Identifying essential proteins using modified-monkey algorithm (MMA)

Proteins are the most essential macromolecules needed for the normal flow of life. Essential proteins play a key role to control other proteins in an interaction network for the growth and understanding of the molecular mechanism of cellular life. Though there are many computational algorithms for essential drug discovery depending on nature of essential proteins, but still lots of improvements and optimizations are required. In this work, modified-Monkey algorithm (MMA) is proposed for the identification of essential proteins in protein-protein interaction network (PPIN). Nature of a monkey can be distinctly described in three processes like climb, watch-jump, and somersault in different problem spaces. These processes of monkey traversal are plotted in PPIN with objective to find out essential proteins. Performance of MMA is assessed with other existing essential protein prediction methodologies, including Eigenvector Centrality (EC), Betweenness Centrality (BC), Network Centrality (NC) and others also. The proposed methodology has achieved higher success rates in comparison to the existing state-of-art model.     

Computational prediction of miRNA/mRNA duplexomes at the whole human genome scale reveals functional subnetworks of interacting genes with embedded miRNA annealing motifs

Perfect annealing between microRNAs (miRNAs) and messenger RNAs (mRNAs) was computationally searched at a broad scale in the human genome to determine whether theoretical pairing is restrictively represented in functional subnetworks or is randomly distributed. Massive RNA interference (RNAi) pairing motifs in genes constitute a remarkable subnetwork that displays highly genetically and biochemically interconnected genes. These analyses show unexpected repertoires of genes defined by their congruence in comatching with miRNAs at numerous sites and by their interconnection based on protein/protein interactions or proteins regulating the activity of others. This offers insights into the putatively coregulated homeostasis of large networks of genes by RNAi, whereas other networks seem to be independent of this regulatory mode. Genes accordingly defined by theoretical RNAi pairing cluster mainly in subnetworks related to cellular, metabolic and developmental processes and their regulation. Indeed, genes harboring numerous potential sites of hybridization with miRNAs are highly enriched with GO terms depicting the abovementioned processes and are grouped in a subnetwork of genes that are significantly more highly connected than they would be according to a random distribution. The significant number of interacting genes that present numerous potential comatches with miRNAs suggests that they may be under the control of the integrative and concerted action of multiple miRNAs.     

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.     

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.     

Population dynamics simulations of functional model proteins

In order to probe the fundamental principles that govern protein evolution, we use a minimalist model of proteins to provide a mapping from genotype to phenotype. The model is based on physically realistic forces of protein folding and includes an explicit definition of protein function. Thus, we can find the fitness of a sequence from its ability to fold to a stable structure and perform a function. We study the fitness landscapes of these functional model proteins, that is, the set of all sequences mapped on to their corresponding fitnesses and connected to their one mutant neighbors. Through population dynamics simulations we directly study the influence of the nature of the fitness landscape on evolution. Populations are observed to move to a steady state, the distribution of which can often be predicted prior to the population dynamics simulations from the nature of the fitness landscape and a quantity analogous to a partition function. In this paper, we develop a scheme for predicting the steady-state population on a fitness landscape, based on the nature of the fitness landscape, thereby obviating the need for explicit population dynamics simulations and providing some insight into the impact on molecular evolution of the nature of fitness landscapes. Poor predictions are indicative of fitness landscapes that consist of a series of weakly connected sublandscapes.     

Adsorption of lysozyme and α-lactalbumin on poly(styrenesulphonate) latices 2. Proton titrations

A study on the titration behaviour of hen's egg lysozyme (LSZ) and milk α-lactalbumin (LAC) is presented. Titration curves for the proteins in their native state, after exposure to denaturing agents, and adsorbed on poly(styrenesulphonate) (PSS) latices are compared. Titrations of the proteins in the presence of guanidinium hydrochloride and sodium dodecyl sulphate (SDS) show that electrostatic interactions, more than alterations in the chemical environment, affect the dissociation of the charged groups on the protein molecule. The titration of adsorbed (apo)-LAC is similar to that of the SDS-denatured state, independent of the surface coverage. The titration of LSZ depends on the degree of coverage, suggesting different modes of adsorption. The two conformers of LAC, i.e. apo-LAC and Ca-LAC, are compared to test the influence of the structural stability of the protein on the titration behaviour. In solution, the two conformers of LAC titrate differently, but after adsorption on to a PSS latex surface the titration behaviour is practically the same. This points to a similar adsorbed state, with expulsion of the Ca²⁺ ion from the Ca-containing form.     

