Chapter 4: Protein interactions and disease

Extracellular Matrix Derived From Dental Pulp Stem Cells Promotes Mineralization

Background: Extracellular matrix (ECM) plays a pivotal role in many physiological processes. ECM macromolecules and associated factors differ according to tissues, impact cell differentiation, and tissue homeostasis. Dental pulp ECM may differ from other oral tissues and impact mineralization. Thus, the present study aimed to identify the matrisome of ECM proteins derived from human dental pulp stem cells (DPSCs) and its ability to regulate mineralization even in cells which do not respond to assaults by mineralization, the human gingival fibroblasts (GF).

Methods: ECM were extracted from DPSCs cultured in normal growth medium supplemented with L-ascorbic acid (N-ECM) or in osteogenic induction medium (OM-ECM). ECM decellularization (dECM) was performed using 0.5% triton X-100 in 20 mM ammonium hydroxide after 21 days. Mass spectrometry and proteomic analysis identified and quantified matrisome proteins.

Results: The dECM contained ECM proteins but lacked cellular components and mineralization. Interestingly, collagens (COL6A1, COL6A2, and COL6A3) and elastic fibers (FBN1, FBLN2, FN1, and HSPG2) were significantly represented in N-ECM, while annexins (ANXA1, ANXA4, ANXA5, ANXA6, ANXA7, and ANXA11) were significantly overdetected in OM-ECM. GF were reseeded on N-dECM and OM-dECM and cultured in normal or osteogenic medium. GF were able to attach and proliferate on N-dECM and OM-dECM. Both dECM enhanced mineralization of GF at day 14 compared to tissue culture plate (TCP). In addition, OM-dECM promoted higher mineralization of GF than N-dECM although cultured in growth medium.

Conclusions: ECM derived from DPSCs proved to be osteoinductive, and this knowledge supported cell-derived ECM can be further utilized for tissue engineering of mineralized tissues.

The evolving biology of the proton‐coupled folate transporter: New insights into regulation, structure, and mechanism

The human proton‐coupled folate transporter (PCFT; SLC46A1) or hPCFT was identified in 2006 as the principal folate transporter involved in the intestinal absorption of dietary folates. A rare autosomal recessive hereditary folate malabsorption syndrome is attributable to human SLC46A1 variants. The recognition that hPCFT was highly expressed in many tumors stimulated substantial interest in its potential for cytotoxic drug targeting, taking advantage of its high‐level transport activity under acidic pH conditions that characterize many tumors and its modest expression in most normal tissues. To better understand the basis for variations in hPCFT levels between tissues including human tumors, studies have examined the transcriptional regulation of hPCFT including the roles of CpG hypermethylation and critical transcription factors and cis elements. Additional focus involved identifying key structural and functional determinants of hPCFT transport that, combined with homology models based on structural homologies to the bacterial transporters GlpT and LacY, have enabled new structural and mechanistic insights. Recently, cryo‐electron microscopy structures of chicken PCFT in a substrate‐free state and in complex with the antifolate pemetrexed were reported, providing further structural insights into determinants of (anti)folate recognition and the mechanism of pH‐regulated (anti)folate transport by PCFT. Like many major facilitator proteins, hPCFT exists as a homo‐oligomer, and evidence suggests that homo‐oligomerization of hPCFT monomeric proteins may be important for its intracellular trafficking and/or transport function. Better understanding of the structure, function and regulation of hPCFT should facilitate the rational development of new therapeutic strategies for conditions associated with folate deficiency, as well as cancer.

Prediction of allosteric sites and signaling: Insights from benchmarking datasets

Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.

Probing GPCR Dimerization Using Peptides

G protein-coupled receptors (GPCRs) are the largest class of membrane proteins and the most common and extensively studied pharmacological target. Numerous studies over the last decade have confirmed that GPCRs do not only exist and function in their monomeric form but in fact, have the ability to form dimers or higher order oligomers with other GPCRs, as well as other classes of receptors. GPCR oligomers have become increasingly attractive to investigate as they have the ability to modulate the pharmacological responses of the receptors which in turn, could have important functional roles in diseases, such as cancer and several neurological & neuropsychiatric disorders. Despite the growing evidence in the field of GPCR oligomerisation, the lack of structural information, as well as targeting the 'undruggable' protein-protein interactions (PPIs) involved in these complexes, has presented difficulties. Outside the field of GPCRs, targeting PPIs has been widely studied, with a variety of techniques being investigated; from small-molecule inhibitors to disrupting peptides. In this review, we will demonstrate several physiologically relevant GPCR dimers and discuss an array of strategies and techniques that can be employed when targeting these complexes, as well as provide ideas for future development.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Systematic discovery of signaling pathways linking immune activation to schizophrenia

Immune activation has been shown to play a critical role in the development of schizophrenia; however its underlying mechanism remains unknown. Our report demonstrates a high-quality protein interaction network for schizophrenia (SCZ Network), constructed using our “neighborhood walk” approach in combination with “random walk with restart”. The spatiotemporal expression pattern of the genes in this disease network revealed two developmental stages sensitive to perturbation by immune activation: mid-to late gestation, and adolescence. Furthermore, we induced immune activation at these stages in mice, carried out transcriptome sequencing on the mouse brains, and illustrated clear potential molecular pathways and key regulators correlating maternal immune activation during gestation and an increased risk for schizophrenia after a second immune activation at puberty. This work provides not only valuable resources for the study on molecular mechanisms underlying schizophrenia, but also a systematic strategy for the discovery of molecular pathways of complex mental disorders.

