Tools for protein-protein interaction network analysis in cancer research

Proteomic-Based Discovery of Predictive Biomarkers for Drug Therapy Response and Personalized Medicine in Chronic Immune Thrombocytopenia

Purpose

ITP is the most prevalent autoimmune blood disorder. The lack of predictive biomarkers for therapeutic response is a major challenge for physicians caring of chronic ITP patients. This study is aimed at identifying predictive biomarkers for drug therapy responses.

Methods

2D gel electrophoresis (2-DE) was performed to find differentially expressed proteins. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS) analysis was performed to identify protein spots. The Cytoscape software was employed to visualize and analyze the protein-protein interaction (PPI) network. Then, enzyme-linked immunosorbent assays (ELISA) were used to confirm the results of the proteins detected in the blood. The DAVID online software was used to explore the Gene Ontology and pathways involved in the disease.

Results

Three proteins, including APOA1, GC, and TF, were identified as hub-bottlenecks and confirmed by ELISA. Enrichment analysis results showed the importance of several biological processes and pathway, such as the PPAR signaling pathway, complement and coagulation cascades, platelet activation, vitamin digestion and absorption, fat digestion and absorption, cell adhesion molecule binding, and receptor binding. Conclusion and Clinical Relevance. Our results indicate that plasma proteins (APOA1, GC, and TF) can be suitable biomarkers for the prognosis of the response to drug therapy in ITP patients.

Community detection in Epstein-Barr virus associated carcinomas and role of tyrosine kinase in etiological mechanisms for oncogenesis

BACKGROUND

Epstein-Barr virus (EBV) affects more than 90% of global population. The role of the virus in causing infectious mononucleosis (IM) affecting B-cells and epithelial cells and in the development of EBV associated cancers is well documented. Investigating the associated interactions can pave way for the discovery of novel therapeutic targets for EBV associated lymphoproliferative (Burkitt's Lymphoma and Hodgkin's Lymphoma) and non-lymphoproliferative diseases (Gastric cancer and Nasopharyngeal cancer).

METHODS

Based on the DisGeNET (v7.0) data set, we constructed a disease-gene network to identify genes that are involved in various carcinomas, viz. Gastric cancer (GC), Nasopharyngeal cancer (NPC), Hodgkin's lymphoma (HL) and Burkitt's lymphoma (BL). We identified communities in the disease-gene network and performed functional enrichment using over-representation analysis to detect significant biological processes/pathways and the interactions between them.

RESULT

We identified the modular communities to explore the relation of this common causative pathogen (EBV) with different carcinomas such as GC, NPC, HL and BL. Through network analysis we identified the top 10 genes linked with EBV associated carcinomas as CASP10, BRAF, NFKBIA, IFNA2, GSTP1, CSF3, GATA3, UBR5, AXIN2 and POLE. Further, the tyrosine-protein kinase (ABL1) gene was significantly over-represented in 3 out of 9 critical biological processes, viz. in regulatory pathways in cancer, the TP53 network and the Imatinib and chronic myeloid leukemia biological processes. Consequently, the EBV pathogen appears to target critical pathways involved in cellular growth arrest/apoptosis. We make our case for BCR-ABL1 tyrosine-kinase inhibitors (TKI) for further clinical investigations in the inhibition of BCR-mediated EBV activation in carcinomas for better prognostic and therapeutic outcomes.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Protein–Protein Interaction (PPI) Network of Zebrafish Oestrogen Receptors: A Bioinformatics Workflow

Protein–protein interaction (PPI) is involved in every biological process that occurs within an organism. The understanding of PPI is essential for deciphering the cellular behaviours in a particular organism. The experimental data from PPI methods have been used in constructing the PPI network. PPI network has been widely applied in biomedical research to understand the pathobiology of human diseases. It has also been used to understand the plant physiology that relates to crop improvement. However, the application of the PPI network in aquaculture is limited as compared to humans and plants. This review aims to demonstrate the workflow and step-by-step instructions for constructing a PPI network using bioinformatics tools and PPI databases that can help to predict potential interaction between proteins. We used zebrafish proteins, the oestrogen receptors (ERs) to build and analyse the PPI network. Thus, serving as a guide for future steps in exploring potential mechanisms on the organismal physiology of interest that ultimately benefit aquaculture research.

