Nienke Moret, Changchang Liu, Benjamin M. Gyori, John A. Bachman, Albert Steppi, Rahil Taujale, Liang-Chin Huang, Clemens Hug, Matt Berginski, Shawn Gomez, Natarajan Kannan, and Peter K. Sorger. 2020. “
Exploring the understudied human kinome for research and therapeutic opportunities.” bioRxiv.
Publisher's VersionAbstractThe functions of protein kinases have been heavily studied and inhibitors for many human kinases have been developed into FDA-approved therapeutics. A substantial fraction of the human kinome is nonetheless understudied. In this paper, members of the NIH Understudied Kinome Consortium mine public data on “dark” kinases to estimate the likelihood that they are functional. We start with a re-analysis of the human kinome and describe the criteria for creation of an inclusive set of 710 kinase domains and a curated set of 557 protein kinase like (PKL) domains. Nearly all PKLs are expressed in one or more CCLE cell lines and a substantial number are also essential in the Cancer Dependency Map. Dark kinases are frequently differentially expressed or mutated in The Cancer Genome Atlas and other disease databases and investigational and approved kinase inhibitors appear to inhibit them as off-target activities. Thus, it seems likely that the dark human kinome contains multiple biologically important genes, a subset of which may be viable drug targets.
Kee-Myoung Nam, Benjamin M. Gyori, Silviana V. Amethyst, Daniel J. Bates, and Jeremy Gunawardena. 2020. “
Robustness and parameter geography in post-translational modification systems.” PLOS Computational Biology, 16, 5, Pp. 1-50.
Publisher's VersionAbstractAuthor summary Biological organisms are often said to have robust properties but it is difficult to understand how such robustness arises from molecular interactions. Here, we use a mathematical model to study how the molecular mechanism of protein modification exhibits the property of multiple internal states, which has been suggested to underlie memory and decision making. The robustness of this property is revealed by the size and shape, or “geography,” of the parametric region in which the property holds. We use advances in reducing model complexity and in rapidly solving the underlying equations, to extensively sample parameter points in an 8-dimensional space. We find that under realistic molecular assumptions the size of the region is surprisingly small, suggesting that generating multiple internal states with such a mechanism is much harder than expected. While the shape of the region appears straightforward, we find surprising complexity in how the region grows with increasing amounts of the modified substrate. Our approach uses statistical analysis of data generated from a model, rather than from experiments, but leads to precise mathematical conjectures about parameter geography and biological robustness.