scholarly journals A novel method for protein-protein interaction site prediction using phylogenetic substitution models

2011 ◽  
Vol 80 (1) ◽  
pp. 126-141 ◽  
Author(s):  
David La ◽  
Daisuke Kihara
Keyword(s):  
Protein Interaction ◽  
Interaction Site ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Substitution Models ◽  
Novel Method ◽  
Interaction Site Prediction

scholarly journals Algorithmic approaches to protein-protein interaction site prediction

2015 ◽  
Vol 10 (1) ◽  
pp. 7 ◽  
Author(s):  
Tristan T Aumentado-Armstrong ◽  
Bogdan Istrate ◽  
Robert A Murgita
Keyword(s):  
Protein Interaction ◽  
Interaction Site ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Algorithmic Approaches ◽  
Interaction Site Prediction

Deep for Protein–Protein Interaction Site Prediction

2021 ◽  
pp. 263-288
Author(s):  
Arian R. Jamasb ◽  
Ben Day ◽  
Cătălina Cangea ◽  
Pietro Liò ◽  
Tom L. Blundell
Keyword(s):  
Protein Interaction ◽  
Interaction Site ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Interaction Site Prediction

metaPIS: A Sequence-based Meta-server for Protein Interaction Site Prediction

2012 ◽  
Vol 20 (2) ◽  
pp. 218-230
Author(s):  
Junfeng Huang ◽  
Riqiang Deng ◽  
Jinwen Wang ◽  
Hongkai Wu ◽  
Yuanyan Xiong ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Interaction Site ◽  
Site Prediction ◽  
Interaction Site Prediction

scholarly journals EGAT: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction

2020 ◽  
Author(s):  
Sazan Mahbub ◽  
Md Shamsuzzoha Bayzid
Keyword(s):  
Transfer Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Protein Protein Interactions ◽  
Attention Networks ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Interaction Site Prediction ◽  
First Time

AbstractMotivationProtein-protein interactions are central to most biological processes. However, reliable identification of protein-protein interaction (PPI) sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites.ResultsWe present EGAT, a highly accurate deep learning based method for PPI site prediction, where we have introduced a novel edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved remarkable improvement over the best alternate methods. Furthermore, EGAT offers a more interpretable framework than the typical black-box deep neural networks.AvailabilityEGAT is freely available as an open source project at https://github.com/Sazan-Mahbub/EGAT.

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