1 research outputs found
Anti-parallel coiled coils structure prediction by support vector machine classification
Transactions On Computational Systems Biology V, 4070, pp. 1-8. http://dx.doi.org/10.1007/11790105_1Coiled coils is an important 3-D protein structure with two or more
stranded alpha-helical motif wounded around to form a “knobs-into-holes” structure.
In this paper we propose an SVM classification approach to predict the antiparallel
coiled coils structure based on the primary amino acid sequence. The
training dataset for the machine learning are collected from SOCKET database
which is a SOCKET algorithm predicted coiled coils database. Total 41 sequences
of at least two heptad repeats of the anti-parallel coiled coils motif are extracted
from 12 proteins as the positive datasets. Total 37 of non coiled coils sequences
and parallel coiled coils motif are extracted from 5 proteins as negative datasets.
The normalized positional weight matrix on each heptad register a, b, c, d, e, f and
g is from SOCKET database and is used to generate the positional weight on each
entry. We performed SVM classification using the cross-validated datasets as
training and testing groups. Our result shows 73% accuracy on the prediction of
anti-parallel coiled coils based on the cross-validated data. The result suggests a
useful approach of using SVM to classify the anti-parallel coiled coils based on
the primary amino acid sequence