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A high-quality molecular network for schizophrenia (SCZ Network)

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A landscape of molecular pathways linking immune activation and schizophrenia

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The spatiotemporal network dynamics revealing stages susceptible to immune activation

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Identification of the molecular pathways and regulators in the immune-activated brain

Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes

Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction

OBJECTIVE

To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

MATERIALS AND METHODS

We propose a deep learning framework named Graph Convolutional Network for Drug Synergy (GraphSynergy). GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order topological relationships in the PPI network of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxicity scores. An attention component is also introduced in GraphSynergy, which aims to capture the pivotal proteins that play a part in both PPI network and biomolecular interactions between drug combinations and cancer cell lines.

RESULTS

GraphSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the 2 latest drug combination datasets. Specifically, GraphSynergy achieves accuracy values of 0.7553 (11.94% improvement compared to DeepSynergy, the latest published drug combination prediction algorithm) and 0.7557 (10.95% improvement compared to DeepSynergy) on DrugCombDB and Oncology-Screen datasets, respectively. Furthermore, the proteins allocated with high contribution weights during the training of GraphSynergy are proved to play a role in view of molecular functions and biological processes, such as transcription and transcription regulation.

CONCLUSION

The introduction of topological relations between drug combination and cell line within the PPI network can significantly improve the capability of synergistic drug combination identification.

Bioinspired Solid-State Nanochannel Sensors: From Ionic Current Signals, Current, and Fluorescence Dual Signals to Faraday Current Signals

Inspired from bioprotein channels of living organisms, constructing "abiotic" analogues, solid-state nanochannels, to achieve "smart" sensing towards various targets, is highly seductive. When encountered with certain stimuli, dynamic switch of terminal modified probes in terms of surface charge, conformation, fluorescence property, electric potential as well as wettability can be monitored via transmembrane ionic current, fluorescence intensity, faraday current signals of nanochannels and so on. Herein, the modification methodologies of nanochannels and targets-detecting application are summarized in ions, small molecules, as well as biomolecules, and systematically reviewed are the nanochannel-based detection means including 1) by transmembrane current signals; 2) by the coordination of current- and fluorescence-dual signals; 3) by faraday current signals from nanochannel-based electrode. The coordination of current and fluorescence dual signals offers great benefits for synchronous temporal and spatial monitoring. Faraday signals enable the nanoelectrode to monitor both redox and non-redox components. Notably, by incorporation with confined effect of tip region of a needle-like nanopipette, glorious in-vivo monitoring is conferred on the nanopipette detector at high temporal-spatial resolution. In addition, some outlooks for future application in reliable practical samples analysis and leading research endeavors in the related fantastic fields are provided.

RNA BioMolecular Electronics: towards new tools for biophysics and biomedicine

The last half-century has witnessed the birth and development of a new multidisciplinary field at the edge between materials science, nanoscience, engineering, and chemistry known as Molecular Electronics. This field deals with the electronic properties of individual molecules and their integration as active components in electronic circuits and has also been applied to biomolecules, leading to BioMolecular Electronics and opening new perspectives for single-molecule biophysics and biomedicine. Herein, we provide a brief introduction and overview of the BioMolecular electronics field, focusing on nucleic acids and potential applications for these measurements. In particular, we review the recent demonstration of the first single-molecule electrical detection of a biologically-relevant nucleic acid. We also show how this could be used to study biomolecular interactions and applications in liquid biopsy for early cancer detection, among others. Finally, we discuss future perspectives and challenges in the applications of this fascinating research field.

Tau Protein Interaction Partners and Their Roles in Alzheimer's Disease and Other Tauopathies

Tau protein plays a critical role in the assembly, stabilization, and modulation of microtubules, which are important for the normal function of neurons and the brain. In diseased conditions, several pathological modifications of tau protein manifest. These changes lead to tau protein aggregation and the formation of paired helical filaments (PHF) and neurofibrillary tangles (NFT), which are common hallmarks of Alzheimer's disease and other tauopathies. The accumulation of PHFs and NFTs results in impairment of physiological functions, apoptosis, and neuronal loss, which is reflected as cognitive impairment, and in the late stages of the disease, leads to death. The causes of this pathological transformation of tau protein haven't been fully understood yet. In both physiological and pathological conditions, tau interacts with several proteins which maintain their proper function or can participate in their pathological modifications. Interaction partners of tau protein and associated molecular pathways can either initiate and drive the tau pathology or can act neuroprotective, by reducing pathological tau proteins or inflammation. In this review, we focus on the tau as a multifunctional protein and its known interacting partners active in regulations of different processes and the roles of these proteins in Alzheimer's disease and tauopathies.

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.

Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins

Infectious diseases in humans appear to be one of the most primary public health issues. Identification of novel disease-associated proteins will furnish an efficient recognition of the novel therapeutic targets. Here, we develop a Graph Convolutional Network (GCN)-based model called PINDeL to identify the disease-associated host proteins by integrating the human Protein Locality Graph and its corresponding topological features. Because of the amalgamation of GCN with the protein interaction network, PINDeL achieves the highest accuracy of 83.45% while AUROC and AUPRC values are 0.90 and 0.88, respectively. With high accuracy, recall, F1-score, specificity, AUROC, and AUPRC, PINDeL outperforms other existing machine-learning and deep-learning techniques for disease gene/protein identification in humans. Application of PINDeL on an independent dataset of 24320 proteins, which are not used for training, validation, or testing purposes, predicts 6448 new disease-protein associations of which we verify 3196 disease-proteins through experimental evidence like disease ontology, Gene Ontology, and KEGG pathway enrichment analyses. Our investigation informs that experimentally-verified 748 proteins are indeed responsible for pathogen-host protein interactions of which 22 disease-proteins share their association with multiple diseases such as cancer, aging, chem-dependency, pharmacogenomics, normal variation, infection, and immune-related diseases. This unique Graph Convolution Network-based prediction model is of utmost use in large-scale disease-protein association prediction and hence, will provide crucial insights on disease pathogenesis and will further aid in developing novel therapeutics.

