Integrating Protein Interaction Research Articles

Deciphering FFAR4 Mediated Functional Pathways and Potential Drug Targets in Diabetes Mellitus: An Integrated Protein Interaction Network Analysis and Kinetic Simulation

Deciphering FFAR4 Mediated Functional Pathways and Potential Drug Targets in Diabetes Mellitus: An Integrated Protein Interaction Network Analysis and Kinetic Simulation

Targeted degradation of zDHHC-PATs decreases substrate S-palmitoylation.

Reversible S-palmitoylation of protein cysteines, catalysed by a family of integral membrane zDHHC-motif containing palmitoyl acyl transferases (zDHHC-PATs), controls the localisation, activity, and interactions of numerous integral and peripheral membrane proteins. There are compelling reasons to want to inhibit the activity of individual zDHHC-PATs in both the laboratory and the clinic, but the specificity of existing tools is poor. Given the extensive conservation of the zDHHC-PAT active site, development of isoform-specific competitive inhibitors is highly challenging. We therefore hypothesised that proteolysis-targeting chimaeras (PROTACs) may offer greater specificity to target this class of enzymes. In proof-of-principle experiments we engineered cell lines expressing tetracycline-inducible Halo-tagged zDHHC5 or zDHHC20, and evaluated the impact of Halo-PROTACs on zDHHC-PAT expression and substrate palmitoylation. In HEK-derived FT-293 cells, Halo-zDHHC5 degradation significantly decreased palmitoylation of its substrate phospholemman, and Halo-zDHHC20 degradation significantly diminished palmitoylation of its substrate IFITM3, but not of the SARS-CoV-2 spike protein. In contrast, in a second kidney derived cell line, Vero E6, Halo-zDHHC20 degradation did not alter palmitoylation of either IFITM3 or SARS-CoV-2 spike. We conclude from these experiments that PROTAC-mediated targeting of zDHHC-PATs to decrease substrate palmitoylation is feasible. However, given the well-established degeneracy in the zDHHC-PAT family, in some settings the activity of non-targeted zDHHC-PATs may substitute and preserve substrate palmitoylation.

Proteostasis networks in aging: novel insights from text-mining approaches

Aging is a topic of paramount importance in an increasingly elderly society and has been the focus of extensive research. Protein homeostasis (proteostasis) decline is a hallmark in aging and several age-related diseases, but which specific proteins and mechanisms are involved in proteostasis (de)regulation during the aging process remain largely unknown. Here, we used different text-mining tools complemented with protein–protein interaction data to address this complex topic. Analysis of the integrated protein interaction networks identified novel proteins and pathways associated to proteostasis mechanisms and aging or age-related disorders, indicating that this approach is useful to identify previously unknown links and for retrieving information of potential novel biomarkers or therapeutic targets.

Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions.

Design of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.

Integrating Protein Interaction Surface Prediction with a Fragment-Based Drug Design: Automatic Design of New Leads with Fragments on Energy Surfaces.

Protein-protein interactions (PPIs) have emerged in the past years as significant pharmacological targets in the development of new therapeutics due to their key roles in determining pathological pathways. Herein, we present fragments on energy surfaces, a simple and general design strategy that integrates the analysis of the dynamic and energetic signatures of proteins to unveil the substructures involved in PPIs, with docking, selection, and combination of drug-like fragments to generate new PPI inhibitor candidates. Specifically, structural representatives of the target protein are used as inputs for the blind physics-based prediction of potential protein interaction surfaces using the matrix of low coupling energy decomposition method. The predicted interaction surfaces are subdivided into overlapping windows that are used as templates to direct the docking and combination of fragments representative of moieties typically found in active drugs. This protocol is then applied and validated using structurally diverse, important PPI targets as test systems. We demonstrate that our approach facilitates the exploration of the molecular diversity space of potential ligands, with no requirement of prior information on the location and properties of interaction surfaces or on the structures of potential lead compounds. Importantly, the hit molecules that emerge from our ab initio design share high chemical similarity with experimentally tested active PPI inhibitors. We propose that the protocol we describe here represents a valuable means of generating initial leads against difficult targets for further development and refinement.

Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data

Many studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein–protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors located in neighboring tissues or in the same cell types, would induce machine make error classification due to the similar gene expression profiles. Furthermore, we observed some patients may exhibit better prognosis if their tumors often misjudged into normal samples. As far as we know, we are the first to generate thousands of cancer networks to predict and classify multiple cancer types with CNN architecture. We believe that our model not only can be applied to cancer diagnosis and prognosis, but also promote the discovery of multiple cancer biomarkers.

