Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer

Abstract

Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the “guilt-by-association” principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

“…BLAST scores were used as the edge metric for full sequence‐based networks, and TM‐Align scores as the edge metric for structure‐based networks. Both of these methods have previously been used as metrics for sequence and structural comparison, respectively. For active site comparison, we used active site profiling, a method previously developed to identify and quantitatively compare active site microenvironments .…”

Construction and Analysis of Protein-Protein Interaction Network to Identify the Molecular Mechanism in Laryngeal Cancer

“…This is due to the well-established notion that a disease, especially cancer, is usually a result of complex interactions and communications within a large set of proteins and other molecules. Such integrative analysis has been applied to study lung cancer (Wu et al 2016), brain cancer (Engin et al 2013), prostate cancer (Chen et al 2016), and non-cancer diseases such as diabetes (Vyas et al 2016) and polycystic ovarian syndrome (PCOS) (Mohamed-Hussein & Harun 2009). In this study, we analysed laryngeal cancer-related protein-protein interaction (PPI) networks to improve our understanding of the complexity of the molecular pathways that underlie laryngeal cancer to identify the dynamic processes involved in cancer progression.…”

ERRATUM: "Identifying Mutation Specific Cancer Pathways Using a Structurally Resolved Protein Interaction Network"

“…Recently, several groups have published high-resolution three-dimensional (3D) PPI networks [8][9][10] that include the molecular details of binding interfaces. Applications of these to investigate inherited disease mutations [11][12][13][14][15] have suggested that a) nsSNVs located at protein interfaces result in distinct phenotypes from those located in the protein core 9,10 , b) known disease associated variants outside of the core are enriched at residues participating in protein interaction interfaces 10 c) in particular, in-frame disease mutations are enriched at interface regions of interacting proteins 9 and d) disease mutations at distinct interfaces of the same protein can be associated with distinct disease phenotypes 9 . In cancer, 3D location of mutations at an interface has served as evidence that protein interactions may be important for metastasis site determination.…”