The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Utilizing the Scale-Invariant Feature Transform Algorithm to Align Distance Matrices Facilitates Systematic Protein Structure Comparison
Open accessPreprint — published as 10.1093/bioinformatics/btae064 · Paper page
Abstract
Protein structure comparison is pivotal for deriving homological relationships, elucidating protein functions, and understanding evolutionary developments. The burgeoning field of in-silico protein structure prediction now yields billions of models with near-experimental accuracy, necessitating sophisticated tools for discerning structural similarities among proteins, particularly when sequence similarity is limited. Here, we have developed the ADAMS pipeline, which synergizes the distance matrix alignment method with the scale-invariant feature transform (SIFT) algorithm, streamlining protein structure comparison on a proteomic scale. Utilizing a computer vision-centric strategy for contrasting disparate distance matrices, ADAMS adeptly alleviates challenges associated with proteins characterized by a high degree of structural flexibility. Our findings indicate that ADAMS achieves a level of performance and accuracy on par with Foldseek, while maintaining similar speed. Crucially, ADAMS overcomes certain limitations of Foldseek in handling structurally flexible proteins, establishing it as an efficacious tool for in-depth protein structure analysis with heightened accuracy.
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
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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: 27
- 2023, Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2, Proceedings of the National Academy of Sciences, 120, e2304302120, 10.1073/pnas.2304302120 · DOI · Paper page
- 10.1006/jmbi.1990.9999 · DOI · Paper page
- 10.1016/j.sbi.2011.03.011 · DOI
- 10.1126/science.abj8754 · DOI · Paper page
- 10.1186/s12964-022-00821-7 · DOI · Paper page
- 10.1093/bioinformatics/btp163 · DOI · Paper page
- 10.1016/j.jmb.2021.167336 · DOI · Paper page
- Greener, J.G. and Jamali, K. Fast protein structure searching using structure graph embeddings. bioRxiv 2022:2022.2011.2028.518224. · DOI · Paper page
- 2020, Using Dali for Protein Structure Comparison, Structural Bioinformatics: Methods and Protocols, 2112, 29, 10.1007/978-1-0716-0270-6_3 · DOI
- 10.1002/pro.4519 · DOI
- 10.1006/jmbi.1993.1489 · DOI · Paper page
- Hultqvist, G. , et al. Emergence and evolution of an interaction between intrinsically disordered proteins. Elife 2017;6. · DOI
- 10.1534/genetics.118.301307 · DOI
- 10.1093/molbev/msw054 · DOI
- 10.1126/science.ade2574 · DOI · Paper page
- 10.1016/j.devcel.2008.02.015 · DOI
- 10.1038/S41592-022-01488-1 · DOI · Paper page
- 10.1093/protein/11.9.739 · DOI
- 10.1038/msb.2011.75 · DOI
- 2017, A review of resolution measures and related aspects in 3D Electron Microscopy, Progress in Biophysics & Molecular Biology, 124, 1, 10.1016/j.pbiomolbio.2016.09.005 · DOI
- 10.1038/nbt.3988 · DOI · Paper page
- 10.1093/nar/gku1028 · DOI
- Trivedi, R. and Nagarajaram, H.A. Intrinsically Disordered Proteins: An Overview. Int J Mol Sci 2022;23(22). · DOI
- 10.1038/s41587-41023-01773-41580 · DOI
- 10.1142/S0219720008003461 · DOI
- 10.1093/nar/gkl395 · DOI
- 10.1093/nar/gki524 · DOI · Paper page
Metadata
- Authors
- Guo, Z., Wang, Y., & Ou, G.
- Affiliations
- Center for Life Sciences (CN)
- Corresponding author
- Ou, Guangshuo
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2023-11-16
- Volume
- 40
- Issue
- 3
- ISSN
- 1367-4803 (ISSN-L)
- PMC
- PMC10924749
- OpenAlex
- W4391575987
- Cited by
- 6
- References
- 2
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 2
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (31671444, 31730052, 31861143042, 2017YFA0102900, 31871352, 31991190)
- National Key Research and Development Program of China (2017YFA0102900, 2019YFA0508401)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure comparison, scale-invariant feature transform, structural similarity, structural flexibility, homology detection
- MeSH terms
- Algorithms, Computers, Proteins, Software, Proteomics
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
