MULAN: multimodal protein language model for sequence and structure encoding.

ABSTRACT:

Motivation

Most protein language models (PLMs) produce high-quality representations using only protein sequences. However, incorporating known protein structures is important for many prediction tasks, leading to increased interest in structure-aware PLMs. Currently, structure-aware PLMs are either trained from scratch or add significant parameter overhead for the structure encoder.

Results

In this study, we propose MULAN, a MULtimodal PLM for both sequence and ANgle-based structure encoding. MULAN has a pre-trained sequence encoder and an introduced parameter-efficient Structure Adapter, which are then fused and trained together. Based on the evaluation of nine downstream tasks, MULAN models of various sizes show a quality improvement compared to both sequence-only ESM2 and

SUBMITTER: Frolova D 

PROVIDER: S-EPMC12452268 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature