Semantically predicting protein functions based on protein functional connectivity

BackgroundThe current availability of public protein–protein interaction (PPI) databases which are usually modelled as PPI networks has led to the rapid development of protein function prediction approaches. The existing network-based prediction approaches mainly focus on the topological similarities between immediately interacting proteins, neglecting the protein functional connectivity which is the functional tightness between proteins. In this paper, we attempt to predict the functions of unannotated proteins based on PPI networks by incorporating the protein functional connectivity, as well as the similarity of protein functions, into the prediction procedure.ResultsAn approach named Semantic protein function Prediction based on protein Functional Connectivity (SPFC) is proposed to achieve a higher accuracy in predicting functions of unannotated protein. We define the functional connectivity and function addition for each protein, and incorporate them into the prediction. We evaluated the SPFC on real PPI datasets and the experiment results show that the SPFC method is more effective in function prediction than other network-based approaches.ConclusionIncorporating the functional connectivity of each protein into the function prediction can significantly improve the accuracy of protein prediction.     

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.     

Core and peripheral connectivity based cluster analysis over PPI network

A number of methods have been proposed in the literature of protein–protein interaction (PPI) network analysis for detection of clusters in the network. Clusters are identified by these methods using various graph theoretic criteria. Most of these methods have been found time consuming due to involvement of preprocessing and post processing tasks. In addition, they do not achieve high precision and recall consistently and simultaneously. Moreover, the existing methods do not employ the idea of core-periphery structural pattern of protein complexes effectively to extract clusters. In this paper, we introduce a clustering method named CPCA based on a recent observation by researchers that a protein complex in a PPI network is arranged as a relatively dense core region and additional proteins weakly connected to the core. CPCA uses two connectivity criterion functions to identify core and peripheral regions of the cluster. To locate initial node of a cluster we introduce a measure called DNQ (Degree based Neighborhood Qualification) index that evaluates tendency of the node to be part of a cluster. CPCA performs well when compared with well-known counterparts. Along with protein complex gold standards, a co-localization dataset has also been used for validation of the results.     

Comprehensive structural analysis of the open and closed conformations of Theileria annulata enolase by molecular modelling and docking

Theileria annulata is an apicomplexan parasite which is responsible for tropical theileriosis in cattle. Due to resistance of T. annulata against commonly used antitheilerial drug, new drug candidates should be identified urgently. Enolase might be a druggable protein candidate which has an important role in glycolysis, and could also be related to several cellular functions as a moonlight protein. In this study; we have described three-dimensional models of open and closed conformations of T. annulata enolase by homology modeling method for the first time with the comprehensive domain, active site and docking analyses. Our results show that the enolase has similar folding patterns within enolase superfamily with conserved catalytic loops and active site residues. We have described specific insertions, possible plasminogen binding sites, electrostatic potential surfaces and positively charged pockets as druggable regions in T. annulata enolase.     

Do cancer proteins really interact strongly in the human protein-protein interaction network?

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer.     

Attractors of hypertrophic cardiomyopathy using maximal cliques and attract methods

BackgroundOur study was designed to identify the differential attractor modules related with hypertrophic cardiomyopathy (HCM) by integrating clustering-based on maximal cliques algorithm and Attract method.MethodsWe firstly recruited the HCM-related microarray data from ArrayExpress database. Next, protein–protein interaction (PPI) networks of normal and HCM were constructed and re-weighted using spearman correlation coefficient (SCC). Then, maximal cliques were found from the PPI networks through the clustering-based on maximal cliques approach. Afterwards, highly overlapped cliques were eliminated or merged according to the interconnectivity, and then modules were obtained. Subsequently, we used Attract method to identify differential attractor modules, following by the pathway enrichment analyses for genes in differential attractor modules.ResultsAfter removing the cliques with nodes less than or equal to 4, 926 and 1118 maximal cliques in normal and HCM PPI networks were obtained for module analysis. Then, we obtained 32 and 55 modules from the PPI networks of normal and HCM, respectively. By comparing with normal condition, there were 5 module pairs with the same or similar gene composition. Significantly, based on attract method, we found that these 5 modules were differential attractors. Pathway enrichment analyses indicated that proteasome, ribosome and oxidative phosphorylation were the significant pathways.ConclusionsProteasome, ribosome and oxidative phosphorylation might play pathophysiological roles in HCM.     

Assessment of high-confidence protein–protein interactome in yeast

The identification of protein–protein interactions (PPIs) and their networks is vitally important to systemically define and understand the roles of proteins in biological systems. In spite of development of numerous experimental systems to detect PPIs and diverse research on assessment of the quality of the obtained data, a consensus – highly reliable, almost complete – interactome of Saccharomyces cerevisiae is not presented yet. In this work, we proposed an unsupervised statistical approach to create a high-confidence yeast PPI network. For this, we assembled databases of interacting protein pairs for yeast and obtained an extremely large PPI dataset which comprises of 135 154 non-redundant interactions between 6191 yeast proteins. A scoring scheme considering eight heterogeneous biological features resulted with a broad score distribution and a highly reliable network consisting of 29 046 physical interactions with scores higher than the threshold value of 0.85, for which sensitivity, specificity and coverage were 86%, 68%, and 72%, respectively. We evaluated our method by comparing it with other scoring schemes and showed that reducing the noise inherent in experimental PPIs via our scoring scheme further increased the accuracy. Current study is expected to increase the efficiency of the methodologies in biological research which make use of protein interaction networks.     

