A local average connectivity-based method for identifying essential proteins from the network level

Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein-protein interactions has produced unprecedented opportunities for detecting protein essentiality from the network level. Essential proteins have been found to be more abundant among those highly connected proteins. However, there exist a number of highly connected proteins which are not essential. By analyzing these proteins, we find that few of their neighbors interact with each other. Thus, we propose a new local method, named LAC, to determine a protein's essentiality by evaluating the relationship between a protein and its neighbors. The performance of LAC is validated based on the yeast protein interaction networks obtained from two different databases: DIP and BioGRID. The experimental results of the two networks show that the number of essential proteins predicted by LAC clearly exceeds that explored by Degree Centrality (DC). More over, LAC is also compared with other seven measures of protein centrality (Neighborhood Component (DMNC), Betweenness Centrality (BC), Closeness Centrality (CC), Bottle Neck (BN), Information Centrality (IC), Eigenvector Centrality (EC), and Subgraph Centrality (SC)) in identifying essential proteins. The comparison results based on the validations of sensitivity, specificity, F-measure, positive predictive value, negative predictive value, and accuracy consistently show that LAC outweighs these seven previous methods.     

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.     

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.     

Development of Catalyst Technology for Producing Methyl Methacrylate (MMA) by Direct Methyl Esterification

The direct methyl esterification reaction for MMA was commercialized. Precisely synthesized Pd₃Pb₁ catalyst and the catalysis at the proposed reductive oxidation reaction condition (PROC) realized and guaranteed high MMA yield and long catalyst life. Controlled Pd distribution in a catalyst particle drastically decreased Pd loss from the catalyst during reaction. Unit processes are introduced as well.     

蛋白质相互作用预测、设计与调控

蛋白质相互作用是生命活动在分子水平上的基本事件. 蛋白质相互作用的三维图像可以给出关键生命活动过程的分子细节. 了解蛋白质相互作用的原理有助于揭示生命活动的机制, 并在此基础上开展有重要价值的蛋白质设计. 本文对于蛋白质相互作用预测、设计和调控研究的近期进展进行了总结归纳, 介绍了作者实验室在相关领域的研究进展, 并对今后的研究方向进行了展望. 主要包括: (1) 蛋白质相互作用网络、蛋白质相互作用机制和蛋白质复合物结构计算分析; (2) 基于序列、结合位点以及复合物结构的蛋白质相互作用预测; (3)蛋白质相互作用设计方法; (4) 利用化学分子调控蛋白质相互作用的方法; (5) 针对蛋白质相互作用的蛋白质药物设计方法.     

Enzyme classification using multiclass support vector machine and feature subset selection

Proteins are the macromolecules responsible for almost all biological processes in a cell. With the availability of large number of protein sequences from different sequencing projects, the challenge with the scientist is to characterize their functions. As the wet lab methods are time consuming and expensive, many computational methods such as FASTA, PSI-BLAST, DNA microarray clustering, and Nearest Neighborhood classification on protein–protein interaction network have been proposed. Support vector machine is one such method that has been used successfully for several problems such as protein fold recognition, protein structure prediction etc. Cai et al. in 2003 have used SVM for classifying proteins into different functional classes and to predict their function. They used the physico-chemical properties of proteins to represent the protein sequences. In this paper a model comprising of feature subset selection followed by multiclass Support Vector Machine is proposed to determine the functional class of a newly generated protein sequence. To train and test the model for its performance, 32 physico-chemical properties of enzymes from 6 enzyme classes are considered. To determine the features that contribute significantly for functional classification, Sequential Forward Floating Selection (SFFS), Orthogonal Forward Selection (OFS), and SVM Recursive Feature Elimination (SVM-RFE) algorithms are used and it is observed that out of 32 properties considered initially, only 20 features are sufficient to classify the proteins into its functional classes with an accuracy ranging from 91% to 94%. On comparison it is seen that, OFS followed by SVM performs better than other methods. Our model generalizes the existing model to include multiclass classification and to identify most significant features affecting the protein function.     

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.     

