Protein-protein interactions essentials: key concepts to building and analyzing interactome networks

Clinical significance of LOXL4 expression and features of LOXL4-associated protein-protein interaction network in esophageal squamous cell carcinoma

Lysyl oxidase-like 4 (LOXL4), a member of the LOX family proteins, catalyzes oxidative deamination of lysine residues in collagen and elastin, which are responsible for maintaining extracellular matrix homeostasis. In this study, the mRNA expression of LOXL4 in seven esophageal squamous cell carcinoma (ESCC) cell lines and 15 ESCC pairs of clinical samples were examined. Furthermore, LOXL4 protein levels in the ESCC cell lines were determined using western blotting. With the use of immunofluorescence, LOXL4 was observed to be localized primarily in the cytoplasm, but was also present in the nucleus. In addition, the results indicated that the upregulated expression of LOXL4 was associated with poor survival in patients with ESCC even following curative resection (P = 0.010). Similar Kaplan-Meier estimator curves for proteins that interact with LOXL4, SUV39H1 (P = 0.014) and COL2A1 (P = 0.011), were plotted. The analyses based on the protein-protein interaction network depicted the expression of LOXL4 and its associated proteins as well as their functions, suggesting that LOXL4 and its associated proteins may serve a significant role in the development and progression of ESCC. In conclusion, the results of the present study suggest that LOXL4 is a potential biomarker for patients with ESCC, as well as SUV39H1 and COL2A1, and high expression levels of these genes are associated with poor prognosis in patients with ESCC.

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.

Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks

Background

Detecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems.

Methods

In this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores.

Results

The experimental results on three different datasets (DIP, MIPS and Krogan) show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes.

Conclusions

Our proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods.

MorCVD: A Unified Database for Host-Pathogen Protein-Protein Interactions of Cardiovascular Diseases Related to Microbes

Microbe induced cardiovascular diseases (CVDs) are less studied at present. Host-pathogen interactions (HPIs) between human proteins and microbial proteins associated with CVD can be found dispersed in existing molecular interaction databases. MorCVD database is a curated resource that combines 23,377 protein interactions between human host and 432 unique pathogens involved in CVDs in a single intuitive web application. It covers endocarditis, myocarditis, pericarditis and 16 other microbe induced CVDs. The HPI information has been compiled, curated, and presented in a freely accessible web interface (http://morcvd.sblab-nsit.net/About). Apart from organization, enrichment of the HPI data was done by adding hyperlinked protein ID, PubMed, gene ontology records. For each protein in the database, drug target and interactors (same as well as different species) information has been provided. The database can be searched by disease, protein ID, pathogen name or interaction detection method. Interactions detected by more than one method can also be listed. The information can be presented in tabular form or downloaded. A comprehensive help file has been developed to explain the various options available. Hence, MorCVD acts as a unified resource for retrieval of HPI data for researchers in CVD and microbiology.

Functional expression and impact of testis-specific gene antigen 10 in breast cancer: a combined in vitro and in silico analysis

Introduction: Testis-specific gene antigen 10 (TSGA10) is a less-known gene, which is involved in the vague biological paths of different cancers. Here, we investigated the TSGA10 expression using different concentrations of glucose under hypoxia and also its interaction with the hypoxia-inducible factor 1 (HIF-1). Methods: The breast cancer MDA-MB-231 and MCF-7 cells were cultured with different concentrations of glucose (5.5, 11.0 and 25.0 mM) under normoxia/hypoxia for 24, 48, and 72 hours and examined for the HIF-1α expression and cell migration by Western blotting and scratch assays. The qPCR was employed to analyze the expression of TSGA10. Three-dimensional (3D) structure and the energy minimization of the interacting domain of TSGA10 were performed by MODELLER v9.17 and Swiss-PDB viewer v4.1.0/UCSF Chimera v1.11. The UCSF Chimera v1.13.1 and Hex 6.0 were used for the molecular docking simulation. The Cytoscape v3.7.1 and STRING v11.0 were used for protein-protein interaction (PPI) network analysis. The HIF-1a related hypoxia pathways were obtained from BioModels database and reconstructed in CellDesigner v4.4.2. Results: The increased expression of TSGA10 was found to be significantly associated with the reduced metastasis in the MDA-MB-231 cells, while an inverse relationship was seen between the TSGA10 mRNA level and cellular migration but not in the MCF-7 cells. The C-terminal domain of TSGA10 interacted with HIF-1α with high affinity, resulting in PPI network with 10 key nodes (HIF-1α, VEGFA, HSP90AA1, AKT1, ARNT, TP53, TSGA10, VHL, JUN, and EGFR). Conclusions: Collectively, TSGA10 functional expression alters under the hyper-/hypo-glycemia and hypoxia, which indicates its importance as a candidate bio-target for the cancer therapy.

A Comprehensive Survey of Tools and Software for Active Subnetwork Identification

A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose.

Tuning the properties of a cyclic RGD-containing tetrapeptide through backbone fluorination

Stereoselective fluorination is investigated as a method for modulating the properties of a cyclic RGD-containing tetrapeptide. Three key outcomes of fluorination are assessed: (i) the effect on peptide cyclisation efficiency; (ii) the ability to fine-tune the molecular conformation; and (iii) the effect on the cyclic peptides' biological activity. Fluorination is found to exert pronounced effects against all three criteria.