GalaxyHeteromer: protein heterodimer structure prediction by template-based and ab initio docking

Protein–protein interactions play crucial roles in diverse biological processes, including various disease progressions. Atomistic structural details of protein–protein interactions may provide important information that can facilitate the design of therapeutic agents. GalaxyHeteromer is a freely available automatic web server (http://galaxy.seoklab.org/heteromer) that predicts protein heterodimer complex structures from two subunit protein sequences or structures. When subunit structures are unavailable, they are predicted by template- or distance-prediction-based modelling methods. Heterodimer complex structures can be predicted by both template-based and ab initio docking, depending on the template's availability. Structural templates are detected from the protein structure database based on both the sequence and structure similarities. The templates for heterodimers may be selected from monomer and homo-oligomer structures, as well as from hetero-oligomers, owing to the evolutionary relationships of heterodimers with domains of monomers or subunits of homo-oligomers. In addition, the server employs one of the best ab initio docking methods when heterodimer templates are unavailable. The multiple heterodimer structure models and the associated scores, which are provided by the web server, may be further examined by user to test or develop functional hypotheses or to design new functional molecules.

PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data

Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

Toward Intracellular Bioconjugation Using Transition-Metal-Free Techniques

Bioconjugation is the chemical strategy of covalent modification of biomolecules, using either an external reagent or other biomolecules. Since its inception in the twentieth century, the technique has grown by leaps and bounds, and has a variety of applications in chemical biology. However, it is yet to reach its full potential in the study of biochemical processes in live cells, mainly because the bioconjugation strategies conflict with cellular processes. This has mostly been overcome by using transition metal catalysts, but the presence of metal centers limit them to in vitro use, or to the cell surface. These hurdles can potentially be circumvented by using metal-free strategies. However, the very modifications that are necessary to make such metal-free reactions proceed effectively may impact their biocompatibility. This is because biological processes are easily perturbed and greatly depend on the prevailing inter- and intracellular environment. With this taken into consideration, this review analyzes the applicability of the transition-metal-free strategies reported in this decade to the study of biochemical processes in vivo.

Targeting SARS-CoV-2 Spike Protein/ACE2 Protein-Protein Interactions: a Computational Study

The spike glycoprotein (S) of the SARS-CoV-2 virus surface plays a key role in receptor binding and virus entry. The S protein uses the angiotensin converting enzyme (ACE2) for entry into the host cell and binding to ACE2 occurs at the receptor binding domain (RBD) of the S protein. Therefore, the protein-protein interactions (PPIs) between the SARS-CoV-2 RBD and human ACE2, could be attractive therapeutic targets for drug discovery approaches designed to inhibit the entry of SARS-CoV-2 into the host cells. Herein, with the support of machine learning approaches, we report structure-based virtual screening as an effective strategy to discover PPIs inhibitors from ZINC database. The proposed computational protocol led to the identification of a promising scaffold which was selected for subsequent binding mode analysis and that can represent a useful starting point for the development of new treatments of the SARS-CoV-2 infection.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

Phytochemistry, pharmacodynamics, and pharmacokinetics of a classic Chinese herbal formula Danggui Beimu Kushen Wan: A review

Danggui Beimu Kushen Wan (DBKW) is a classic herbal formula for difficult urination and has been widely used for urinary-related disorders and cancers in current clinical practice. This study aimed to comprehensively review the phytochemistry, pharmacodynamics, and pharmacokinetics of DBKW in experimental studies. We searched 21 databases to identify experimental studies of DBKW. We also searched 11 databases to identify and summarize compounds from DBKW's ingredients. A total of 423 studies of DBKW were identified and 15 studies were included. For Angelicae Sinensis Radix (ASR) and Sophorae Flavescentis Radix (SFR), 2,425 and 2,843 studies were identified, and 42 and 33 studies were included, respectively. Eight compounds were found in the whole formula, 408 compounds from ASR, and 277 compounds from SFR. DBKW may have anticancer effects (inhibiting the growth of tumors, regulating cell proliferation, inducing tumor cell apoptosis, suppressing invasion and metastasis of cancer, enhancing the therapeutic effects of chemotherapy, and relieving toxicity of chemotherapy) and have benefits on chronic prostatitis (reducing inflammation, inhibiting oxidation, regulating sex hormone, and stimulating immune system). The pharmacokinetics of the seven primary compounds from DBKW were also summarized. DBKW contains multiple compounds that may act on more than one pathway of the conditions simultaneously.

