Title: Sub-similarity matching based on data mining with dihedral angles

Authors: Egemen Berki Cimen; Fatih Akin; R. Murat Demirer

Addresses: Computer Engineering Department, Istanbul Kultur University, Bakırköy, Istanbul 34156, Turkey ' Computer Engineering Department, Istanbul Kultur University, Bakırköy, Istanbul 34156, Turkey ' Computer Engineering Department, Istanbul Kultur University, Bakırköy, Istanbul 34156, Turkey

Abstract: Protein sub-similarity matching remains largely unknown even though it is becoming one of the most important open problems in bioinformatics for drug and vaccine design. Variations in human immune responses to vaccines are, and thus responses, fail. We propose a new matching and protein alignment method based on clustering and Longest Common Subsequence (LCS) techniques. After clustering, we found LCS between a candidate protein and meningitis outer membrane antigen for each candidate. Each similarity was scored, and closest similarities were determined with statistical methods. We located three closely matching proteins among a total of 50 human immune system proteins. Moreover, we selected a HIV-1 related protein from one of scenarios, because it revealed a relationship between HIV and meningitis patients. We also found that Ω main chain torsion angle for atoms CA, C and N is the best angle for determining sub-similarities between meningitis antigen and immune proteins.

Keywords: proteins; 3D dihedral angles; clustering; classification; LCS; longest common subsequence; R programming; dynamic programming; sub-similarity matching; data mining; bioinformatics; drug design; vaccine design; protein alignment; HIV; meningitis antigens; immune proteins.

DOI: 10.1504/IJCBDD.2013.052207

International Journal of Computational Biology and Drug Design, 2013 Vol.6 No.1/2, pp.131 - 145

Published online: 20 Feb 2013 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article