Holistic similarity-based prediction of phosphorylation sites for understudied kinases.

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time-consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase-kinase similarity network was generated by merging the sequence-, functional-, protein-domain- and 'STRING'-related similarities. Thus, besides sequence data, protein-protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the 'TK', 'Other', 'STE', 'CAMK', 'TKL', 'CMGC', 'AGC', 'CK1' and 'Atypical' groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.

How many kinases are druggable? A review of our current understanding.

There are over 500 human kinases ranging from very well-studied to almost completely ignored. Kinases are tractable and implicated in many diseases, making them ideal targets for medicinal chemistry campaigns, but is it possible to discover a drug for each individual kinase? For every human kinase, we gathered data on their citation count, availability of chemical probes, approved and investigational drugs, PDB structures, and biochemical and cellular assays. Analysis of these factors highlights which kinase groups have a wealth of information available, and which groups still have room for progress. The data suggest a disproportionate focus on the more well characterized kinases while much of the kinome remains comparatively understudied. It is noteworthy that tool compounds for understudied kinases have already been developed, and there is still untapped potential for further development in this chemical space. Finally, this review discusses many of the different strategies employed to generate selectivity between kinases. Given the large volume of information available and the progress made over the past 20 years when it comes to drugging kinases, we believe it is possible to develop a tool compound for every human kinase. We hope this review will prove to be both a useful resource as well as inspire the discovery of a tool for every kinase.

The Kinase Chemogenomic Set (KCGS): An Open Science Resource for Kinase Vulnerability Identification.

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.

NEK Family Review and Correlations with Patient Survival Outcomes in Various Cancer Types.

The Never in Mitosis Gene A (NIMA)-related kinases (NEKs) are a group of serine/threonine kinases that are involved in a wide array of cellular processes including cell cycle regulation, DNA damage repair response (DDR), apoptosis, and microtubule organization. Recent studies have identified the involvement of NEK family members in various diseases such as autoimmune disorders, malignancies, and developmental defects. Despite the existing literature exemplifying the importance of the NEK family of kinases, this family of protein kinases remains understudied. This report seeks to provide a foundation for investigating the role of different NEKs in malignancies. We do this by evaluating the 11 NEK family kinase gene expression associations with patients' overall survival (OS) from various cancers using the Kaplan-Meier Online Tool (KMPlotter) to correlate the relationship between mRNA expression of NEK1-11 in various cancers and patient survival. Furthermore, we use the Catalog of Somatic Mutations in Cancer (COSMIC) database to identify NEK family mutations in cancers of different tissues. Overall, the data suggest that the NEK family has varying associations with patient survival in different cancers with tumor-suppressive and tumor-promoting effects being tissue-dependent.

MAP3K Family Review and Correlations with Patient Survival Outcomes in Various Cancer Types.

The mitogen-activated protein kinase (MAPK) pathways are ubiquitous in cellular signaling and are essential for proper biological functions. Disruptions in this signaling axis can lead to diseases such as the development of cancer. In this review, we discuss members of the MAP3K family and correlate their mRNA expression levels to patient survival outcomes in different cancers. Furthermore, we highlight the importance of studying the MAP3K family due to their important roles in the larger, overall MAPK pathway, relationships with cancer progression, and the understudied status of these kinases.

Expression of Novel Kinase MAP3K19 in Various Cancers and Survival Correlations.

Mitogen Activated Protein (MAP) kinases are a category of serine/threonine kinases that have been demonstrated to regulate intracellular events including stress responses, developmental processes, and cancer progression Although many MAP kinases have been extensively studied in various disease processes, MAP3K19 is an understudied kinase whose activities have been linked to lung disease and fibroblast development. In this manuscript, we use bioinformatics databases starBase, GEPIA, and KMPlotter, to establish baseline expressions of MAP3K19 in different tissue types and its correlation with patient survival in different cancers.

Pan-Cancer Analysis of Human Kinome Gene Expression and Promoter DNA Methylation Identifies Dark Kinase Biomarkers in Multiple Cancers.

Kinases are a group of intracellular signaling molecules that play critical roles in various biological processes. Even though kinases comprise one of the most well-known therapeutic targets, many have been understudied and therefore warrant further investigation. DNA methylation is one of the key epigenetic regulators that modulate gene expression. In this study, the human kinome's DNA methylation and gene expression patterns were analyzed using the level-3 TCGA data for 32 cancers. Unsupervised clustering based on kinome data revealed the grouping of cancers based on their organ level and tissue type. We further observed significant differences in overall kinase methylation levels (hyper- and hypomethylation) between the tumor and adjacent normal samples from the same tissue. Methylation expression quantitative trait loci (meQTL) analysis using kinase gene expression with the corresponding methylated probes revealed a highly significant and mostly negative association (~92%) within 1.5 kb from the transcription start site (TSS). Several understudied (dark) kinases (PKMYT1, PNCK, BRSK2, ERN2, STK31, STK32A, and MAPK4) were also identified with a significant role in patient survival. This study leverages results from multi-omics data to identify potential kinase markers of prognostic and diagnostic importance and further our understanding of kinases in cancer.

The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer.

The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment-the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively "drugged." Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app (http://cki.ccs.miami.edu) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study.