Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction
Open accessAbstract
Motivation: Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. Results: We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Availability and Implementation: Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. Contact: deane@stats.ox.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.
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The biggest challenge in ab initio modelling remains selection of the best loop models among those generated.
“The biggest challenge in such ab initio modelling remains selection of best loop models among those generated [102].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [102]
Hybrid methods such as Sphinx combine knowledge-based and ab initio approaches to provide better all-round predictions irrespective of the presence or absence of a priori structural information.
“Hybrid methods such as Sphinx [102] combine knowledge-based and ab initio approaches to provide better all-round predictions irrespective of the presence or absence of a priori structural information.”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [102]
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“The biggest challenge in such ab initio modelling remains selection of best loop models among those generated [102].”
BackgroundMethodSelf-citationAntibody H3 structure prediction
Marks, C., & Deane, C. M. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2017.01.010 · Cited by 56 · see citing works
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Self-citation
- Authors
- Marks, C., Nowak, J., Klostermann, S., Georges, G., Dunbar, J., Shi, J., Kelm, S., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Department of Statistics, University of Oxford, Oxford, UK
- Roche Pharma AG (Germany) (DE)
- UCB Pharma (United Kingdom) (GB)
- Corresponding author
- Deane, Charlotte M
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-01-16
- Volume
- 33
- Issue
- 9
- Pages
- 1346-1353
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC5408792
- OpenAlex
- W2574510864
- Cited by
- 56
- References
- 62
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 2 · 2023: 6 · 2022: 5 · 2021: 11 · 2020: 10 · 2019: 10 · 2018: 5 · 2017: 3
- FWCI
- 3.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/G037280/1)
- UCB Pharma Ltd and Roche
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- loop conformation, antibody heavy chain, decoy generation, protein structure prediction, sphinx
- MeSH terms
- Algorithms, Animals, Antibodies, Models, Molecular, Protein Conformation, Software, Computational Biology, Knowledge Bases
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