Proteome-wide prediction of protein-protein interactions from high-throughput data

Proteome landscape and interactome of voltage-gated potassium channel 1.6 (Kv1.6) of the murine ophthalmic artery and neuroretina

The voltage-gated potassium channel 1.6 (Kv1.6) plays a vital role in ocular neurovascular beds and exerts its modulatory functions via interaction with other proteins. However, the interactome and their potential roles remain unknown. Here, the global proteome landscape of the ophthalmic artery (OA) and neuroretina was mapped, followed by the determination of Kv1.6 interactome and validation of its functionality and cellular localization. Microfluorimetric analysis of intracellular [K+] and Western blot validated the native functionality and cellular expression of the recombinant Kv1.6 channel protein. A total of 54, 9 and 28 Kv1.6-interacting proteins were identified in the mouse OA and, retina of mouse and rat, respectively. The Kv1.6-protein partners in the OA, namely actin cytoplasmic 2, alpha-2-macroglobulin and apolipoprotein A-I, were implicated in the maintenance of blood vessel integrity by regulating integrin-mediated adhesion to extracellular matrix and Ca2+ flux. Many retinal protein interactors, particularly the ADP/ATP translocase 2 and cytoskeleton protein tubulin, were involved in endoplasmic reticulum stress response and cell viability. Three common interactors were found in all samples comprising heat shock cognate 71 kDa protein, Ig heavy constant gamma 1 and Kv1.6 channel. This foremost in-depth investigation enriched and identified the elusive Kv1.6 channel and, elucidated its complex interactome.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

A Comparative Computational Analysis Approach to Predict Significant Protein-Protein Interactions of Human and Vancomycin Resistant Enterococcus faecalis (VRE) to Prioritize Potential Drug Targets

Background:

Various challenges exist in the treatment of infectious diseases due to the significant rise in drug resistance, resulting in the failure of antibiotic treatment. As a consequence, a dire need has arisen for the rethinking of the drug discovery cycle because of the challenge of drug resistance. The underlying cause of the infectious diseases depends upon associations within the Host-pathogen Protein- Protein Interactions (HP-PPIs) network, which represents a key to unlock new pathogenesis mechanisms. Hence, the elucidation of significant PPIs is a promising approach for the identification of potential drug targets.

Objective:

Identification of the most significant HP-PPIs and their partners, and targeting them to prioritize potential new drug targets against Vancomycin-resistant Enterococcus faecalis (VRE).

Methods:

We applied a computational approach based on one of the emerging techniques i.e. Interolog methodology to predict the significant Host-Pathogen PPIs. Structure-Based Studies were applied to model shortlisted protein structures and validate them through PSIPRED, PROCHECK, VERIFY3D, and ERRAT tools. Furthermore, 18,000 drug-like compounds from the ZINC library were docked against these proteins to study protein-chemical interactions using the AutoDock based molecular docking method.

Results:

The study resulted in the identification of 118 PPIs for Enterococcus faecalis, and prioritized two novel drug targets i.e. Exodeoxyribonuclease (ExoA) and ATP-dependent Clp protease proteolytic subunit (ClpP). Consequently, the docking program ranked 2,670 and 3,154 compounds as potential binders against Exodeoxyribonuclease and ATP-dependent Clp protease proteolytic subunit, respectively.

Conclusion:

Thereby, the current study enabled us to identify and prioritize potential PPIs in VRE and their interacting proteins in human hosts along with the pool of novel drug candidates.

Delineating infection strategies of Leishmania donovani secretory proteins in Human through host-pathogen protein Interactome prediction

Interactions of Leishmania donovani secretory virulence factors with the host proteins and their interplay during the infection process in humans is poorly studied in Visceral Leishmaniasis. Lack of a holistic study of pathway level de-regulations caused due to these virulence factors leads to a poor understanding of the parasite strategies to subvert the host immune responses, secure its survival inside the host and further the spread of infection to the visceral organs. In this study, we propose a computational workflow to predict host-pathogen protein interactome of L.donovani secretory virulence factors with human proteins combining sequence-based Interolog mapping and structure-based Domain Interaction mapping techniques. We further employ graph theoretical approaches and shortest path methods to analyze the interactome. Our study deciphers the infection paths involving some unique and understudied disease-associated signaling pathways influencing the cellular phenotypic responses in the host. Our statistical analysis based in silico knockout study unveils for the first time UBC, 1433Z and HS90A mediator proteins as potential immunomodulatory candidates through which the virulence factors employ the infection paths. These identified pathways and novel mediator proteins can be effectively used as possible targets to control and modulate the infection process further aiding in the treatment of Visceral Leishmaniasis.