Melatonin reduces proliferation and promotes apoptosis of bladder cancer cells by suppressing O-GlcNAcylation of cyclin-dependent-like kinase 5

Melatonin helps to maintain circadian rhythm, exerts anticancer activity, and plays key roles in regulation of glucose homeostasis and energy metabolism. Glycosylation, a form of metabolic flux from glucose or other monosaccharides, is a common post-translational modification. Dysregulated glycosylation, particularly O-GlcNAcylation, is often a biomarker of cancer cells. In this study, elevated O-GlcNAc level in bladder cancer was inhibited by melatonin treatment. Melatonin treatment inhibited proliferation and migration and enhanced apoptosis of bladder cancer cells. Proteomic analysis revealed reduction in cyclin-dependent-like kinase 5 (CDK5) expression by melatonin. O-GlcNAc modification determined the conformation of critical T-loop domain on CDK5 and further influenced the CDK5 stability. The mechanism whereby melatonin suppressed O-GlcNAc level was based on decreased glucose uptake and metabolic flux from glucose to UDP-GlcNAc, and consequent reduction in CDK5 expression. Melatonin treatment, inhibition of O-GlcNAcylation by OSMI-1, or mutation of key O-GlcNAc site strongly suppressed in vivo tumor growth. Our findings indicate that melatonin reduces proliferation and promotes apoptosis of bladder cancer cells by suppressing O-GlcNAcylation of CDK5.

Three-Dimensional Interfacing of Cells with Hierarchical Silicon Nano/Microstructures for Midinfrared Interrogation of In Situ Captured Proteins

Label-free optical detection of biomolecules is currently limited by a lack of specificity rather than sensitivity. To exploit the much more characteristic refractive index dispersion in the mid-infrared (IR) regime, we have engineered three-dimensional IR-resonant silicon micropillar arrays (Si-MPAs) for protein sensing. By exploiting the unique hierarchical nano- and microstructured design of these Si-MPAs attained by CMOS-compatible silicon-based microfabrication processes, we achieved an optimized interrogation of surface protein binding. Based on spatially resolved surface functionalization, we demonstrate controlled three-dimensional interfacing of mammalian cells with Si-MPAs. Spatially controlled surface functionalization for site-specific protein immobilization enabled efficient targeting of soluble and membrane proteins into sensing hotspots directly from cells cultured on Si-MPAs. Protein binding to Si-MPA hotspots at submonolayer level was unambiguously detected by conventional Fourier transform IR spectroscopy. The compatibility with cost-effective CMOS-based microfabrication techniques readily allows integration of this novel IR transducer into fully fledged bioanalytical microdevices for selective and sensitive protein sensing.

Network analysis reveals essential proteins that regulate sodium-iodide symporter expression in anaplastic thyroid carcinoma

Anaplastic thyroid carcinoma (ATC) is the most rare and lethal form of thyroid cancer and requires effective treatment. Efforts have been made to restore sodium-iodide symporter (NIS) expression in ATC cells where it has been downregulated, yet without complete success. Systems biology approaches have been used to simplify complex biological networks. Here, we attempt to find more suitable targets in order to restore NIS expression in ATC cells. We have built a simplified protein interaction network including transcription factors and proteins involved in MAPK, TGFβ/SMAD, PI3K/AKT, and TSHR signaling pathways which regulate NIS expression, alongside proteins interacting with them. The network was analyzed, and proteins were ranked based on several centrality indices. Our results suggest that the protein interaction network of NIS expression regulation is modular, and distance-based and information-flow-based centrality indices may be better predictors of important proteins in such networks. We propose that the high-ranked proteins found in our analysis are expected to be more promising targets in attempts to restore NIS expression in ATC cells.

A multidimensional systems biology analysis of cellular senescence in aging and disease

BACKGROUND

Cellular senescence, a permanent state of replicative arrest in otherwise proliferating cells, is a hallmark of aging and has been linked to aging-related diseases. Many genes play a role in cellular senescence, yet a comprehensive understanding of its pathways is still lacking.

