ArticleBioinformatics (Oxford, England)2013
ThreaDom: extracting protein domain boundary information from multiple threading alignments.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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49 citing papers in PubMed.
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- Semi-supervised segmentation of RNA 3D structures using density-based clustering.Computational and structural biotechnology journal · 2025Article
- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A Structural Proteomics Exploration of Synphilin-1 and Alpha-Synuclein Interaction in Pathogenesis of Parkinson's Disease.Biomolecules · 2024Article
- Bioinformatics approach for prediction and analysis of the Non-Structural Protein 4B (NSP4B) of the Zika virus.Journal, genetic engineering & biotechnology · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Article
- A unified approach to protein domain parsing with inter-residue distance matrix.Bioinformatics (Oxford, England) · 2023Article
- I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.Nature protocols · 2022Review
- Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps.BMC bioinformatics · 2022Article
- Progressive assembly of multi-domain protein structures from cryo-EM density maps.Nature computational science · 2022Article
- Res-Dom: predicting protein domain boundary from sequence using deep residual network and Bi-LSTM.Bioinformatics advances · 2022Article
- Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.Proteins · 2021Article
- Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.Cell reports methods · 2021Article
- The male germline-specific protein MAPS is indispensable for pachynema progression and fertility.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Protein domain identification methods and online resources.Computational and structural biotechnology journal · 2021Review
- FUpred: detecting protein domains through deep-learning-based contact map prediction.Bioinformatics (Oxford, England) · 2020Article
- The Sequence and Three-Dimensional Structure Characterization of Snake Venom Phospholipases B.Frontiers in molecular biosciences · 2020Article
- Structure-Function Studies and Mechanism of Action of Snake Venom L-Amino Acid Oxidases.Frontiers in pharmacology · 2020Review
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4 authors.
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Abstract
motivationProtein domains are subunits that can fold and evolve independently. Identification of domain boundary locations is often the first step in protein folding and function annotations. Most of the current methods deduce domain boundaries by sequence-based analysis, which has low accuracy. There is no efficient method for predicting discontinuous domains that consist of segments from separated sequence regions. As template-based methods are most efficient for protein 3D structure modeling, combining multiple threading alignment information should increase the accuracy and reliability of computational domain predictions.
resultWe developed a new protein domain predictor, ThreaDom, which deduces domain boundary locations based on multiple threading alignments. The core of the method development is the derivation of a domain conservation score that combines information from template domain structures and terminal and internal alignment gaps. Tested on 630 non-redundant sequences, without using homologous templates, ThreaDom generates correct single- and multi-domain classifications in 81% of cases, where 78% have the domain linker assigned within ±20 residues. In a second test on 486 proteins with discontinuous domains, ThreaDom achieves an average precision 84% and recall 65% in domain boundary prediction. Finally, ThreaDom was examined on 56 targets from CASP8 and had a domain overlap rate 73, 87 and 85% with the target for Free Modeling, Hard multiple-domain and discontinuous domain proteins, respectively, which are significantly higher than most domain predictors in the CASP8. Similar results were achieved on the targets from the most recently CASP9 and CASP10 experiments. AVAILABILITY: http://zhanglab.ccmb.med.umich.edu/ThreaDom/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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