Charge transfer initiation of methylmethacrylate by isopropylamine

Methylmethacrylate (MMA) can be initiated by charge transfer complexes (i) formed by the interaction of aliphatic amines and MMA and (ii) formed by the interaction of aliphatic amines and carbon tetrachloride in a solvent like N-N dimethylformamide (DMF), dimethyl sulphoxide (DMSO) or chloroform. This paper reports the polymerization of MMA by isopropylamine (IPA) in the presence of CCl₄ in DMSO at 30. The rate of polymerization, R_p increases rapidly with CCl₄ up to a concentration of 0.25 mol l^?1 but, for a higher concentration, it is practically independent of the CCl₄ concentration. R_p is proportional to (IPA concentration)^{1 2} and to power of (MMA concentration)^1.30 when [CCl₄] ? [IPA]. The average rate constant, k, is 2.1 × 10^?6 l mol^{· 1} sec^{? 1}.     

Accessible Mannitol‐Based Amphiphiles (MNAs) for Membrane Protein Solubilisation and Stabilisation

Integral membrane proteins are amphipathic molecules crucial for all cellular life. The structural study of these macromolecules starts with protein extraction from the native membranes, followed by purification and crystallisation. Detergents are essential tools for these processes, but detergent‐solubilised membrane proteins often denature and aggregate, resulting in loss of both structure and function. In this study, a novel class of agents, designated mannitol‐based amphiphiles (MNAs), were prepared and characterised for their ability to solubilise and stabilise membrane proteins. Some of MNAs conferred enhanced stability to four membrane proteins including a G protein‐coupled receptor (GPCR), the β₂ adrenergic receptor (β₂AR), compared to both n‐dodecyl‐d ‐maltoside (DDM) and the other MNAs. These agents were also better than DDM for electron microscopy analysis of the β₂AR. The ease of preparation together with the enhanced membrane protein stabilisation efficacy demonstrates the value of these agents for future membrane protein research.     

Study on grafting of different types of acrylic monomers onto natural rubber by γ-rays

 A comparative study of various acrylic monomers for grafting onto natural rubber was done. The stability of natural rubber latex (NRL) against coagulum with monomer, mechanical properties of grafted rubbers and percent of grafting were investigated. The NRL with monomers, methylacrylate (MA), ethylacrylate (EA) and n-butylacrylate (n-BA), is unstable but it is stable with methyl methacrylate (MMA), n-butyl methacrylate (BMA) and cyclohexyl methacrylate (CHMA). The mechanical properties and degree of grafting attained a maximum at a total radiation dose of 4 kGy. The values of tensile properties of MMA and CHMA grafted rubbers are almost similar, and higher than those of BMA grafted rubbers. On the other hand, the degree of grafting for CHMA is higher than those of MMA and BMA grafted rubbers. The infrared (IR) spectra of monomer grafted natural rubber were also studied.     

The influence of solute-solvent interactions on the kinetics and stereospecificity of polymerization-the relevance of aromatic solvent shifts observed on the nmr spectrum of methyl methacrylate

The effect of certain aromatic compounds on the PMR spectrum of methyl methacrylate (MMA) was investigated. The magnitude of observed aromatic-induced shifts decreased in the order benzene ? styrene > chlorobenzene ≈ bromobenzene.Assuming that the interaction arises from a stoichiometric 1:1 complex, equilibrium parameters for the MMA-benzene interaction have been estimated. ΔH ± S.E. (ΔH) = ?(8 ± 4) kJ mol^?1. These effects are likely to have a small influence on the kinetics of copolymerization with aromatic monomers and polymerization in aromatic solvent. The stereochemistry of the solute-solvent interactions suggests that MMA takes a cis-conformation in solution, which is relevant to the mechanism of stereoregular polymerizations of this monomer.     

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.     

Painting Proteins with Covalent Labels: What's In the Picture?

