Strategy toward Kinase-Selective Drug Discovery

Kinase drug selectivity is the ground challenge in cancer
research. Due to the structurally similar kinase drug pockets, off-target
inhibitor toxicity has been a major cause for clinical trial failures.
The pockets are similar but not identical. Here, we describe a transformation
invariant protocol to identify distinct geometric features in the
drug pocket that can distinguish one kinase from all others. We integrate
available experimental structures with the artificial intelligence-based
structural kinome, performing a kinome-wide structural bioinformatic
analysis to establish the structural principles of kinase drug selectivity.
We generate the structural landscape from the experimental kinase–ligand
complexes and propose a binary network that encapsulates the information.
The results show that all kinases contain binary units that are shared
by less than seven other kinases in the kinome. 331 kinases contain
unique binary units that may distinguish them from all others. The
structural features encoded by these binary units in the network represent
the inhibitor-accessible geometric space that may capture the kinome-wide
selectivity. Our proposed binary network with the unsupervised clustering
can serve as a general structural bioinformatic protocol for extracting
the distinguishing structural features for any protein from their
families. We apply the binary network to epidermal growth factor receptor
tyrosine kinase inhibitor selectivity by targeting the gate area and
the AKT1 serine/threonine kinase selectivity by binding to the αC-helix
region and the allosteric pocket. Finally, we develop the cross-platform
software, KDS (Kinase Drug Selectivity), for customized visualization
and analysis of the binary networks in the human kinome (https://github.com/CBIIT/KDS).