Intrinsic disorder in integral membrane proteins.

The well-defined roles and specific protein-protein interactions of many integral membrane proteins (IMPs), such as those functioning as receptors for extracellular matrix proteins and soluble growth factors, easily align with considering IMP structure as a classical "lock-and-key" concept. Nevertheless, continued advances in understanding protein conformation, such as those which established the widespread existence of intrinsically disordered proteins (IDPs) and especially intrinsically disordered regions (IDRs) in otherwise three-dimensionally organized proteins, call for ongoing reevaluation of transmembrane proteins. Here, we present basic traits of IDPs and IDRs, and, for some select single-span IMPs, consider the potential functional advantages intrinsic disorder might provide and the possible conformational impact of disease-associated mutations. For transmembrane proteins in general, we highlight several investigational approaches, such as biophysical and computational methods, stressing the importance of integrating them to produce a more-complete mechanistic model of disorder-containing IMPs. These procedures, when synergized with in-cell assessments, will likely be key in translating in silico and in vitro results to improved understanding of IMP conformational flexibility in normal cell physiology as well as disease, and will help to extend their potential as therapeutic targets.

Inferring Virus-Host relationship between HPV and its host Homo sapiens using protein interaction network

Human papilloma virus (HPV) is a serious threat to human life globally with over 100 genotypes including cancer causing high risk HPVs. Study on protein interaction maps of pathogens with their host is a recent trend in ‘omics’ era and has been practiced by researchers to find novel drug targets. In current study, we construct an integrated protein interaction map of HPV with its host human in Cytoscape and analyze it further by using various bioinformatics tools. We found out 2988 interactions between 12 HPV and 2061 human proteins among which we identified MYLK, CDK7, CDK1, CDK2, JAK1 and 6 other human proteins associated with multiple viral oncoproteins. The functional enrichment analysis of these top-notch key genes is performed using KEGG pathway and Gene Ontology analysis, which reveals that the gene set is enriched in cell cycle a crucial cellular process, and the second most important pathway in which the gene set is involved is viral carcinogenesis. Among the viral proteins, E7 has the highest number of associations in the network followed by E6, E2 and E5. We found out a group of genes which is not targeted by the existing drugs available for HPV infections. It can be concluded that the molecules found in this study could be potential targets and could be used by scientists in their drug design studies.

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis.

Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.

The energetics of protein-lipid interactions as viewed by molecular simulations.

Membranes are formed from a bilayer containing diverse lipid species with which membrane proteins interact. Integral, membrane proteins are embedded in this bilayer, where they interact with lipids from their surroundings, whilst peripheral membrane proteins bind to lipids at the surface of membranes. Lipid interactions can influence the function of membrane proteins, either directly or allosterically. Both experimental (structural) and computational approaches can reveal lipid binding sites on membrane proteins. It is, therefore, important to understand the free energies of these interactions. This affords a more complete view of the engagement of a particular protein with the biological membrane surrounding it. Here, we describe many computational approaches currently in use for this purpose, including recent advances using both free energy and unbiased simulation methods. In particular, we focus on interactions of integral membrane proteins with cholesterol, and with anionic lipids such as phosphatidylinositol 4,5-bis-phosphate and cardiolipin. Peripheral membrane proteins are exemplified via interactions of PH domains with phosphoinositide-containing membranes. We summarise the current state of the field and provide an outlook on likely future directions of investigation.

Expression, purification, and structural analysis of the full-length human integral membrane protein γ-sarcoglycan

Mutation of the gene encoding γ-sarcoglycan (SGCG), an integral membrane protein responsible for maintaining the integrity of the muscle cell sarcolemma, results in Limb-Girdle Muscular Dystrophy (LGMD), a congenital disease with no current treatment options. This member of the sarcoglycan glycoprotein family is a vital component of the Dystrophin Complex, which together facilitate normal muscle function. However, very little is known about the structure and dynamics of these proteins, and of membrane glycoproteins in general. This is due to a number of factors, including their complexity, heterogeneity and highly-specific native environments. The expression, purification, and structural study of membrane proteins is further impeded by their hydrophobic nature and consequent propensity to aggregate in aqueous solutions. Here, we report the first successful expression and purification of milligram quantities of full-length recombinant SGCG, utilizing fusion protein-guided overexpression to inclusion bodies in Escherichia coli. Purification of SGCG from the fusion protein, TrpΔLE, was facilitated using chemical cleavage. Cleavage products were then isolated by size-exclusion chromatography. Successful purification of the protein was confirmed using SDS-PAGE and mass spectroscopy. Finally, solution nuclear magnetic resonance spectroscopy of uniformly 15N-labeled SGCG in detergent environments was performed, yielding the first spectra of the full-length membrane glycoprotein, SGCG. These results represent the initial structural studies of SGCG, laying the foundation for further investigation on the interaction and dynamics of other integral membrane proteins. More specifically, this data allows for opportunities in the future for enhanced treatment modalities and cures for LGMD.

