EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction

• URL: http://arxiv.org/abs/2302.12177v3
• Date: Tue, 23 Jul 2024 03:32:32 GMT
• Title: EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
• Authors: Yang Zhang, Zhewei Wei, Ye Yuan, Chongxuan Li, Wenbing Huang,
• Abstract summary: Predicting the binding sites of target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a protein as a 3D image by spatially clustering its atoms into voxels. This work proposes EquiPocket, an E(3)-equivariant Graph Neural Network (GNN) for binding site prediction.
• Score: 49.674494450107005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting the binding sites of target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a protein as a 3D image by spatially clustering its atoms into voxels and then feed the voxelized protein into a 3D CNN for prediction. However, the CNN-based methods encounter several critical issues: 1) defective in representing irregular protein structures; 2) sensitive to rotations; 3) insufficient to characterize the protein surface; 4) unaware of protein size shift. To address the above issues, this work proposes EquiPocket, an E(3)-equivariant Graph Neural Network (GNN) for binding site prediction, which comprises three modules: the first one to extract local geometric information for each surface atom, the second one to model both the chemical and spatial structure of protein and the last one to capture the geometry of the surface via equivariant message passing over the surface atoms. We further propose a dense attention output layer to alleviate the effect incurred by variable protein size. Extensive experiments on several representative benchmarks demonstrate the superiority of our framework to the state-of-the-art methods.

Related papers

• Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs [26.727436310732692]
  We propose a novel self-supervised method to pretrain 3D graph neural networks on 3D protein structures. We experimentally show that our proposed pertaining strategy leads to significant improvements up to 6%.
  arXiv  Detail & Related papers  (2024-06-20T09:34:31Z)
• Exploiting Hierarchical Interactions for Protein Surface Learning [52.10066114039307]
  Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet) Our method outperforms the prior state-of-the-art method by 2.3% in site prediction task and 3.2% in interaction matching task.
  arXiv  Detail & Related papers  (2024-01-17T14:10:40Z)
• Multi-Scale Representation Learning on Proteins [78.31410227443102]
  This paper introduces a multi-scale graph construction of a protein -- HoloProt. The surface captures coarser details of the protein, while sequence as primary component and structure captures finer details. Our graph encoder then learns a multi-scale representation by allowing each level to integrate the encoding from level(s) below with the graph at that level.
  arXiv  Detail & Related papers  (2022-04-04T08:29:17Z)
• Directed Weight Neural Networks for Protein Structure Representation Learning [16.234990522729348]
  We propose the Directed Weight Neural Network for better capturing geometric relations among different amino acids. Our new framework supports a rich set of geometric operations on both classical and SO(3)--representation features. It achieves state-of-the-art performance on various computational biology applications related to protein 3D structures.
  arXiv  Detail & Related papers  (2022-01-28T13:41:56Z)
• Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking [57.2037357017652]
  We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position. Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment.
  arXiv  Detail & Related papers  (2021-11-15T18:46:37Z)
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity [52.67037774136973]
  Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. We propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool)
  arXiv  Detail & Related papers  (2021-07-21T03:34:09Z)
• G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation [41.66010308405784]
  We introduce a joint geometric-neural networks approach for comparing, deforming and generating 3D protein structures. Our method is able to generate plausible structures, different from the structures in the training data.
  arXiv  Detail & Related papers  (2021-06-22T16:52:48Z)
• Spherical convolutions on molecular graphs for protein model quality assessment [0.0]
  In this work, we propose Spherical Graph Convolutional Network (S-GCN) that processes 3D models of proteins represented as molecular graphs. Within the framework of the protein model quality assessment problem, we demonstrate that the proposed spherical convolution method significantly improves the quality of model assessment.
  arXiv  Detail & Related papers  (2020-11-16T14:22:36Z)
• BERTology Meets Biology: Interpreting Attention in Protein Language Models [124.8966298974842]
  We demonstrate methods for analyzing protein Transformer models through the lens of attention. We show that attention captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure. We also present a three-dimensional visualization of the interaction between attention and protein structure.
  arXiv  Detail & Related papers  (2020-06-26T21:50:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.