Protein Protein-protein Interactions Research Articles

Engagement of intrinsic disordered proteins in protein-protein interaction.

Proteins from the intrinsically disordered group (IDP) focus the attention of many researchers engaged in protein structure analysis. The main criteria used in their identification are lack of secondary structure and significant structural variability. This variability takes forms that cannot be identified in the X-ray technique. In the present study, different criteria were used to assess the status of IDP proteins and their fragments recognized as intrinsically disordered regions (IDRs). The status of the hydrophobic core in proteins identified as IDPs and in their complexes was assessed. The status of IDRs as components of the ordering structure resulting from the construction of the hydrophobic core was also assessed. The hydrophobic core is understood as a structure encompassing the entire molecule in the form of a centrally located high concentration of hydrophobicity and a shell with a gradually decreasing level of hydrophobicity until it reaches a level close to zero on the protein surface. It is a model assuming that the protein folding process follows a micellization pattern aiming at exposing polar residues on the surface, with the simultaneous isolation of hydrophobic amino acids from the polar aquatic environment. The use of the model of hydrophobicity distribution in proteins in the form of the 3D Gaussian distribution described on the protein particle introduces the possibility of assessing the degree of similarity to the assumed micelle-like distribution and also enables the identification of deviations and mismatch between the actual distribution and the idealized distribution. The FOD (fuzzy oil drop) model and its modified FOD-M version allow for the quantitative assessment of these differences and the assessment of the relationship of these areas to the protein function. In the present work, the sections of IDRs in protein complexes classified as IDPs are analyzed. The classification "disordered" in the structural sense (lack of secondary structure or high flexibility) does not always entail a mismatch with the structure of the hydrophobic core. Particularly, the interface area, often consisting of IDRs, in many analyzed complexes shows the compliance of the hydrophobicity distribution with the idealized distribution, which proves that matching to the structure of the hydrophobic core does not require secondary structure ordering.

Changes in the C-terminal, N-terminal, and histidine regions of amelogenin reveal the role of oligomer quaternary structure on adsorption and hydroxyapatite mineralization.

Adsorption interactions between amelogenin and calcium phosphate minerals are believed to be important to amelogenin's function in enamel formation, however, the role of specific amino acid residues and domains within the protein in controlling adsorption is not well known. We synthesized "mechanistic probes" by systematically removing charged regions of amelogenin in order to elucidate their roles. The probes included amelogenin without the charged residues in the N-terminus (SEKR), without two, three, or eight histidines (H) in the central protein region (H2, H3, H8), or without the C-terminal residues (Delta). In-situ atomic force microscopy (AFM) adsorption studies onto hydroxyapatite (HAP) single crystals confirmed that the C-terminus was the dominant domain in promoting adsorption. We propose that subtle changes in protein-protein interactions for proteins with histidines and N-terminal residues removed resulted in changes in the oligomer quaternary size and structure that also affected protein adsorption. HAP mineralization studies revealed that the oligomer-HAP binding energy and protein layer thickness were factors in controlling the amorphous calcium phosphate (ACP) to HAP induction time. Our studies with mechanistic probes reveal the importance of the oligomer quaternary structure in controlling amelogenin adsorption and HAP mineralization.

Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses

Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of pathogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3–23% better area under curve (AUC) score than its competitors.

Application of Centrality Measures for Potential Drug Targets: Review

Protein-Protein Interactions (PPI) have important role in drug binding with the Proteins called drug targets. For identifying the potential drug targets there are different techniques. In this paper we are presenting application of Centrality Measures for identifying the drug targets. Centrality measure indicates importance of node in the graph or network. Protein-Protein Interactions for proteins which are involved in a particular disease are identified and centrality measures will be calculated based on the graph built suing the PPI interactions. Further the nodes which are playing crucial role will be identified using the various centrality measures and these drug targets can be used for drug discovery of a particular disease through insilico docking studies.

