STAR : Predicting recombination sites from amino acid sequence

Bauer, Denis C., Boden, Mikael, Thier, Ricarda, & Gillam, Elizabeth M. (2006) STAR : Predicting recombination sites from amino acid sequence. BMC Bioinformatics, 7(437).

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Designing novel proteins with site-directed recombination has enormous prospects. By locating effective recombination sites for swapping sequence parts, the probability that hybrid sequences have the desired properties is increased dramatically. The prohibitive requirements for applying current tools led us to investigate machine learning to assist in finding useful recombination sites from amino acid sequence alone.


We present STAR, Site Targeted Amino acid Recombination predictor, which produces a score indicating the structural disruption caused by recombination, for each position in an amino acid sequence. Example predictions contrasted with those of alternative tools, illustrate STAR'S utility to assist in determining useful recombination sites. Overall, the correlation coefficient between the output of the experimentally validated protein design algorithm SCHEMA and the prediction of STAR is very high (0.89).


STAR allows the user to explore useful recombination sites in amino acid sequences with unknown structure and unknown evolutionary origin. The predictor service is available from

Impact and interest:

5 citations in Scopus
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4 citations in Web of Science®

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ID Code: 77438
Item Type: Journal Article
Refereed: Yes
Keywords: Amino acid sequence, Correlation coefficient, Evolutionary origin, Hybrid sequences, Novel proteins, Protein design, Amino acids, Proteins, Tools, Stars, algorithm, article, artificial intelligence, controlled study, DNA recombination, Internet, prediction, probability, scoring system
DOI: 10.1186/1471-2105-7-437
ISSN: 1471-2105
Divisions: Current > Schools > School of Clinical Sciences
Current > QUT Faculties and Divisions > Faculty of Health
Copyright Owner: Copyright 2006 Bauer et al; licensee BioMed Central Ltd.
Copyright Statement: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Deposited On: 20 Oct 2014 23:55
Last Modified: 19 Dec 2016 02:10

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