Structure‐Based Target‐Specific Screening Leads to Small‐Molecule CaMKII Inhibitors

Abstract

Target-specific scoring methods are more commonly used to identify small-molecule inhibitors among compounds docked to a target of interest. Top candidates that emerge from these methods have rarely been tested for activity and specificity across a family of proteins. Here, we dock a chemical library to CaMKIIδ, a member of the Ca2+/calmodulin (CaM)-dependent protein kinase (CaMK) family, and rescore the resulting protein-compound structures using SVMSP, a target-specific method that we previously developed. Among the 35 selected candidates, three hits were identified, such as quinazoline compound 1 (KIN-1), which inhibited CaMKIIδ kinase activity with single-digit micromolar IC50. Activity across the kinome was assessed by profiling analogs of 1, namely 6 (KIN-236), and an analog of hit compound 2 (KIN-15), namely 14 (KIN-332), against 337 kinases. Interestingly, for 6 (KIN-236), CaMKIIδ and homolog CaMKIIγ were among the top 10 targets. Among the top 25 targets of 6 (KIN-236), IC50 values ranged from 5 to 22 μM. Compound 14 (KIN-332) was not specific towards CaMKII kinases, but the compound inhibited two kinases with sub-micromolar IC50s among the top 25. Derivatives of 1 were tested against several kinases including several members of the CaMK family. The data afforded a limited structure-activity relationship study. Molecular dynamics simulations with explicit-solvent followed by end-point MM-GBSA free energy calculations revealed strong engagement of specific residues within the ATP-binding pocket, and also changes in the dynamics as a result of binding. This work suggests that target-specific scoring approaches like SVMSP may hold promise for the identification of small-molecule kinase inhibitors that exhibit some level of specificity towards the target of interest across a large number of proteins.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

“…SVM-based methods have been prominently used to develop a target-specific SVM-based scoring model (SVM-SP) 330 for the identification of small-molecule inhibitors among the protein− ligand complexes generated by automated ligand docking. 331 The inclusion of the statistical pairwise potentials of docked protein−ligand pairs has led to a general scoring function (SVMGen). 332 MIEC-SVM 333 is an SBVS approach that uses an SVM model to discriminate between active and inactive molecules.…”

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

“…Indeed, the performance of an SF differs from target to target 137 . Therefore, researchers have developed ML‐based, target‐specific SFs to improve the efficiency of existing SFs for kinases, 138–141 histone methyltransferases, 142 cyclin‐dependent kinases and G protein‐coupled receptors (GPCRs), 137 and cytochrome P450 aromatase 143 …”

Towards an Enrichment Optimization Algorithm (EOA)‐based Target Specific Docking Functions for Virtual Screening

“…Several methods for the derivation of target specific scoring functions in the context of docking were reported in the literature [26,31–36] . These methods mostly applied various machine learning algorithms either to re‐derive the weights associated with the energy components that make up specific scoring functions or to develop new functions based on descriptors derived from predicted binding modes.…”

“…Several methods for the derivation of target specific scoring functions in the context of docking were reported in the literature. [26,[31][32][33][34][35][36] These methods mostly applied various machine learning algorithms either to re-derive the weights associated with the energy components that make up specific scoring functions or to develop new functions based on descriptors derived from predicted binding modes. However, except of one case, [37] all of these functions were used for pose rescoring rather than for ligand re-docking and none were based on the optimization of a VS-aware metric as suggested in this work.…”