학술논문
Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes
Document Type
Conference
Author
Source
2017 Data Compression Conference (DCC) DCC Data Compression Conference (DCC), 2017. :340-349 Apr, 2017
Subject
Language
ISSN
2375-0359
Abstract
Computational analyses of the growing corpus of three-dimensional (3D) structures of proteins have revealed a limited set of recurrent substructural themes, termed super-secondary structures. Knowledge of super-secondary structures is important for the study of protein evolution and for the modeling of proteins with unknown structures. Characterizing a comprehensive dictionary of these super-secondary structures has been an unanswered computational challenge in protein structural studies. This paper presents an unsupervised method for learning such a comprehensive dictionary using the statistical framework of lossless compression on a database comprised of concise geometric representations of protein 3D folding patterns. The best dictionary is defined as the one that yields the most compression of the database. Here we describe the inference methodology and the statistical models used to estimate the encoding lengths. An interactive website for this dictionary is available at http://lcb.infotech.monash.edu.au/proteinConcepts/scop100/dictionary.html.