Using the structural kinome to systematize kinase drug discovery

TL;DR

This paper reviews in silico structure-based methods, especially the function-site interaction fingerprint approach, for systematizing kinase drug discovery by analyzing the human kinome's structural data and exploring new opportunities with machine learning.

Contribution

It introduces a systematic framework using structural kinome data and in silico methods to improve kinase drug discovery and highlights new opportunities with machine learning techniques.

Findings

Structural kinome analysis aids kinase selectivity understanding.

Function-site interaction fingerprint approach enhances kinase-ligand characterization.

Machine learning broadens drug discovery opportunities.

Abstract

Kinase-targeted drug design is challenging. It requires designing inhibitors that can bind to specific kinases when all kinase catalytic domains share a common folding scaffold that binds ATP. Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery. This begins with deciphering the kinase-ligand characteristics, analyzing the structure-activity relationships, and prioritizing the desired drug molecules across the whole kinome. Currently, there are more than 300 kinases with released PDB structures, which provides a substantial structural basis to gain these necessary insights. Here, we review in silico structure-based methods - notably, a function-site interaction fingerprint approach used in exploring the complete human kinome. In silico methods can be explored synergistically with multiple cell-based or protein-based assay platforms such as KINOMEscan. We conclude with new drug discovery opportunities associated with kinase signaling networks and using machine/deep learning techniques broadly referred to as structural biomedical data science.