Rita Casadio (a, b), Mario Compiani
(a, c), Piero Fariselli (a) and Pier Luigi
Martelli (a)
(a) CIRB, Biocomputing unit, University
of Bologna;
(b) Laboratory of Biophysics, Dept.of Biology, Via Irnerio 42,
I-40126-Bologna, Italy;
(c) Dept of Chemical Sciences, University of Camerino; casadio@kaiser.alma.unibo.it,
compiani@camserv.unicam.it, (piero, gigi)@lipid.biocomp.unibo.it
Abstract
A data base of minimally frustrated alpha helical segments is
defined by filtering a set comprising 822 non redundant proteins,
which contain 4783 alpha helical structures. The data base definition
is performed using a neural network-based alpha helix predictor,
whose outputs are rated according to an entropy criterion. A comparison
with the presently available experimental results indicates that
a subset of the data base contains the initiation sites of protein
folding experimentally detected and also protein fragments which
fold into stable isolated alpha helices. This suggests the usage
of the data base (and/or of the predictor) to highlight patterns
which govern the stability of alpha helices in proteins and the
helical behavior of isolated protein fragments.
Keywords
Alpha helix prediction; neural networks; minimal entropy criterion;
protein folding; self-stabilizing alpha helices.