Protein Structure Classification Using Geometric Invariants and Dynamic Programming

Aniket Dalal; Sandeep Deshmukh; Pramod P. Wangikar

doi:10.2174/092986607781483877

Abstract

Classification of newly determined protein structures is important in understanding their function and mechanism of action. Currently available methods employ a global structure alignment strategy and are computationally expensive. We propose a two-step methodology with a quick screen to significantly reduce the number of candidate structures followed by global structure alignment of the query structure with the reduced set. We represent a protein structure as a sequence of local structures, codified in the form of geometric invariants. Geometric invariants are quantities that remain unchanged under transformations such as translation and rotation. Protein structures represented as multi-attribute sequences are aligned via dynamic programming to identify close neighbors of the query structure. The query structure is then compared with this reduced dataset using conventional structure comparison methods to predict its functional class. For a typical protein structure, the screening method was able to reduce the protein data bank to mere 200 proteins while preserving structurally closest neighbor in the reduced set. This has resulted in 30 to 60 fold improvement in the execution time. We present the results of leave-one-out classification experiment on ASTRAL-95 domains and comparison with SCOP classification hierarchy.

Keywords: Nearest neighbor classification, cosine similarity, affine gap penalty, protein structure alignment

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