ArticleProteins2021
Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.
Article in Proteins, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data.Nature methods · 2024Article
- Article
- 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
- Characterization of Treponema denticola Major Surface Protein (Msp) by Deletion Analysis and Advanced Molecular Modeling.Journal of bacteriology · 2022Article
- DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction.Nucleic acids research · 2022Article
- Deep learning methods for 3D structural proteome and interactome modeling.Current opinion in structural biology · 2022Review
- Refinement of AlphaFold2 models against experimental and hybrid cryo-EM density maps.QRB discovery · 2022Article
- Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.Proteins · 2021Article
- Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies.Molecules (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
This article reports and analyzes the results of protein contact and distance prediction by our methods in the 14th Critical Assessment of techniques for protein Structure Prediction (CASP14). A new deep learning-based contact/distance predictor was employed based on the ensemble of two complementary coevolution features coupling with deep residual networks. We also improved our multiple sequence alignment (MSA) generation protocol with wholesale meta-genome sequence databases. On 22 CASP14 free modeling (FM) targets, the proposed model achieved a top-L/5 long-range precision of 63.8% and a mean distance bin error of 1.494. Based on the predicted distance potentials, 11 out of 22 FM targets and all of the 14 FM/template-based modeling (TBM) targets have correctly predicted folds (TM-score >0.5), suggesting that our approach can provide reliable distance potentials for ab initio protein folding.
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