Book chapter
Anti-parallel coiled coils structure prediction by support vector machine classification
TRANSACTIONS ON COMPUTATIONAL SYSTEMS BIOLOGY V, v 4070, pp 1-8
01 Jan 2006
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 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 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.
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Details
- Title
- Anti-parallel coiled coils structure prediction by support vector machine classification
- Creators
- Zhong HuangYun LiXiaohua Hu
- Publication Details
- TRANSACTIONS ON COMPUTATIONAL SYSTEMS BIOLOGY V, v 4070, pp 1-8
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 8
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000240081600001
- Other Identifier
- 991019170613304721
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods