USING INFORMATION THEORY TO DISCOVER SIDE CHAIN ROTAMER CLASSES: ANALYSIS OF THE EFFECTS OF LOCAL BACKBONE STRUCTURE

An understanding of the regularities in the side chain conformations of proteins and how these are related to local backbone structures is important for protein modeling and design. Previous work using regular secondary structures and regular divisions of the backbone dihedral angle data has shown that these rotamers are sensitive to the protein's local backbone conformation. In this preliminary study, we demonstrate a method for combining a more general backbone structure model with an objective clustering algorithm to investigate the effects of backbone structures on side chain rotamer classes and distributions. For the local structure classification, we use the Structural Building Blocks (SBB) categories, which represent all types of secondary structure, including regular structures, capping structures, and loops. For classification of side chain data, we use Minimum Message Length (MML) clustering from information theory. We show an example of how MML clustering on data classified by backbone SBBs can reveal different distributions of rotamer classes among the SBBs. Using these preliminary results, some of the characteristics of a rotamer library created using MML clustering on SBB dependent rotamer data are demonstrated.