Finding low-conductance sets with dense interactions (FLCD) for better protein complex prediction

BackgroundIntuitively, proteins in the same protein complexes should highly interact with each other but rarely interact with the other proteins in protein-protein interaction (PPI) networks. Surprisingly, many existing computational algorithms do not directly detect protein complexes based on both of these topological properties. Most of them, depending on mathematical definitions of either “modularity” or “conductance”, have their own limitations: Modularity has the inherent resolution problem ignoring small protein complexes; and conductance characterizes the separability of complexes but fails to capture the interaction density within complexes.ResultsIn this paper, we propose a two-step algorithm FLCD (Finding Low-Conductance sets with Dense interactions) to predict overlapping protein complexes with the desired topological structure, which is densely connected inside and well separated from the rest of the networks. First, FLCD detects well-separated subnetworks based on approximating a potential low-conductance set through a personalized PageRank vector from a protein and then solving a mixed integer programming (MIP) problem to find the minimum-conductance set within the identified low-conductance set. At the second step, the densely connected parts in those subnetworks are discovered as the protein complexes by solving another MIP problem that aims to find the dense subnetwork in the minimum-conductance set.ConclusionExperiments on four large-scale yeast PPI networks from different public databases demonstrate that the complexes predicted by FLCD have better correspondence with the yeast protein complex gold standards than other three state-of-the-art algorithms (ClusterONE, LinkComm, and SR-MCL). Additionally, results of FLCD show higher biological relevance with respect to Gene Ontology (GO) terms by GO enrichment analysis.

Triage by ranking to support the curation of protein interactions.

Today, molecular biology databases are the cornerstone of knowledge sharing for life and health sciences. The curation and maintenance of these resources are labour intensive. Although text mining is gaining impetus among curators, its integration in curation workflow has not yet been widely adopted. The Swiss Institute of Bioinformatics Text Mining and CALIPHO groups joined forces to design a new curation support system named nextA5. In this report, we explore the integration of novel triage services to support the curation of two types of biological data: protein–protein interactions (PPIs) and post-translational modifications (PTMs). The recognition of PPIs and PTMs poses a special challenge, as it not only requires the identification of biological entities (proteins or residues), but also that of particular relationships (e.g. binding or position). These relationships cannot be described with onto-terminological descriptors such as the Gene Ontology for molecular functions, which makes the triage task more challenging. Prioritizing papers for these tasks thus requires the development of different approaches. In this report, we propose a new method to prioritize articles containing information specific to PPIs and PTMs. The new resources (RESTful APIs, semantically annotated MEDLINE library) enrich the neXtA5 platform. We tuned the article prioritization model on a set of 100 proteins previously annotated by the CALIPHO group. The effectiveness of the triage service was tested with a dataset of 200 annotated proteins. We defined two sets of descriptors to support automatic triage: the first set to enrich for papers with PPI data, and the second for PTMs. All occurrences of these descriptors were marked-up in MEDLINE and indexed, thus constituting a semantically annotated version of MEDLINE. These annotations were then used to estimate the relevance of a particular article with respect to the chosen annotation type. This relevance score was combined with a local vector-space search engine to generate a ranked list of PMIDs. We also evaluated a query refinement strategy, which adds specific keywords (such as ‘binds’ or ‘interacts’) to the original query. Compared to PubMed, the search effectiveness of the nextA5 triage service is improved by 190% for the prioritization of papers with PPIs information and by 260% for papers with PTMs information. Combining advanced retrieval and query refinement strategies with automatically enriched MEDLINE contents is effective to improve triage in complex curation tasks such as the curation of protein PPIs and PTMs. Database URL: http://candy.hesge.ch/nextA5

Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information.

The identification of essential proteins in protein-protein interaction (PPI) networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on network topology have been proposed to detect essential proteins from PPI networks. However, most of the current approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology annotation information. In this paper, we propose a novel centrality measure, called TEO, for identifying essential proteins by combining network topology, gene expression profiles, and GO information. To evaluate the performance of the TEO method, we compare it with five other methods (degree, betweenness, NC, Pec, and CowEWC) in detecting essential proteins from two different yeast PPI datasets. The simulation results show that adding GO information can effectively improve the predicted precision and that our method outperforms the others in predicting essential proteins.