Network Analysis Reveals TNF as a Major Hub of Reactive Inflammation Following Spinal Cord Injury

Spinal cord injury (SCI) leads to reactive inflammation and other harmful events that limit spinal cord regeneration. We propose an approach for studying the mechanisms at the levels of network topology, gene ontology, signaling pathways, and disease inference. We treated inflammatory mediators as toxic chemicals and retrieved the genes and interacting proteins associated with them via a set of biological medical databases and software. We identified >10,000 genes associated with SCI. Tumor necrosis factor (TNF) had the highest scores, and the top 30 were adopted as core data. In the core interacting protein network, TNF and other top 10 nodes were the major hubs. The core members were involved in cellular responses and metabolic processes, as components of the extracellular space and regions, in protein-binding and receptor-binding functions, as well as in the TNF signaling pathway. In addition, both seizures and SCI were highly associated with TNF levels; therefore, for achieving a better curative effect on SCI, TNF and other major hubs should be targeted together according to the theory of network intervention, rather than a single target such as TNF alone. Furthermore, certain drugs used to treat epilepsy could be used to treat SCI as adjuvants.

In Silico Approaches for TRP Channel Modulation

The implication of several TRP ion channels (e.g., TRPV1) in diverse physiological and pathological processes has signaled them as pivotal drug targets. Consequently, the identification of selective and potent ligands for these channels is of great interest in pharmacology and biomedicine. However, a major challenge in the design of modulators is ensuring the specificity for their intended targets. In recent years, the emergence of high-resolution structures of ion channels facilitates the computer-assisted drug design at molecular levels. Here we describe some computational methods and general protocols to discover channel modulators, including homology modelling, docking and virtual screening, and structure-based peptide design.

The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs)

Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.

The Galaxy Platform for Reproducible Affinity Proteomic Mass Spectrometry Data Analysis

Affinity proteomics (AP-MS) is growing in importance for characterizing protein-protein interactions (PPIs) in the form of protein complexes and signaling networks. The AP-MS approach necessitates several different software tools, integrated into reproducible and accessible workflows. However, if the scientist (e.g., a bench biologist) lacks a computational background, then managing large AP-MS datasets can be challenging, manually formatting AP-MS data for input into analysis software can be error-prone, and data visualization involving dozens of variables can be laborious. One solution to address these issues is Galaxy, an open source and web-based platform for developing and deploying user-friendly computational pipelines or workflows. Here, we describe a Galaxy-based platform enabling AP-MS analysis. This platform enables researchers with no prior computational experience to begin with data from a mass spectrometer (e.g., peaklists in mzML format) and perform peak processing, database searching, assignment of interaction confidence scores, and data visualization with a few clicks of a mouse. We provide sample data and a sample workflow with step-by-step instructions to quickly acquaint users with the process.

APID database: redefining protein-protein interaction experimental evidences and binary interactomes

The collection and integration of all the known protein–protein physical interactions within a proteome framework are critical to allow proper exploration of the protein interaction networks that drive biological processes in cells at molecular level. APID Interactomes is a public resource of biological data (http://apid.dep.usal.es) that provides a comprehensive and curated collection of `protein interactomes’ for more than 1100 organisms, including 30 species with more than 500 interactions, derived from the integration of experimentally detected protein-to-protein physical interactions (PPIs). We have performed an update of APID database including a redefinition of several key properties of the PPIs to provide a more precise data integration and to avoid false duplicated records. This includes the unification of all the PPIs from five primary databases of molecular interactions (BioGRID, DIP, HPRD, IntAct and MINT), plus the information from two original systematic sources of human data and from experimentally resolved 3D structures (i.e. PDBs, Protein Data Bank files, where more than two distinct proteins have been identified). Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions (following the `ontology and controlled vocabulary’: www.ebi.ac.uk/ols/ontologies/mi; developed by `HUPO PSI-MI’). Within this data mining framework, all interaction detection methods have been grouped into two main types: (i) `binary’ physical direct detection methods and (ii) `indirect’ methods. As a result of these redefinitions, APID provides unified protein interactomes including the specific `experimental evidences’ that support each PPI, indicating whether the interactions can be considered `binary’ (i.e. supported by at least one binary detection method) or not.

Identification of hub genes and outcome in colon cancer based on bioinformatics analysis

Background

Colon cancer is one of the leading malignant neoplasms worldwide. Until now, the concrete mechanisms of colonic cancerogenesis are largely unknown; identification of driven genes and pathways is, therefore, of great importance for monitoring and conquering this disease. This study aims to explore the potential biomarkers and therapeutic targets for colon cancer treatment.

Methods

The gene expression profile of GSE44076 from Gene Expression Omnibus database, including 98 primary colon cancers and 98 normal distant colon mucosa, was deeply analyzed. GEO2R tool was used to screen the differentially expressed genes (DEGs) between colon cancer tissues and normal samples. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed for screening DEGs using Database for Annotation, Visualization and Integrated Discovery database and Panther database. Moreover, Search Tool for the Retrieval of Interacting Genes, Cytoscape software, and Molecular Complex Detection plug-in were used to visualize the protein-protein interaction of these DEGs.

