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

Xiaomin Fang, Jie Gao, Jing Hu, Lihang Liu, Yang Xue +2 more
4/16/2024
q-bio.BMcs.AI

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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, 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.

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Cite this paper

@article{fang2024helixfoldmultimer,
  title  = {HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights},
  author = {Xiaomin Fang and Jie Gao and Jing Hu and Lihang Liu and Yang Xue and Xiaonan Zhang and Kunrui Zhu},
  year   = {2024},
  eprint = {2404.10260},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.10260v2}
}

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