Protein contact prediction using metagenome sequence data and residual neural networks
By Qi Wu, Zhenling Peng, Ivan V. Anishchenko, Qian Cong, David Baker, Jianyi Yang. 2019
79 citations recorded by the source corpus.
MOTIVATION Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the conventional UniProt database. Here we aim to solve this issue by exploring the rich sequence data from the metagenome sequencing projects. RESULTS Based on the improved MSA constructed from the metagenome sequence data, we developed MapPred, a new deep learning-based contact prediction method. MapPred consists of two component methods, DeepMSA and DeepMeta, both trained with the residual neural networks. DeepMSA was inspired by the recent method DeepCov, which was trained on 441 matrices of covariance features. By considering the symmetry of contact map, we reduced the number of matrices to 231, which makes the training more efficient in DeepMSA. Experiments show that DeepMSA outperforms DeepCov by 10-13% in precision. DeepMeta works by combining predicted contacts and other sequence profile features. Experiments on three benchmark datasets suggest that the contribution from the metagenome sequence data is significant with P-values less than 4.04E-17. MapPred is shown to be complementary and comparable the state-of-the-art methods. The success of MapPred is attributed to three factors: the deeper MSA from the metagenome sequence data, improved feature design in DeepMSA, and optimized training by the residual neural networks. AVAILABILITY AND IMPLEMENTATION http://yanglab.nankai.edu.cn/mappred/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Subfields: Molecular Biology
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In Molecular Biology
- Yang Zhang
- Robin Pearce
- Sergey Ovchinnikov
- David Baker
- Ivan V. Anishchenko
- Jianyi Yang
- Hahnbeom Park
- Zhenling Peng
Citations in the corpus
- Improved protein structure prediction using predicted interresidue orientations (2019, methodology): “1b2) (29) against the metagenome sequence database (20) in case not enough sequences were collected at previous steps.”
- Toward the solution of the protein structure prediction problem (2021, background): “Later MSA construction methods (183, 184) con fi rmed the usefulness of metagenome-derived MSAs for improving contact prediction (168, 183, 184), threading results for distantly homologous targets (99, 184), and the ability to model proteins that belong to families with unknown structures (25, 185).”
- The trRosetta server for fast and accurate protein structure prediction (2021, background): “For example, it is also worth searching the metagenome sequence databases with hmmsearch 26 to enrich the MSA generation 27,28 .”