HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911878989479936 |
|---|---|
| author | Fang, Xiaomin Gao, Jie Hu, Jing Liu, Lihang Xue, Yang Zhang, Xiaonan Zhu, Kunrui |
| author_facet | Fang, Xiaomin Gao, Jie Hu, Jing Liu, Lihang Xue, Yang Zhang, Xiaonan Zhu, Kunrui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10260 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights Fang, Xiaomin Gao, Jie Hu, Jing Liu, Lihang Xue, Yang Zhang, Xiaonan Zhu, Kunrui Biomolecules Artificial Intelligence 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. |
| title | HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights |
| topic | Biomolecules Artificial Intelligence |
| url | https://arxiv.org/abs/2404.10260 |