ArticleProteins2019
Deep-learning contact-map guided protein structure prediction in CASP13.
Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 2 of them syntheses that pooled it.
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Who cites it
106 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.Frontiers in immunology · 2020Pooled it
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- AI-driven insights into protein misfolding and innate immunity in neurodegenerative diseases.Frontiers in immunology · 2026Review
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- A comprehensive application of FiveFold for conformation ensemble-based protein structure prediction.Scientific reports · 2025Review
- Artificial intelligence and first-principle methods in protein redesign: A marriage of convenience?Protein science : a publication of the Protein Society · 2025Article
- Predicting RNA Structure Utilizing Attention from Pretrained Language Models.Journal of chemical information and modeling · 2025Article
- M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling.BMC bioinformatics · 2025Article
- AlphaFold2, SPINE-X, and Seder on Four Hard CASP Targets.Methods in molecular biology (Clifton, N.J.) · 2025Article
- ProCaliper: functional and structural analysis, visualization, and annotation of proteins.Bioinformatics advances · 2025Article
- Weighted families of contact maps to characterize conformational ensembles of (highly-)flexible proteins.Bioinformatics (Oxford, England) · 2024Article
- PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.BMC bioinformatics · 2024Article
- Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- A significance score for protein-protein interaction models through random docking.Protein science : a publication of the Protein Society · 2024Article
- Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction.Journal of chemical information and modeling · 2024Article
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46 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
We report the results of two fully automated structure prediction pipelines, "Zhang-Server" and "QUARK", in CASP13. The pipelines were built upon the C-I-TASSER and C-QUARK programs, which in turn are based on I-TASSER and QUARK but with three new modules: (a) a novel multiple sequence alignment (MSA) generation protocol to construct deep sequence-profiles for contact prediction; (b) an improved meta-method, NeBcon, which combines multiple contact predictors, including ResPRE that predicts contact-maps by coupling precision-matrices with deep residual convolutional neural-networks; and (c) an optimized contact potential to guide structure assembly simulations. For 50 CASP13 FM domains that lacked homologous templates, average TM-scores of the first models produced by C-I-TASSER and C-QUARK were 28% and 56% higher than those constructed by I-TASSER and QUARK, respectively. For the first time, contact-map predictions demonstrated usefulness on TBM domains with close homologous templates, where TM-scores of C-I-TASSER models were significantly higher than those of I-TASSER models with a P-value <.05. Detailed data analyses showed that the success of C-I-TASSER and C-QUARK was mainly due to the increased accuracy of deep-learning-based contact-maps, as well as the careful balance between sequence-based contact restraints, threading templates, and generic knowledge-based potentials. Nevertheless, challenges still remain for predicting quaternary structure of multi-domain proteins, due to the difficulties in domain partitioning and domain reassembly. In addition, contact prediction in terminal regions was often unsatisfactory due to the sparsity of MSAs. Development of new contact-based domain partitioning and assembly methods and training contact models on sparse MSAs may help address these issues.
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