Energy-based Graph Convolutional Networks for Scoring Protein Docking Models

• URL: http://arxiv.org/abs/1912.12476v1
• Date: Sat, 28 Dec 2019 15:57:17 GMT
• Title: Energy-based Graph Convolutional Networks for Scoring Protein Docking Models
• Authors: Yue Cao and Yang Shen
• Abstract summary: Two problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We propose a novel graph convolutional kernel that pool interacting nodes' features through edge features so that generalized interaction energies can be learned directly from graph data. The resulting energy-based graph convolutional networks (EGCN) with multi-head attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes.
• Score: 19.09624358779376
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative to predict such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent proteins' and encounter complexes' 3D structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that pool interacting nodes' features through edge features so that generalized interaction energies can be learned directly from graph data. The resulting energy-based graph convolutional networks (EGCN) with multi-head attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN has significantly improved ranking for a CAPRI test set involving homology docking; and is comparable for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.

Related papers

• Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation [153.92387500677023]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The proposed graph Transformer encoder combines graph convolutions and self-attentions in a Transformer to model both local and global interactions. We also propose a novel self-guided pre-training method for graph representation learning.
  arXiv  Detail & Related papers  (2024-01-15T14:36:38Z)
• EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction [49.674494450107005]
  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.
  arXiv  Detail & Related papers  (2023-02-23T17:18:26Z)
• HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction [0.0]
  We present a novel deep learning architecture consisting of a 3-dimensional convolutional neural network and two graph convolutional networks. HAC-Net obtains state-of-the-art results on the PDBbind v.2016 core set. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction.
  arXiv  Detail & Related papers  (2022-12-23T16:14:53Z)
• E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking [20.266157559473342]
  We propose E3Bind, an end-to-end equivariant network that iteratively updates the ligand pose. E3Bind models the protein-ligand interaction through careful consideration of the geometric constraints in docking. Experiments on standard benchmark datasets demonstrate the superior performance of our end-to-end trainable model.
  arXiv  Detail & Related papers  (2022-10-12T10:25:54Z)
• Protein 3D structure-based neural networks highly improve the accuracy in compound-protein binding affinity prediction [7.059949221160259]
  We develop Fast Evolutional Attention and Thoroughgoing-graph Neural Networks (FeatNN) to facilitate the application of protein 3D structure information for predicting compound-protein binding affinities (CPAs) FeatNN considerably outperforms various state-of-the-art baselines in CPA prediction with the Pearson value elevated by about 35.7%.
  arXiv  Detail & Related papers  (2022-03-30T00:44:15Z)
• 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)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)
• Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction [54.93890176891602]
  We propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph. We also propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation.
  arXiv  Detail & Related papers  (2020-12-17T17:44:01Z)
• CatGCN: Graph Convolutional Networks with Categorical Node Features [99.555850712725]
  CatGCN is tailored for graph learning when the node features are categorical. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification.
  arXiv  Detail & Related papers  (2020-09-11T09:25:17Z)

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.