2024/03/05 by Kangjie Zheng, Siyu Long, Zheng, Kangjie +15 · 3 citations
Biochemistry, Genetics and Molecular Biology · #Biomolecules (q-bio.BM) #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Genetics, Bioinformatics, and Biomedical Research #Genomics and Phylogenetic Studies #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2403.12995
openalex publication_date 2024/03/05 · openalex created_date 2024/03/23 · openalex updated_date 2026/07/28
Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pre-training on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins. The source codes of ESM-AA are publicly released at https://github.com/zhengkangjie/ESM-AA.