Results

A total of 497 DEGs were obtained, including 129 upregulated genes mainly enriched in Hippo signaling pathway, Wnt signaling pathway, and cytokine-cytokine receptor interaction and 368 downregulated genes enriched in retinol metabolism, steroid hormone biosynthesis, drug metabolism, and chemical carcinogenesis. Using Molecular Complex Detection software, three important modules were selected from the protein-protein interaction network. Moreover, 20 hub genes with high degree of connectivity were selected, including COL1A1, CXCL5, GNG4, TIMP1, and so on. The Kaplan-Meier analysis for overall survival and correlation analysis were applied among the hub genes.

Conclusion

Taken together, DEGs, especially the hub genes such as COL1A1, might be the driven genes in colon cancer progression. More importantly, they might be the novel biomarkers for diagnosis and guiding therapeutic strategies of colon cancer.

PPIExp: A Web-Based Platform for Integration and Visualization of Protein-Protein Interaction Data and Spatiotemporal Proteomics Data

Integrating spatiotemporal proteomics data with protein-protein interaction (PPI) data can help researchers make an in-depth exploration of their proteins of interest in a dynamic manner. However, there is still a lack of proper tools for the biologists who usually have few programming skills to construct a PPI network for a protein list, visualize active PPI subnetworks, and then select key nodes for further study. We propose a web-based platform named PPIExp that can automatically construct a PPI network, perform clustering analysis according to protein abundances, and perform functional enrichment analysis. More importantly, it provides multiple effective visualization interfaces, such as the interface to display the PPI network map, the interface to display a dendrogram and heatmap for the clustering result, and the interface to display the expression pattern of a selected protein. To visualize the active PPI subnetworks in specific space or time, it provides buttons to highlight the differentially expressed proteins under each condition on the network map. Additionally, to help researchers determine which proteins are worth further attention, PPIExp provides extensive one-click interactive operations to map node centrality measures to node size on the network and highlight three types of proteins, that is, the proteins in an enriched functional term, the coexpressed proteins selected from the dendgrogram and heatmap, and the proteins input by users. PPIExp is available at http://www.fgvis.com/expressvis/PPIExp .

A nearest-neighbors network model for sequence data reveals new insight into genotype distribution of a pathogen

BACKGROUND

Sequence similarity networks are useful for classifying and characterizing biologically important proteins. Threshold-based approaches to similarity network construction using exact distance measures are prohibitively slow to compute and rely on the difficult task of selecting an appropriate threshold, while similarity networks based on approximate distance calculations compromise useful structural information.

RESULTS

We present an alternative network representation for a set of sequence data that overcomes these drawbacks. In our model, called the Directed Weighted All Nearest Neighbors (DiWANN) network, each sequence is represented by a node and is connected via a directed edge to only the closest sequence, or sequences in the case of ties, in the dataset. Our contributions span several aspects. Specifically, we: (i) Apply an all nearest neighbors network model to protein sequence data from three different applications and examine the structural properties of the networks; (ii) Compare the model against threshold-based networks to validate their semantic equivalence, and demonstrate the relative advantages the model offers; (iii) Demonstrate the model's resilience to missing sequences; and (iv) Develop an efficient algorithm for constructing a DiWANN network from a set of sequences. We find that the DiWANN network representation attains similar semantic properties to threshold-based graphs, while avoiding weaknesses of both high and low threshold graphs. Additionally, we find that approximate distance networks, using BLAST bitscores in place of exact edit distances, can cause significant loss of structural information. We show that the proposed DiWANN network construction algorithm provides a fourfold speedup over a standard threshold based approach to network construction. We also identify a relationship between the centrality of a sequence in a similarity network of an Anaplasma marginale short sequence repeat dataset and how broadly that sequence is dispersed geographically.

CONCLUSION

We demonstrate that using approximate distance measures to rapidly construct similarity networks may lead to significant deficiencies in the structure of that network in terms centrality and clustering analyses. We present a new network representation that maintains the structural semantics of threshold-based networks while increasing connectedness, and an algorithm for constructing the network using exact distance measures in a fraction of the time it would take to build a threshold-based equivalent.

Uncovering the mechanism of Maxing Ganshi Decoction on asthma from a systematic perspective: A network pharmacology study

Maxing Ganshi Decoction (MXGSD) is used widely for asthma over thousands of years, but its underlying pharmacological mechanisms remain unclear. In this study, systematic and comprehensive network pharmacology was utilized for the first time to reveal the potential pharmacological mechanisms of MXGSD on asthma. Specifically, we collected 141 bioactive components from the 600 components in MXGSD, which shared 52 targets common to asthma-related ones. In-depth network analysis of these 52 common targets indicated that asthma might be a manifestation of systemic neuro-immuno-inflammatory dysfunction in the respiratory system, and MXGSD could treat asthma through relieving airway inflammation, improving airway remodeling, and increasing drug responsiveness. After further cluster and enrichment analysis of the protein-protein interaction network of MXGSD bioactive component targets and asthma-related targets, we found that the neurotrophin signaling pathway, estrogen signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway might serve as the key points and principal pathways of MXGSD gene therapy for asthma from a systemic and holistic perspective, and also provides a novel idea for the development of new drugs for asthma.

