4.3 Article

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

期刊

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
卷 87, 期 12, 页码 1092-1099

出版社

WILEY
DOI: 10.1002/prot.25779

关键词

deep learning; machine learning; metagenomics; neural networks; protein contact prediction; protein structure prediction

资金

  1. Francis Crick Institute [FC001002]
  2. H2020 European Research Council [695558]
  3. European Research Council (ERC) [695558] Funding Source: European Research Council (ERC)

向作者/读者索取更多资源

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: .

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