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

Nerve Injury-Induced Neuronal PAP-I Maintains Neuropathic Pain by Activating Spinal Microglia

Pancreatitis-associated proteins (PAPs) display multiple functions in visceral diseases. Previous studies showed that the expression level of PAP-I was low in the DRG of naive rats but was de novo expressed after peripheral nerve injury. However, its role in neuropathic pain remains unknown. We found that PAP-I expression was continuously upregulated in the DRG neurons from rat spared nerve injury models, and transported toward the spinal dorsal horn to act as a proinflammatory factor. Intrathecal delivery of PAP-I enhanced sensory hyperalgesia, whereas PAP-I deficiency by either gene knockout or antibody application alleviated tactile allodynia at the maintenance phase after spared nerve injury. Furthermore, PAP-I functioned by activating the spinal microglia via C-C chemokine receptor Type 2 that participated in neuropathic pain. Inhibition of either microglial activation or C-C chemokine receptor Type 2 abolished the PAP-I-induced hyperalgesia. Thus, PAP-I mediates the neuron-microglial crosstalk after peripheral nerve injury and contributes to the maintenance of neuropathic pain.SIGNIFICANCE STATEMENT Neuropathic pain is maladaptive pain condition, and the maintaining mechanism is largely unclear. Here we reveal that, after peripheral nerve injury, PAP-I can be transported to the spinal dorsal horn and is crucial in the progression of neuropathic pain. Importantly, we prove that PAP-I mainly functions through activating the spinal microglia via the CCR2-p38 MAPK pathway. Furthermore, we confirm that the proinflammatory effect of PAP-I is more prominent after the establishment of neuropathic pain, thus indicating that microglia also participate in the maintenance phase of neuropathic pain.

The translational network for metabolic disease - from protein interaction to disease co-occurrence

Background

The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing ‘n-of-1 utility’ (n potential diseases of one patient) to human disease network—the translational disease network.

Results

We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network.

Conclusions

The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.

A Cross-Linking-Aided Immunoprecipitation/Mass Spectrometry Workflow Reveals Extensive Intracellular Trafficking in Time-Resolved, Signal-Dependent Epidermal Growth Factor Receptor Proteome

Ligand binding to the cell surface receptors initiates signaling cascades that are commonly transduced through a protein-protein interaction (PPI) network to activate a plethora of response pathways. However, tools to capture the membrane PPI network are lacking. Here, we describe a cross-linking-aided mass spectrometry workflow for isolation and identification of signal-dependent epidermal growth factor receptor (EGFR) proteome. We performed protein cross-linking in cell culture at various time points following EGF treatment, followed by immunoprecipitation of endogenous EGFR and analysis of the associated proteins by quantitative mass spectrometry. We identified 140 proteins with high confidence during a 2 h time course by data-dependent acquisition and further validated the results by parallel reaction monitoring. A large proportion of proteins in the EGFR proteome function in endocytosis and intracellular protein transport. The EGFR proteome was highly dynamic with distinct temporal behavior; 10 proteins that appeared in all time points constitute the core proteome. Functional characterization showed that loss of the FYVE domain-containing proteins altered the EGFR intracellular distribution but had a minor effect on EGFR proteome or signaling. Thus, our results suggest that the EGFR proteome include functional regulators that influence EGFR signaling and bystanders that are captured as the components of endocytic vesicles. The high-resolution spatiotemporal information of these molecules facilitates the delineation of many pathways that could determine the strength and duration of the signaling, as well as the location and destination of the receptor.

Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory

Leptospirosis is the most emerging zoonotic disease of epidemic potential caused by pathogenic species of Leptospira. The bacterium invades the host system and causes the disease by interacting with the host proteins. Analyzing these pathogen-host protein interactions (PHPIs) may provide deeper insight into the disease pathogenesis. For this analysis, inter-species as well as intra-species protein interactions networks of Leptospira interrogans and human were constructed and investigated. The topological analyses of these networks showed lesser connectivity in inter-species network than intra-species, indicating the perturbed nature of the inter-species network. Hence, it can be one of the reasons behind the disease development. A total of 35 out of 586 PHPIs were identified as key interactions based on their sub-cellular localization. Two outer membrane proteins (GpsA and MetXA) and two periplasmic proteins (Flab and GlyA) participating in PHPIs were found conserved in all pathogenic, intermediate and saprophytic spp. of Leptospira. Furthermore, the bacterial membrane proteins involved in PHPIs were found playing major roles in disruption of the immune systems and metabolic processes within host and thereby causing infectious disease. Thus, the present results signify that the membrane proteins participating in such interactions hold potential to serve as effective immunotherapeutic candidates for vaccine development.

Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information

Non-coding RNA (ncRNA) plays a crucial role in numerous biological processes including gene expression and post-transcriptional gene regulation. The biological function of ncRNA is mostly realized by binding with related proteins. Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research. The major challenge at this stage is the waste of a great deal of redundant time and resource consumed on classification in traditional interaction pattern prediction methods. Fortunately, an efficient classifier named LightGBM can solve this difficulty of long time consumption. In this study, we employed LightGBM as the integrated classifier and proposed a novel computational model for predicting ncRNA and protein interactions. More specifically, the pseudo-Zernike Moments and singular value decomposition algorithm are employed to extract the discriminative features from protein and ncRNA sequences. On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively. The experimental results of 10-fold cross-validation shown that the proposed method performs much better than existing methods in predicting ncRNA-protein interaction patterns, which could be used as a useful tool in proteomics research.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome

Background

Proteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory.

Results

We present SPRINT (Scoring PRotein INTeractions), a new sequence-based algorithm and tool for predicting protein-protein interactions. We comprehensively compare SPRINT with state-of-the-art programs on seven most reliable human PPI datasets and show that it is more accurate while running orders of magnitude faster and using very little memory.

Conclusion

SPRINT is the only sequence-based program that can effectively predict the entire human interactome: it requires between 15 and 100 min, depending on the dataset. Our goal is to transform the very challenging problem of predicting the entire human interactome into a routine task.

Availability

The source code of SPRINT is freely available from https://github.com/lucian-ilie/SPRINT/ and the datasets and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1871-x) contains supplementary material, which is available to authorized users.

TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation

The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions.

AVAILABILITY AND IMPLEMENTATION

http://www.vin.bg.ac.rs/180/tools/tfpred.php CONTACT: vladaper@vinca.rs; nevenav@vinca.rsSupplementary information: Supplementary data are available at Bioinformatics online.

SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations

Predicting the effect of amino acid substitutions on protein–protein affinity (typically evaluated via the change of protein binding free energy) is important for both understanding the disease-causing mechanism of missense mutations and guiding protein engineering. In addition, researchers are also interested in understanding which energy components are mostly affected by the mutation and how the mutation affects the overall structure of the corresponding protein. Here we report a webserver, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) webserver, which addresses the demand for tools for predicting the change of protein binding free energy. SAAMBE is an easy to use webserver, which only requires that a coordinate file be inputted and the user is provided with various, but easy to navigate, options. The user specifies the mutation position, wild type residue and type of mutation to be made. The server predicts the binding free energy change, the changes of the corresponding energy components and provides the energy minimized 3D structure of the wild type and mutant proteins for download. The SAAMBE protocol performance was tested by benchmarking the predictions against over 1300 experimentally determined changes of binding free energy and a Pearson correlation coefficient of 0.62 was obtained. How the predictions can be used for discriminating disease-causing from harmless mutations is discussed. The webserver can be accessed via http://compbio.clemson.edu/saambe_webserver/.