Segmental,Domain‐Selective Perdeuteration and Small‐Angle Neutron Scattering for Structural Analysis of Multi‐Domain Proteins

Multi‐domain proteins play critical roles in fine‐tuning essential processes in cellular signaling and gene regulation. Typically, multiple globular domains that are connected by flexible linkers undergo dynamic rearrangements upon binding to protein, DNA or RNA ligands. RNA binding proteins (RBPs) represent an important class of multi‐domain proteins, which regulate gene expression by recognizing linear or structured RNA sequence motifs. Here, we employ segmental perdeuteration of the three RNA recognition motif (RRM) domains in the RBP TIA‐1 using Sortase A mediated protein ligation. We show that domain‐selective perdeuteration combined with contrast‐matched small‐angle neutron scattering (SANS), SAXS and computational modeling provides valuable information to precisely define relative domain arrangements. The approach is generally applicable to study conformational arrangements of individual domains in multi‐domain proteins and changes induced by ligand binding.     

Segmental,Domain‐Selective Perdeuteration and Small‐Angle Neutron Scattering for Structural Analysis of Multi‐Domain Proteins

Multi‐domain proteins play critical roles in fine‐tuning essential processes in cellular signaling and gene regulation. Typically, multiple globular domains that are connected by flexible linkers undergo dynamic rearrangements upon binding to protein, DNA or RNA ligands. RNA binding proteins (RBPs) represent an important class of multi‐domain proteins, which regulate gene expression by recognizing linear or structured RNA sequence motifs. Here, we employ segmental perdeuteration of the three RNA recognition motif (RRM) domains in the RBP TIA‐1 using Sortase A mediated protein ligation. We show that domain‐selective perdeuteration combined with contrast‐matched small‐angle neutron scattering (SANS), SAXS and computational modeling provides valuable information to precisely define relative domain arrangements. The approach is generally applicable to study conformational arrangements of individual domains in multi‐domain proteins and changes induced by ligand binding.     

The prediction of amphiphilic alpha-helices

A number of sequence-based analyses have been developed to identify protein segments, which are able to form membrane interactive amphiphilic alpha-helices. Earlier techniques attempted to detect the characteristic periodicity in hydrophobic amino acid residues shown by these structure and included the Molecular Hydrophobic Potential (MHP), which represents the hydrophobicity of amino acid residues as lines of isopotential around the alpha-helix and analyses based on Fourier transforms. These latter analyses compare the periodicity of hydrophobic residues in a putative alpha-helical sequence with that of a test mathematical function to provide a measure of amphiphilicity using either the Amphipathic Index or the Hydrophobic Moment. More recently, the introduction of computational procedures based on techniques such as hydropathy analysis, homology modelling, multiple sequence alignments and neural networks has led to the prediction of transmembrane alpha-helices with accuracies of the order of 95% and transmembrane protein topology with accuracies greater than 75%. Statistical approaches to transmembrane protein modeling such as hidden Markov models have increased these prediction levels to an even higher level. Here, we review a number of these predictive techniques and consider problems associated with their use in the prediction of structure / function relationships, using alpha-helices from G-coupled protein receptors, penicillin binding proteins, apolipoproteins, peptide hormones, lytic peptides and tilted peptides as examples.     

Role of cooperativity in protein folding and protein mosaic assemblage relevance for protein conformational diseases

Biological systems are organized in intricate and highly structured networks with hierarchies and multiple scales. Cells can be considered as "meso-scale level" systems placed between the "macro-scale level" (systems of cellular networks) and the "micro-scale level" (systems of molecular networks). In fact, cells represent complex biochemical machineries made by networks of molecules connected by biochemical reactions. Thus, the brain should be studied as a system of "networks of networks". Recently, the existence of a Global Molecular Network (GMN) enmeshing the entire CNS was proposed. This proposal is based on the evidence that the extra-cellular matrix is a dynamic molecular structure capable of storing and releasing signals and of interacting with receptors and proteins on the cell membranes. Proteins have a special role in molecular networks since they can be assembled into high-order molecular complexes, which have been defined as Protein Mosaics (PM). Protein monomers in a PM (the "tesserae" of the mosaic) can interact via classical and non-classical cooperativity behaviour involving allosteric interactions. In the present paper, new features of allostery and cooperativity for protein folding, assemblage and topological features of PM will be discussed. Against this background, alterations in PM via allosteric modulations and non-classical cooperativity mechanisms may lead to protein aggregates like beta amyloid fibrils. Such aggregates cause pathological changes in the GMN structure and function leading to neurodegenerative diseases such as Alzheimer's disease. Thus, a novel view of the so called Protein Conformational Diseases (PCD) is proposed.     

