.png){kind=icon}
返回
ComplexQA: a deep graph learning approach for protein complex structure assessment
.png){kind=icon}
PRE
AI
DOI：10.1093/bib/bbad287.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Motive In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residual structural information in 3D space and the sequence-level constraints. Results: We benchmark our method to other state-d -art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by Alpha Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes. Availability: https://github.com/Cao-Labs/ComplexQA.git. Contact: caora@plu.edu
Keyword:
deep graph learning
protein complex assessment
machine learning
protein interface
期刊
IF:
7.7
论文数:
5.8K
被引数:
2.7W
机构
引用论文
分享收藏GOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction.png)
BIOPHYSICAL JOURNAL
OAAI
BIOPHYSICAL JOURNALIF3.1
OAAI分享收藏
Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM通过使用PRISM匹配界面上的进化和结构相似性，在蛋白质组尺度上预测蛋白质-蛋白质相互作用NATURE PROTOCOLSOAAI
NATURE PROTOCOLSIF16
OAAI分享收藏
查看更多内容