Interactive protein network of FXIII-A1 in lipid rafts of activated and non-activated platelets

Lipid-rafts are defined as membrane microdomains enriched in cholesterol and glycosphingolipids within platelet plasma membrane. Lipid raft-mediated clot retraction requires factor XIII and other interacting proteins. The aim of this study was to investigate the proteins that interact with factor XIII in raft and non-raft domains of activated and non-activated platelet plasma membrane. By lipidomics analysis, we identified cholesterol- and sphingomyelin-enriched areas as lipid rafts. Platelets were activated by thrombin. Proteomics analysis provided an overview of the pathways in which proteins of rafts and non-rafts participated in the interaction network of FXIII-A1, a catalytic subunit of FXIII. "Platelet activation" was the principal pathway among KEGG pathways for proteins of rafts, both before and after activation. Network analysis showed four types of interactions (activation, binding, reaction, and catalysis) in raft and non-raft domains in interactive network of FXIII-A1. FXIII-A1 interactions with other proteins in raft domains and their role in homeostasis highlight the specialization of the raft domain in clot retraction via the Factor XIII protein network.

The biochemical and mass spectrometric profiling of the dystrophin complexome from skeletal muscle

The development of advanced mass spectrometric methodology has decisively enhanced the analytical capabilities for studies into the composition and dynamics of multi-subunit protein complexes and their associated components. Large-scale complexome profiling is an approach that combines the systematic isolation and enrichment of protein assemblies with sophisticated mass spectrometry-based identification methods. In skeletal muscles, the membrane cytoskeletal protein dystrophin of 427 kDa forms tight interactions with a variety of sarcolemmal, cytosolic and extracellular proteins, which in turn associate with key components of the extracellular matrix and the intracellular cytoskeleton. A major function of this enormous assembly of proteins, including dystroglycans, sarcoglycans, syntrophins, dystrobrevins, sarcospan, laminin and cortical actin, is postulated to stabilize muscle fibres during the physical tensions of continuous excitation-contraction-relaxation cycles. This article reviews the evidence from recent proteomic studies that have focused on the characterization of the dystrophin-glycoprotein complex and its central role in the establishment of the cytoskeleton-sarcolemma-matrisome axis. Proteomic findings suggest a close linkage of the core dystrophin complex with a variety of protein species, including tubulin, vimentin, desmin, annexin, proteoglycans and collagens. Since the almost complete absence of dystrophin is the underlying cause for X-linked muscular dystrophy, a more detailed understanding of the composition, structure and plasticity of the dystrophin complexome may have considerable biomedical implications.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

Agarose gel shift assay reveals that calreticulin favors substrates with a quaternary structure in solution

Here we present an agarose gel shift assay that, in contrast to other electrophoresis approaches, is loaded in the center of the gel. This allows proteins to migrate in either direction according to their isoelectric points. Therefore, the presented assay enables a direct visualization, separation, and prefractionation of protein interactions in solution independent of isoelectric point. We demonstrate that this assay is compatible with immunochemical methods and mass spectrometry. The assay was used to investigate interactions with several potential substrates for calreticulin, a chaperone that is involved in different biological aspects through interaction with other proteins. The current analytical assays used to investigate these interactions are mainly spectroscopic aggregation assays or solid phase assays that do not provide a direct visualization of the stable protein complex but rather provide an indirect measure of interactions. Therefore, no interaction studies between calreticulin and substrates in solution have been investigated previously. The results presented here indicate that calreticulin has a preference for substrates with a quaternary structure and primarily β-sheets in their secondary structure. It is also demonstrated that the agarose gel shift assay is useful in the study of other protein interactions and can be used as an alternative method to native polyacrylamide gel electrophoresis.

Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs

Background

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.

Results

We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.

Conclusions

This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0535-y) contains supplementary material, which is available to authorized users.

Using network clustering to predict copy number variations associated with health disparities

Substantial health disparities exist between African Americans and Caucasians in the United States. Copy number variations (CNVs) are one form of human genetic variations that have been linked with complex diseases and often occur at different frequencies among African Americans and Caucasian populations. Here, we aimed to investigate whether CNVs with differential frequencies can contribute to health disparities from the perspective of gene networks. We inferred network clusters from human gene/protein networks based on two different data sources. We then evaluated each network cluster for the occurrences of known pathogenic genes and genes located in CNVs with different population frequencies, and used false discovery rates to rank network clusters. This approach let us identify five clusters enriched with known pathogenic genes and with genes located in CNVs with different frequencies between African Americans and Caucasians. These clustering patterns predict two candidate causal genes located in four population-specific CNVs that play potential roles in health disparities

LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information

Protein-protein interaction (PPI) detection is one of the central goals of functional genomics and systems biology. Knowledge about the nature of PPIs can help fill the widening gap between sequence information and functional annotations. Although experimental methods have produced valuable PPI data, they also suffer from significant limitations. Computational PPI prediction methods have attracted tremendous attentions. Despite considerable efforts, PPI prediction is still in its infancy in complex multicellular organisms such as humans. Here, we propose a novel ensemble learning method, LocFuse, which is useful in human PPI prediction. This method uses eight different genomic and proteomic features along with four types of different classifiers. The prediction performance of this classifier selection method was found to be considerably better than methods employed hitherto. This confirms the complex nature of the PPI prediction problem and also the necessity of using biological information for classifier fusion. The LocFuse is available at: http://lbb.ut.ac.ir/Download/LBBsoft/LocFuse.

BIOLOGICAL SIGNIFICANCE

The results revealed that if we divide proteome space according to the cellular localization of proteins, then the utility of some classifiers in PPI prediction can be improved. Therefore, to predict the interaction for any given protein pair, we can select the most accurate classifier with regard to the cellular localization information. Based on the results, we can say that the importance of different features for PPI prediction varies between differently localized proteins; however in general, our novel features, which were extracted from position-specific scoring matrices (PSSMs), are the most important ones and the Random Forest (RF) classifier performs best in most cases. LocFuse was developed with a user-friendly graphic interface and it is freely available for Linux, Mac OSX and MS Windows operating systems.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

Protein-protein interaction network of the marine microalga Tetraselmis subcordiformis: prediction and application for starch metabolism analysis

Under stressful conditions, the non-model marine microalga Tetraselmis subcordiformis can accumulate a substantial amount of starch, making it a potential feedstock for the production of fuel ethanol. Investigating the interactions of the enzymes and the regulatory factors involved in starch metabolism will provide potential genetic manipulation targets for optimising the starch productivity of T. subcordiformis. For this reason, the proteome of T. subcordiformis was utilised to predict the first protein-protein interaction (PPI) network for this marine alga based on orthologous interactions, mainly from the general PPI repositories. Different methods were introduced to evaluate the credibility of the predicted interactome, including the confidence value of each PPI pair and Pfam-based and subcellular location-based enrichment analysis. Functional subnetworks analysis suggested that the two enzymes involved in starch metabolism, starch phosphorylase and trehalose-phosphate synthase may be the potential ideal genetic engineering targets.

Protein-Protein Interactions as New Targets for Ion Channel Drug Discovery

Protein-protein interactions (PPI) are key molecular elements that provide the basis of signaling in virtually all cellular processes. The precision and specificity of these molecular interactions have ignited a strong interest in pursuing PPI surfaces as new targets for drug discovery, especially against ion channels in the central nervous system (CNS) where selectivity and specificity are vital for developing drugs with limited side effects. Ion channels are large transmembrane domain proteins assembled with multiple regulatory proteins binding to the intracellular portion of channels. These macromolecular complexes are difficult to isolate, purify and reconstitute, posing a significant barrier in targeting these PPI for drug discovery purposes. Here, we will provide a short overview of salient features of PPI and discuss successful studies focusing on protein-channel interactions that could inspire new drug discovery campaigns targeting ion channel complexes.

Protein-protein interaction networks: probing disease mechanisms using model systems

Protein-protein interactions (PPIs) and multi-protein complexes perform central roles in the cellular systems of all living organisms. In humans, disruptions of the normal patterns of PPIs and protein complexes can be causative or indicative of a disease state. Recent developments in the biological applications of mass spectrometry (MS)-based proteomics have expanded the horizon for the application of systematic large-scale mapping of physical interactions to probe disease mechanisms. In this review, we examine the application of MS-based approaches for the experimental analysis of PPI networks and protein complexes, focusing on the different model systems (including human cells) used to study the molecular basis of common diseases such as cancer, cardiomyopathies, diabetes, microbial infections, and genetic and neurodegenerative disorders.