Identification of potential SARS-CoV-2 entry inhibitors by targeting the interface region between the spike RBD and human ACE2

Coronavirus disease 2019 (COVID-19) is a fatal infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The virus infection is initiated upon recognition and binding of the spike (S) protein receptor-binding domain (RBD) to the host cell surface receptor, angiotensin-converting enzyme 2 (ACE2). Blocking the interaction between S protein and ACE2 receptor is a novel approach to prevent the viral entry into the host cell. The present study is aimed at the identification of small molecules which can disrupt the interaction between SARS-CoV-2 S protein and human ACE2 receptor by binding to the interface region. A chemical library consisting of 1,36,191 molecules were screened for drug-like compounds based on Lipinski's rule of five, Verber's rule and in silico toxicity parameters. The filtered drug-like molecules were next subjected to molecular docking in the interface region of RBD. The best three hits viz; ZINC64023823, ZINC33039472 and ZINC00991597 were further taken for molecular dynamics (MD) simulation studies and binding free energy evaluations using Molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) and Molecular mechanics-Generalized Born surface area (MM-GBSA). The protein-ligand complexes showed stable trajectories throughout the simulation time. ZINC33039472 exhibited binding free energy value lower as compared to the control (emodin) with a higher contribution by gas-phase energy and van der Waals energy to the total binding free energy. Thus, ZINC33039472 is identified to be a promising interfacial binding molecule which can inhibit the interaction between the viral S protein and human ACE2 receptor which would consequently help in the management of the disease.

Protein detection enabled using functionalised silk-binding peptides on a silk-coated optical fibre

We report a new approach to functionalise optical fibres to enable protein sensing, which controls the sensor molecule location either within the fibre tip coating or isolated to its exterior. This control dictates suitability for protein sensing.

Protein–protein interaction network: an emerging tool for understanding fish disease in aquaculture

Protein–protein interactions (PPIs) play integral roles in a wide range of biological processes that regulate the overall growth, development, physiology and disease in living organisms. With the advancement of high‐throughput sequencing technologies, increasing numbers of PPI networks are being predicted and annotated, and these contribute greatly towards the understanding of pathogenesis and the discovery of novel drug targets for the treatment of diseases. The use of this tool is gaining popularity in the identification, understanding and treatment of diseases in humans and plants. Due to the importance of aquaculture in tackling the global food crisis by producing cheap and high‐quality protein source, the maintenance of the overall health status of aquaculture species is essential. With the increasing omics data on aquaculture species, the PPI network is an emerging tool for fish health maintenance. In this review, we first introduce the concept of PPI network, how they are discovered and their general application. Then, the current status of aquaculture and disease in aquaculture are discussed. The different applications of PPI network in aquaculture fish disease management such as biomarker identification, mechanism prediction, understanding of host–pathogen interaction, understanding of pathogen co‐infection interaction, and potential development of vaccines and treatments are subsequently highlighted. It is hoped that this emerging tool – PPI network – would deepen our understanding of the pathogenesis of various diseases and hasten the prevention and treatment processes in aquaculture species.

A Nobel-Winning Scientist: Aziz Sancar and the Impact of his Work on the Molecular Pathology of Neoplastic Diseases

Aziz Sancar, Nobel Prize winning Turkish scientist, made several discoveries which had a major impact on molecular sciences, particularly disciplines that focus on carcinogenesis and cancer treatment, including molecular pathology. Cloning the photolyase gene, which was the initial step of his work on DNA repair mechanisms, discovery of the "Maxicell" method, explanation of the mechanism of nucleotide excision repair and transcription-coupled repair, discovery of "molecular matchmakers", and mapping human excision repair genes at single nucleotide resolution constitute his major research topics. Moreover, Sancar discovered the cryptochromes, the clock genes in humans, in 1998, and this discovery led to substantial progress in the understanding of the circadian clock and the introduction of the concept of "chrono-chemoterapy" for more effective therapy in cancer patients. This review focuses on Aziz Sancar's scientific studies and their reflections on molecular pathology of neoplastic diseases. While providing a new perspective for researchers working in the field of pathology and molecular pathology, this review is also an evidence of how basic sciences and clinical sciences complete each other.

Topology-driven protein-protein interaction network analysis detects genetic sub-networks regulating reproductive capacity

Understanding the genetic regulation of organ structure is a fundamental problem in developmental biology. Here, we use egg-producing structures of insect ovaries, called ovarioles, to deduce systems-level gene regulatory relationships from quantitative functional genetic analysis. We previously showed that Hippo signalling, a conserved regulator of animal organ size, regulates ovariole number in Drosophila melanogaster. To comprehensively determine how Hippo signalling interacts with other pathways in this regulation, we screened all known signalling pathway genes, and identified Hpo-dependent and Hpo-independent signalling requirements. Network analysis of known protein-protein interactions among screen results identified independent gene regulatory sub-networks regulating one or both of ovariole number and egg laying. These sub-networks predict involvement of previously uncharacterised genes with higher accuracy than the original candidate screen. This shows that network analysis combining functional genetic and large-scale interaction data can predict function of novel genes regulating development.

The Challenges of OSCC Diagnosis: Salivary Cytokines as Potential Biomarkers

Fast, economic, and noninvasive, molecular analysis of saliva has the potential to become a diagnostic tool of reference for several local and systemic diseases, oral cancer included. The diagnosis of Oral Squamous Cell Carcinoma (OSCC) can be performed using high specificity and sensibility biomarkers that can be encountered in the biological fluids. Recent advances in salivary proteomics have underlined the potential use of salivary biomarkers as early diagnosis screening tools for oral neoplasia. In this respect, over 100 salivary molecules have been described and proposed as oral cancer biomarkers, out of which cytokines are among the most promising. Besides being directly involved in inflammation and immune response, the role of salivary cytokines in tumor growth and progression linked them to the incidence of oral malignant lesions. This review summarizes the existing studies based on the use of salivary cytokines as potential oral cancer biomarkers, their involvement in the malignant process based on their type, and ther influence upon prognostic and metastatic rates.

