 My name is Anna Penchinger and I am an associate investigator of the National Center for Biotechnology Information of the National Institute of Health. And today I am going to present our paper, Publishing Human Mutation, with my co-authors Kosaka Kisimoto and Inga Ragoism. In the paper is entitled, Oncogenic Potential is related to activating effect of cancer with single and double somatic mutations in receptor tyrosine kinases. Receptor tyrosine kinases transuse signals from the extracellular matrix to the cytoplasm and they contain extracellular, transmembrane, juxtamembrane and catalytic kinase domains. Binding of ligand by the extracellular regions may induce demerization, cross-phase fibrillation and initiate the cascade of events which lead to regulation of gene expression implicated in cell division, cellular homeostasis and survival. It has been shown that kinases especially receptor tyrosine kinases are frequently mutated and activated in cancer and only a small fraction of these mutations is inherited. Due to the efforts of structural biologists, structures of different receptor tyrosine kinases were solved in active and inactive states. Structural analysis showed significant differences in conformations between the active and inactive forms of kinase domains and suggested that mechanisms of activation are linked to transitions between active and inactive conformations. However, the role of many mutations in the activation of kinases still remains unclear. In this work, we examined the energetic effect of cancer mutations on both active and inactive conformations of kinase domain in relation to the oncogenic potential in order to quantify the coupling between mutations, kinase stability and activity. First, we performed the analysis of the mutation spectra and tried to classify the mutation sites according to different mutation probabilities, debounding that each site should belong to only one class of mutations. The mutation spectrum of each class was approximated by the Poisson distribution and variations in mutation frequencies among sites of the same class were assumed to be due to the random reasons. An analysis of the mutation spectrum of different receptor tyrosine kinases revealed four classes of sites. But what is important here that the second class did not contain mutation hard spots since numerous sites with no mutations were also included in this class. While several obvious hard spots were present in the third and fourth class of sites, and thus we chose the threshold of 10 mutations or 10 samples for determining the mutation hard spots. Therefore, we separated all mutations into classes based on their frequency in the samples into class A, class B and class C. Next we measured the effect of the mutations, first of single mutations, on the stability and activity of the kinase domain. We collected pairs of structures of the same receptor tyrosine kinase in active and inactive states and mapped about 400 unique mutations onto active and inactive structures. Then we calculated the difference in unfolding free energy between wild type and mutated states. This figure shows the distribution of delta-delta-g values for active states. And you can see from this figure that all three distributions for active states are shifted to the positive values which indicate that mutations destabilize active structures. It's true that inactive structures, only mutations from class C destabilizing active structures significantly compare to random mutations. This figure shows the distribution of the difference in the unfolding free energy between the active and inactive states upon mutations. Positive values correspond to the tendency for the activation by mutation. And you can see from this figure that the distribution of class C is shifted to the positive side while mutations from the A and B classes do not show a significant effect on the stability compared to random mutations. All this indicates that the frequently observed mutations with the number of samples of 10 and higher have a different effect on the active and inactive states leading to the increased population of active states and overall activation of the kinase. In summary, here we showed that single mutations destabilize active states but to a lesser degree than the random mutations. Moreover, to a lesser degree than the single mutations destabilized inactive states, overall this led to kinase activation. Next, we tried to answer the question if we can predict the activating effect of mutations from the mutation spectra. In importantly, we showed that there exists a relationship between the statistics-based estimate of oncogenic potential of mutation and its activation effect. Namely, more frequent mutations have a higher activating effect. This effect is not linear though and for frequent mutations from 10 or more samples, the activity increases radically upon mutations. We also analyzed double mutations when two mutations occurred in the same gene, in the same patient. This figure shows distribution of similar properties of difference of double mutations on active and inactive states. And this third figure shows the distribution corresponding to cancel double mutations is significantly shifted to positive values compared to random double mutations, which points in turn to a tendency for activation for doublet. Moreover, we found a positive epistasis for double mutations. Indeed, activation effect of double mutations was higher than the total of individual single mutations. This suggests that overall double mutations have a synergetic effect and this trend is especially pronounced for double mutations observed in more than one sample. The observed and simulated spectra of double mutations were found to be statistically different from each other, which points to the different mechanisms of their origin. In summary, in our study, we attempted to link the stability of receptor tyrosine kinases with their oncogenic potential and differential activity, which combined with other experimental data may provide insight into the mechanisms of activation of different pathways by cancer mutations and may help to design effective cancer drugs.