Identification of Small-Molecule Frequent Hitters of Glutathione S-Transferase–Glutathione Interaction

In high-throughput screening (HTS) campaigns, the binding of glutathione S-transferase (GST) to glutathione (GSH) is used for detection of GST-tagged proteins in protein-protein interactions or enzyme assays. However, many false-positives, so-called frequent hitters (FH), arise that either prevent GST/GSH interaction or interfere with assay signal generation or detection. To identify GST-FH compounds, we analyzed the data of five independent AlphaScreen-based screening campaigns to classify compounds that inhibit the GST/GSH interaction. We identified 53 compounds affecting GST/GSH binding but not influencing His-tag/Ni2+-NTA interaction and general AlphaScreen signals. The structures of these 53 experimentally identified GST-FHs were analyzed in chemoinformatic studies to categorize substructural features that promote interference with GST/GSH binding. Here, we confirmed several existing chemoinformatic filters and more importantly extended them as well as added novel filters that specify compounds with anti–GST/GSH activity. Selected compounds were also tested using different antibody-based GST detection technologies and exhibited no interference clearly demonstrating specificity toward their GST/GSH interaction. Thus, these newly described GST-FH will further contribute to the identification of FH compounds containing promiscuous substructures. The developed filters were uploaded to the OCHEM website (http://ochem.eu) and are publicly accessible for analysis of future HTS results.

Protein functional properties prediction in sparsely-label PPI networks through regularized non-negative matrix factorization.

BackgroundPredicting functional properties of proteins in protein-protein interaction (PPI) networks presents a challenging problem and has important implication in computational biology. Collective classification (CC) that utilizes both attribute features and relational information to jointly classify related proteins in PPI networks has been shown to be a powerful computational method for this problem setting. Enabling CC usually increases accuracy when given a fully-labeled PPI network with a large amount of labeled data. However, such labels can be difficult to obtain in many real-world PPI networks in which there are usually only a limited number of labeled proteins and there are a large amount of unlabeled proteins. In this case, most of the unlabeled proteins may not connected to the labeled ones, the supervision knowledge cannot be obtained effectively from local network connections. As a consequence, learning a CC model in sparsely-labeled PPI networks can lead to poor performance.ResultsWe investigate a latent graph approach for finding an integration latent graph by exploiting various latent linkages and judiciously integrate the investigated linkages to link (separate) the proteins with similar (different) functions. We develop a regularized non-negative matrix factorization (RNMF) algorithm for CC to make protein functional properties prediction by utilizing various data sources that are available in this problem setting, including attribute features, latent graph, and unlabeled data information. In RNMF, a label matrix factorization term and a network regularization term are incorporated into the non-negative matrix factorization (NMF) objective function to seek a matrix factorization that respects the network structure and label information for classification prediction.ConclusionExperimental results on KDD Cup tasks predicting the localization and functions of proteins to yeast genes demonstrate the effectiveness of the proposed RNMF method for predicting the protein properties. In the comparison, we find that the performance of the new method is better than those of the other compared CC algorithms especially in paucity of labeled proteins.

Finding trans-regulatory genes and protein complexes modulating meiotic recombination hotspots of human, mouse and yeast.

BackgroundThe regulatory mechanism of recombination is one of the most fundamental problems in genomics, with wide applications in genome wide association studies (GWAS), birth-defect diseases, molecular evolution, cancer research, etc. Recombination events cluster into short genomic regions called “recombination hotspots”. Recently, a zinc finger protein PRDM9 was reported to regulate recombination hotspots in human and mouse genomes. In addition, a 13-mer motif contained in the binding sites of PRDM9 is found to be enriched in human hotspots. However, this 13-mer motif only covers a fraction of hotspots, indicating that PRDM9 is not the only regulator of recombination hotspots. Therefore, the challenge of discovering other regulators of recombination hotspots becomes significant. Furthermore, recombination is a complex process. Hence, multiple proteins acting as machinery, rather than individual proteins, are more likely to carry out this process in a precise and stable manner. Therefore, the extension of the prediction of individual trans-regulators to protein complexes is also highly desired.ResultsIn this paper, we introduce a pipeline to identify genes and protein complexes associated with recombination hotspots. First, we prioritize proteins associated with hotspots based on their preference of binding to hotspots and coldspots. Second, using the above identified genes as seeds, we apply the Random Walk with Restart algorithm (RWR) to propagate their influences to other proteins in protein-protein interaction (PPI) networks. Hence, many proteins without DNA-binding information will also be assigned a score to implicate their roles in recombination hotspots. Third, we construct sub-PPI networks induced by top genes ranked by RWR for various species (e.g., yeast, human and mouse) and detect protein complexes in those sub-PPI networks.ConclusionsThe GO term analysis show that our prioritizing methods and the RWR algorithm are capable of identifying novel genes associated with recombination hotspots. The trans-regulators predicted by our pipeline are enriched with epigenetic functions (e.g., histone modifications), demonstrating the epigenetic regulatory mechanisms of recombination hotspots. The identified protein complexes also provide us with candidates to further investigate the molecular machineries for recombination hotspots. Moreover, the experimental data and results are available on our web site http://www.ntu.edu.sg/home/zhengjie/data/RecombinationHotspot/NetPipe/.