Mapping the underlying mechanisms of fibrinogen benzothiazole drug interactions using computational and experimental approaches

Three-dimensional conformational crystallographic binding-modes are of paramount importance to understand the docking mechanism of protein-ligand interactions and to identify potential "leading drugs" conformers towards rational drugs-design. Herein, we present an integrated computational-experimental study tackling the problem of multiple binding modes among the ligand 3-(2-Benzothiazolylthio)-propane sulfonic acid (BTS) and the fibrinogen receptor (E-region). Based on molecular docking simulations, we found that the free energy of binding values for nine of different BTS-docking complexes (i.e., BTS-pose_1-9) were very close. We have also identified a docking-mechanism of BTS-interaction mainly based on non-covalent hydrophobic interactions with H-bond contacts stabilizing the fibrinogen-BTS docking complexes. Interestingly, the different BTS-poses_1-9 were found to be able to block the fibrinogen binding site (E-region) by inducing local perturbations in effector and allosteric residues, reducing the degree of collectivity in its flexibility normal modes. As such, we theoretically suggest that the BTS-binding modes can significantly affect the physiological condition of the unoccupied fibrinogen protein structure by bringing global and local perturbations in the frequency domain spectra. The proposed theoretical mechanisms, the interactions involved and the conformational changes suggested, were further corroborated by different experimental techniques such as isothermal titration calorimetry (ITC), zeta potential, UV-vis, fluorescence and small angle X-ray scattering (SAXS). The combined results shall open new avenues towards the application of complex supra-molecular information in rational drugs-design.

A systems biology-driven approach to construct a comprehensive protein interaction network of influenza A virus with its host

BACKGROUND

Influenza A virus (IAV) infection is a serious public health problem not only in South East Asia but also in European and African countries. Scientists are using network biology to dig deep into the essential host factors responsible for regulation of virus infections. Researchers can explore the virus invasion into the host cells by studying the virus-host relationship based on their protein-protein interaction network.

METHODS

In this study, we present a comprehensive IAV-host protein-protein interaction network that is obtained based on the literature-curated protein interaction datasets and some important interaction databases. The network is constructed in Cytoscape and analyzed with its plugins including CytoHubba, CytoCluster, MCODE, ClusterViz and ClusterOne. In addition, Gene Ontology and KEGG enrichment analyses are performed on the highly IAV-associated human proteins. We also compare the current results with those from our previous study on Hepatitis C Virus (HCV)-host protein-protein interaction network in order to find out valuable information.

RESULTS

We found out 1027 interactions among 829 proteins of which 14 are viral proteins and 815 belong to human proteins. The viral protein NS1 has the highest number of associations with human proteins followed by NP, PB2 and so on. Among human proteins, LNX2, MEOX2, TFCP2, PRKRA and DVL2 have the most interactions with viral proteins. Based on KEGG pathway enrichment analysis of the highly IAV-associated human proteins, we found out that they are enriched in the KEGG pathway of basal cell carcinoma. Similarly, the result of KEGG analysis of the common host factors involved in IAV and HCV infections shows that these factors are enriched in the infection pathways of Hepatitis B Virus (HBV), Viral Carcinoma, measles and certain other viruses.

CONCLUSION

It is concluded that the list of proteins we identified might be used as potential drug targets for the drug design against the infectious diseases caused by Influenza A Virus and other viruses.

Network Analyses of the Differential Expression of Heat Shock Proteins in Glioma

Heat shock protein (HSP) is a family of highly conserved protein, which exists widely in various organisms and has a variety of important physiological functions. Currently, there is no systematic analysis of HSPs in human glioma. The aim of this study was to investigate the characteristics of HSPs through constructing protein-protein interaction network (PPIN) considering the expression level of HSPs in glioma. After the identification of the differentially expressed HSPs in glioma tissues, a specific PPIN was constructed and found that there were many interactions between the differentially expressed HSPs in glioma. Subcellular localization analysis shows that HSPs and their interacting proteins distribute from the cell membrane to the nucleus in a multilayer structure. By functional enrichment analysis, gene ontology analysis, and Kyoto Encyclopedia of Genes and Genomes pathway analysis, the potential function of HSPs and two meaningful enrichment pathways was revealed. In addition, nine HSPs (DNAJA4, DNAJC6, DNAJC12, HSPA6, HSP90B1, DNAJB1, DNAJB6, DNAJC10, and SERPINH1) are prognostic markers for human brain glioma. These analyses provide a full view of HSPs about their expression, biological process, as well as clinical significance in glioma.

Affimers as an alternative to antibodies for protein biomarker enrichment

INTRODUCTION

Assessing the specificity of protein binders is an essential first step in protein biomarker assay development. Affimers are novel protein binders and can potentially replace antibodies in multiple protein capture-based assays. Affimers are selected for their high specificity against the target protein and have benefits over antibodies like batch-to-batch reproducibility and are stable across a wide range of chemical conditions. Here we mimicked a typical initial screening of affimers and commercially available monoclonal antibodies against two non-related proteins, IL-37b and proinsulin, to assess the potential of affimers as alternative to antibodies.

