Abstract
Coiled 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 two stranded anti-parallel 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 two stranded anti-parallel coiled coils motif are extracted from 12 proteins as the positive datasets. Total 37 of non coiled coils sequences and two stranded 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 two stranded anti-parallel coiled coils based on the cross-validated data. The result suggests a useful approach of using SVM to classify the two stranded anti-parallel coiled coils based on the primary amino acid sequence.
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Huang, Z., Li, Y., Hu, X. (2005). SVM Classification to Predict Two Stranded Anti-parallel Coiled Coils based on Protein Sequence Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424857_40
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DOI: https://doi.org/10.1007/11424857_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25862-9
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