STAR: predicting recombination sites from amino acid sequence.
BACKGROUND
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.


RESULTS
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).


CONCLUSIONS
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 http://pprowler.itee.uq.edu.au/star.
Bauer
Denis C
DC
Institute for Molecular Bioscience, The University of Queensland, QLD 4072, Australia. d.bauer@imb.uq.edu.au
Bodén
Mikael
M
Thier
Ricarda
R
Gillam
Elizabeth M
EM
D000465:Algorithms; D000595:Amino Acid Sequence; D019943:Amino Acid Substitution; D001665:Binding Sites; D008969:Molecular Sequence Data; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D011994:Recombinant Proteins / Q000737:chemistry* / Q000378:metabolism*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D011506:Proteins; D011994:Recombinant Proteins