RESULTS

We develop CellAge (http://genomics.senescence.info/cells), a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build cellular senescence protein-protein interaction and co-expression networks. Clusters in the networks are enriched for cell cycle and immunological processes. Network topological parameters also reveal novel potential cellular senescence regulators. Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence.

CONCLUSIONS

Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence.

Discovering Innovative Drugs Targeting Both Cancer and Cardiovascular Disease by Shared Protein-Protein Interaction Network Analyses

Cancer and cardiovascular disease (CVD) have a common co-occurrence. Both diseases display overlapping pathophysiology and risk factors, suggesting shared biological mechanisms. Conditions such as obesity, diabetes, hypertension, smoking, poor diet, and inadequate physical activity can cause both heart disease and cancer. The burgeoning field of onco-cardiology aims to develop diagnostics and innovative therapeutics for both diseases through targeting shared mechanisms and molecular targets. In this overarching context, this expert review presents an analysis of the protein-protein interaction (PPI) networks for onco-cardiology drug discovery. Several PPI complexes such as MDM2-TP53 and CDK4-pRB have been studied for their tumor-suppressive functions. In addition, XIAP-SMAC, RAC1-GEF, Sur-2ESX, and TP53-BRCA1 are other PPI complexes that offer potential breakthrough for onco-cardiology therapeutics innovation. As both cancer and CVD share biological mechanisms to a certain degree, the PPI network analyses for onco-cardiology drug discovery are promising for addressing comorbid diseases in the spirit of systems medicine. We discuss the emerging architecture of PPI networks in cancer and CVD and prospects and challenges for their exploitation toward therapeutics applications. Finally, we emphasize that PPIs that were once thought to be undruggable have become potential new class of innovative drug targets.

Cellular energy stress induces AMPK-mediated regulation of glioblastoma cell proliferation by PIKE-A phosphorylation

Phosphoinositide 3-kinase enhancer-activating Akt (PIKE-A), which associates with and potentiates Akt activity, is a pro-oncogenic factor that play vital role in cancer cell survival and growth. However, PIKE-A physiological functions under energy/nutrient deficiency are poorly understood. The AMP-activated protein kinase (AMPK) is an evolutionarily conserved serine/threonine kinase that is a principal regulator of energy homeostasis and has a critical role in metabolic disorders and cancers. In this present study, we show that cellular energy stress induces PIKE-A phosphorylation mediated by AMPK activation, thereby preventing its carcinogenic action. Moreover, AMPK directly phosphorylates PIKE-A Ser-351 and Ser-377, which become accessible for the interaction with 14-3-3β, and in turn stimulates nuclear translocation of PIKE-A. Nuclear PIKE-A associates with CDK4 and then disrupts CDK4-cyclinD1 complex and inhibits the Rb pathway, resulting in cancer cell cycle arrest. Our data uncover a molecular mechanism and functional significance of PIKE-A phosphorylation response to cellular energy status mediated by AMPK.

Random Matrix Analysis for Gene Interaction Networks in Cancer Cells

Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding the disease. Although cancer is considered to originate from the topological alteration of a huge molecular interaction network in cellular systems, the theoretical study to investigate such complex networks is still insufficient. It is necessary to predict the behavior of a huge complex interaction network from the behavior of a finite size network. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings P(s) of interaction matrices of gene networks in human cancer cells. The interaction matrices are computed using the Cancer Network Galaxy (TCNG) database which is a repository of gene interactions inferred by a Bayesian network model. 256 NCBI GEO entries regarding gene expressions in human cancer cells have been used for the inference. We observe the Wigner distribution of P(s) when the gene networks are dense networks that have more than ~38,000 edges. In the opposite case, when the networks have smaller numbers of edges, the distribution P(s) becomes the Poisson distribution. We investigate relevance of P(s) both to the sparseness of the networks and to edge frequency factor which is the reliance (likelihood) of the inferred gene interactions.

Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer

Background

Recent studies have shown that the crosstalk between microRNA (miRNA) sponges plays an important role in human cancers. However, the co-regulation roles of miRNA sponges in protein-protein interactions (PPIs) are still unknown.

