Toward data-driven identification of kingdom-specific protein sequence motifs

Corrine F. Elliott; Kristin Linscott; Satrio Husodo; Joseph Chappell; Jinze Liu

doi:10.1109/BIBM.2018.8621241

Toward data-driven identification of kingdom-specific protein sequence motifs

Corrine F. Elliott, Kristin Linscott, Satrio Husodo, Joseph Chappell, Jinze Liu

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

Abstract

Biological researchers have proposed the existence of protein domains specific to individual taxonomic kingdoms of organism that do not participate directly in catalytic activity and yet are essential to genetic complementation of loss-of-function mutations [1]. Under the scope of this project, we design and implement a computational algorithm for unsupervised identification of new kingdom-specific sequence motifs to distinguish protein domains warranting empirical investigation. We execute this algorithm on sequences for a protein with empirically documented kingdom-specific domain, and validate the results with respect to biological realism by mapping to a 3-D protein structure and comparing against existing protein annotations.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Pages	2221-2228
Number of pages	8
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621241
State	Published - Jan 21 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	12/3/18 → 12/6/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

classification
feature identification
information theory
protein alignment
sequence alignment

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBM.2018.8621241

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Elliott, C. F., Linscott, K., Husodo, S., Chappell, J., & Liu, J. (2019). Toward data-driven identification of kingdom-specific protein sequence motifs. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2221-2228). Article 8621241 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). https://doi.org/10.1109/BIBM.2018.8621241

Elliott, Corrine F. ; Linscott, Kristin ; Husodo, Satrio et al. / Toward data-driven identification of kingdom-specific protein sequence motifs. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. 2019. pp. 2221-2228 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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abstract = "Biological researchers have proposed the existence of protein domains specific to individual taxonomic kingdoms of organism that do not participate directly in catalytic activity and yet are essential to genetic complementation of loss-of-function mutations [1]. Under the scope of this project, we design and implement a computational algorithm for unsupervised identification of new kingdom-specific sequence motifs to distinguish protein domains warranting empirical investigation. We execute this algorithm on sequences for a protein with empirically documented kingdom-specific domain, and validate the results with respect to biological realism by mapping to a 3-D protein structure and comparing against existing protein annotations.",

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Elliott, CF, Linscott, K, Husodo, S, Chappell, J & Liu, J 2019, Toward data-driven identification of kingdom-specific protein sequence motifs. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621241, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, pp. 2221-2228, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621241

Toward data-driven identification of kingdom-specific protein sequence motifs. / Elliott, Corrine F.; Linscott, Kristin; Husodo, Satrio et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. 2019. p. 2221-2228 8621241 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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

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N2 - Biological researchers have proposed the existence of protein domains specific to individual taxonomic kingdoms of organism that do not participate directly in catalytic activity and yet are essential to genetic complementation of loss-of-function mutations [1]. Under the scope of this project, we design and implement a computational algorithm for unsupervised identification of new kingdom-specific sequence motifs to distinguish protein domains warranting empirical investigation. We execute this algorithm on sequences for a protein with empirically documented kingdom-specific domain, and validate the results with respect to biological realism by mapping to a 3-D protein structure and comparing against existing protein annotations.

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Elliott CF, Linscott K, Husodo S, Chappell J, Liu J. Toward data-driven identification of kingdom-specific protein sequence motifs. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. 2019. p. 2221-2228. 8621241. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621241

Toward data-driven identification of kingdom-specific protein sequence motifs

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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