Optimizing the location and the number of the maximal scoring subsequences with constrained segment lengths with MaxSubSeq
Piero Fariselli1, Pier Luigi Martelli2, Ivan Rossi and Rita Casadio
1piero@lipid.biocomp.unibo.it, Department of Biology CIRB, University of Bologna; 2 Department of Biology CIRB, University of Bologna
A problem in predicting the topography of transmembrane proteins is the
optimal localization of the transmembrane segments along the protein
sequences, provided that each residue is associated with a propensity of
being or not included in the transmembrane protein region. From previous
work it is known that post-processing of propensity signals with suited
algorithms can greatly improve the quality and the accuracy of the
predictions. We developed a general dynamic programming-like algorithm
(MaxSubSeq, Maximal SubSequence) specifically designed to optimise the
number and length of segments with constrained length in a given protein
sequence. The results on the MaxSubSeq applications to both helical and
beta strand transmembrane segments show that our algorithm can increase of
5-10 percentage points with respect to the original outputs derived with
different methods (Bioinformatics 19:500-505 (2003)). Our algorithm is
devised to be used independently of the predictive method and is available
through the web interface at
http://gpcr.biocomp.unibo.it
. A possible application of MaxSubSeq will be presented and is
related to the 3D modeling of voltage dependent anion channels (VDAC) in
eukaryotes (Casadio et al., FEBS Lett 2002, 520:1-7).