Rare Copy Number Variants Identify Novel Genes in Sporadic Total Anomalous Pulmonary Vein Connection

Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart anomaly. Several genes have been associated TAPVC but the mechanisms remain elusive. To search novel CNVs and candidate genes, we screened a cohort of 78 TAPVC cases and 100 healthy controls for rare copy number variants (CNVs) using whole exome sequencing (WES). Then we identified pathogenic CNVs by statistical comparisons between case and control groups. After that, we identified altogether eight pathogenic CNVs of seven candidate genes (PCSK7, RRP7A, SERHL, TARP, TTN, SERHL2, and NBPF3). All these seven genes have not been described previously to be related to TAPVC. After network analysis of these candidate genes and 27 known pathogenic genes derived from the literature and publicly database, PCSK7 and TTN were the most important genes for TAPVC than other genes. Our study provides novel candidate genes potentially related to this rare congenital birth defect (CHD) which should be further fundamentally researched and discloses the possible molecular pathogenesis of TAPVC.

Molecular characterization of the interaction of sialic acid with the periplasmic binding protein from Haemophilus ducreyi

The primary role of bacterial periplasmic binding proteins is sequestration of essential metabolites present at a low concentration in the periplasm and making them available for active transporters that transfer these ligands into the bacterial cell. The periplasmic binding proteins (SiaPs) from the tripartite ATP-independent periplasmic (TRAP) transport system that transports mammalian host-derived sialic acids have been well studied from different pathogenic bacteria, including Haemophilus influenzae, Fusobacterium nucleatum, Pasteurella multocida, and Vibrio cholerae SiaPs bind the sialic acid N-acetylneuraminic acid (Neu5Ac) with nanomolar affinity by forming electrostatic and hydrogen-bonding interactions. Here, we report the crystal structure of a periplasmic binding protein (SatA) of the ATP-binding cassette (ABC) transport system from the pathogenic bacterium Haemophilus ducreyi The structure of Hd-SatA in the native form and sialic acid-bound forms (with Neu5Ac and N-glycolylneuraminic acid (Neu5Gc)), determined to 2.2, 1.5, and 2.5 Å resolutions, respectively, revealed a ligand-binding site that is very different from those of the SiaPs of the TRAP transport system. A structural comparison along with thermodynamic studies suggested that similar affinities are achieved in the two classes of proteins through distinct mechanisms, one enthalpically driven and the other entropically driven. In summary, our structural and thermodynamic characterization of Hd-SatA reveals that it binds sialic acids with nanomolar affinity and that this binding is an entropically driven process. This information is important for future structure-based drug design against this pathogen and related bacteria.

Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells

The information of how two proteins interact is embedded in the atomic details of their binding interfaces. These interactions, spatial-temporally coordinating each other as a network in a variable cytoplasmic environment, dominate almost all biological functions. A feasible and reliable computational model is highly demanded to realistically simulate these cellular processes and unravel the complexities beneath them. We therefore present a multiscale framework that integrates simulations on two different scales. The higher-resolution model incorporates structural information of proteins and energetics of their binding, while the lower-resolution model uses a highly simplified representation of proteins to capture the long-time-scale dynamics of a system with multiple proteins. Through a systematic benchmark test and two practical applications of biomolecular systems with specific cellular functions, we demonstrated that this method could be a powerful approach to understand molecular mechanisms of dynamic interactions between biomolecules and their functional impacts with high computational efficiency.

Prediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks

Background

Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from protein sequences. In contrast, protein interactions have been less investigated.

Objectives

As protein interactions usually occur in the same or adjacent places, using this feature to find the location would be efficient and impressive. This study did not aim at increasing the total accuracy of the conducted research. The study has focused on the features of the proteins’ interaction and their employment which lead to a higher accuracy.

Materials and Methods

In this study, we have examined the protein interaction network as one of the features for prediction of the protein localization and its effects on the prediction results. In this regards, we have gathered some of the most common features including Amino Acid Composition, Dipeptide Compositions, Pseudo Amino Acid Compositions (PseAAC), Position Specific Scoring Matrix (PSSM), Functional Domain, Gene Ontology information, and the Pair-wise sequence alignment. The results of the classification are compared to the ones using protein interactions. For achieving this goal different machine learning algorithms were tested.

Results

The best-obtained results of using single feature set obtained using SVM classifier for PseAAC feature. The accuracy of combining all features with PPI data, using the Decision Tree and Random Forest classifiers, was 82.49% and 83.35%, respectively. In another experiment, using just protein interaction data with the different cutting points resulted in obtaining an accuracy of 93.035% for the protein location prediction.

Conclusion

In total, it was shown that protein(s) interaction has a significant impact on the prediction of the mitochondrial proteins’ location. This feature can separately distinguish the locations well. Using this feature the accuracy of the results is raised up to 5%.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

The Application of Dynamic Models to the Exploration of β1-AR Overactivation as a Cause of Heart Failure

High titer of β₁-adrenoreceptor autoantibodies (β₁-AA) has been reported to appear in heart failure patients. It induces sustained β₁-adrenergic receptor (β₁-AR) activation which leads to heart failure (HF), but the mechanism is as yet unclear. In order to investigate the mechanisms causing β₁-AR non-desensitization, we studied the beating frequency of the neonatal rat cardiomyocytes (NRCMs) under different conditions (an injection of isoprenaline (ISO) for one group and β₁-AA for the other) and established three dynamic models in order to best describe the true relationships shown in medical experiments; one model used a control group of healthy rats; then in HF rats one focused on conformation changes in β₁-AR; the other examined interaction between β₁-AR and β₂-adrenergic receptors (β₂-AR). Comparing the experimental data and corresponding Akaike information criterion (AIC) values, we concluded that the interaction model was the most likely mechanism. We used mathematical methods to explore the mechanism for the development of heart failure and to find potential targets for prevention and treatment. The aim of the paper was to provide a strong theoretical basis for the clinical development of personalized treatment programs. We also carried out sensitivity analysis of the initial concentration β₁-AA and found that they had a noticeable effect on the fitting results.

LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains

Protein-peptide interactions form an important subset of the total protein interaction network in the cell and play key roles in signaling and regulatory networks, and in major biological processes like cellular localization, protein degradation, and immune response. In this work, we have described the LMDIPred web server, an online resource for generalized prediction of linear peptide sequences that may bind to three most prevalent and well-studied peptide recognition modules (PRMs)—SH3, WW and PDZ. We have developed support vector machine (SVM)-based prediction models that achieved maximum Matthews Correlation Coefficient (MCC) of 0.85 with an accuracy of 94.55% for SH3, MCC of 0.90 with an accuracy of 95.82% for WW, and MCC of 0.83 with an accuracy of 92.29% for PDZ binding peptides. LMDIPred output combines predictions from these SVM models with predictions using Position-Specific Scoring Matrices (PSSMs) and string-matching methods using known domain-binding motif instances and regular expressions. All of these methods were evaluated using a five-fold cross-validation technique on both balanced and unbalanced datasets, and also validated on independent datasets. LMDIPred aims to provide a preliminary bioinformatics platform for sequence-based prediction of probable binding sites for SH3, WW or PDZ domains.

Proteomic analysis of canine oral tumor tissues using MALDI-TOF mass spectrometry and in-gel digestion coupled with mass spectrometry (GeLC MS/MS) approaches

Oral tumors, including highly invasive and metastatic oral melanoma (OM), non-tonsillar oral squamous cell carcinoma (OSCC) and benign tumors (BN), are common neoplasms in dogs. Although these tumors behave differently, limited data of their protein expression profiles have been exhibited, particularly at the proteome level. The present study aimed to i.) characterize peptide-mass fingerprints (PMFs) and identify potential protein candidates of OM, OSCC, BN and normal control subjects, using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and liquid chromatography tandem mass spectrometry (LC-MS/MS), ii.) identify potential protein candidates associated with the diseases, using in-gel digestion coupled with mass spectrometric analysis (GeLC-MS/MS) and iii.) search for relationships between chemotherapy drugs and disease-perturbed proteins. A distinct cluster of each sample group and unique PMFs with identified protein candidates were revealed. The unique peptide fragment at 2,274 Da of sacsin molecular chaperone (SACS) was observed in early-stage OM whereas the fragment at 1,958 Da of sodium voltage-gated channel alpha subunit 10 (SCN10A) was presented in early- and late-stage OM. The peptide mass at 2,316 Da of Notch1 appeared in early-stage OM and benign oral tumors while the peptide mass at 2,505 Da of glutamate ionotropic receptor N-methyl-D-aspartate type subunit 3A (GRIN3A) was identified in all groups. Markedly expressed proteins from GeLC-MS/MS included Jumonji domain containing 1C (JMJD1C) in benign tumors, inversin (INVS) and rho guanine nucleotide exchange factor 28 (ARHGEF28) in OM, BTB domain-containing 16 (BTBD16) in OSCC, and protein tyrosine phosphatase non-receptor type 1 (PTPN1), BRCA2, DNA repair associated (BRCA2), WW domain binding protein 2 (WBP2), purinergic receptor P2Y1 and proteasome activator subunit 4 (PSME4) in all cancerous groups. The network connections between these proteins and chemotherapy drugs, cisplatin and doxorubicin, were also demonstrated. In conclusion, this study unveiled the unique PMFs and novel candidate protein markers of canine oral tumors.

LuTHy: a double-readout bioluminescence-based two-hybrid technology for quantitative mapping of protein-protein interactions in mammalian cells

Information on protein-protein interactions (PPIs) is of critical importance for studying complex biological systems and developing therapeutic strategies. Here, we present a double-readout bioluminescence-based two-hybrid technology, termed LuTHy, which provides two quantitative scores in one experimental procedure when testing binary interactions. PPIs are first monitored in cells by quantification of bioluminescence resonance energy transfer (BRET) and, following cell lysis, are again quantitatively assessed by luminescence-based co-precipitation (LuC). The double-readout procedure detects interactions with higher sensitivity than traditional single-readout methods and is broadly applicable, for example, for detecting the effects of small molecules or disease-causing mutations on PPIs. Applying LuTHy in a focused screen, we identified 42 interactions for the presynaptic chaperone CSPα, causative to adult-onset neuronal ceroid lipofuscinosis (ANCL), a progressive neurodegenerative disease. Nearly 50% of PPIs were found to be affected when studying the effect of the disease-causing missense mutations L115R and ∆L116 in CSPα with LuTHy. Our study presents a robust, sensitive research tool with high utility for investigating the molecular mechanisms by which disease-associated mutations impair protein activity in biological systems.

