TY  - JOUR
UR  - https://doi.org/10.1002/prot.25779
VL  - 87
A1  - Kandathil, S
A1  - Greener, J
A1  - Jones, DT
EP  - 1099
N2  - 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.
ID  - discovery10077561
N1  - © 2019 The Authors. Proteins: Structure, Function, and Bioinformatics published by Wiley Periodicals, Inc.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
PB  - Wiley-Blackwell
TI  - Prediction of inter-residue contacts with DeepMetaPSICOV in CASP13
Y1  - 2019/11/11/
AV  - public
JF  - Proteins
KW  - Protein contact prediction
KW  -  Protein structure prediction
KW  -  Neural networks
KW  -  Machine learning
KW  -  Deep learning
KW  -  Metagenomics
IS  - 12
SP  - 1092
SN  - 0887-3585
ER  -