Chainsaw: protein domain segmentation with fully convolutional neural networks
Open accessPreprint — published as 10.1093/bioinformatics/btae296 · Paper page
Abstract
0.1 Motivation: Protein domains are fundamental units of protein structure and play a pivotal role in understanding folding, function, evolution, and design. The advent of accurate structure prediction techniques has resulted in an influx of new structural data, making the partitioning of these structures into domains essential for inferring evolutionary relationships and functional classification. 0.2 Results: This manuscript presents Chainsaw, a supervised learning approach to domain parsing that achieves accuracy that surpasses current state-of-the-art methods. Chainsaw uses a fully convolutional neural network which is trained to predict the probability that each pair of residues is in the same domain. Domain predictions are then derived from these pairwise predictions using an algorithm that searches for the most likely assignment of residues to domains given the set of pairwise co-membership probabilities. Chainsaw matches CATH domain annotations in 78% of protein domains versus 72% for the next closest method. When predicting on AlphaFold models expert human evaluators were twice as likely to prefer Chainsaw’s predictions versus the next best method. 0.3 Availability and Implementation: Code implementation of Chainsaw is available at github.com/JudeWells/chainsaw .
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- Authors
- Wells, J., Hawkins-Hooker, A., Bordin, N., Sillitoe, I., Paige, B., & Orengo, C.
- Affiliations
- University College London (GB)
- Institute of Structural and Molecular Biology (GB)
- Corresponding author
- Orengo, Christine
- Venue
- Bioinformatics
- Publisher
- openRxiv
- Published
- 2023-07-19
- Volume
- 40
- Issue
- 5
- ISSN
- 1367-4803 (ISSN-L)
- PMC
- PMC11256964
- OpenAlex
- W4396730528
- Cited by
- 53
- References
- 29
- Citations by year
- 2026: 17 · 2025: 21 · 2024: 13 · 2023: 1
- FWCI
- 6.94
- 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)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Alan Turing Institute
- Medical Research Council (HDR-23005)
- Engineering and Physical Sciences Research Council (EP/S021566/1)
- Biotechnology and Biological Sciences Research Council (BB/W018802/1)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein structure annotation, fully convolutional network, AlphaFold models, protein structure prediction
- MeSH terms
- Protein Domains, Algorithms, Humans, Proteins, Software, Neural Networks, Computer, Computational Biology, Databases, Protein
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