Toward Data-Driven Identification Of Kingdom-Specific Protein Sequence Motifs

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.