ArticleBioinformatics (Oxford, England)2019
ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.
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85 citing papers in PubMed.
- Machine Learning-Guided Engineering of Protein Phase Separation Properties in Immune Regulation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MAKA-Map: Real-Valued Distance Prediction for Protein Folding Mechanisms via a Hybrid Neural Framework Integrating the Mamba and Kolmogorov-Arnold Networks.Biomolecules · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks.Computational and structural biotechnology journal · 2025Article
- DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity.BMC bioinformatics · 2024Article
- Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective.Clinical and translational medicine · 2024Review
- Investigating Medin Cleavage Accessibility in MfgE8: Conformational Insights Derived from Molecular Dynamics Simulations and AlphaFold2 Models.bioRxiv : the preprint server for biology · 2024Article
- The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction.Topics in current chemistry (Cham) · 2024Review
- Protein language model-embedded geometric graphs power inter-protein contact prediction.eLife · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting.Briefings in bioinformatics · 2024Article
- Article
- Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction.Nature communications · 2023Article
- PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries.bioRxiv : the preprint server for biology · 2023Article
- PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries.Bioinformatics advances · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Prediction of the tetramer protein complex interaction based on CNN and SVM.Frontiers in genetics · 2023Article
- Article
- Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks.Nature communications · 2022Article
25 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
Abstract
motivationContact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank.
resultsWe developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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