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Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations.

ABSTRACT: The Ras/MAPK syndromes ('RASopathies') are a class of developmental disorders caused by germline mutations in 15 genes encoding proteins of the Ras/mitogen-activated protein kinase (MAPK) pathway frequently involved in cancer. Little is known about the molecular mechanisms underlying the differences in mutations of the same protein causing either cancer or RASopathies. Here, we shed light on 956 RASopathy and cancer missense mutations by combining protein network data with mutational analyses based on 3D structures. Using the protein design algorithm FoldX, we predict that most of the missense mutations with destabilising energies are in structural regions that control the activation of proteins, and only a few are predicted to compromise protein folding. We find a trend that energy changes are higher for cancer compared to RASopathy mutations. Through network modelling, we show that partly compensatory mutations in RASopathies result in only minor downstream pathway deregulation. In summary, we suggest that quantitative rather than qualitative network differences determine the phenotypic outcome of RASopathy compared to cancer mutations.

SUBMITTER: Kiel C

PROVIDER: S-EPMC4188041 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations.

Kiel Christina C Serrano Luis L

Molecular systems biology 20140506

The Ras/MAPK syndromes ('RASopathies') are a class of developmental disorders caused by germline mutations in 15 genes encoding proteins of the Ras/mitogen-activated protein kinase (MAPK) pathway frequently involved in cancer. Little is known about the molecular mechanisms underlying the differences in mutations of the same protein causing either cancer or RASopathies. Here, we shed light on 956 RASopathy and cancer missense mutations by combining protein network data with mutational analyses ba ...[more]

PMID: 24803665

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Project description:Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.

Project description:BackgroundDevelopment of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains.ResultsIn this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a fivefold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein-protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein-protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy.ConclusionsIn this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph ). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

Project description:BackgroundRASopathies are a group of syndromes showing clinical overlap caused by mutations in genes affecting the RAS-MAPK pathway. Consequent disruption on cellular signaling leads and is driven by phosphoproteome remodeling. However, we still lack a comprehensive picture of the different key players and altered downstream effectors.MethodsAn in silico interactome of RASopathy proteins was generated using pathway enrichment analysis/STRING tool, including identification of main hub proteins. We also integrated phosphoproteomic and immunoblotting studies using previous published information on RASopathy proteins and their neighbors in the context of RASopathy syndromes. Data from Phosphosite database ( www.phosphosite.org ) was collected in order to obtain the potential phosphosites subjected to regulation in the 27 causative RASopathy proteins. We compiled a dataset of dysregulated phosphosites in RASopathies, searched for commonalities between syndromes in harmonized data, and analyzed the role of phosphorylation in the syndromes by the identification of key players between the causative RASopathy proteins and the associated interactome.ResultsIn this study, we provide a curated data set of 27 causative RASopathy genes, identify up to 511 protein-protein associations using pathway enrichment analysis/STRING tool, and identify 12 nodes as main hub proteins. We found that a large group of proteins contain tyrosine residues and their biological processes include but are not limited to the nervous system. Harmonizing published RASopathy phosphoproteomic and immunoblotting studies we identified a total of 147 phosphosites with increased phosphorylation, whereas 47 have reduced phosphorylation. The PKB signaling pathway is the most represented among the dysregulated phosphoproteins within the RASopathy proteins and their neighbors, followed by phosphoproteins implicated in the regulation of cell proliferation and the MAPK pathway.ConclusionsThis work illustrates the complex network underlying the RASopathies and the potential of phosphoproteomics for dissecting the molecular mechanisms in these syndromes. A combined study of associated genes, their interactome and phosphorylation events in RASopathies, elucidates key players and mechanisms to direct future research, diagnosis and therapeutic windows.

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Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations.

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Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations.

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