BACKGROUND: Protein kinases are among the largest druggable family of signaling 
proteins, involved in various human diseases, including cancers and 
neurodegenerative disorders. Despite their clinical relevance, nearly 30% of the 
545 human protein kinases remain highly understudied. Comparative genomics is a 
powerful approach for predicting and investigating the functions of understudied 
kinases. However, an incomplete knowledge of kinase orthologs across fully 
sequenced kinomes severely limits the application of comparative genomics 
approaches for illuminating understudied kinases. Here, we introduce KinOrtho, a 
query- and graph-based orthology inference method that combines full-length and 
domain-based approaches to map one-to-one kinase orthologs across 17 thousand 
species.
RESULTS: Using multiple metrics, we show that KinOrtho performed better than 
existing methods in identifying kinase orthologs across evolutionarily divergent 
species and eliminated potential false positives by flagging sequences without a 
proper kinase domain for further evaluation. We demonstrate the advantage of 
using domain-based approaches for identifying domain fusion events, highlighting 
a case between an understudied serine/threonine kinase TAOK1 and a metabolic 
kinase PIK3C2A with high co-expression in human cells. We also identify 
evolutionary fission events involving the understudied OBSCN kinase domains, 
further highlighting the value of domain-based orthology inference approaches. 
Using KinOrtho-defined orthologs, Gene Ontology annotations, and machine 
learning, we propose putative biological functions of several understudied 
kinases, including the role of TP53RK in cell cycle checkpoint(s), the 
involvement of TSSK3 and TSSK6 in acrosomal vesicle localization, and potential 
functions for the ULK4 pseudokinase in neuronal development.
CONCLUSIONS: In sum, KinOrtho presents a novel query-based tool to identify 
one-to-one orthologous relationships across thousands of proteomes that can be 
applied to any protein family of interest. We exploit KinOrtho here to identify 
kinase orthologs and show that its well-curated kinome ortholog set can serve as 
a valuable resource for illuminating understudied kinases, and the KinOrtho 
framework can be extended to any protein-family of interest.