METHODS

Binding specificity of anti-IL-37b and anti-proinsulin affimers and antibodies was investigated via magnetic bead-based capture of their recombinant protein targets in human plasma. Captured proteins were analyzed using SDS-PAGE, Coomassie blue staining, Western blotting and LC-MS/MS-based proteomics.

RESULTS

All affimers and antibodies were able to bind their target protein in human plasma. Gel and LC-MS/MS analysis showed that both affimer and antibody-based captures resulted in co-purified background proteins. However, affimer-based captures showed the highest relative enrichment of IL-37b and proinsulin.

CONCLUSIONS

For both proteins tested, affimers show higher specificity in purifying their target proteins from human plasma compared to monoclonal antibodies. These results indicate that affimers are promising antibody-replacement tools for protein biomarker assay development.

The design and development of covalent protein-protein interaction inhibitors for cancer treatment

Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.

Mutation effect estimation on protein-protein interactions using deep contextualized representation learning

The functional impact of protein mutations is reflected on the alteration of conformation and thermodynamics of protein–protein interactions (PPIs). Quantifying the changes of two interacting proteins upon mutations is commonly carried out by computational approaches. Hence, extensive research efforts have been put to the extraction of energetic or structural features on proteins, followed by statistical learning methods to estimate the effects of mutations on PPI properties. Nonetheless, such features require extensive human labors and expert knowledge to obtain, and have limited abilities to reflect point mutations. We present an end-to-end deep learning framework, MuPIPR (Mutation Effects in Protein–protein Interaction PRediction Using Contextualized Representations), to estimate the effects of mutations on PPIs. MuPIPR incorporates a contextualized representation mechanism of amino acids to propagate the effects of a point mutation to surrounding amino acid representations, therefore amplifying the subtle change in a long protein sequence. On top of that, MuPIPR leverages a Siamese residual recurrent convolutional neural encoder to encode a wild-type protein pair and its mutation pair. Multi-layer perceptron regressors are applied to the protein pair representations to predict the quantifiable changes of PPI properties upon mutations. Experimental evaluations show that, with only sequence information, MuPIPR outperforms various state-of-the-art systems on estimating the changes of binding affinity for SKEMPI v1, and offers comparable performance on SKEMPI v2. Meanwhile, MuPIPR also demonstrates state-of-the-art performance on estimating the changes of buried surface areas. The software implementation is available at https://github.com/guangyu-zhou/MuPIPR.

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization

Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔG_bind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔG_bind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔG_bind data points on purified proteins to generate ΔΔG_bind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ΔΔG_bind for this interaction could be quantified with high accuracy over the range of 12 kcal mol⁻¹ displayed by various BPTI single mutants.

Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.

Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome

RESEARCH QUESTION

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach?

METHODS

Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways.

RESULTS

The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity.

CONCLUSIONS

These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.

The oncogenic potential of small nuclear ribonucleoprotein polypeptide G: a comprehensive and perspective view

Small nuclear ribonucleoprotein polypeptide G (SNRPG), often referred to as Smith protein G (SmG), is an indispensable component in the biogenesis of spliceosomal uridyl-rich small nuclear ribonucleoprotein particles (U snRNPs; U1, U2, U4 and U5), which are precursors of both the major and minor spliceosome. SNRPG has attracted significant attention because of its implicated roles in tumorigenesis and tumor development. Suggestive evidence of its varying expression levels has been reported in different types of cancers, which include breast cancer, lung cancer, prostate cancer and colon cancer. The accumulating evidence suggests that the splicing machinery component plays a significant role in the initiation and progression of cancers. SNRPG has a wide interaction network, and its functions are predominantly mediated by protein-protein interactions (PPIs), making it a promising anti-cancer therapeutic target in PPI-focused drug technology. Understanding its roles in tumorigenesis and tumor development is an indispensable arsenal in the development of molecular-targeted therapies. Several antitumor drugs linked to splicing machinery components have been reported in different types of cancers and some have already entered the clinic. However, targeting SNRPG as a drug development tool has been an overlooked and underdeveloped strategy in cancer therapy. In this article, we present a comprehensive and perspective view on the oncogenic potential of SNRPG in PPI-focused drug discovery.

A Parametric Targetability Evaluation Approach for Vitiligo Proteome Extracted through Integration of Gene Ontologies and Protein Interaction Topologies

Vitiligo is a well-known skin disorder with complex etiology. Vitiligo pathogenesis is multifaceted with many ramifications. A computational systemic path was designed to first propose candidate disease proteins by merging properties from protein interaction networks and gene ontology terms. All in all, 109 proteins were identified and suggested to be involved in the onset of disease or its progression. Later, a composite approach was employed to prioritize vitiligo disease proteins by comparing and benchmarking the properties against standard target identification criteria. This includes sequence-based, structural, functional, essentiality, protein-protein interaction, vulnerability, secretability, assayability, and druggability information. The existing information was seamlessly integrated into efficient pipelines to propose a novel protocol for assessment of targetability of disease proteins. Using the online data resources and the scripting, an illustrative list of 68 potential drug targets was generated for vitiligo. While this list is broadly consistent with the research community's current interest in certain specific proteins, and suggests novel target candidates that may merit further study, it can still be modified to correspond to a user-specific environment, either by adjusting the weights for chosen criteria (i.e., a quantitative approach) or by changing the considered criteria (i.e., a qualitative approach).

