Kandathil_et_al-2019-Proteins__Structure,_Function,_and_Bioinformatics.pdf (1.67 MB)
Prediction of interresidue contacts with DeepMetaPSICOV in CASP13.
journal contributionposted on 2020-01-09, 16:42 authored by Shaun M Kandathil, Joe G Greener, David T Jones
In this article, we describe our efforts in contact prediction in the CASP13 experiment. We employed a new deep learning-based contact prediction tool, DeepMetaPSICOV (or DMP for short), together with new methods and data sources for alignment generation. DMP evolved from MetaPSICOV and DeepCov and combines the input feature sets used by these methods as input to a deep, fully convolutional residual neural network. We also improved our method for multiple sequence alignment generation and included metagenomic sequences in the search. We discuss successes and failures of our approach and identify areas where further improvements may be possible. DMP is freely available at: https://github.com/psipred/DeepMetaPSICOV. This article is protected by copyright. All rights reserved.
Crick (Grant ID: 10002, Grant title: Jones FC001002)
Deep learningMachine learningMetagenomicsNeural networksProtein contact predictionProtein structure predictiondeep learningmachine learningmetagenomicsneural networksprotein contact predictionprotein structure predictionJones - sec06 Biological Sciences08 Information and Computing Sciences01 Mathematical SciencesBioinformatics