The Role of Protein Interactions in Mediating Essentiality and Synthetic Lethality

Genes are characterized as essential if their knockout is associated with a lethal phenotype, and these “essential genes” play a central role in biological function. In addition, some genes are only essential when deleted in pairs, a phenomenon known as synthetic lethality. Here we consider genes displaying synthetic lethality as “essential pairs” of genes, and analyze the properties of yeast essential genes and synthetic lethal pairs together. As gene duplication initially produces an identical pair or sets of genes, it is often invoked as an explanation for synthetic lethality. However, we find that duplication explains only a minority of cases of synthetic lethality. Similarly, disruption of metabolic pathways leads to relatively few examples of synthetic lethality. By contrast, the vast majority of synthetic lethal gene pairs code for proteins with related functions that share interaction partners. We also find that essential genes and synthetic lethal pairs cluster in the protein-protein interaction network. These results suggest that synthetic lethality is strongly dependent on the formation of protein-protein interactions. Compensation by duplicates does not usually occur mainly because the genes involved are recent duplicates, but is more commonly due to functional similarity that permits preservation of essential protein complexes. This unified view, combining genes that are individually essential with those that form essential pairs, suggests that essentiality is a feature of physical interactions between proteins protein-protein interactions, rather than being inherent in gene and protein products themselves.

Molecule of the month: Synaptic plasticity – Protein miRNA interactions

miRNAs (microRNAs) are small RNAs that are transcribed from intron and other non-coding RNAs. miRNAs are utilized by the cell's molecular biological machinery to help control transcription [1]. There are more than 500 known miRNAs involved in neuronal function including development, transport, and neuronal plasticity. [2] However, what of the neuronal proteins that interacts with these miRNAs? In this month's Editorial, we examine a few protein-protein interactions of proteins that interact with the miRNAs and demonstrate additional network pathways in which these proteins function in neurons. The protein product Atx2 (Ataxin-2 protein) is implicated in the human neurodegenerative disease, spinocerebellar ataxia. In studies in the brains of the fruit fly, Drosophila, Atx2 was identified as a miRNA pathway component. Both miRNA and Atx2 components were involved in long-term plasticity of synapses in these studies. Interestingly, the control of translation occurs locally in proximity to the involved synapses. In addition, Atx2 interacts with the Me31B family DEAD box helicases - DHH1 – (also termed DDX helicases) that interact with Argonaute (Ago) and affect function of miRNAs [1, 3] (Figure 1). Figure 1 Protein interactions with Atx2, Ago, and DDX. In this figure, line-colors and various interactions with other genes are red Down-regulation, green Up-regulation, beige Regulation, purple Co-expression, brown Physical Interaction, turquoise dotted Predicted ... The contributions of miRNAs to synaptic plasticity are extensive study. For example, CREB (cAMP responsive element binding protein), MECP2 (methyl CpG binding protein 2), and FMRP (fragile X mental retardation protein) interact with miRNAs in their role as mediators of synaptic plasticity within dendrites. The RISC machinery is utilized post-DICER action, producing the miRNAs in situ as above. Post-translation inhibition occurs for these proteins following bursts of synaptic activity mediated by the rapid increase of calcium levels. These activities occur in mouse forebrain and are involved in longterm memory in Drosophila [5] (Figure 2). Figure 2 Protein interactions with CREB, MECP2, and FMRP. In this figure, line-colors and various interactions with other genes are red Down-regulation, green Up-regulation, beige Regulation, purple Co-expression, brown Physical Interaction, turquoise dotted Predicted ... The two figures illustrate various gene interactions, up to 100 [4]. It is left as a puzzle for the interested reader to identify the various genes and their functions in the figures [6].