Exploring the Bile Stress Response of Lactobacillus mucosae LM1 through Exoproteome Analysis

Lactobacillus sp. have long been studied for their great potential in probiotic applications. Recently, proteomics analysis has become a useful tool for studies on potential lactobacilli probiotics. Specifically, proteomics has helped determine and describe the physiological changes that lactic acid bacteria undergo in specific conditions, especially in the host gut. In particular, the extracellular proteome, or exoproteome, of lactobacilli contains proteins specific to host– or environment–microbe interactions. Using gel-free, label-free ultra-high performance liquid chromatography tandem mass spectrometry, we explored the exoproteome of the probiotic candidate Lactobacillus mucosae LM1 subjected to bile treatment, to determine the proteins it may use against bile stress in the gut. Bile stress increased the size of the LM1 exoproteome, secreting ribosomal proteins (50S ribosomal protein L27 and L16) and metabolic proteins (lactate dehydrogenase, phosphoglycerate kinase, glyceraldehyde-3-phosphate dehydrogenase and pyruvate dehydrogenases, among others) that might have moonlighting functions in the LM1 bile stress response. Interestingly, membrane-associated proteins (transporters, peptidase, ligase and cell division protein ftsH) were among the key proteins whose secretion were induced by the LM1 bile stress response. These specific proteins from LM1 exoproteome will be useful in observing the proposed bile response mechanisms via in vitro experiments. Our data also reveal the possible beneficial effects of LM1 to the host gut.     

Weighted edge based clustering to identify protein complexes in protein–protein interaction networks incorporating gene expression profile

Protein complex detection from protein–protein interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes, not all protein complexes have high density. Moreover, existing methods focus more on static PPI networks and usually overlook the dynamic nature of protein complexes. Here, we propose a new method, Weighted Edge based Clustering (WEC), to identify protein complexes based on the weight of the edge between two interacting proteins, where the weight is defined by the edge clustering coefficient and the gene expression correlation between the interacting proteins. Our WEC method is capable of detecting highly inter-connected and co-expressed protein complexes. The experimental results of WEC on three real life data shows that our method can detect protein complexes effectively in comparison with other highly cited existing methods.Availability: The WEC tool is available at http://agnigarh.tezu.ernet.in/~rosy8/shared.html.     

Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study

Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein–protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.     

Protein function prediction using neighbor relativity in protein–protein interaction network

There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called “Neighbor Relativity Coefficient” (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network.     

A three-phase method for identifying functionally related protein groups in weighted PPI networks

Identifying significant protein groups is of great importance for further understanding protein functions. This paper introduces a novel three-phase heuristic method for identifying such groups in weighted PPI networks. In the first phase a variable neighborhood search (VNS) algorithm is applied on a weighted PPI network, in order to support protein complexes by adding a minimum number of new PPIs. In the second phase proteins from different complexes are merged into larger protein groups. In the third phase these groups are expanded by a number of 2-level neighbor proteins, favoring proteins that have higher average gene co-expression with the base group proteins. Experimental results show that: (i) the proposed VNS algorithm outperforms the existing approach described in literature and (ii) the above-mentioned three-phase method identifies protein groups with very high statistical significance.     

A network-based pharmacology study of active compounds and targets of Fritillaria thunbergii against influenza

Seasonal and pandemic influenza infections are serious threats to public health and the global economy. Since antigenic drift reduces the effectiveness of conventional therapies against the virus, herbal medicine has been proposed as an alternative. Fritillaria thunbergii (FT) have been traditionally used to treat airway inflammatory diseases such as coughs, bronchitis, pneumonia, and fever-based illnesses. Herein, we used a network pharmacology-based strategy to predict potential compounds from Fritillaria thunbergii (FT), target genes, and cellular pathways to better combat influenza and influenza-associated diseases. We identified five compounds, and 47 target genes using a compound-target network (C-T). Two compounds (beta-sitosterol and pelargonidin) and nine target genes (BCL2, CASP3, HSP90AA1, ICAM1, JUN, NOS2, PPARG, PTGS1, PTGS2) were identified using a compound-influenza disease target network (C-D). Protein-protein interaction (PPI) network was constructed and we identified eight proteins from nine target genes formed a network. The compound-disease-pathway network (C-D-P) revealed three classes of pathways linked to influenza: cancer, viral diseases, and inflammation. Taken together, our systems biology data from C-T, C-D, PPI and C-D-P networks predicted potent compounds from FT and new therapeutic targets and pathways involved in influenza.     

