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
- Francis Crick Institute [FC001002]
- H2020 European Research Council [695558]
- European Research Council (ERC) [695558] Funding Source: European Research Council (ERC)
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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