ArticleBioinformatics (Oxford, England)2020
Protein contact prediction using metagenome sequence data and residual neural networks.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- H2GnnDTI: hierarchical heterogeneous graph neural networks for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Prediction of Disordered Linear Interacting Peptides with CLIP.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Drug-target affinity prediction with extended graph learning-convolutional networks.BMC bioinformatics · 2024Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Increasing the Efficacy of Seproxetine as an Antidepressant Using Charge-Transfer Complexes.Molecules (Basel, Switzerland) · 2022Article
- Reaching alignment-profile-based accuracy in predicting protein secondary and tertiary structural properties without alignment.Scientific reports · 2022Article
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- Deep learning methods for 3D structural proteome and interactome modeling.Current opinion in structural biology · 2022Review
- SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model.Bioinformatics (Oxford, England) · 2022Article
- Review
- DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins.Proteins · 2022Article
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Inter-Residue Distance Prediction From Duet Deep Learning Models.Frontiers in genetics · 2022Article
- Using metagenomic data to boost protein structure prediction and discovery.Computational and structural biotechnology journal · 2022Review
- The trRosetta server for fast and accurate protein structure prediction.Nature protocols · 2021Review
- Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Improved 3-D Protein Structure Predictions using Deep ResNet Model.The protein journal · 2021Article
- Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.Briefings in bioinformatics · 2021Article
- Toward the solution of the protein structure prediction problem.The Journal of biological chemistry · 2021Review
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Authors and funding
6 authors.
Funding
Abstract
motivationAlmost 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.
resultsBased 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.
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