ArticleBiology methods & protocols2024
Multimodal pretraining for unsupervised protein representation learning.
Article in Biology methods & protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- LINKER:Journal of chemical information and modeling · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- A unified framework combining linear and 3D molecular features for robust drug-protein interaction prediction.Cell reports methods · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Multimodal Learning of Protein-Protein Interactions for Accurate Binding Affinity Prediction.ACS omega · 2025Article
- Article
- Topological machine learning for protein-nucleic acid binding affinity changes upon mutation.Machine learning: science and technology · 2025Article
- PLiSAGE: enhancing protein-ligand interaction prediction with multimodal surface and geometry encoding.Bioinformatics (Oxford, England) · 2025Article
- MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.BMC biology · 2025Article
- Multimodal deep learning for allergenic proteins prediction.BMC biology · 2025Article
- $\mathcal{S}$ able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm.Briefings in bioinformatics · 2025Article
- SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions.Nature communications · 2024Article
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2 authors.
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Abstract
Proteins are complex biomolecules essential for numerous biological processes, making them crucial targets for advancements in molecular biology, medical research, and drug design. Understanding their intricate, hierarchical structures, and functions is vital for progress in these fields. To capture this complexity, we introduce Multimodal Protein Representation Learning (MPRL), a novel framework for symmetry-preserving multimodal pretraining that learns unified, unsupervised protein representations by integrating primary and tertiary structures. MPRL employs Evolutionary Scale Modeling (ESM-2) for sequence analysis, Variational Graph Auto-Encoders (VGAE) for residue-level graphs, and PointNet Autoencoder (PAE) for 3D point clouds of atoms, each designed to capture the spatial and evolutionary intricacies of proteins while preserving critical symmetries. By leveraging Auto-Fusion to synthesize joint representations from these pretrained models, MPRL ensures robust and comprehensive protein representations. Our extensive evaluation demonstrates that MPRL significantly enhances performance in various tasks such as protein-ligand binding affinity prediction, protein fold classification, enzyme activity identification, and mutation stability prediction. This framework advances the understanding of protein dynamics and facilitates future research in the field. Our source code is publicly available at https://github.com/HySonLab/Protein_Pretrain.
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