Stabilization centers and protein stability

The well-balanced stability of protein structures allows large-scale fluctuations, which are indispensable in many biochemical functions, ensures the long-term persistence of the equilibrium structure and it regulates the degradation of proteins to provide amino acids for biosynthesis. This balance is studied in the present work with two sets of proteins by analyzing stabilization centers, defined as certain clusters of residues involved in cooperative long-range interactions. One data set contains 56 proteins, which belong to 16 families of homologous proteins, derived from organisms of various physiological temperatures. The other set is composed of 31 major histocompatibility complex (MHC)–peptide complexes, which represent peptide transporters complexed with peptide ligands that apparently contribute to the stabilization of the MHC proteins themselves. We show here that stabilization centers, which had been identified as special clusters of residues that protect the protein structure, evolved to serve also as regulators of function – related degradation of useless protein as part of protein housekeeping. Received: 25 August 2000 / Accepted: 6 September 2000 / Published online: 21 December 2000     

Equation of state for monomolecular films of melittin at air-water interface

The spread monolayers of proteins at the air-water interface have been reported to be very useful model membrane systems. The charged protein monolayers have been analyzed by using the Gouy-Chapman (-Stern) models. These models gave satisfactory analyses of “non-membrane” proteins, but could not be used for the data of charged melittin monolayers (“membrane protein”). In order to describe these data, a new discrete (net) charge model is developed, and the equation of state for these two-dimensional films is discussed herein. This study shows, for the first time, that discrete (net) charges are present in charged melittin (a peptide with 26 amino acids) monolyers. The measured surface pressure,Π, and surface potential,Δψ, are analyzed with the help of the discrete charge model.     

Non-covalent strategy for activating separation and detection functionality by use of the multifunctional host molecule thiacalixarene

This review article proposes a non-covalent strategy for activating separation and detection functionality; this strategy acts not through extensive organic synthesis to a covalently constructed molecular receptor, but by combining a simple molecular platform with a chemical “field” or functional component. For such a platform, we employed thiacalixarenes—calixarenes in which the bridging methylene groups are replaced with sulfur—to demonstrate usefulness of the non-covalent strategy and the multifunctionality of thiacalixarene. Thiacalixarene exhibits inherent abilities to recognize metal ions by coordinating with the bridging sulfur and adjacent phenol oxygen, as well as to include organic guest molecules in the cavity. Moreover, the non-covalent coupling of thiacalixarene provides systems with functions higher than thiacalixarene by itself. The functions described in this paper are as follows: (1) a 200-fold pre-concentration of heavy metal ions such as Cu^II, Cd^II, and Pb^II; (2) a pre-column derivatization reagent for the highly selective and sensitive determination of Ni^II, Al^III, Fe^III, and Ti^IV at sub-ppb levels with reversed-phase HPLC; (3) the self-assembled formation of a luminescence receptor with Tb^III ions for the detection of 10^?10 M levels of 1-ethylquinolinium guest; and (4) a sensing system for 10^?9 M levels of Ag^I ions by the formation of the Ag^I-Tb^III-thiacalixarene ternary supramolecular complex. These examples support the non-covalent strategy as a highly promising way to obtain functions beyond that of a molecular platform. In addition, these diverse functions indicate the multifunctionality of thiacalixarene as well as its suitability to the non-covalent strategy, since the inherent functional groups—such as the bridging sulfur, phenol oxygen, p-substituent, aromatic ring, and hydrophobic cavity—synergistically perform the functions.     

Recent Advances in the Chemical Biology of N-Glycans

Asparagine-linked N-glycans on proteins have diverse structures, and their functions vary according to their structures. In recent years, it has become possible to obtain high quantities of N-glycans via isolation and chemical/enzymatic/chemoenzymatic synthesis. This has allowed for progress in the elucidation of N-glycan functions at the molecular level. Interaction analyses with lectins by glycan arrays or nuclear magnetic resonance (NMR) using various N-glycans have revealed the molecular basis for the recognition of complex structures of N-glycans. Preparation of proteins modified with homogeneous N-glycans revealed the influence of N-glycan modifications on protein functions. Furthermore, N-glycans have potential applications in drug development. This review discusses recent advances in the chemical biology of N-glycans.     

Trans-, cis-, and dihydro-resveratrol: a comparative study

Because of the shifted focus in life science research from genome analyses to genetic and protein function analyses, we now know functions of numerous proteins. These analyses, including those of newly identified proteins, are expected to contribute to the identification of proteins of therapeutic value in various diseases. Consequently, pharmacoproteomic-based drug discovery and development of protein therapies attracted a great deal of attention in recent years. Clinical applications of most of these proteins are, however, limited because of their unexpectedly low therapeutic effects, resulting from the proteolytic degradation in vivo followed by rapid removal from the circulatory system. Therefore, frequent administration of excessively high dose of a protein is required to observe its therapeutic effect in vivo. This often results in impaired homeostasis in vivo and leads to severe adverse effects. To overcome these problems, we have devised a method for chemical modification of proteins with polyethylene glycol (PEGylation) and other water-soluble polymers. In addition, we have established a method for creating functional mutant proteins (muteins) with desired properties, and developed a site-specific polymer-conjugation method to further improve their therapeutic potency. In this review, we are introducing our original protein-drug innovation system mentioned above.