Handling Noise in Protein Interaction Networks

Protein-protein interactions (PPIs) can be conveniently represented as networks, allowing the use of graph theory for their study. Network topology studies may reveal patterns associated with specific organisms. Here, we propose a new methodology to denoise PPI networks and predict missing links solely based on the network topology, the organization measurement (OM) method. The OM methodology was applied in the denoising of the PPI networks of two Saccharomyces cerevisiae datasets (Yeast and CS2007) and one Homo sapiens dataset (Human). To evaluate the denoising capabilities of the OM methodology, two strategies were applied. The first strategy compared its application in random networks and in the reference set networks, while the second strategy perturbed the networks with the gradual random addition and removal of edges. The application of the OM methodology to the Yeast and Human reference sets achieved an AUC of 0.95 and 0.87, in Yeast and Human networks, respectively. The random removal of 80% of the Yeast and Human reference set interactions resulted in an AUC of 0.71 and 0.62, whereas the random addition of 80% interactions resulted in an AUC of 0.75 and 0.72, respectively. Applying the OM methodology to the CS2007 dataset yields an AUC of 0.99. We also perturbed the network of the CS2007 dataset by randomly inserting and removing edges in the same proportions previously described. The false positives identified and removed from the network varied from 97%, when inserting 20% more edges, to 89%, when 80% more edges were inserted. The true positives identified and inserted in the network varied from 95%, when removing 20% of the edges, to 40%, after the random deletion of 80% edges. The OM methodology is sensitive to the topological structure of the biological networks. The obtained results suggest that the present approach can efficiently be used to denoise PPI networks.

Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks

Rapid reconstruction of genome-scale protein-protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e., pairs of proteins that do not interact) is still a major issue that needs to be properly addressed in computational modeling. In this study, we take advantage of the very limited experimentally verified negative data from Negatome to infer more negative data for computational modeling. We assume that the paralogs or orthologs of two non-interacting proteins also do not interact with high probability. We coin an assumption as "Neglog" this assumption is to some extent supported by paralogous/orthologous structure conservation. To reduce the risk of bias toward the negative data from Negatome, we combine Neglog with less biased random sampling according to a certain ratio to construct training data. L₂-regularized logistic regression is used as the base classifier to counteract noise and train on a large dataset. Computational results show that the proposed Neglog method outperforms pure random sampling method with sound biological interpretability. In addition, we find that independent test on negative data is indispensable for bias control, which is usually neglected by existing studies. Lastly, we use the Neglog method to validate the PPIs in STRING, which are supported by gene ontology (GO) enrichment analyses.

A New Approach for the Diagnosis of Myelodysplastic Syndrome Subtypes Based on Protein Interaction Analysis

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematological malignancies with a high risk of transformation to acute myeloid leukemia (AML). MDS are associated with posttranslational modifications of proteins and variations in the protein expression levels. In this work, we present a novel interactomic diagnostic method based on both protein array and surface plasmon resonance biosensor technology, which enables monitoring of protein-protein interactions in a label-free manner. In contrast to conventional methods based on the detection of individual biomarkers, our presented method relies on measuring interactions between arrays of selected proteins and patient plasma. We apply this method to plasma samples obtained from MDS and AML patients, as well as healthy donors, and demonstrate that even a small protein array comprising six selected proteins allows the method to discriminate among different MDS subtypes and healthy donors.

An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation

Phosphorylation of proteins is a key way cells regulate function, both at the individual protein level and at the level of signaling pathways. Kinases are responsible for phosphorylation of substrates, generally on serine, threonine, or tyrosine residues. Though particular sequence patterns can be identified that dictate whether a residue will be phosphorylated by a specific kinase, these patterns are not highly predictive of phosphorylation. The availability of large scale proteomic and phosphoproteomic data sets generated using mass-spectrometry-based approaches provides an opportunity to study the important relationship between kinase activity, substrate specificity, and phosphorylation. In this study, we analyze relationships between protein abundance and phosphopeptide abundance across more than 150 tumor samples and show that phosphorylation at specific phosphosites is not well correlated with overall kinase abundance. However, individual kinases show a clear and statistically significant difference in correlation among known phosphosite targets for that kinase and randomly selected phosphosites. We further investigate relationships between phosphorylation of known activating or inhibitory sites on kinases and phosphorylation of their target phosphosites. Combined with motif-based analysis, this approach can predict novel kinase targets and show which subsets of a kinase's target repertoire are specifically active in one condition versus another.

Protein-protein interaction modulators: advances, successes and remaining challenges

Modulating disease-relevant protein-protein interactions (PPIs) using small-molecule inhibitors is a quite indispensable diagnostic and therapeutic strategy in averting pathophysiological cues and disease progression. Over the years, targeting intracellular PPIs as drug design targets has been a challenging task owing to their highly dynamic and expansive interfacial areas (flat, featureless and relatively large). However, advances in PPI-focused drug discovery technology have been reported and a few drugs are already on the market, with some potential drug-like candidates already in clinical trials. In this article, we review the advances, successes and remaining challenges in the application of small molecules as valuable PPI modulators in disease diagnosis and therapeutics.