Lipids in the Processes of Exo- and Endocytosis of Synaptic Vesicles

The transmission of information between neurons inthe brain and between neurons and other excitatory cells(muscle and secretory cells) at the periphery occurs at spe-cialized locations, i.e., synapses. The presynaptic nerveending (NE) in the vast majority of synapses contains largenumbers of synaptic vesicles filled with a chemical trans-mitter (mediator, neurotransmitter). In response to a nerveimpulse, vesicles fuse with the presynaptic membrane (exo-cytosis), releasing a portion (quantum) of transmitter intothe synaptic cleft. Reaching receptors on the postsynapticmembrane, the transmitter activates them, with the resultthat an electrical signal arises in the postsynaptic cell [1, 3].Along with the processes of exocytosis, the intense forma-tion of new vesicles (endocytosis) occur in the NE, whichprevents depletion of vesicles in conditions of high-fre-quency activation of the synapse. After endocytosis, thevesicles are again filled with neurotransmitter, transported,and placed in line for repeated exocytosis. In other words,nerve endings involve a process of circulation (recycling) ofsynaptic vesicles, which is termed the vesicular cycle [1, 2,9, 13]. Naturally, the processes of the vesicular cycle deter-mine the intensity of transmitter secretion and, ultimately,the strength of the synaptic contact.Recent years have seen major scientific advances instudies of the roles of different proteins in protein-proteininteractions in the mechanisms of exoendocytosis and vesi-cle transport (Fig. 1). Only now have investigators started topay detailed attention to the significance of lipids for thefunctioning of nerve endings. This is not surprising, as rad-ical transformations take place in membrane geometry dur-ing the vesicular cycle, along with biochemical and struc-tural “metamorphoses” in their lipids, and changes in thepattern of the activity of many lipid-modifying enzymes,which “prepare” the vesicle membrane and nerve endingsfor exo- and endocytosis. Activation of the key proteins ofexo- and endocytosis and vesicular transport is significant-ly accelerated by particular lipids and their derivatives. It isno less important that the sites of exo- and endocytosis haveunique lipid compositions (and unique protein composi-tions), despite multiple cycles of vesicle fusion and recov-

Removing bias against membrane proteins in interaction networks

BackgroundCellular interaction networks can be used to analyze the effects on cell signaling and other functional consequences of perturbations to cellular physiology. Thus, several methods have been used to reconstitute interaction networks from multiple published datasets. However, the structure and performance of these networks depends on both the quality and the unbiased nature of the original data. Due to the inherent bias against membrane proteins in protein-protein interaction (PPI) data, interaction networks can be compromised particularly if they are to be used in conjunction with drug screening efforts, since most drug-targets are membrane proteins.ResultsTo overcome the experimental bias against PPIs involving membrane-associated proteins we used a probabilistic approach based on a hypergeometric distribution followed by logistic regression to simultaneously optimize the weights of different sources of interaction data. The resulting less biased genome-scale network constructed for the budding yeast Saccharomyces cerevisiae revealed that the starvation pathway is a distinct subnetwork of autophagy and retrieved a more integrated network of unfolded protein response genes. We also observed that the centrality-lethality rule depends on the content of membrane proteins in networks.ConclusionsWe show here that the bias against membrane proteins can and should be corrected in order to have a better representation of the interactions and topological properties of protein interaction networks.

Protein-Protein Interactions of Proteins from the ESAT-6 Family ofMycobacterium tuberculosis

In the present study, we demonstrate that, in analogy with the genes encoding ESAT-6 and CFP-10, the genes rv0287 and rv0288 from the ESAT-6 gene family are cotranscribed. Using Western-Western blotting and protein-print overlay methodologies, we demonstrate that ESAT-6 and CFP-10, as well as the protein pair Rv0288/Rv0287, interact pairwise in a highly specific way. Most notably, the ESAT-6 proteins interact directly with Rv3873, a possible cell envelope component of the ESAT-6 secretion pathway.