Abstract
CORA is a suite of programs for multiply aligning and analyzing protein structural families to identify the consensus positions and capture their most conserved structural characteristics (e.g., residue accessibility, torsional angles, and global geometry as described by inter-residue vectors/contacts). Knowledge of these structurally conserved positions, which are mostly in the core of the fold and of their properties, significantly improves the identification and classification of newly-determined relatives. Information is encoded in a consensus three-dimensional (3D) template and relatives found by a sensitive alignment method, which employs a new scoring scheme based on conserved residue contacts. By encapsulating these critical "core" features, templates perform more reliably in recognizing distant structural relatives than searches with representative structures. Parameters for 3D-template generation and alignment were optimized for each structural class (mainly-alpha, mainly-beta, alpha-beta), using representative superfold families. For all families selected, the templates gave significant improvements in sensitivity and selectivity in recognizing distant structural relatives. Furthermore, since templates contain less than 70% of fold positions and compare fewer positions when aligning structures, scans are at least an order of magnitude faster than scans using selected structures. CORA was subsequently tested on eight other broad structural families from the CATH database. Diagnostics plots are generated automatically and provide qualitative assistance for classifying newly determined relatives. They are demonstrated here by application to the large globin-like fold family. CORA templates for both homologous superfamilies and fold families will be stored in CATH and used to improve the classification and analysis of newly determined structures.