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Computational prediction and characterization of the kinase CDK5 inhibitory mechanism of pathological activity in Alzheimer’s disease

Wednesday, September 16, 2015 — Poster Session I

3:30 p.m. – 5:00 p.m.
FAES Terrace
CIT
COMPBIO-6

Authors

  • A Cardone
  • SA Hassan
  • M Brady
  • RD Sriram
  • HC Pant

Abstract

The cyclin-dependent kinase CDK5 plays a fundamental role in the nervous system development and neuronal migration. CDK5 physiological activity is regulated by its activator p35. Under neuronal insults such as A-beta toxicity as well as Ca2+-induced oxidative stress, p35 is proteolyzed into a fragment p10 and a pathological activator p25, which binds CDK5 and deregulates and hyperactivates its activity. This results in the development of plaques and neurofibrillary tangles, hallmarks of Alzheimer’s disease. Past efforts to inhibit the pathological activity have directly targeted the phosphorylation mechanism by preventing ATP-binding. However, this might cause toxic effects due to their binding to all the kinases. Our in vitro experimental assays show that truncated fragments from p35 effectively inhibit the CDK5-p25 activity. The smallest fragment derived from p25, the 24 amino acid peptide p5, appears to be an ideal therapeutic candidate since it selectivity inhibits CDK5-p25 instead of the physiological CDK5-p35, thus decreasing toxicity. The binding modes and inhibitory mechanism of p5 is unknown. Here, we used recent developments in computer modeling and simulations to study CDK5-p5 association. We have identified a high-affinity, high-population CDK5-p5 binding mode that inhibits the aberrant activity by competing for binding with p25. The main physicochemical characteristics that drive association allowed us to characterize the pharmacophore with the goal of developing small drug-like compounds against AD. A reduced set of amino acids at the CDK5/p5 interface predicted to interact with p5 provides valuable information for targeted mutagenesis studies to map the binding site experimentally.

Category: Computational Biology