A new approach to protein fold recognition
Abstract
The prediction of protein tertiary structure from sequence using molecular energy calculations has not yet been successful; an alternative strategy of recognizing known motifs or folds in sequences looks more promising. We present here a new approach to fold recognition, whereby sequences are fitted directly onto the backbone coordinates of known protein structures. Our method for protein fold recognition involves automatic modelling of protein structures using a given sequence, and is based on the frameworks of known protein folds. The plausibility of each model, and hence the degree of compatibility between the sequence and the proposed structure, is evaluated by means of a set of empirical potentials derived from proteins of known structure. The novel aspect of our approach is that the matching of sequences to backbone coordinates is performed in full three-dimensional space, incorporating specific pair interactions explicitly.
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The principle that tertiary structure models aid structural template retrieval has long been understood and is the basis of protein threading.
“Of course, this principle has been well understood for some time, and is the basis of protein threading (45,46), but the ease and speed with which a rough candidate model can be generated for a query sequence has the potential to accelerate and improve the sensitivity of structural template searches.”
From Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins (2020) · cites this paper as [46]
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Self-citation
- Authors
- Jones, D. T., Taylort, W. R., & Thornton, J. M.
- Affiliations
- University College London (GB)
- National Institute for Medical Research (GB)
- Venue
- Nature
- Published
- 1992-07
- Volume
- 358
- Issue
- 6381
- Pages
- 86-89
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2033496784
- Cited by
- 1,221
- References
- 20
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 9 · 2023: 7 · 2022: 15 · 2021: 29 · 2020: 21 · 2019: 21 · 2018: 16 · 2017: 20 · 2016: 22 · 2015: 15 · 2014: 23 · 2013: 31 · 2012: 26
- FWCI
- 17.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein fold recognition, protein structure prediction, sequence-structure alignment, empirical potentials, pairwise interactions, homology modeling
- MeSH terms
- Algorithms, Amino Acid Sequence, Models, Theoretical, Molecular Sequence Data, Myoglobin, Phycocyanin, Protein Conformation, Proteins, Structure-Activity Relationship, Thermodynamics, Sequence Alignment
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