ArticleBioinformatics (Oxford, England)2023
A unified approach to protein domain parsing with inter-residue distance matrix.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 38 citations in OpenAlex.
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- AFragmenter: schema-free, tuneable protein domain segmentation for AlphaFold protein structures.Bioinformatics (Oxford, England) · 2025Article
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- Foldclass and Merizo-search: scalable structural similarity search for single- and multi-domain proteins using geometric learning.Bioinformatics (Oxford, England) · 2025Article
- Tandem Repeats Provide Evidence for Convergent Evolution to Similar Protein Structures.Genome biology and evolution · 2025Article
- Semi-supervised segmentation of RNA 3D structures using density-based clustering.Computational and structural biotechnology journal · 2025Article
- Exploring structural diversity across the protein universe with The Encyclopedia of Domains.Science (New York, N.Y.) · 2024Article
- Protein domain embeddings for fast and accurate similarity search.Genome research · 2024Article
- Deep learning for the PSIPRED Protein Analysis Workbench.Nucleic acids research · 2024Article
- Freeprotmap: waiting-free prediction method for protein distance map.BMC bioinformatics · 2024Article
- Chainsaw: protein domain segmentation with fully convolutional neural networks.Bioinformatics (Oxford, England) · 2024Article
- A genomic analysis reveals the diversity of cellulosome displaying bacteria.Frontiers in microbiology · 2024Article
- Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.Nature communications · 2023Article
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
motivationIt is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing.
resultsBased on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis. AVAILABILITY AND IMPLEMENTATION: https://yanglab.nankai.edu.cn/UniDoc/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.