Systems biology in hepatology: approaches and applications

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

Inter-residue, inter-protein and inter-family coevolution: bridging the scales

Interacting proteins coevolve at multiple but interconnected scales, from the residue-residue over the protein-protein up to the family-family level. The recent accumulation of enormous amounts of sequence data allows for the development of novel, data-driven computational approaches. Notably, these approaches can bridge scales within a single statistical framework. Although being currently applied mostly to isolated problems on single scales, their immense potential for an evolutionary informed, structural systems biology is steadily emerging.

GC[Formula: see text]NMF: A Novel Matrix Factorization Framework for Gene-Phenotype Association Prediction

Gene-phenotype association prediction can be applied to reveal the inherited basis of human diseases and facilitate drug development. Gene-phenotype associations are related to complex biological processes and influenced by various factors, such as relationship between phenotypes and that among genes. While due to sparseness of curated gene-phenotype associations and lack of integrated analysis of the joint effect of multiple factors, existing applications are limited to prediction accuracy and potential gene-phenotype association detection. In this paper, we propose a novel method by exploiting weighted graph constraint learned from hierarchical structures of phenotype data and group prior information among genes by inheriting advantages of Non-negative Matrix Factorization (NMF), called Weighted Graph Constraint and Group Centric Non-negative Matrix Factorization (GC[Formula: see text]NMF). Specifically, first we introduce the depth of parent-child relationships between two adjacent phenotypes in hierarchical phenotypic data as weighted graph constraint for a better phenotype understanding. Second, we utilize intra-group correlation among genes in a gene group as group constraint for gene understanding. Such information provides us with the intuition that genes in a group probably result in similar phenotypes. The model not only allows us to achieve a high-grade prediction performance, but also helps us to learn interpretable representation of genes and phenotypes simultaneously to facilitate future biological analysis. Experimental results on biological gene-phenotype association datasets of mouse and human demonstrate that GC[Formula: see text]NMF can obtain superior prediction accuracy and good understandability for biological explanation over other state-of-the-arts methods.

Identifying genes as potential prognostic indicators in patients with serous ovarian cancer resistant to carboplatin using integrated bioinformatics analysis

Serous ovarian cancer (SOC) accounts for >50% of all epithelial ovarian cancers. However, patients with SOC present with various degrees of response to platinum‑based chemotherapy and, thus, their survival may differ. The present study aimed to identify the candidate genes involved in the carcinogenesis and drug resistance of SOC by analyzing the microarray datasets GDS1381 and GDS3592. GDS1381 and GDS3592 were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/gds/). A total of 219 differentially expressed genes (DEGs) were identified. Potential genes that may predict the response to carboplatin and, thus, the prognosis of SOC were analyzed. The enriched functions and pathways of DEGs included extracellular region, extracellular space and extracellular exosome, among others. Upon screening the upregulated and downregulated genes on the connectivity map, 10 small‑molecule drugs were identified that may be helpful in improving drug sensitivity in patients with ovarian cancer. A total of 30 hub genes were screened for further analysis after constructing the protein‑to‑protein interaction network. Through survival analysis, comparison of genes across numerous analyses, and immunohistochemistry, GNAI1, non‑structural maintenance of chromosomes (non‑SMC) condensin I complex subunit H (NCAPH), matrix metallopeptidase 9 (MMP9), aurora kinase A (AURKA) and enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) were identified as the key molecules that may be involved in the carcinogenesis and carboplatin resistance of SOC. In conclusion, GNAI1, NCAPH, MMP9, AURKA and EZH2 should be examined in further studies for the possibility of their participation in the carcinogenesis and carboplatin response of SOC.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Recent Progress in CFTR Interactome Mapping and Its Importance for Cystic Fibrosis

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride channel found in secretory epithelia with a plethora of known interacting proteins. Mutations in the CFTR gene cause cystic fibrosis (CF), a disease that leads to progressive respiratory illness and other complications of phenotypic variance resulting from perturbations of this protein interaction network. Studying the collection of CFTR interacting proteins and the differences between the interactomes of mutant and wild type CFTR provides insight into the molecular machinery of the disease and highlights possible therapeutic targets. This mini review focuses on functional genomics and proteomics approaches used for systematic, high-throughput identification of CFTR-interacting proteins to provide comprehensive insight into CFTR regulation and function.

A Biomolecular Network Driven Proteinic Interaction in HCV Clearance

Hepatitis C virus infection causes chronic liver disease that leads to cancer-related mortality. Presently around 30% of the HCV (infected) affected population get rid of the infection through spontaneous disease clearance. This phenomenon is conducted by a set of reported immune candidate genes. Hence, this study focuses only on these immune-response related genes with aid of network approach, where the idea is to disseminate the network for better understanding of key functional genes and their transcription control activity. Based on the network analysis the IFNG, TNF, IFNB1, STAT1, NFKB1, STAT3, SOCS1, and MYD88 genes are prioritized as hub genes along with their common transcription factors (TFs), IRF9, NFKB1, and STAT1. The dinucleotide frequency of TF binding elements indicated GG-rich motifs in these regulatory elements. On the other hand, gene enrichment report suggests the regulation of response to interferon gamma signaling pathway, which plays central role in the spontaneous HCV clearance. Therefore, our study tends to prioritize the genes, TFs, and their regulatory pathway towards HCV clearance. Even so, the resultant hub genes and their TFs and TF binding elements could be crucial in underscoring the clearance activity in specific populations.

