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 PaperResearchpeer-review

    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.

    LanguageEnglish
    Title of host publicationProceedings - DCC 2017, 2017 Data Compression Conference
    Subtitle of host publication4 - 7 April 2017, Snowbird, Utah, USA
    EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
    Place of PublicationPiscataway, NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages340-349
    Number of pages10
    ISBN (Electronic)9781509067213
    ISBN (Print)9781509067220
    DOIs
    StatePublished - 8 May 2017
    EventData Compression Conference 2017 - Snowbird, United States
    Duration: 4 Apr 20177 Apr 2017
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7921793 (IEEE Conference Proceedings)

    Publication series

    NameData Compression Conference. Proceedings
    PublisherI E E E Computer Society
    ISSN (Print)1068-0314

    Conference

    ConferenceData Compression Conference 2017
    Abbreviated titleDCC 2017
    CountryUnited States
    CitySnowbird
    Period4/04/177/04/17
    Internet address

    Keywords

    • Minimum Message Length
    • MML
    • Protein structure
    • super-secondary structural patterns

    Cite this

    Subramanian, R., Allison, L., Stuckey, P. J., de la Banda, M. G., Abramson, D., Lesk, A. M., & Konagurthu, A. S. (2017). Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes. In A. Bilgin, M. W. Marcellin, J. Serra-Sagrista, & J. A. Storer (Eds.), Proceedings - DCC 2017, 2017 Data Compression Conference: 4 - 7 April 2017, Snowbird, Utah, USA (pp. 340-349). [7923707] (Data Compression Conference. Proceedings). Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers. DOI: 10.1109/DCC.2017.46
    Subramanian, Ramanan ; Allison, Lloyd ; Stuckey, Peter J ; de la Banda, Maria Garcia ; Abramson, David ; Lesk, Arthur M ; Konagurthu, Arun S. / Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes. Proceedings - DCC 2017, 2017 Data Compression Conference: 4 - 7 April 2017, Snowbird, Utah, USA. editor / Ali Bilgin ; Michael W. Marcellin ; Joan Serra-Sagrista ; James A. Storer. Piscataway, NJ : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 340-349 (Data Compression Conference. Proceedings).
    @inproceedings{f8344ee36f9b4906a97676443f3e2bcb,
    title = "Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes",
    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.",
    keywords = "Minimum Message Length, MML, Protein structure, super-secondary structural patterns",
    author = "Ramanan Subramanian and Lloyd Allison and Stuckey, {Peter J} and {de la Banda}, {Maria Garcia} and David Abramson and Lesk, {Arthur M} and Konagurthu, {Arun S}",
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    Subramanian, R, Allison, L, Stuckey, PJ, de la Banda, MG, Abramson, D, Lesk, AM & Konagurthu, AS 2017, Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes. in A Bilgin, MW Marcellin, J Serra-Sagrista & JA Storer (eds), Proceedings - DCC 2017, 2017 Data Compression Conference: 4 - 7 April 2017, Snowbird, Utah, USA., 7923707, Data Compression Conference. Proceedings, IEEE, Institute of Electrical and Electronics Engineers, Piscataway, NJ, pp. 340-349, Data Compression Conference 2017, Snowbird, United States, 4/04/17. DOI: 10.1109/DCC.2017.46

    Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes. / Subramanian, Ramanan; Allison, Lloyd; Stuckey, Peter J; de la Banda, Maria Garcia; Abramson, David; Lesk, Arthur M; Konagurthu, Arun S.

    Proceedings - DCC 2017, 2017 Data Compression Conference: 4 - 7 April 2017, Snowbird, Utah, USA. ed. / Ali Bilgin; Michael W. Marcellin; Joan Serra-Sagrista; James A. Storer. Piscataway, NJ : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 340-349 7923707 (Data Compression Conference. Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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    Subramanian R, Allison L, Stuckey PJ, de la Banda MG, Abramson D, Lesk AM et al. Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes. In Bilgin A, Marcellin MW, Serra-Sagrista J, Storer JA, editors, Proceedings - DCC 2017, 2017 Data Compression Conference: 4 - 7 April 2017, Snowbird, Utah, USA. Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 340-349. 7923707. (Data Compression Conference. Proceedings). Available from, DOI: 10.1109/DCC.2017.46