Results

In this study, we propose a multi-step method called miRSCoPPI to infer miRNA sponge co-regulation of PPIs. We focus on investigating breast cancer (BRCA) related miRNA sponge co-regulation, by integrating heterogeneous data, including miRNA, long non-coding RNA (lncRNA) and messenger RNA (mRNA) expression data, experimentally validated miRNA-target interactions, PPIs and lncRNA-target interactions, and the list of breast cancer genes. We find that the inferred BRCA-related miRSCoPPI network is highly connected and scale free. The top 10% hub genes in the BRCA-related miRSCoPPI network have potential biological implications in breast cancer. By utilizing a graph clustering method, we discover 17 BRCA-related miRSCoPPI modules. Through pathway enrichment analysis of the modules, we find that several modules are significantly enriched in pathways associated with breast cancer. Moreover, 10 modules have good performance in classifying breast tumor and normal samples, and can act as module signatures for prognostication. By using putative computationally predicted miRNA-target interactions, we have consistent results with those obtained using experimentally validated miRNA-target interactions, indicating that miRSCoPPI is robust in inferring miRNA sponge co-regulation of PPIs in human breast cancer.

Conclusions

Taken together, the results demonstrate that miRSCoPPI is a promising tool for inferring BRCA-related miRNA sponge co-regulation of PPIs and it can help with the understanding of the co-regulation roles of miRNA sponges on the PPIs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1672-2) contains supplementary material, which is available to authorized users.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa.

A novel framework for inferring condition-specific TF and miRNA co-regulation of protein-protein interactions

Recent studies have shown that transcription factors (TFs) and microRNAs (miRNAs), while independently regulate their downstream targets, collaborate with each other to regulate gene expression. However, their synergistic roles in protein-protein interactions (PPIs) remain mostly unknown. In this paper, we present a novel framework (called CoRePPI) for inferring TF and miRNA co-regulation of PPIs. Particularly, CoRePPI is aimed at discovering the co-regulation specific to a condition of interest, by using heterogeneous data, including miRNA and messenger RNA (mRNA) expression profiles, putative miRNA targets, TF targets and PPIs. CoRePPI firstly finds the network motifs indicating the co-regulation of PPIs by TFs and miRNAs in tumor and normal conditions separately. Then by identifying the differential motifs found in one condition but not in the other, it builds the networks consisting of TFs, miRNAs and their co-regulated PPIs specific to different conditions respectively. To validate CoRePPI, we apply it to the Pan-Cancer dataset which includes the expression profiles of 12 cancer types from TCGA. Through network topology analysis, we found that the tumor and normal CoRePPI networks are scale-free. Furthermore, the results of differential and intersected network analysis between the tumor and normal CoRePPI networks suggest that only a small fraction of the regulatory relationships between TFs and miRNAs are conserved in both conditions but they co-regulate different downstream PPIs in tumor and normal conditions; and in different conditions the majority of the regulatory relationships between TFs and miRNAs are different although they may regulate the same PPIs in their respective conditions. The CoRePPI sub-networks constructed for the three types of cancers (breast cancer, lung cancer and ovarian cancer) are all scale-free, and the intersection of these CoRePPI sub-networks can be utilized as the biomarker CoRePPI sub-network of the three types of cancers. The PPI enrichment analyses of the tumor and normal CoRePPI networks suggest that the co-regulating TFs and miRNAs are significantly associated with the specific biological processes, diseases and pathways. In addition, comparing with the two non-condition-specific approaches, the tumor CoRePPI network is found to have the most enriched cancer-related PPIs. Altogether, the results uncover the combined regulatory patterns of TFs and miRNAs on the PPIs, and may provide new insights for research in cancer-associated TFs and miRNAs.

Network Topologies Decoding Cervical Cancer

According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.

Proteins interaction network and modeling of IGVH mutational status in chronic lymphocytic leukemia

Background

Chronic lymphocytic leukemia (CLL) is an incurable malignancy of mature B-lymphocytes, characterized as being a heterogeneous disease with variable clinical manifestation and survival. Mutational statuses of rearranged immunoglobulin heavy chain variable (IGVH) genes has been consider one of the most important prognostic factors in CLL, but despite of its proven value to predict the course of the disease, the regulatory programs and biological mechanisms responsible for the differences in clinical behavior are poorly understood.

