Computational Analysis of 3D Protein Structures

Abstract

Analysis of 3-dimensional (3D) protein structures plays an important role in bioinformatics. Here, we present four methods for four different types of protein structure analyses: alignment, database search, classification, and clustering. Firstly, we propose a new method that carries out precise structural alignment by means of aligning their distance profiles, followed by an iterative refinement. Secondly, we propose a new index-based method for rapid structural database searching. It builds an inverted index of secondary structure element (SSE) pairs, and uses this index for ranking of the database proteins with respect to a query. Thirdly, we develop a new protein structure classification method based on a nearest neighbor scheme integrated with active learning. It adopts the filter-and-refine strategy, and utilizes a two-tier abstract representation of protein structures. Finally, we propose a method for clustering protein-protein interfaces. We carefully choose a set of representative interfaces from PDB (Protein Data Bank); characterize them as interface matrices; encode them as feature vectors based on the different submatrix types contained in them; and cluster them using a version of nearest-neighbor clustering algorithm. We can discover a number of statistically and biologically significant clusters.