Knowledge about the structural and biophysical properties of proteins when they are free in solution and/or in complexes with other molecules is essential for understanding the biological processes that proteins regulate. Such knowledge is also important to drug discovery efforts, particularly those focused on the development of therapeutic agents with protein targets. In the last decade a variety of different covalent labeling techniques have been used in combination with mass spectrometry to probe the solution-phase structures and biophysical properties of proteins and protein—ligand complexes. Highlighted here are five different mass spectrometry—based covalent labeling strategies including: continuous hydrogen/deuterium (H/D) exchange labeling, hydroxyl radical-mediated footprinting, SUPREX (stability of unpurified proteins from rates of H/D exchange), PLIMSTEX (protein-ligand interaction by mass spectrometry, titration, and H/D exchange), and SPROX (stability of proteins from rates of oxidation). The basic experimental protocols used in each of the above-cited methods are summarized along with the kind of biophysical information they generate. Also discussed are the relative strengths and weaknesses of the different methods for probing the wide range of conformational states that proteins and protein-ligand complexes can adopt when they are in solution.     

Experimental and Theoretical Studies on the Enantioseparation and Chiral Recognition of Mandelate and Cyclohexylmandelate on Permethylated ��-Cyclodextrin Chiral Stationary Phase

Enantioseparations of methyl mandelate (MMA) and methyl ??-cyclohexylmandelate (MCHMA) on permethylated ??-cyclodextrin (PM-??-CD) chiral stationary phase were explored in detail using high-performance liquid chromatography. The influence of the concentration of organic modifiers, along with the column temperature, was studied. In addition, the thermodynamics parameters of the enantioseparations were determined to discuss driven power in the process of enantioseparations. In addition, host?guest complexation of PM-??-CD with MMA enantiomers was simulated by quantum mechanics PM3 method for understanding the chiral recognition mechanism. The experimental results showed that the retention factor (k), separation factor (??), and resolution factor (Rs) for MMA and MCHMA resolved on the PM-??-CD column all generally decreased with the increase of methanol content, which indicated that the main chiral recognition mechanism is that the hydrophobic portions of MMA and MCHMA are included in the hydrophobic cavity of PM-??-CD to form inclusion complexes. In addition, there is an excellent linear relationship between the logarithms of retention factors (k) of MMA and MCHMA enantiomers and 1/T. It was demonstrated that the enantioseparations of MMA and MCHMA on PM-??-CD chiral column were enthalpy-driven processes. The modeling results can correctly predict the retention order and provide an atomistic account of how chiral discrimination takes place. It is found that the most stable structure of (R)-MMA/PM-??-CD complex is different with that of (S)-MMA/PM-??-CD complex. The main driving forces responsible for chiral recognition are hydrophobic forces and weak hydrogen bondings.     

New azo-peroxidic initiators in the radical polymerization of styrene and methyl methacrylate

Two new azo-perester compounds, di-tert-butyl-6,6′-azobis-(6-cyanoperoxyheptanoate) (6,6-di-tBu) and di-tert-amyl-6,6′-azobis-(6-cyanoperoxyheptanoate) (6,6-di-tAm), synthesized on the basis of 6,6′-azobis-(6-cyanoheptanoic acid) (ACHpA), were investigated for their use in the radical polymerization of styrene (S) and methyl methacrylate (MMA). Their characteristics are given, including chemical (IR spectra), thermal (DSC) and kinetic, i.e., thermal decomposition studied by volumetric and gas chromatographic methods. The rate constants and activation energies of the decomposition of both the azo and perester bonds were determined. The new azo-peresters were utilized to initiate the radical solution polymerizations of S and MMA at 60 °C. The kinetic parameters of the processes, i.e., polymerization rate and overall rate constant, were determined. Subsequently, the polymerization products were characterized by IR and DSC. It was found that the perester groups were present in the obtained polymers, and hence, the polymers are “active” for further polymerization.     

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.     

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.     

Mechanism of Charge Transfer Initiation by the Dimethyl Phenyl Phosphine/Fe(III) Catalyst System

The dimethyl phenyl phosphine (DMPP) initiated polymerization of methyl methacrylate (MMA) in dimethylsulfoxide was studied. Polymerization of MMA in this system required the presence of transition metal ions like Fe³⁺ or Cu²⁺. Kinetic studies showed that the propagation was free radical in nature. An interaction between DMPP and MMA was detected spectrophotometrically. A proposed mechanism involves a transition metal ion-activated dipole interaction between the carbonyl oxygen and the phosphorus atom with the ultimate formation of a methyl methacrylate type of free radical.     

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.