2024/04/16 by Xiaomin Fang, Fang, Xiaomin, Jie Gao +11 · 2 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Artificial Intelligence (cs.AI) #Biomolecules (q-bio.BM) #Enzyme Structure and Function #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning in Bioinformatics #Protein Structure and Dynamics
paper · pdf · doi:10.48550/arxiv.2404.10260
openalex publication_date 2024/04/16 · openalex created_date 2024/04/18 · openalex updated_date 2026/07/28
While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field. This challenge is particularly pronounced in scenarios involving complexes with protein chains from different species, such as antigen-antibody interactions, where accuracy often falls short. Limited by the accuracy of complex prediction, tasks based on precise protein-protein interaction analysis also face obstacles. In this report, we highlight the ongoing advancements of our protein complex structure prediction model, HelixFold-Multimer, underscoring its enhanced performance. HelixFold-Multimer provides precise predictions for diverse protein complex structures, especially in therapeutic protein interactions. Notably, HelixFold-Multimer achieves remarkable success in antigen-antibody and peptide-protein structure prediction, greatly surpassing AlphaFold 3. HelixFold-Multimer is now available for public use on the PaddleHelix platform, offering both a general version and an antigen-antibody version. Researchers can conveniently access and utilize this service for their development needs.