Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome.

Abstract

The ability of a small molecule to interact with multiple target proteins provides the molecular basis of polypharmacology. So-defined compound promiscuity is intensely investigated in drug discovery. For example, for kinase inhibitors, the interplay between target selectivity and promiscuity plays a decisive role for different therapeutic applications. The "promiscuity cliff" (PC) concept was introduced previously to aid in promiscuity analysis. A PC is defined as a pair of structurally similar compounds with a large difference in promiscuity. Accordingly, PCs can reveal small structural modifications that might be responsible for selectivity or multi-target activity. In network representations, PCs form clusters of varying size and complexity that are difficult to analyze interactively. Herein, we introduce a computational method to systematically identify PC pathways, which are particularly rich in structure-promiscuity information, and extract them from PC clusters. PC pathways provide informative templates for experimental design. In a proof-of-concept investigation, we have applied the new computational approach to systematically identify pathways in more than 600 PC clusters formed by inhibitors of the human kinome, demonstrating the utility of the method and revealing many interesting promiscuity patterns.