ArticleBioinformatics (Oxford, England)2020
FUpred: detecting protein domains through deep-learning-based contact map prediction.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline 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
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- AFragmenter: schema-free, tuneable protein domain segmentation for AlphaFold protein structures.Bioinformatics (Oxford, England) · 2025Article
- Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.Molecular biotechnology · 2025Review
- Treponema denticola major surface protein (Msp): a key player in periodontal pathogenicity and immune evasion.Archives of microbiology · 2025Review
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- Hierarchical Analysis of Protein Structures: From Secondary Structures to Protein Units and Domains.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Semi-supervised segmentation of RNA 3D structures using density-based clustering.Computational and structural biotechnology journal · 2025Article
- Protein domain embeddings for fast and accurate similarity search.Genome research · 2024Article
- Chainsaw: protein domain segmentation with fully convolutional neural networks.Bioinformatics (Oxford, England) · 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
- Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.Nature communications · 2023Article
- Article
- A unified approach to protein domain parsing with inter-residue distance matrix.Bioinformatics (Oxford, England) · 2023Article
- Global genomic epidemiology of chromosomally mediated non-enzymatic carbapenem resistance inFrontiers in microbiology · 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
- Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology.International journal of molecular sciences · 2022Article
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6 authors.
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Abstract
motivationProtein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence.
resultsWe developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with coevolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2549 proteins and generated correct single- and multi-domain classifications with a Matthew's correlation coefficient of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method. For proteins with discontinuous domains, the domain boundary detection and normalized domain overlapping scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/FUpred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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