Data integration and predictive modeling methods for multi-omics datasets

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions

Background

Protein post-translational modification (PTM) plays an essential role in various cellular processes that modulates the physical and chemical properties, folding, conformation, stability and activity of proteins, thereby modifying the functions of proteins. The improved throughput of mass spectrometry (MS) or MS/MS technology has not only brought about a surge in proteome-scale studies, but also contributed to a fruitful list of identified PTMs. However, with the increase in the number of identified PTMs, perhaps the more crucial question is what kind of biological mechanisms these PTMs are involved in. This is particularly important in light of the fact that most protein-based pharmaceuticals deliver their therapeutic effects through some form of PTM. Yet, our understanding is still limited with respect to the local effects and frequency of PTM sites near pharmaceutical binding sites and the interfaces of protein-protein interaction (PPI). Understanding PTM’s function is critical to our ability to manipulate the biological mechanisms of protein.

Results

In this study, to understand the regulation of protein functions by PTMs, we mapped 25,835 PTM sites to proteins with available three-dimensional (3D) structural information in the Protein Data Bank (PDB), including 1785 modified PTM sites on the 3D structure. Based on the acquired structural PTM sites, we proposed to use five properties for the structural characterization of PTM substrate sites: the spatial composition of amino acids, residues and side-chain orientations surrounding the PTM substrate sites, as well as the secondary structure, division of acidity and alkaline residues, and solvent-accessible surface area. We further mapped the structural PTM sites to the structures of drug binding and PPI sites, identifying a total of 1917 PTM sites that may affect PPI and 3951 PTM sites associated with drug-target binding. An integrated analytical platform (CruxPTM), with a variety of methods and online molecular docking tools for exploring the structural characteristics of PTMs, is presented. In addition, all tertiary structures of PTM sites on proteins can be visualized using the JSmol program.

Conclusion

Resolving the function of PTM sites is important for understanding the role that proteins play in biological mechanisms. Our work attempted to delineate the structural correlation between PTM sites and PPI or drug-target binding. CurxPTM could help scientists narrow the scope of their PTM research and enhance the efficiency of PTM identification in the face of big proteome data. CruxPTM is now available at http://csb.cse.yzu.edu.tw/CruxPTM/.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0506-1) contains supplementary material, which is available to authorized users.

Functional Analysis of Human Hub Proteins and Their Interactors Involved in the Intrinsic Disorder-Enriched Interactions

Some of the intrinsically disordered proteins and protein regions are promiscuous interactors that are involved in one-to-many and many-to-one binding. Several studies have analyzed enrichment of intrinsic disorder among the promiscuous hub proteins. We extended these works by providing a detailed functional characterization of the disorder-enriched hub protein-protein interactions (PPIs), including both hubs and their interactors, and by analyzing their enrichment among disease-associated proteins. We focused on the human interactome, given its high degree of completeness and relevance to the analysis of the disease-linked proteins. We quantified and investigated numerous functional and structural characteristics of the disorder-enriched hub PPIs, including protein binding, structural stability, evolutionary conservation, several categories of functional sites, and presence of over twenty types of posttranslational modifications (PTMs). We showed that the disorder-enriched hub PPIs have a significantly enlarged number of disordered protein binding regions and long intrinsically disordered regions. They also include high numbers of targeting, catalytic, and many types of PTM sites. We empirically demonstrated that these hub PPIs are significantly enriched among 11 out of 18 considered classes of human diseases that are associated with at least 100 human proteins. Finally, we also illustrated how over a dozen specific human hubs utilize intrinsic disorder for their promiscuous PPIs.

Protein-protein interactions: detection, reliability assessment and applications

Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.

In vivo evidence for homo- and heterodimeric interactions of Arabidopsis thaliana dehydrins AtCOR47, AtERD10, and AtRAB18

Dehydrins (DHNs) are intrinsically disordered proteins that play central roles in plant abiotic stress responses; however, how they work remains unclear. Herein, we report the in planta subcellular localization of Arabidopsis thaliana DHNs AtCOR47, AtERD10, and AtRAB18 through GFP translational fusions. To explore the dimerization ability of the Arabidopsis acidic DHNs AtCOR47 and AtERD10, we conducted an in planta DHN binding assay using the Bimolecular Fluorescence Complementation (BiFC) technique. Our analyses revealed homodimeric interactions for AtCOR47 and AtERD10; interestingly, heterodimeric associations also occurred with these DHNs, and these interactions were observed in the cytosol of tobacco cells. Furthermore, we evaluated whether Arabidopsis basic DHNs, such as AtRAB18, could also interact with itself and/or with AtCOR47 and AtERD10 in the BiFC system. Our data revealed homodimeric RAB18 complexes in the nucleus and cytosol, while heterodimeric associations between AtRAB18 and acidic DHNs occurred only in the cytosol. Finally, we demonstrated the presence of heterodimeric complexes among Arabidopsis AtCOR47, AtERD10, and AtRAB18 DHNs with their acidic ortholog the OpsDHN1 from Opuntia streptacantha; these heterodimeric interactions showed different subcellular distributions. Our results guide DHN research toward a new scenario where DHN/DHN oligomerization could be explored as a part of their molecular mechanism.

