The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Merizo: a rapid and accurate domain segmentation method using invariant point attention
Open accessPreprint — published as 10.1038/s41467-023-43934-4 · Paper page
Abstract
A bstract: The AlphaFold Protein Structure Database (AFDB), containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the information within the AFDB is precluded by an urgent need of tools that allow the efficient traversal, discovery and documentation of its contents. Identifying the regions that correspond to domains in the AFDB is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for accurate domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on a subset of AFDB models via self-distillation, enabling it to be applicable to both experimental and AFDB models. As proof of concept, we apply Merizo to models of the human proteome, and identify 40,818 putative domains that can be matched to CATH representative domains. Merizo is available at https://github.com/psipred/Merizo .
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Flagged first, then by citations
Research works printed but not linked: 37
- 10.1016/S0969-2126(97)00260-8 · DOI
- 2021, Cath: increased structural coverage of functional space, Nucleic acids research, 49, D266, 10.1093/nar/gkaa1079 · DOI · Paper page
- 2014, Ecod: an evolutionary classification of protein domains, PLoS computational biology, 10, e1003926, 10.1371/journal.pcbi.1003926 · DOI · Paper page
- 10.1093/NAR/GKAA913 · DOI · Paper page
- 10.1093/nar/gkz1064 · DOI
- 10.1073/pnas.70.3.697 · DOI
- 2021, Completeness and consistency in structural domain classifications, ACS omega, 6, 15698, 10.1021/acsomega.1c00950 · DOI
- 1994, Parser for protein folding units, Proteins: Structure, Function, and Bioinformatics, 19, 256, 10.1002/prot.340190309 · DOI
- 1995, Continuous and discontinuous domains: an algorithm for the automatic generation of reliable protein domain definitions, Protein Science, 4, 872, 10.1002/pro.5560040507 · DOI
- 1995, A procedure for detecting structural domains in proteins, Protein Science, 4, 103, 10.1002/pro.5560040113 · DOI
- 2007, Cathe-dral: a fast and effective algorithm to predict folds and domain boundaries from multidomain protein structures, PLoS computational biology, 3, e232, 10.1371/journal.pcbi.0030232 · DOI
- Yuexu Jiang , Duolin Wang , and Dong Xu . Deepdom: Predicting protein domain boundary from sequence alone using stacked bidirectional lstm. In BIOCOMPUTING 2019: Proceedings of the Pacific Symposium, pages 66–75. World Scientific, 2018. · DOI
- Sajid Mahmud , Zhiye Guo , Farhan Quadir , Jian Liu , and Jianlin Cheng . Multi-head attention-based u-nets for predicting protein domain boundaries using 1d sequence features and 2d distance maps. bioRxiv, 2022. · DOI
- 2020, Fupred: detecting protein domains through deep-learning-based contact map prediction, Bioinformatics, 36, 3749, 10.1093/bioinformatics/btaa217 · DOI
- 2023, A unified approach to protein domain parsing with inter-residue distance matrix, Bioinformatics, 39, btad070, 10.1093/bioinformatics/btad070 · DOI
- 10.1126/sciadv.1600552 · DOI
- Zhong-ze Yu , Chun-xiang Peng , Jun Liu , Biao Zhang , Xiao-gen Zhou , and Gui-jun Zhang . Dombpred: protein domain boundary prediction based on domain-residue clustering using inter-residue distance. IEEE/ACM Transactions on Computational Biology and Bioinformatics, pages 1–1, 2022.
- 10.1093/NAR/GKAB1061 · DOI · Paper page
- 10.1002/pro.4548 · DOI
- 2020, Multi-scale structural analysis of proteins by deep semantic segmentation, Bioinformatics, 36, 1740, 10.1093/bioinformatics/btz650 · DOI
- 10.1093/bioinformatics/16.7.613 · DOI · Paper page
- 2023, Classification of domains in predicted structures of the human proteome, Proceedings of the National Academy of Sciences, 120, e2214069120, 10.1073/pnas.2214069120 · DOI
- 2021, Highly accurate protein structure prediction for the human proteome, Nature, 596, 590, 10.1038/s41586-021-03828-1 · DOI · Paper page
- 10.1016/0022-2836(89)90084-3 · DOI
- Michel van Kempen , Stephanie S Kim , Charlotte Tumescheit , Milot Mirdita , Jeongjae Lee , Cameron LM Gilchrist , Johannes Söding , and Martin Steinegger . Fast and accurate protein structure search with foldseek. Nature Biotechnology, pages 1–4, 2023. · DOI · Paper page
- 10.1186/s12859-019-2669-9 · DOI
- 10.1002/pro.3749 · DOI
- 2017, Uniclust databases of clustered and deeply annotated protein sequences and alignments, Nucleic acids research, 5, D170
- 10.1093/nar/28.1.235 · DOI · Paper page
- Joe G Greener and Kiarash Jamali . Fast protein structure searching using structure graph embeddings. bioRxiv, 2022. · DOI · Paper page
- Zeming Lin , Halil Akin , Roshan Rao , Brian Hie , Zhongkai Zhu , Wenting Lu , Nikita Smetanin , Robert Verkuil , Ori Kabeli , Yaniv Shmueli , et al. Evolutionary-scale prediction of atomic level protein structure with a language model. bioRxiv, pages 2022–07, 2022.
- Ruidong Wu , Fan Ding , Rui Wang , Rui Shen , Xiwen Zhang , Shitong Luo , Chenpeng Su , Zuofan Wu , Qi Xie , Bonnie Berger , Jianzhu Ma , and Jian Peng . High-resolution de novo structure prediction from primary sequence. bioRxiv, 2022.
- Konstantin Weissenow , Michael Heinzinger , Martin Steinegger , and Burkhard Rost . Ultra-fast protein structure prediction to capture effects of sequence variation in mutation movies. bioRxiv, pages 2022–11, 2022. · DOI
- 10.1093/bioinformatics/17.3.282 · DOI
- Jianlin Su , Yu Lu , Shengfeng Pan , Ahmed Murtadha , Bo Wen , and Yunfeng Liu . Roformer: Enhanced transformer with rotary position embedding. arXiv preprint arXiv:2104.09864, 2021. · arXiv
- Robin Strudel , Ricardo Garcia , Ivan Laptev , and Cordelia Schmid . Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7262–7272, 2021. · DOI
- Ofir Press , Noah A Smith , and Mike Lewis . Train short, test long: Attention with linear biases enables input length extrapolation. arXiv preprint arXiv:2108.12409, 2021. · arXiv
Metadata
- Authors
- Lau, A. M., Kandathil, S. M., & Jones, D. T.
- Affiliations
- University College London (GB)
- Corresponding author
- Jones, David T.
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2023-02-21
- Volume
- 14
- Issue
- 1
- Pages
- 8445-8445
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10730818
- OpenAlex
- W4389947479
- Cited by
- 47
- References
- 1
- Citations by year
- 2026: 20 · 2025: 15 · 2024: 11 · 2023: 1
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- Funders
- UK Research and Innovation (BB/T019409/1)
- Research Councils UK (BB/T019409/1)
- Biotechnology and Biological Sciences Research Council (BB/W008556/1, BB/T019379/2, BB/T019409/1, BB/T019379/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- AlphaFold2, self-distillation, deep learning, human proteome, protein structure
- MeSH terms
- Protein Domains, Humans, Proteins, Protein Structure, Tertiary, Genomics, Databases, Protein
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
