Regions of Similarity: A Novel Graph Theoretical Protein Structure Comparison and Analysis Technique

EasyChair Preprint 1823

Abstract

All existing protein structure comparison methods return a score for similarity, but few give a deep underlying look at the parts of the structures which match. Zemla’s Global Distance Test (GDT) partially does by identifying the largest region of a pair of structures whose superposition errors all fall under some threshold, but the region and its errors are dependent on that superposition, and smaller regions are not identified. By converting the C distances matrices of two structures into a graph, a maximum clique analysis can be used to identify the largest non-overlapping regions of similarity between structures. These regions can easily be visualized, and they lend themselves to a deep analysis of the underlying similarities between structures, complementing existing methods of comparison by providing additional information that is not readily available. Additionally, when applied to an analysis such as that performed for each CASP experiment, models which correctly represent each domain in a multi-domain structure but whose orientations differ from the native will be immediately apparent. A regions of similarity analysis can be performed on multi-domain targets without a priori knowledge of the domains.

Keyphrases: CASP, Max Clique, conformational comparative analysis, maximum clique, protein structure, protein structure comparison, protein structure prediction, structural bioinformatics

@booklet{EasyChair:1823,
  author    = {Aaron Maus and Christopher Summa},
  title     = {Regions of Similarity: A Novel Graph Theoretical Protein Structure Comparison and Analysis Technique},
  howpublished = {EasyChair Preprint 1823},
  year      = {EasyChair, 2019}}