Methods

In this study, (i) we performed differential gene expression analysis between the IGVH statuses using multiple and independent CLL cohorts in microarrays platforms, based on this information, (ii) we constructed a simplified protein-protein interaction (PPI) network and (iii) investigated its structure and critical genes. This provided the basis to (iv) develop a Boolean model, (v) infer biological regulatory mechanism and (vi) performed perturbation simulations in order to analyze the network in dynamic state.

Results

The result of topological analysis and the Boolean model showed that the transcriptional relationships of IGVH mutational status were determined by specific regulatory proteins (PTEN, FOS, EGR1, TNF, TGFBR3, IFGR2 and LPL). The dynamics of the network was controlled by attractors whose genes were involved in multiple and diverse signaling pathways, which may suggest a variety of mechanisms related with progression occurring over time in the disease. The overexpression of FOS and TNF fixed the fate of the system as they can activate important genes implicated in the regulation of process of adhesion, apoptosis, immune response, cell proliferation and other signaling pathways related with cancer.

Conclusion

The differences in prognosis prediction of the IGVH mutational status are related with several regulatory hubs that determine the dynamic of the system.

Electronic supplementary material

The online version of this article (doi:10.1186/s12976-015-0008-z) contains supplementary material, which is available to authorized users.

Identification of intrinsically disordered regions in PTEN and delineation of its function via a network approach

Intrinsically disordered proteins (IDPs) are proteins that lack stable higher order structures for the entire protein molecule or a significant portion of it. The discovery of IDPs evolved as an antithesis to the conventional structure-function paradigm wherein a higher order structure dictates protein function. Over the last decade, a number of proteins with functionally relevant unstructured regions have been discovered, which includes tumor suppressor PTEN. The protein domains that lack structure provide "hot-spots" for post-translational modifications (PTMs) and protein-protein interactions (PPIs), which facilitate their regulation and participation in multiple cellular processes. Consequently, dysregulation in IDPs contribute to aberrant cellular pathophysiology. Herein, we present PTEN and its translational isoform PTEN-L as a hybrid protein possessing ordered domain and intrinsically disordered C-terminal and an N-terminal tails. We review the role of intrinsic disorder in PTEN function and propose a methodology for the use of intrinsic disorder to study PTEN-regulated higher order protein-networks by associating basic principles of network biology to functional pathway analysis at the systems level.

Cancer networks and beyond: interpreting mutations using the human interactome and protein structure

Over recent years, with the advances in next-generation sequencing, a large number of cancer mutations have been identified and accumulated in public repositories. Coupled to this is our increased ability to generate detailed interactome maps that help to enrich our knowledge of the biological implications of cancer mutations. As a result, network analysis approaches have become an invaluable tool to predict and interpret mutations that are associated with tumour survival and progression. Our understanding of cancer mechanisms is further enhanced by mapping protein structure information to such networks. Here we review the current methodologies for annotating the functional impacts of cancer mutations, which range from analysis of protein structures to protein-protein interaction network studies.

The roles of PIKE in tumorigenesis

Tumorigenesis is the process by which normal cells evolve the capacity to evade and overcome the constraints usually placed upon their growth and survival. To ensure the integrity of organs and tissues, the balance of cell proliferation and cell death is tightly maintained. The proteins controlling this balance are either considered oncogenes, which promote tumorigenesis, or tumor suppressors, which prevent tumorigenesis. Phosphoinositide 3-kinase enhancer (PIKE) is a family of GTP-binding proteins that possess anti-apoptotic functions and play an important role in the central nervous system. Notably, accumulating evidence suggests that PIKE is a proto-oncogene involved in tumor progression. The PIKE gene (CENTG1) is amplified in a variety of human cancers, leading to the resistance against apoptosis and the enhancement of invasion. In this review, we will summarize the functions of PIKE proteins in tumorigenesis and discuss their potential implications in cancer therapy.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.