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Quantitative Biology > Biomolecules

Title: HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights

Abstract: 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, surpassing AlphaFold-Multimer by several folds. 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.
Subjects: Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI)
Cite as: arXiv:2404.10260 [q-bio.BM]
  (or arXiv:2404.10260v1 [q-bio.BM] for this version)

Submission history

From: Xiaomin Fang [view email]
[v1] Tue, 16 Apr 2024 03:29:37 GMT (689kb,D)

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