Stronger selective constraint on downstream genes in the oxidative phosphorylation pathway of cetaceans

The oxidative phosphorylation (OXPHOS) pathway is an efficient way to produce energy via adenosine triphosphate (ATP), which is critical for sustaining an energy supply for cetaceans in a hypoxic environment. Several studies have shown that natural selection may shape the evolution of the genes involved in OXPHOS. However, how network architecture drives OXPHOS protein sequence evolution remains poorly explored. Here, we investigated the evolutionary patterns of genes in the OXPHOS pathway across six cetacean genomes within the framework of a functional network. Our results show a negative correlation between the strength of purifying selection and pathway position. This result indicates that downstream genes were subjected to stronger evolutionary constraints than upstream genes, which may be due to the dual function of ATP synthase in the OXPHOS pathway. Additionally, there was a positive correlation between codon usage bias and omega (ω = dN/dS) and a negative correlation with synonymous substitution rate (dS), indicating that the stronger selective constraint on genes (with less biased codon usage) along the OXPHOS pathway is attributable to an increase in the rate of synonymous substitution. Surprisingly, there was no significant correlation between protein-protein interactions and the evolutionary estimates, implying that highly connected enzymes may not always show greater evolutionary constraints. Compared with that observed for terrestrial mammals, we found that the signature of positive selection detected in five genes (ATP5J, LHPP, PPA1, UQCRC1 and UQCRQ) was cetacean-specific, reflecting the importance of OXPHOS for survival in hypoxic, aquatic environments.

An uncertain model-based approach for identifying dynamic protein complexes in uncertain protein-protein interaction networks

Background

Recently, researchers have tried to integrate various dynamic information with static protein-protein interaction (PPI) networks to construct dynamic PPI networks. The shift from static PPI networks to dynamic PPI networks is essential to reveal the cellular function and organization. However, it is still impossible to construct an absolutely reliable dynamic PPI networks due to the noise and incompletion of high-throughput experimental data.

Results

To deal with uncertain data, some uncertain graph models and theories have been proposed to analyze social networks, electrical networks and biological networks. In this paper, we construct the dynamic uncertain PPI networks to integrate the dynamic information of gene expression and the topology information of high-throughput PPI data. The dynamic uncertain PPI networks can not only provide the dynamic properties of PPI, which are neglected by static PPI networks, but also distinguish the reliability of each protein and PPI by the existence probability. Then, we use the uncertain model to identify dynamic protein complexes in the dynamic uncertain PPI networks.

Conclusion

We use gene expression data and different high-throughput PPI data to construct three dynamic uncertain PPI networks. Our approach can achieve the state-of-the-art performance in all three dynamic uncertain PPI networks. The experimental results show that our approach can effectively deal with the uncertain data in dynamic uncertain PPI networks, and improve the performance for protein complex identification.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4131-6) contains supplementary material, which is available to authorized users.

Patterns produced by dried droplets of protein binary mixtures suspended in water

Patterns formed by the evaporation of a drop containing biological molecules have provided meaningful information about certain pathologies. In this context, several works propose the study of protein solutions as a model to understand the formation of deposits of biological fluids. Generally, dry droplets of proteins in a saline solution create complex aggregates. Here, we present an experimental study on the formation of patterns produced by the evaporation of droplet suspensions containing a protein binary mixture. We explore the structural aspect of such deposits by using optical and atomic force microscopy. We found that salt is unnecessary for the formation of complex structures such as crystal clusters, dendritic and undulated branches, and interlocked chains. Such structural features allow us to differentiate among protein binary mixtures. Finally, we discuss the potential use of this finding to reveal the presence of a protein suspensions, the folded and unfolded state of a protein, as well as their structural changes.

Vicus: Exploiting local structures to improve network-based analysis of biological data

Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network’s local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks.

Networks are a representation of choice for many problems in biology and medicine including protein interactions, metabolic pathways, evolutionary biology, cancer subtyping and disease modeling to name a few. The key to much of network analysis lies in the spectrum decomposition represented by eigenvectors of the network Laplacian. While possessing many desirable algebraic properties, Laplacian lacks the power to capture fine-grained structure of the underlying network. Our novel matrix, Vicus, introduced in this work, takes advantage of the local structure of the network while preserving algebraic properties of the Laplacian. We show that using Vicus in spectral methods leads to superior performance across fundamental biological tasks such as dimensionality reduction in single cell analysis, identifying genes for cancer subtyping and identifying protein modules in a PPI network. We postulate, that in tasks where it is important to take into account local network information, spectral-based methods should be using Vicus matrix in place of Laplacian.

Probing the Protein-Protein Interaction Network of Proteins Causing Maturity Onset Diabetes of the Young

Protein-protein interactions (PPIs) play vital roles in various cellular pathways. Most of the proteins perform their responsibilities by interacting with an enormous number of proteins. Understanding these interactions of the proteins and their interacting partners has shed light toward the field of drug discovery. Also, PPIs enable us to understand the functions of a protein by understanding their interacting partners. Consequently, in the current study, PPI network of the proteins causing MODY (Maturity Onset Diabetes of the Young) was drawn, and their correlation in causing a disease condition was marked. MODY is a monogenic type of diabetes caused by autosomal dominant inheritance. Extensive research on transcription factor and their corresponding genetic pathways have been studied over the last three decades, yet, very little is understood about the molecular modalities of highly dynamic interactions between transcription factors, genomic DNA, and the protein partners. The current study also reveals the interacting patterns of the various transcription factors. Consequently, in the current work, we have devised a PPI analysis to understand the plausible pathway through which the protein leads to a deformity in glucose uptake.