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Prediction of interresidue contacts with DeepMetaPSICOV in CASP13 |
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| Authors: | Shaun M Kandathil Joe G Greener David T Jones |
| Institution: | 1. Department of Computer Science, University College London, London, UK;2. Department of Computer Science, University College London, London, UK
Biomedical Data Science Laboratory, The Francis Crick Institute, London, UK |
| Abstract: | 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 . |
| Keywords: | deep learning machine learning metagenomics neural networks protein contact prediction protein structure prediction |