The computational assignment of three-dimensional structures to newly determined protein sequences is becoming an increasingly important element in experimental structure determination and in structural genomics (Fischer et al. 2001a). In particular, fold-recognition methods aim to predict approximate three-dimensional (3D) models for proteins bearing no evident sequence similarity to any protein of known structure (see the review by Cymerman et al., this Vol.). The assignment is carried out by searching a library of known structures (usually obtained from the Protein Data Bank) for a compatible fold.
Keywords
- Protein Structure Prediction
- Fold Recognition
- Protein Secondary Structure Prediction
- Protein Fold Recognition
- Protein Structure Prediction Server
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Bujnicki, J.M., Fischer, D. (2008). ‘Meta’Approaches to Protein Structure Prediction. In: Bujnicki, J.M. (eds) Practical Bioinformatics. Nucleic Acids and Molecular Biology, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74268-5_2
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