Screening and Identification of Potential Prognostic Biomarkers in Adrenocortical Carcinoma.

Objective: Adrenocortical carcinoma (ACC) is a rare but aggressive malignant cancer that has been attracting growing attention over recent decades. This study aims to integrate protein interaction networks with gene expression profiles to identify potential biomarkers with prognostic value in silico. Methods: Three microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) according to the normalization annotation information. Enrichment analyses were utilized to describe biological functions. A protein–protein interaction network (PPI) of the DEGs was developed, and the modules were analyzed using STRING and Cytoscape. LASSO Cox regression was used to identify independent prognostic factors. The Kaplan–Meier method for the integrated expression score was applied to analyze survival outcomes. A receiver operating characteristic (ROC) curve was constructed with area under curve (AUC) analysis to determine the diagnostic ability of the candidate biomarkers. Results: A total of 150 DEGs and 24 significant hub genes with functional enrichment were identified as candidate prognostic biomarkers. LASSO Cox regression suggested that ZWINT, PRC1, CDKN3, CDK1 and CCNA2 were independent prognostic factors in ACC. In multivariate Cox analysis, the integrated expression scores of the modules showed statistical significance in predicting disease-free survival (DFS, P = 0.019) and overall survival (OS, P < 0.001). Meanwhile, ROC curves were generated to validate the ability of the Cox model to predict prognosis. The AUC index for the integrated genes scores was 0.861 (P < 0.0001). Conclusion: In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of ACC. The expression levels of ZWINT, PRC1, CDKN3, CDK1 and CCNA2 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ACC. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in ACC.

Integrating Gene and Protein Expression Reveals Perturbed Functional Networks in Alzheimer’s Disease

SUMMARYAsymptomatic and symptomatic Alzheimer’s disease (AD) subjects may present with equivalent neuropathological burdens but have significantly different antemortem cognitive decline rates. Using the transcriptome as a proxy for functional state, we selected 414 expression profiles of symptomatic AD subjects and age-matched non-demented controls from a community-based neuropathological study. By combining brain tissue-specific protein interactomes with gene networks, we identified functionally distinct composite clusters of genes that reveal extensive changes in expression levels in AD. Global expression for clusters broadly corresponding to synaptic transmission, metabolism, cell cycle, survival, and immune response were downregulated, while the upregulated cluster included largely uncharacterized processes. We propose that loss of EGR3 regulation mediates synaptic deficits by targeting the synaptic vesicle cycle. Our results highlight the utility of integrating protein interactions with gene perturbations to generate a comprehensive framework for characterizing alterations in the molecular network as applied to AD.

Cell cycle and protein complex dynamics in discovering signaling pathways.

Signaling pathways are responsible for the regulation of cell processes, such as monitoring the external environment, transmitting information across membranes, and making cell fate decisions. Given the increasing amount of biological data available and the recent discoveries showing that many diseases are related to the disruption of cellular signal transduction cascades, in silico discovery of signaling pathways in cell biology has become an active research topic in past years. However, reconstruction of signaling pathways remains a challenge mainly because of the need for systematic approaches for predicting causal relationships, like edge direction and activation/inhibition among interacting proteins in the signal flow. We propose an approach for predicting signaling pathways that integrates protein interactions, gene expression, phenotypes, and protein complex information. Our method first finds candidate pathways using a directed-edge-based algorithm and then defines a graph model to include causal activation relationships among proteins, in candidate pathways using cell cycle gene expression and phenotypes to infer consistent pathways in yeast. Then, we incorporate protein complex coverage information for deciding on the final predicted signaling pathways. We show that our approach improves the predictive results of the state of the art using different ranking metrics.

Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles.

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to "omics" data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. The performed computational experiments suggest that in terms of accuracy the predictive performance of our proposed method was better than those of other machine learning methods such as SVM or Random Forest. Moreover, the computational results also indicate that the underlying protein network structure assists to enhance the predictions. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis.

