Book chapter
SVM Classification to Predict Two Stranded Anti-parallel Coiled Coils based on Protein Sequence Data
Computational Science and Its Applications – ICCSA 2005, pp 374-380
2005
Featured in Collection : UN Sustainable Development Goals @ Drexel
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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Details
- Title
- SVM Classification to Predict Two Stranded Anti-parallel Coiled Coils based on Protein Sequence Data
- Creators
- Zhong Huang - Thomas Jefferson UniversityYun Li - Drexel UniversityXiahohua Hu - Drexel UniversityZhuoran Huang - WELL Center
- Publication Details
- Computational Science and Its Applications – ICCSA 2005, pp 374-380
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science; Center for Weight, Eating and Lifestyle Science (WELL) [Historical]
- Web of Science ID
- WOS:000229696900040
- Scopus ID
- 2-s2.0-24944517127
- Other Identifier
- 991019173457604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods