High accuracy prediction of β‐turns and their types using propensities and multiple alignments

We have developed a method that predicts both the presence and the type of β‐turns, using a straightforward approach based on propensities and multiple alignments. The propensities were calculated classically, but the way to use them for prediction was completely new: starting from a tetrapeptide sequence on which one wants to evaluate the presence of a β‐turn, the propensity for a given residue is modified by taking into account all the residues present in the multiple alignment at this position. The evaluation of a score is then done by weighting these propensities by the use of Position‐specific score matrices generated by PSI‐BLAST. The introduction of secondary structure information predicted by PSIPRED or SSPRO2 as well as taking into account the flanking residues around the tetrapeptide improved the accuracy greatly. This latter evaluated on a database of 426 reference proteins (previously used on other studies) by a sevenfold crossvalidation gave very good results with a Matthews Correlation Coefficient (MCC) of 0.42 and an overall prediction accuracy of 74.8%; this places our method among the best ones. A jackknife test was also done, which gave results within the same range. This shows that it is possible to reach neural networks accuracy with considerably less computional cost and complexity. Furthermore, propensities remain excellent descriptors of amino acid tendencies to belong to β‐turns, which can be useful for peptide or protein engineering and design. For β‐turn type prediction, we reached the best accuracy ever published in terms of MCC (except for the irregular type IV) in the range of 0.25–0.30 for types I, II, and I′ and 0.13–0.15 for types VIII, II′, and IV. To our knowledge, our method is the only one available on the Web that predicts types I′ and II′. The accuracy evaluated on two larger databases of 547 and 823 proteins was not improved significantly. All of this was implemented into a Web server called COUDES (French acronym for: Chercher Où Une Déviation Existe Sûrement), which is available at the following URL: http://bioserv.rpbs.jussieu.fr/Coudes/index.html within the new bioinformatics platform RPBS. Proteins 2005;59. © 2005 Wiley‐Liss, Inc.

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