Unofficial TensorFlow implementation of Protein Interface Prediction using Graph Convolutional Networks.

Related tags

Deep Learning prediction protein protein-protein-interaction graph-convolutional-networks graph-convolutional-network graph-neural-networks graph-neural-network
Overview

[TensorFlow] Protein Interface Prediction using Graph Convolutional Networks

Unofficial TensorFlow implementation of Protein Interface Prediction using Graph Convolutional Networks [1].

Usage

$ unzip dataset.zip
$ python run.py

Dataset

Number of samples

Set Complex Positive Negative
Training 140 12866 (9.1 %) 128660 (90.9 %)
Validation 35 3138 (0.2 %) 31380 (99.8 %)
Test 55 4871 (0.1 %) 4953446 (99.9 %)

Sample view for Ligand and Receptor respectively.

Feature description

Graph
│
├── Vertex: 70 features
│    ├── Windowed Position Specific Scoring Matrix: 20
│    ├── Relative Accessible Surface Area: 1
│    ├── Residue Depth: 2
│    ├── Protrusion Index: 6
│    ├── Hydrophobicity: 1
│    └── Half Sphere Amino Acid Composition: 40
└── Edge: 2 features
     ├── Average Atomic Distance: 1
     └── CCαO Angle: 1

Three equations for each neural network in this repository. Each equation is provided as net00_pipgcn_navg.py, net01_pipgcn_neavg.py, and net02_pipgcn_odepn.py

Result

  • Refer that the results shown below are temporary results. (AUROC: 0.81421)
  • The model tuning process is not finished completely.

Loss graph.

Results of the test process. The first figure (upper side) shows the negative interaction score between ligand and receptor. The lower side figures show a histogram of negative interaction score of negative labeled and positive labeled sample.

Requirements

Contribution

  • YeongHyeon Park : PIPGCN implementation (including neural network, data management)
  • Myung Jin Kim and Yeong Beom Kim : review (double-check)

Reference

[1] Fout, Alex M. Protein interface prediction using graph convolutional networks. Diss. Colorado State University, 2017.
[2] Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks. https://zenodo.org/record/1127774#.WkLewGGnGcY

You might also like...
Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

This is a fork of Fairseq(-py) with implementations of the following models: Pervasive Attention - 2D Convolutional Neural Networks for Sequence-to-Se

Maha 490 Dec 15, 2022
Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Noah Getz 3 Jun 22, 2022
PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

null 11 May 19, 2022
A PyTorch implementation of
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

ClusterGCN ⠀⠀ A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019). A

Benedek Rozemberczki 697 Dec 27, 2022
Official DGL implementation of
Official DGL implementation of "Rethinking High-order Graph Convolutional Networks"

SE Aggregation This is the implementation for Rethinking High-order Graph Convolutional Networks. Here we show the codes for citation networks as an e

Tianqi Zhang (张天启) 32 Jul 19, 2022
An unofficial personal implementation of UM-Adapt, specifically to tackle joint estimation of panoptic segmentation and depth prediction for autonomous driving datasets.

Semisupervised Multitask Learning This repository is an unofficial and slightly modified implementation of UM-Adapt[1] using PyTorch. This code primar

Abhinav Atrishi 11 Nov 25, 2022
Implementation and replication of ProGen, Language Modeling for Protein Generation, in Jax

ProGen - (wip) Implementation and replication of ProGen, Language Modeling for Protein Generation, in Pytorch and Jax (the weights will be made easily

Phil Wang 71 Dec 1, 2022
Official implementation of
Official implementation of "Generating 3D Molecules for Target Protein Binding"

Generating 3D Molecules for Target Protein Binding This is the official implementation of the GraphBP method proposed in the following paper. Meng Liu

DIVE Lab, Texas A&M University 74 Dec 7, 2022
Implementation of the GVP-Transformer, which was used in the paper
Implementation of the GVP-Transformer, which was used in the paper "Learning inverse folding from millions of predicted structures" for de novo protein design alongside Alphafold2

GVP Transformer (wip) Implementation of the GVP-Transformer, which was used in the paper Learning inverse folding from millions of predicted structure

Phil Wang 19 May 6, 2022
Owner
YeongHyeon Park
YeongHyeon Park
GitHub
Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction".

GNN_PPI Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction". Lear

Ursa Zrimsek 2 Dec 14, 2022
MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

MVGCN MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. Developer: Fu Hait

null 13 Dec 1, 2022
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 9, 2022
This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].

CG3 This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning]. R

null 12 Oct 28, 2022
A geometric deep learning pipeline for predicting protein interface contacts.

A geometric deep learning pipeline for predicting protein interface contacts.

null 44 Dec 30, 2022
A Protein-RNA Interface Predictor Based on Semantics of Sequences

PRIP PRIP：A Protein-RNA Interface Predictor Based on Semantics of Sequences installation gensim==3.8.3 matplotlib==3.1.3 xgboost==1.3.3 prettytable==2

李优 0 Mar 25, 2022
Generative Models for Graph-Based Protein Design

Graph-Based Protein Design This repo contains code for Generative Models for Graph-Based Protein Design by John Ingraham, Vikas Garg, Regina Barzilay

John Ingraham 159 Dec 15, 2022
Graph-based community clustering approach to extract protein domains from a predicted aligned error matrix

Using a predicted aligned error matrix corresponding to an AlphaFold2 model , returns a series of lists of residue indices, where each list corresponds to a set of residues clustering together into a pseudo-rigid domain.

Tristan Croll 24 Nov 23, 2022
Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training Code for our paper "Predicting lncRNA–protein interactio

zhanglabNKU 1 Nov 29, 2022
Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

DeepCDR Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network This work has been accepted to ECCB2020 and was also published in the

Qiao Liu 50 Dec 18, 2022