Abstract
The central role of kinases in virtually all signal transduction networks is the driving motivation for the development of compounds modulating their activity. ATP-mimetic inhibitors are essential tools for elucidating signaling pathways and are emerging as promising therapeutic agents. However, off-target ligand binding and complex and sometimes unexpected kinase/inhibitor relationships can occur for seemingly unrelated kinases, stressing that computational approaches are needed for learning the interaction determinants and for the inference of the effect of small compounds on a given kinase. Recently published high-throughput profiling studies assessed the effects of thousands of small compound inhibitors, covering a substantial portion of the kinome. This wealth of data paved the road for computational resources and methods that can offer a major contribution in understanding the reasons of the inhibition, helping in the rational design of more specific molecules, in the in silico prediction of inhibition for those neglected kinases for which no systematic analysis has been carried yet, in the selection of novel inhibitors with desired selectivity, and offering novel avenues of personalized therapies.
Highlights
The kinome plays a predominant role in signal transduction networks and cellular responses; its involvement in a large number of pathologies is a major impulse for the identification and development of compounds modulating the activity of individual kinases or kinase families
Kinase inhibitor selectivity is generally not inclusively known for the majority of the tested compounds, as kinase research has been principally focused on a small subset of the kinome
Traditional kinase inhibitor analysis is a low-throughput process in which the capability of small compounds to decrease the phosphorylation activity or their binding affinity is measured, but are generally not extended to the characterization of the inhibitory abilities of a given compound against the entire kinome. Such data are mined from the literature and collected in general-purpose databases such as ChEMBL (Gaulton et al, 2012) and STITCH (Kuhn et al, 2014), or in kinase-dedicated public resources such as the CheEMBL Kinase SARfari, or the commercially available Kinase Knowledgebase (KKB) by Eidogen-Sertanty (Oceanside, CA, USA) and the kinase inhibitor database provided by GVK Biosciences (Hyderabad, India)
Summary
The kinome plays a predominant role in signal transduction networks and cellular responses; its involvement in a large number of pathologies is a major impulse for the identification and development of compounds modulating the activity of individual kinases or kinase families. Traditional kinase inhibitor analysis is a low-throughput process in which the capability of small compounds to decrease the phosphorylation activity (usually reported as the IC50 or as the remaining or residual activity of the kinase) or their binding affinity (as its dissociation constant) is measured, but are generally not extended to the characterization of the inhibitory abilities of a given compound against the entire kinome Such data are mined from the literature and collected in general-purpose databases such as ChEMBL (Gaulton et al, 2012) and STITCH (Kuhn et al, 2014), or in kinase-dedicated public resources such as the CheEMBL Kinase SARfari, or the commercially available Kinase Knowledgebase (KKB) by Eidogen-Sertanty (Oceanside, CA, USA) and the kinase inhibitor database provided by GVK Biosciences (Hyderabad, India). In this review we focused on recent computational methods and resources that employ the latest kinase inhibition profiling data but go beyond standard quantitative structure-activity relationship (QSAR) modeling approaches, which are generally specific for a single target, being instead purposely tailored toward kinase inhibition analysis and applied to the whole kinome, taking advantage from the overall kinase domain conservation and from shared binding patterns and characteristics and providing multidimensional structure-activity relationships concerning tens or hundreds of targets at the same time (Goldstein et al, 2008)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
