizable solution for enhancing PLM interpretability across various downstream applications. AVAILABILITY AND IMPLEMENTATION: Source code and models are available at https://github.com/AIT4LIFE-UU/PLM-eXplain/.
Source: Bioinformatics
Institution: Utrecht University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,989
Award: G5616476150
OpenAlex ID: W4416504075