An Integrated Global Analysis of Compartmentalized HRAS Signaling

Modern omics technologies allow us to obtain global information on different types of biological networks. However, integrating these different types of analyses into a coherent framework for a comprehensive biological interpretation remains challenging. Here, we present a conceptual framework that integrates protein interaction, phosphoproteomics, and transcriptomics data. Applying this method to analyze HRAS signaling from different subcellular compartments shows that spatially defined networks contribute specific functions to HRAS signaling. Changes in HRAS protein interactions at different sites lead to different kinase activation patterns that differentially regulate gene transcription. HRAS-mediated signaling is the strongest from the cell membrane, but it regulates the largest number of genes from the endoplasmic reticulum. The integrated networks provide a topologically and functionally resolved view of HRAS signaling. They reveal distinct HRAS functions including the control of cell migration from the endoplasmic reticulum and TP53-dependent cell survival when signaling from the Golgi apparatus.

Classifying mild traumatic brain injuries with functional network analysis

BackgroundTraumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. TBI is a result of an external force damaging brain tissue, accompanied by delayed pathogenic events which aggravate the injury. Molecular responses to different mild TBI subtypes have not been well characterized. TBI subtype classification is an important step towards the development and application of novel treatments. The computational systems biology approach is proved to be a promising tool in biomarker discovery for central nervous system injury.ResultsIn this study, we have performed a network-based analysis on gene expression profiles to identify functional gene subnetworks. The gene expression profiles were obtained from two experimental models of injury in rats: the controlled cortical impact and the fluid percussion injury. Our method integrates protein interaction information with gene expression profiles to identify subnetworks of genes as biomarkers. We have demonstrated that the selected gene subnetworks are more accurate to classify the heterogeneous responses to different injury models, compared to conventional analysis using individual marker genes selected without network information.ConclusionsThe systems approach can lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks will have important prognostic functions for patients who sustain mild TBIs.

Identification on mitogen-activated protein kinase signaling cascades by integrating protein interaction with transcriptional profiling analysis in cotton

Plant mitogen-activated protein kinase (MAPK) cascades play important roles in development and stress responses. In previous studies, we have systematically investigated the mitogen-activated protein kinase kinase (MKK) and MAPK gene families in cotton. However, the complete interactions between MAPK gene family members in MAPK signaling cascade is poorly characterized. Herein, we investigated the mitogen-activated protein kinase kinase kinase (MAPKKK) family members and identified a total of 89 MAPKKK genes in the Gossypium raimondii genome. We cloned 51 MAPKKKs in G. hirsutum and investigated the interactions between MKK and MAPKKK proteins through yeast-two hybrid assays. A total of 18 interactive protein pairs involved in 14 MAPKKKs and six MKKs were found. Among these, 13 interactive pairs had not been reported previously. Gene expression patterns revealed that 12 MAPKKKs were involved in diverse signaling pathways triggered by hormone treatments or abiotic stresses. By combining the MKK-MAPK and MKK-MAPKKK protein interactions with gene expression patterns, 38 potential MAPK signaling modules involved in the complicated cross-talks were identified, which provide a basis on elucidating biological function of the MAPK cascade in response to hormonal and/or stress responses. The systematic investigation in MAPK signaling cascades will lay a foundation for understanding the functional roles of different MAPK cascades in signal transduction pathways, and for the improvement of various defense responses in cotton.

Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1.

Shewanella oneidensis MR-1 can transfer electrons from the intracellular environment to the extracellular space of the cells to reduce the extracellular insoluble electron acceptors (Extracellular Electron Transfer, EET). Benefiting from this EET capability, Shewanella has been widely used in different areas, such as energy production, wastewater treatment, and bioremediation. Genome-wide proteomics data was used to determine the active proteins involved in activating the EET process. We identified 1012 proteins with decreased expression and 811 proteins with increased expression when the EET process changed from inactivation to activation. We then networked these proteins to construct the active protein networks, and identified the top 20 key active proteins by network centralization analysis, including metabolism- and energy-related proteins, signal and transcriptional regulatory proteins, translation-related proteins, and the EET-related proteins. We also constructed the integrated protein interaction and transcriptional regulatory networks for the active proteins, then found three exclusive active network motifs involved in activating the EET process—Bi-feedforward Loop, Regulatory Cascade with a Feedback, and Feedback with a Protein–Protein Interaction (PPI)—and identified the active proteins involved in these motifs. Both enrichment analysis and comparative analysis to the whole-genome data implicated the multiheme c-type cytochromes and multiple signal processing proteins involved in the process. Furthermore, the interactions of these motif-guided active proteins and the involved functional modules were discussed. Collectively, by using network-based methods, this work reported a proteome-wide search for the key active proteins that potentially activate the EET process.

NetSig: network-based discovery from cancer genomes.

Approaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.