A Novel Computational Framework for Structural Classification of Proteins Using Local Geometric Parameter Matching

• Authors: Sumeet Dua;Naveen Kandiraju
• Venue: CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
• Year: 2004

The objective of this study was to develop a novel and fast computational framework for classification of proteins using a series of secondary structure geometric parameter represented by an unexplored dihedral angle of a protein sequence. A dihedral angle is calculated between two planes represented by atom-tuplets [N(i), C(i), N(i+1)] and [C(i), N(i+1), C(i+1)], of adjacent (i and i+1) amino acids of a protein structure. The comparison of two such series of dihedral angles, each representing a different protein structure, is based on subsequence matching which not only gives the extent of match but also provides with the approximate demographic information of the match which then is used in classification of proteins. The technique is tested over 25 proteins belonging to 5 different families randomly selected from Alpha, Beta, Alpha and Beta (alpha/beta) and Multi-domain proteins (alpha and beta) classes. The classification rate is achieved with an accuracy of 88%.