Prediction of specificity-determining residues for small-molecule kinase inhibitors

Abstract

Background: Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificitydetermining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.

Measuring and interpreting the selectivity of protein kinase inhibitors

“…Several groups have looked at using a selection of these methods in order to predict which kinases will be inhibited by a compound. Some good correlations between calculated and experimental data were found as long as the compounds used for training were sufficiently structurally similar to those subsequently tested [21,26,66]. An analysis of the binding modes of many kinase inhibitors, as determined by X-ray crystallography, has suggested simple rules-of-thumb for predicting the orientation of typical ATP-competitive inhibitor scaffolds within the binding site [58].…”

New biochemical approaches towards understanding the Parkinson's disease-associated kinase, LRRK2

“…Very few protein kinase inhibitors are completely specific for their intended targets. Initially, it was assumed that inhibitor selectivity would be dictated by the overall homology between protein kinase domains, but now there is a growing recognition, particularly within the pharmaceutical industry, that the ability of an inhibitor to bind is often determined by a small number of amino acids within the ATP-binding site of the kinase, indicating that the most variable residues in this region are likely to be specificity determinants [12].…”

Computational analysis of kinase inhibitor selectivity using structural knowledge

“…Although kinase sequence similarity searching is a widely used method to predict kinase inhibitor off-target bindings, sequence homology alone cannot fully capture inhibitor selectivity (Fabian et al, 2005;Sheridan et al, 2009). For example, a single residue difference in the binding pocket among the homologous p38 kinase isoforms was enough to define discrete selectivity toward diverse kinase inhibitors (Caffrey et al, 2008). Other computational methods for off-target predictions include chemical similarity inference, inverse docking of inhibitor to multiple kinase structures, or binding site similarity comparison (Caffrey et al, 2008;Kinnings and Jackson, 2009;Kuhn et al, 2006;Sciabola et al, 2008;Subramanian and Sud, 2010;Zahler et al, 2007).…”

“…For example, a single residue difference in the binding pocket among the homologous p38 kinase isoforms was enough to define discrete selectivity toward diverse kinase inhibitors (Caffrey et al, 2008). Other computational methods for off-target predictions include chemical similarity inference, inverse docking of inhibitor to multiple kinase structures, or binding site similarity comparison (Caffrey et al, 2008;Kinnings and Jackson, 2009;Kuhn et al, 2006;Sciabola et al, 2008;Subramanian and Sud, 2010;Zahler et al, 2007). Recently, machine learning approaches such as kernel regression have also been developed for kinase activity profiling where a computational model was trained based on descriptors of protein sequence and chemical structures to predict bioactivities of new drugs (Cichonska et al, 2017).…”