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

doi:10.1109/DCC.2017.46

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 proceeding › Conference Paper › Research › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - DCC 2017, 2017 Data Compression Conference
Subtitle of host publication	4 - 7 April 2017, Snowbird, Utah, USA
Editors	Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
Place of Publication	Piscataway, NJ
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	340-349
Number of pages	10
ISBN (Electronic)	9781509067213
ISBN (Print)	9781509067220
DOIs	https://doi.org/10.1109/DCC.2017.46
Publication status	Published - 8 May 2017
Event	Data Compression Conference 2017 - Snowbird, United States of America Duration: 4 Apr 2017 → 7 Apr 2017 Conference number: 27th https://ieeexplore.ieee.org/xpl/conhome/7921793/proceeding (IEEE Conference Proceedings)

Publication series

Name	Data Compression Conference. Proceedings
Publisher	I E E E Computer Society
ISSN (Print)	1068-0314

Conference

Conference	Data Compression Conference 2017
Abbreviated title	DCC 2017
Country/Territory	United States of America
City	Snowbird
Period	4/04/17 → 7/04/17
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7921793/proceeding (IEEE Conference Proceedings)

Keywords

Minimum Message Length
MML
Protein structure
super-secondary structural patterns

Access to Document

10.1109/DCC.2017.46

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). (Data Compression Conference. Proceedings). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DCC.2017.46

Subramanian, Ramanan ; Allison, Lloyd ; Stuckey, Peter J et al. / 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}",

year = "2017",

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booktitle = "Proceedings - DCC 2017, 2017 Data Compression Conference",

address = "United States of America",

note = "Data Compression Conference 2017, DCC 2017 ; Conference date: 04-04-2017 Through 07-04-2017",

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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. Data Compression Conference. Proceedings, IEEE, Institute of Electrical and Electronics Engineers, Piscataway, NJ, pp. 340-349, Data Compression Conference 2017, Snowbird, Utah, United States of America, 4/04/17. https://doi.org/10.1109/DCC.2017.46

Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes. / Subramanian, Ramanan; Allison, Lloyd; Stuckey, Peter J et al.
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 (Data Compression Conference. Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Statistical Compression of Protein Folding Patterns for Inference of Recurrent Substructural Themes

AU - Subramanian, Ramanan

AU - Allison, Lloyd

AU - Stuckey, Peter J

AU - de la Banda, Maria Garcia

AU - Abramson, David

AU - Lesk, Arthur M

AU - Konagurthu, Arun S

N1 - Conference code: 27th

PY - 2017/5/8

Y1 - 2017/5/8

N2 - 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.

AB - 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.

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KW - Protein structure

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DO - 10.1109/DCC.2017.46

M3 - Conference Paper

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T3 - Data Compression Conference. Proceedings

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EP - 349

BT - Proceedings - DCC 2017, 2017 Data Compression Conference

A2 - Bilgin, Ali

A2 - Marcellin, Michael W.

A2 - Serra-Sagrista, Joan

A2 - Storer, James A.

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway, NJ

T2 - Data Compression Conference 2017

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ER -

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. (Data Compression Conference. Proceedings). doi: 10.1109/DCC.2017.46

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Next-generation Protein Structural comparison using Information Theory

Cite this

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Projects

Next-generation Protein Structural comparison using Information Theory

Cite this