Format

Send to

Choose Destination
See comment in PubMed Commons below
J Theor Biol. 2014 Apr 7;346:8-15. doi: 10.1016/j.jtbi.2013.12.015. Epub 2013 Dec 30.

Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces.

Author information

1
Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan. Electronic address: m.hayat@awkum.edu.pk.
2
Department of Computer Science, National University of Computer and Emerging Science, Peshawar, Pakistan.
3
Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.

Abstract

Proteins are the executants of biological functions in living organisms. Comprehension of protein structure is a challenging problem in the era of proteomics, computational biology, and bioinformatics because of its pivotal role in protein folding patterns. Owing to the large exploration of protein sequences in protein databanks and intricacy of protein structures, experimental and theoretical methods are insufficient for prediction of protein structure classes. Therefore, it is highly desirable to develop an accurate, reliable, and high throughput computational model to predict protein structure classes correctly from polygenetic sequences. In this regard, we propose a promising model employing hybrid descriptor space in conjunction with optimized evidence-theoretic K-nearest neighbor algorithm. Hybrid space is the composition of two descriptor spaces including Multi-profile Bayes and bi-gram probability. In order to enhance the generalization power of the classifier, we have selected high discriminative descriptors from the hybrid space using particle swarm optimization, a well-known evolutionary feature selection technique. Performance evaluation of the proposed model is performed using the jackknife test on three low similarity benchmark datasets including 25PDB, 1189, and 640. The success rates of the proposed model are 87.0%, 86.6%, and 88.4%, respectively on the three benchmark datasets. The comparative analysis exhibits that our proposed model has yielded promising results compared to the existing methods in the literature. In addition, our proposed prediction system might be helpful in future research particularly in cases where the major focus of research is on low similarity datasets.

KEYWORDS:

Bi-gram probability; Multi-profile Bayes; OET-KNN; PSO

PMID:
24384128
DOI:
10.1016/j.jtbi.2013.12.015
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Support Center