Efficient Adaptation of Structure‐Aware Protein Language Models for Diverse Protein Applications

Protein language models (PLMs) trained solely on sequence data have significantly advanced our understanding of protein biology and achieved remarkable performance in protein prediction tasks. However, their lack of three-dimensional (3D) structural features limits their predictive power in applications that rely heavily on 3D conformation. To address this limitation, we developed two structure-aware PLMs, S-PLM1 and S-PLM2, that employ multi-view contrastive learning to align protein sequences with their 3D structures in a unified latent space. S-PLM1 represents structural information using contact maps encoded by a pretrained Swin-Transformer, while S-PLM2 directly encodes 3D backbone coordinates through a Geometric Vector Perceptron (GVP)-based model. The paired sequence-structure data were obtained from AlphaFoldDB. For both models, we designed efficient tuning strategies that enable optimal performance with minimal computational cost. Here, we present detailed protocols for adapting S-PLM1 and S-PLM2 for diverse protein applications. The protocols provide step-by-step guidance on generating structure-aware representations from S-PLMs, fine-tuning them for various protein prediction tasks, and using S-PLM2 to produce structure embeddings for structure-based downstream analyses. We also provide source code and Google Colab implementations for easy customization and deployment. © 2026 Wiley Periodicals LLC. Basic Protocol 1: Generating structure-aware representations of protein sequences Basic Protocol 2: Efficient tuning of structure-aware protein language models for diverse protein applications Basic Protocol 3: Using S-PLM2 to generate protein structure representations and conduct structure-based clustering Support Protocol: Google Colab quick start notebooks.