Dataset Information

Protein interaction disruption in cancer.

ABSTRACT: BACKGROUND:Most methods that integrate network and mutation data to study cancer focus on the effects of genes/proteins, quantifying the effect of mutations or differential expression of a gene and its neighbors, or identifying groups of genes that are significantly up- or down-regulated. However, several mutations are known to disrupt specific protein-protein interactions, and network dynamics are often ignored by such methods. Here we introduce a method that allows for predicting the disruption of specific interactions in cancer patients using somatic mutation data and protein interaction networks. METHODS:We extend standard network smoothing techniques to assign scores to the edges in a protein interaction network in addition to nodes. We use somatic mutations as input to our modified network smoothing method, producing scores that quantify the proximity of each edge to somatic mutations in individual samples. RESULTS:Using breast cancer mutation data, we show that predicted edges are significantly associated with patient survival and known ligand binding site mutations. In-silico analysis of protein binding further supports the ability of the method to infer novel disrupted interactions and provides a mechanistic explanation for the impact of mutations on key pathways. CONCLUSIONS:Our results show the utility of our method both in identifying disruptions of protein interactions from known ligand binding site mutations, and in selecting novel clinically significant interactions. Supporting website with software and data: https://www.cs.cmu.edu/~mruffalo/mut-edge-disrupt/ .

SUBMITTER: Ruffalo M

PROVIDER: S-EPMC6823625 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Protein interaction disruption in cancer.

Ruffalo Matthew M Bar-Joseph Ziv Z

BMC cancer 20190423 1

<h4>Background</h4>Most methods that integrate network and mutation data to study cancer focus on the effects of genes/proteins, quantifying the effect of mutations or differential expression of a gene and its neighbors, or identifying groups of genes that are significantly up- or down-regulated. However, several mutations are known to disrupt specific protein-protein interactions, and network dynamics are often ignored by such methods. Here we introduce a method that allows for predicting the d ...[more]

PMID: 31014259

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Project description:Lactate dehydrogenase A (LDHA) is a critical metabolic enzyme belonging to a family of 2-hydroxy acid oxidoreductases that plays a key role in anaerobic metabolism in the cells. In hypoxia condition, the overexpression of LDHA shifts the metabolic pathway of ATP synthesis from oxidative phosphorylation to aerobic glycolysis and the hypoxia condition is a common phenomenon occurred in the microenvironment of tumor cells; therefore, the inhibition of LDHA is considered to be an excellent strategy for cancer therapy. In this study, we employed in silico methods to design inhibitory peptides for lactate dehydrogenase through the disturbance in tetramerization of the enzyme. Using peptide as an anti-cancer agent is a novel approach for cancer therapy possessing some advantages with respect to the chemotherapeutic drugs such as low toxicity, ease of synthesis, and high target specificity. So peptides can act as appropriate enzyme inhibitor in parallel to chemical compounds. In this study, several computational techniques such as molecular dynamics (MD) simulation, docking and MM-PBSA calculation have been employed to investigate the structural characteristics of the monomer, dimer, and tetramer forms of the enzyme. Analysis of MD simulation and protein-protein interaction showed that the N-terminal arms of each subunit have an important role in enzyme tetramerization to establish active form of the enzyme. Hence, N-terminal arm can be used as a template for peptide design. Then, peptides were designed and evaluated to obtain best binders based on the affinity and physicochemical properties. Finally, the inhibitory effect of the peptides on subunit association was measured by dynamic light scattering (DLS) technique. Our results showed that the designed peptides which mimic the N-terminal arm of the enzyme can successfully target the C-terminal domain and interrupt the bona fide form of the enzyme subunits. The result of this study makes a new avenue to disrupt the assembly process and thereby oppress the function of the LDHA.

Dataset Information

Protein interaction disruption in cancer.

Publications

Protein interaction disruption in cancer.

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