ArticlePLoS computational biology2021
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
- NEFFy: a versatile tool for computing the number of effective sequences.Bioinformatics (Oxford, England) · 2026Article
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.Molecular biotechnology · 2025Review
- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.BMC bioinformatics · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Structure prediction of linear and cyclic peptides using CABS-flex.Briefings in bioinformatics · 2024Article
- Article
- Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship.Communications biology · 2023Article
- Prediction of the tetramer protein complex interaction based on CNN and SVM.Frontiers in genetics · 2023Article
- I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.Nature protocols · 2022Review
- Fast and accurate Ab Initio Protein structure prediction using deep learning potentials.PLoS computational biology · 2022Article
- Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.Bioinformatics (Oxford, England) · 2022Article
- Data Size and Quality Matter: Generating Physically-Realistic Distance Maps of Protein Tertiary Structures.Biomolecules · 2022Article
- Deep learning geometrical potential for high-accuracyiScience · 2022Article
- Progressive assembly of multi-domain protein structures from cryo-EM density maps.Nature computational science · 2022Article
- Deep learning methods for 3D structural proteome and interactome modeling.Current opinion in structural biology · 2022Review
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.Briefings in bioinformatics · 2022Article
- Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.Computational and structural biotechnology journal · 2022Article
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7 authors.
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Abstract
The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.
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