Analyzing Kinase Similarity in Small Molecule and Protein Structural Space to Explore the Limits of Multi-Target Screening

Abstract

While selective inhibition is one of the key assets for a small molecule drug, many diseases can only be tackled by simultaneous inhibition of several proteins. An example where achieving selectivity is especially challenging are ligands targeting human kinases. This difficulty arises from the high structural conservation of the kinase ATP binding sites, the area targeted by most inhibitors. We investigated the possibility to identify novel small molecule ligands with pre-defined binding profiles for a series of kinase targets and anti-targets by in silico docking. The candidate ligands originating from these calculations were assayed to determine their experimental binding profiles. Compared to previous studies, the acquired hit rates were low in this specific setup, which aimed at not only selecting multi-target kinase ligands, but also designing out binding to anti-targets. Specifically, only a single profiled substance could be verified as a sub-micromolar, dual-specific EGFR/ErbB2 ligand that indeed avoided its selected anti-target BRAF. We subsequently re-analyzed our target choice and in silico strategy based on these findings, with a particular emphasis on the hit rates that can be expected from a given target combination. To that end, we supplemented the structure-based docking calculations with bioinformatic considerations of binding pocket sequence and structure similarity as well as ligand-centric comparisons of kinases. Taken together, our results provide a multi-faceted picture of how pocket space can determine the success of docking in multi-target drug discovery efforts.

KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome

“…A structure is known to be more conserved than a sequence, and previous studies have shown that including structural information adds orthogonal information to shed light on unexpected similarities between kinases and off-target effects. , To help detect such relationships between more distantly related kinases, we generated KiSSim kinome trees based on the DFG-in conformations, as described in detail in the KiSSim Tree section, to investigate all-against-all relationships between kinases compared to the sequence-based kinome tree by Manning et al (Figure ). Note that we can base the comparison on structurally resolved kinases only, i.e., 257 out of the roughly 500 human kinases.…”

“…10 The KinCore phylogenetic tree produced by a kinome-wide structure-guided MSA 7,11 overall confirms the assignment from Manning et al 6 but provides higher precision, e.g., regarding previously unassigned kinases. Schmidt et al 12 have recently investigated the similarities between a panel of nine kinasesEGFR, ErbB2, PIK3CA, KDR, BRAF, CDK2, LCK, MET, and p38abased on different pocket encodings, including the pocket sequence identity, pocket structure similarity, interaction fingerprint similarity, and ligand promiscuity. Individual kinase relationships differed according to these different perspectives, while some trends could be observed such as the atypical kinase PIK3CA being an outlier among the otherwise typical kinases in this panel.…”

KiSSim: Predicting off-targets from structural similarities in the kinome

“…Structure is known to be more conserved than sequence, 64 and previous studies have shown that including structural information adds orthogonal information to shed light on unexpected similarities between kinases and off-target effects. 7,12 To help detect such relationships between more distantly related kinases, we generated KiSSim kinome trees based on the DFG-in conformations, as described in detail in the KiSSim tree section, to investigate all-against-all relationships between kinases compared to the sequence-based kinome tree by Manning et al 6 . Note that we can base the comparison on structurally resolved kinases only, i.e., 257 out of the roughly 500 human kinases.…”

“…regarding previously unassigned kinases. Schmidt et al 12 have recently investigated the similarities between a panel of nine kinases -EGFR, ErbB2, PIK3CA, KDR, BRAF, CDK2, LCK, MET, and p38a -based on different pocket encodings, including the pocket sequence identity, pocket structure similarity, interaction fingerprint similarity, and ligand promiscuity. Individual kinase relationships differed according to these different perspectives, while some trends could be observed such as the atypical kinase PIK3CA being an outlier amongst the otherwise typical kinases in this panel.…”

Comprehensive detection and characterization of human druggable pockets through novel binding site descriptors

“…Indeed, Govindaraj and Brylinski 53 showed that docking scores tended to be more correlated in pockets binding to chemically similar ligands than in pockets binding to dissimilar ligands. This opens the possibility of estimating binding site similarity on the basis of docking rankings and enrichments, as explored by Schmidt and co-workers in their analysis of the human kinome 54 . Moreover, inverse virtual screening (i.e.…”