A novel sequence alignment algorithm based on deep learning of the protein folding code
Open accessAbstract
Motivation: From evolutionary interference, function annotation to structural prediction, protein sequence comparison has provided crucial biological insights. While many sequence alignment algorithms have been developed, existing approaches often cannot detect hidden structural relationships in the ‘twilight zone’ of low sequence identity. To address this critical problem, we introduce a computational algorithm that performs protein Sequence Alignments from deep-Learning of Structural Alignments (SAdLSA, silent ‘d’). The key idea is to implicitly learn the protein folding code from many thousands of structural alignments using experimentally determined protein structures. Results: To demonstrate that the folding code was learned, we first show that SAdLSA trained on pure α-helical proteins successfully recognizes pairs of structurally related pure β-sheet protein domains. Subsequent training and benchmarking on larger, highly challenging datasets show significant improvement over established approaches. For challenging cases, SAdLSA is ∼150% better than HHsearch for generating pairwise alignments and ∼50% better for identifying the proteins with the best alignments in a sequence library. The time complexity of SAdLSA is O(N) thanks to GPU acceleration. Availability and implementation: Datasets and source codes of SAdLSA are available free of charge for academic users at http://sites.gatech.edu/cssb/sadlsa/. Supplementary information: Supplementary data are available at Bioinformatics online.
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SAdLSA trained on purely α-helical proteins can align the sequences of distantly related pure β-proteins.
“SAdLSA trained on purely α-helical proteins can align the sequences of distantly related pure β-proteins.”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [35]
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- Authors
- Gao, M., & Skolnick, J.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Gao, Mu
- Skolnick, Jeffrey
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-09-21
- Volume
- 37
- Issue
- 4
- Pages
- 490-496
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC8599902
- OpenAlex
- W3088911218
- Cited by
- 18
- References
- 28
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 6 · 2021: 7 · 2020: 1
- FWCI
- 1.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Division of General Medical Sciences
- National Institutes of Health (R35-118039)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein sequence alignment, structural alignment, deep learning, low sequence identity, twilight zone, structural relationships, alpha-helix proteins, beta-sheet proteins, pairwise sequence alignment, protein structure prediction
- MeSH terms
- Deep Learning, Algorithms, Software, Sequence Alignment, Protein Folding
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