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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Pages2221-2228
Number of pages8
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/TerritorySpain
CityMadrid
Period12/3/1812/6/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • classification
  • feature identification
  • information theory
  • protein alignment
  • sequence alignment

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Fingerprint

Dive into the research topics of 'Toward data-driven identification of kingdom-specific protein sequence motifs'. Together they form a unique fingerprint.

Cite this