Protein kinases control cellular responses to environmental cues by swift and accurate signal processing. Breakdowns in this high-fidelity capability are a driving force in cancer and other diseases. Thus, our limited understanding of which amino acids in the kinase domain encode substrate specificity, the so-called determinants of specificity (DoS), constitutes a major obstacle in cancer signaling. Here, we systematically discover several DoS and experimentally validate three of them, named the αC1, αC3, and APE-7 residues. We demonstrate that DoS form sparse networks of non-conserved residues spanning distant regions. Our results reveal a likely role for inter-residue allostery in specificity and an evolutionary decoupling of kinase activity and specificity, which appear loaded on independent groups of residues. Finally, we uncover similar properties driving SH2 domain specificity and demonstrate how the identification of DoS can be utilized to elucidate a greater understanding of the role of signaling networks in cancer (Creixell et al., 2015 [this issue of Cell]).

Failure to clear antigens causes CD8⁺ T cells to become increasingly hypo-functional, a state known as exhaustion. We combined manually extracted information from published literature with gene expression data from diverse model systems to infer a set of molecular regulatory interactions that underpin exhaustion. Topological analysis and simulation modeling of the network suggests CD8⁺ T cells undergo 2 major transitions in state following stimulation. The time cells spend in the earlier pro-memory/proliferative (PP) state is a fixed and inherent property of the network structure. Transition to the second state is necessary for exhaustion. Combining insights from network topology analysis and simulation modeling, we predict the extent to which each node in our network drives cells towards an exhausted state. We demonstrate the utility of our approach by experimentally testing the prediction that drug-induced interference with EZH2 function increases the proportion of pro-memory/proliferative cells in the early days post-activation.

We implement a finite state machine by representing state, transition rules and input symbols with DNA components. Transitions between states are triggered by a clock signal which allows synchronized, parallel operation of two (or more) state machines. The state machine can be re-programmed by changing the input symbols.

Small molecule inhibitors of BRAF and MEK have proven effective at inhibiting tumor growth in melanoma patients, however this efficacy is limited due to the almost universal development of drug resistance. To provide advanced insight into the signaling responses that occur following kinase inhibition we have performed quantitative (phospho)-proteomics of human melanoma cells treated with either dabrafenib, a BRAF inhibitor; trametinib, a MEK inhibitor or SCH772984, an ERK inhibitor. Over nine experiments we identified 7827 class I phosphorylation sites on 4960 proteins. This included 54 phosphorylation sites that were significantly down-modulated after exposure to all three inhibitors, 34 of which have not been previously reported. Functional analysis of these novel ERK targets identified roles for them in GTPase activity and regulation, apoptosis and cell-cell adhesion. Comparison of the results presented here with previously reported phosphorylation sites downstream of ERK showed a limited degree of overlap suggesting that ERK signaling responses may be highly cell line and cue specific. In addition we identified 26 phosphorylation sites that were only responsive to dabrafenib. We provide further orthogonal experimental evidence for 3 of these sites in human embryonic kidney cells over-expressing BRAF as well as further computational insights using KinomeXplorer. The validated phosphorylation sites were found to be involved in actin regulation, which has been proposed as a novel mechanism for inhibiting resistance development. These results would suggest that the linearity of the BRAF-MEK-ERK module is at least context dependent.