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Quantitative Structure-Activity Relationship Modeling of Kinase Selectivity Profiles.

ABSTRACT: The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure-activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model's performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23.

SUBMITTER: Kothiwale S

PROVIDER: S-EPMC6151389 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Quantitative Structure-Activity Relationship Modeling of Kinase Selectivity Profiles.

Kothiwale Sandeepkumar S Borza Corina C Pozzi Ambra A Meiler Jens J

Molecules (Basel, Switzerland) 20170919 9

The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during d ...[more]

PMID: 28925954

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Project description:Under-expression or overexpression of protein kinases has been shown to be associated with unregulated cell signal transduction in cancer cells. Therefore, there is major interest in designing protein kinase inhibitors as anticancer agents. We have previously reported [WR]5, a peptide containing alternative arginine (R) and tryptophan (W) residues as a non-competitive c-Src tyrosine kinase inhibitor. A number of larger cyclic peptides containing alternative hydrophobic and positively charged residues [WR]x (x = 6-9) and hybrid cyclic-linear peptides, [R6K]W6 and [R5K]W7, containing R and W residues were evaluated for their protein kinase inhibitory potency. Among all the peptides, cyclic peptide [WR]9 was found to be the most potent tyrosine kinase inhibitor. [WR]9 showed higher inhibitory activity (IC50 = 0.21 μM) than [WR]5, [WR]6, [WR]7, and [WR]8 with IC50 values of 0.81, 0.57, 0.35, and 0.33 μM, respectively, against c-Src kinase as determined by a radioactive assay using [γ-33P]ATP. Consistent with the result above, [WR]9 inhibited other protein kinases such as Abl kinase activity with an IC50 value of 0.35 μM, showing 2.2-fold higher inhibition than [WR]5 (IC50 = 0.79 μM). [WR]9 also inhibited PKCa kinase activity with an IC50 value of 2.86 μM, approximately threefold higher inhibition than [WR]5 (IC50 = 8.52 μM). A similar pattern was observed against Braf, c-Src, Cdk2/cyclin A1, and Lck. [WR]9 exhibited IC50 values of <0.25 μM against Akt1, Alk, and Btk. These data suggest that [WR]9 is consistently more potent than other cyclic peptides with a smaller ring size and hybrid cyclic-linear peptides [R6K]W6 and [R5K]W7 against selected protein kinases. Thus, the presence of R and W residues in the ring, ring size, and the number of amino acids in the structure of the cyclic peptide were found to be critical in protein kinase inhibitory potency. We identified three putative binding pockets through automated blind docking of cyclic peptides [WR](5-9). The most populated pocket is located between the SH2, SH3, and N-lobe domains on the opposite side of the ATP binding site. The second putative pocket is formed by the same domains and located on the ATP binding site side of the protein. Finally, a third pocket was identified between the SH2 and SH3 domains. These results are consistent with the non-competitive nature of the inhibition displayed by these molecules. Molecular dynamics simulations of the protein-peptide complexes indicate that the presence of either [WR]5 or [WR]9 affects the plasticity of the protein and in particular the volume of the ATP binding site pocket in different ways. These results suggest that the second pocket is most likely the site where these peptides bind and offer a plausible rationale for the increased affinity of [WR]9.

Dataset Information

Quantitative Structure-Activity Relationship Modeling of Kinase Selectivity Profiles.

Publications

Quantitative Structure-Activity Relationship Modeling of Kinase Selectivity Profiles.

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