Analysis of protein targets in pathogen-host interaction in infectious diseases: a case study on Plasmodium falciparum and Homo sapiens interaction network

Infection and disease progression is the outcome of protein interactions between pathogen and host. Pathogen, the role player of Infection, is becoming a severe threat to life as because of its adaptability toward drugs and evolutionary dynamism in nature. Identifying protein targets by analyzing protein interactions between host and pathogen is the key point. Proteins with higher degree and possessing some topologically significant graph theoretical measures are found to be drug targets. On the other hand, exceptional nodes may be involved in infection mechanism because of some pathway process and biologically unknown factors. In this article, we attempt to investigate characteristics of host-pathogen protein interactions by presenting a comprehensive review of computational approaches applied on different infectious diseases. As an illustration, we have analyzed a case study on infectious disease malaria, with its causative agent Plasmodium falciparum acting as 'Bait' and host, Homo sapiens/human acting as 'Prey'. In this pathogen-host interaction network based on some interconnectivity and centrality properties, proteins are viewed as central, peripheral, hub and non-hub nodes and their significance on infection process. Besides, it is observed that because of sparseness of the pathogen and host interaction network, there may be some topologically unimportant but biologically significant proteins, which can also act as Bait/Prey. So, functional similarity or gene ontology mapping can help us in this case to identify these proteins.

Identification of the iduronate-2-sulfatase proteome in wild-type mouse brain

Iduronate-2-sulfatase (IDS) is a lysosomal enzyme involved in the metabolism of the glycosaminoglycans heparan (HS) and dermatan (DS) sulfate. Mutations on IDS gene produce mucopolysaccharidosis II (MPS II), characterized by the lysosomal accumulation of HS and DS, leading to severe damage of the central nervous system (CNS) and other tissues. In this study, we used a neurochemistry and proteomic approaches to identify the brain distribution of IDS and its interacting proteins on wild-type mouse brain. IDS immunoreactivity showed a robust staining throughout the entire brain, suggesting an intracellular reactivity in nerve cells and astrocytes. By using affinity purification and mass spectrometry we identified 187 putative IDS partners-proteins, mainly hydrolases, cytoskeletal proteins, transporters, transferases, oxidoreductases, nucleic acid binding proteins, membrane traffic proteins, chaperons and enzyme modulators, among others. The interactions with some of these proteins were predicted by using bioinformatics tools and confirmed by co-immunoprecipitation analysis and Blue Native PAGE. In addition, we identified cytosolic IDS-complexes containing proteins from predicted highly connected nodes (hubs), with molecular functions including catalytic activity, redox balance, binding, transport, receptor activity and structural molecule activity. The proteins identified in this study would provide new insights about IDS physiological role into the CNS and its potential role in the brain-specific protein networks.

Construction and analysis of a comprehensive protein interaction network of HCV with its host Homo sapiens

Background

Hepatitis C Virus is becoming a major health problem in Asia and across the globe since it is causing serious liver diseases including liver cirrhosis, chronic hepatitis and hepatocarcinoma (HCC). Protein interaction networks presents us innumerable novel insights into functional constitution of proteome and helps us finding potential candidates for targeting the drugs.

Methods

Here we present a comprehensive protein interaction network of Hepatitis C Virus with its host, constructed by literature curated interactions. The network was constructed and explored using Cytoscape and the results were further analyzed using KEGG pathway, Gene Ontology enrichment analysis and MCODE.

Results

We found 1325 interactions between 12 HCV proteins and 940 human genes, among which 21 were intraviral and 1304 were HCV-Human. By analyzing the network, we found potential human gene list with their number of interactions with HCV proteins. ANXA2 and NR4A1 were interacting with 6 HCV proteins while we found 11 human genes which were interacting with 5 HCV proteins. Furthermore, the enrichment analysis and Gene Ontology of the top genes to find the pathways and the biological processes enriched with those genes. Among the viral proteins, NS3 was interacting with most number of interactors followed by NS5A and so on. KEGG pathway analysis of three set of most HCV- associated human genes was performed to find out which gene products are involved in certain disease pathways. Top 5, 10 and 20 human genes with most interactions were analyzed which revealed some striking results among which the top 10 host genes came up to be significant because they were more related to Influenza A viral infection previously. This insight provides us with a clue that the set of genes are highly enriched in HCV but are not well studied in its infection pathway.

Conclusions

We found out a group of proteins which were rich in HCV viral pathway but there were no drugs targeting them according to the drug repurposing hub. It can be concluded that the cluster we obtained from MCODE contains potential targets for HCV treatment and could be implemented for molecular docking and drug designing further by the scientists.

Inhibition of the TIRAP-c-Jun interaction as a therapeutic strategy for AP1-mediated inflammatory responses

Bacterial endotoxin-induced sepsis causes 30-40% of the deaths in the intensive care unit (ICU) globally, for which there is no pharmacotherapy. Lipopolysaccharide (LPS), a bacterial endotoxin, stimulates the Toll-like receptor (TLR)-4 signalling pathways to upregulate the expression of various inflammatory mediators. Here, we show that the TIRAP and c-Jun protein signalling complex forms in macrophages in response to LPS stimulation, which increases the AP1 transcriptional activity, thereby amplifying the expression of inflammatory mediators. Using a computer-aided molecular docking platform, we identified gefitinib as a putative inhibitor of the TIRAP-c-Jun signalling complex. Further, we demonstrated the ability of gefitinib to inhibit the interaction of TIRAP-c-Jun with in vitro experiments and with a mouse model of sepsis. Importantly, pre-treatment with gefitinib increased the survival of the mice that received a lethal dose of LPS compared to that of the controls. These findings verify the ability of gefitinib to directly disrupt the interaction of TIRAP and c-Jun, thereby inhibiting a major inflammatory response that is often observed in patients experiencing sepsis.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

BACKGROUND

Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.

OBJECTIVE

Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.

METHODS

Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.

RESULTS

GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.

CONCLUSION

In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.