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Comparative analysis of coiled-coil prediction methods

2006; Elsevier BV; Volume: 155; Issue: 2 Linguagem: Inglês

10.1016/j.jsb.2006.03.009

1095-8657

Markus Gruber, Johannes Söding, Andrei N. Lupas,

Machine Learning in Bioinformatics

In this study we compare commonly used coiled-coil prediction methods against a database derived from proteins of known structure. We find that the two older programs COILS and PairCoil/MultiCoil are significantly outperformed by two recent developments: Marcoil, a program built on hidden Markov models, and PCOILS, a new COILS version that uses profiles as inputs; and to a lesser extent by a PairCoil update, PairCoil2. Overall Marcoil provides a slightly better performance over the reference database than PCOILS and is considerably faster, but it is sensitive to highly charged false positives, whereas the weighting option of PCOILS allows the identification of such sequences.