Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining

Natarajan Kannan, Ph.D.
Biochemistry and Molecular Biology
Institute of Bioinformatics
University of Georgia

Wei Lü, Ph.D.
Associate Professor
Department of Structural Biology
Van Andel Research Institute


Lab websites:

  • Natarajan Kannan profile: https://www.bmb.uga.edu/directory/people/natarajan-kannan
  • Dr. Kannan's Lab Website: http://esbg.bmb.uga.edu/index.html
  • Wei Lü profile: https://lulab.vai.org/


Ion channels are a large family of druggable proteins causatively associated with a multitude of human diseases including neurological disorders, cardiovascular diseases, epilepsy, kidney failure, blindness and cancer to name but a few. However, an incomplete understanding of their molecular and cellular functions presents a bottleneck in ongoing drug discovery efforts. By developing cutting edge tools and resources that cater to the unique needs of the ion channel community, the proposed studies will accelerate the functional annotation of understudied members of this family and address the overall mission of the IDG (Illuminating the Druggable Genome) project in illuminating the druggable proteome.

NIH grant number: 1U01 CA271376



  1. Salcedo MV, Gravel N, Keshavarzi A, Huang L, Kochut KJ, Kannan N. Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding. PeerJ. 2023 Oct 18:11:e15815. doi: 10.7717/peerj.15815. eCollection 2023. PMID: 37868056, PMCID: PMC10590106.

Page reviewed on March 8, 2024