Kornev, A. P. | Search bioRxiv

bioRxiv2022

Calculation of centralities in protein kinase A

Topological analysis of amino acid networks is a common method that can help to understand the roles of individual residues. The most popular approach for network construction is to create a connection between residues if they interact. These interactions are usually weighted by absolute values of correlation coefficients or mutual information. Here we argue that connections in such networks have to reflect levels of cohesion within the protein instead of a simple fact of interaction between residues. If this is correct, an indiscriminate combination of correlation and anti-correlation, as well as the all-inclusive nature of the mutual information metrics, should be detrimental for the analysis. To test our hypothesis, we studied amino acid networks of the protein kinase A created by Local Spatial Pattern alignment, a method that can detect conserved patterns formed by C-C{beta} vectors. Our results showed that, in comparison with the traditional methods, this approach is more efficient in detecting functionally important residues. Out of four studied centrality metrics, Closeness centrality was the least efficient measure of residue importance. Eigenvector centrality proved to be ineffective as the spectral gap values of the networks were very low due to the bilobal structure of the kinase. We recommend using joint graphs of Betweenness centrality and Degree centrality to visualize different aspects of amino acid roles. Author Summary Protein structures can be viewed as networks of residues with some of them being a part of highly interconnected hubs and some being connectors between the hubs. Analysis of these networks can be helpful for understanding of possible roles of single amino acids. In this paper, we challenged existing methods for the creation of such networks. A traditional way is to connect residues if they can interact. We propose that residues should be connected only if they retain their mutual positions in space during molecular dynamic simulation, that is they move cohesively. We show that this approach improves the efficiency of the analysis indicating that a significant revision of the existing views on amino acid networks is necessary.

bioinformatics↗

bioRxiv2021

Protein Kinase C gamma Mutations Drive Spinocerebellar Ataxia Type 14 by Impairing Autoinhibition

Spinocerebellar ataxia type 14 (SCA14) is a neurodegenerative disease caused by germline variants in the diacylglycerol (DG)/Ca2+-regulated protein kinase C gamma (PKC{gamma}), leading to Purkinje cell degeneration and progressive cerebellar dysfunction. The majority of the approximately 50 identified variants cluster to the DG-sensing C1 domains. Here, we use a FRET- based activity reporter to show that ataxia-associated PKC{gamma} mutations enhance basal activity by compromising autoinhibition. Although impaired autoinhibition generally leads to PKC degradation, the C1 domain mutations protect PKC{gamma} from phorbol ester-induced downregulation. Furthermore, it is the degree of disrupted autoinhibition, not changes in the amplitude of agonist- stimulated activity, that correlate with disease severity. This enhanced basal signaling rewires the brain phosphoproteome, as assessed by phosphoproteomic analysis of cerebella from mice expressing a human PKC{gamma} transgene harboring a SCA14 C1 domain mutation, H101Y. Validating that the pathology arises from disrupted autoinhibition, we show that the degree of impaired autoinhibition correlates inversely with age of disease onset in patients: mutations that cause high basal activity are associated with early onset, whereas those that only modestly increase basal activity, including a previously undescribed variant, D115Y, are associated with later onset. Molecular modeling indicates that almost all SCA14 variants that are not in the C1 domains are at interfaces with the C1B domain, and bioinformatics analysis reveals that variants in the C1B domain are under-represented in cancer. Thus, clustering of SCA14 variants to the C1B domain provides a unique mechanism to enhance PKC{gamma} basal activity while protecting the enzyme from downregulation, deregulating the cerebellar phosphoproteome. One Sentence SummarySCA14 driver mutations in PKC{gamma} impair autoinhibition, with defect correlating inversely with age of disease onset.

biochemistry↗