.png){kind=icon}
返回
Exploiting Hierarchical Interactions for Protein Surface Learning
.png){kind=icon}
OA
AI
DOI：10.1109/JBHI.2024.3356231.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. However, existing works only consider handcrafted or individually learned chemical features from the atom type and extract geometric features independently. Here, we identify two key properties of effective protein surface learning: 1) relationship among atoms: atoms are linked with each other by covalent bonds to form biomolecules instead of appearing alone, leading to the significance of modeling the relationship among atoms in chemical feature learning. 2) hierarchical feature interaction: the neighboring residue effect validates the significance of hierarchical feature interaction among atoms and between surface points and atoms (or residues). In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions. Extensive experiments demonstrate that our method outperforms the prior state-of-the-art method by 2.3% in site prediction task and 3.2% in interaction matching task, respectively.
Keyword:
Proteins
Protein engineering
Chemicals
Three-dimensional displays
Point cloud compression
Amino acids
Feature extraction
Biology
computer vision
point cloud
protein-protein interaction
surface learning
期刊
IF:
6.8
论文数:
4.7K
被引数:
2.0W
机构
引用论文
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏分享收藏
查看更多内容