Toward data-driven identification of kingdom-specific protein sequence motifs
- Resource Type
- Conference
- Authors
- Elliott, Corrine F.; Linscott, Kristin; Husodo, Satrio; Chappell, Joseph; Liu, Jinze
- Source
- 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2018 IEEE International Conference on. :2221-2228 Dec, 2018
- Subject
- Bioengineering
Computing and Processing
Signal Processing and Analysis
Proteins
Fasteners
Amino acids
Measurement
Protein engineering
Entropy
Computer science
protein alignment
sequence alignment
feature identification
classification
information theory
- Language
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.