Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes

Ramanan Subramanian, Lloyd Allison, Peter J. Stuckey, Maria Garcia De La Banda, David Abramson, Arthur M. Lesk, Arun S. Konagurthu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2017, 2017 Data Compression Conference
EditorsAli Bilgin, Joan Serra-Sagrista, Michael W. Marcellin, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781509067213
StatePublished - May 8 2017
Event2017 Data Compression Conference, DCC 2017 - Snowbird, United States
Duration: Apr 4 2017Apr 7 2017

Publication series

NameData Compression Conference Proceedings
VolumePart F127767
ISSN (Print)1068-0314


Other2017 Data Compression Conference, DCC 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications


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