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.     

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine,biomedical text mining and network analysis

In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein–protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies.     

Fast and reliable protein microarray production by a new drop-in-drop technique

In contrast to DNA microarrays, production of protein microarrays is an immense technological challenge due to high complexity and diversity of proteins. In this paper we investigate three essential aspects of protein microarray fabrication based on the highly parallel and non-contact TopSpot technology: evaporation of probes during long lasting production times, optimization of protein immobilization and improvement of protein microarray reproducibility. Evaporation out of the printhead reservoirs was reduced to a minimum by sealing the reservoirs with gas permeable foils or PDMS frames. This led to dramatically lowered setup times through the possibility of long-term, ready-to-print storage of filled printheads. To optimize immobilization efficiency 128 printing buffers were tested by printing two different proteins onto seven different microarray slide types. This way we were able to reduce the CV of spot diameter on the microarray slide below 1.14%. To remarkably increase protein immobilization efficiency on microarray slides the commonly used EDC-NHS system (a laboratory method for immobilization of proteins) was miniaturized by using a new drop-in-drop printing technique. Additionally the very fast UV cross-linking was used to immobilize antibodies. The optimized system was used to produce antibody microarrays and with it microarray ELISA experiments were performed successfully.     

Monitoring of protein profiles for the optimization of recombinant fermentation processes using public domain databases

The expression of human superoxide dismutase in fed-batch fermentation of E. coli HMS174(DE3)(pET3ahSOD) was studied as model system. Due to the frequently used strong T7 promoter system a high metabolic load is exerted, which triggers stress response mechanisms and finally leads to the differentiation of the host cell. As a consequence, host cell metabolism is partly shifted from growth to survival accompanied by significant alterations of the protein pattern. In terms of process optimization two-dimensional electrophoresis deserves as a powerful tool to monitor these changes on protein level. For the analysis of samples derived from different states of recombinant protein production wide-range Immobiline Dry Strips pH 3-10 were used. In order to establish an efficient procedure for accelerated process optimization and to avoid costly and time-consuming analysis like mass spectrometry (MS), a database approach for the identification of significant changes of the protein pattern was evaluated. On average, 935 spots per gel were detected, whereby 50 are presumably stress-relevant. Out of these, 24 proteins could be identified by using the SWISS-2DPAGE database (www.expasy.ch/ch2d/). The identified proteins are involved in regulatory networks, energy metabolism, purine and pyrimidine nucleotide synthesis and translation. By this database approach, significant fluctuations of individual proteins in relation to recombinant protein production could be identified. Seven proteins show strong alterations (>100%) directly after induction and can therefore be stated as reliable marker proteins for the assessment of stress response. For distinctive interpretation of this highly specific information, a bioinformatic and statistic tool would be essential in order to perceive the role and contribution of individual proteins in stress response.     

Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function: hypotheses and a comprehensive review

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into 'cumulus-type', i.e., those similar to puffy (white) clouds, and 'stratus-type', i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an 'energy transfer' mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by 'multi-trajectories'; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach 'rarely visited' but functionally-related states. We also show the role of disorder in 'spatial games' of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks.     

HashGO: hashing gene ontology for protein function prediction

Gene ontology (GO) is a standardized and controlled vocabulary of terms that describe the molecular functions, biological roles and cellular locations of proteins. GO terms and GO hierarchy are regularly updated as the accumulated biological knowledge. More than 50,000 terms are included in GO and each protein is annotated with several or dozens of these terms. Therefore, accurately predicting the association between proteins and massive GO terms is rather challenging. To accurately predict the association between massive GO terms and proteins, we proposed a method called Hashing GO for protein function prediction (HashGO in short). HashGO firstly adopts a protein-term association matrix to store available GO annotations of proteins. Then, it tailors a graph hashing method to explore the underlying structure between GO terms and to obtain a series of hash functions to compress the high-dimensional protein-term association matrix into a low-dimensional one. Next, HashGO computes the semantic similarity between proteins based on Hamming distance on that low-dimensional matrix. After that, it predicts missing annotations of a protein based on the annotations of its semantic neighbors. Experimental results on archived GO annotations of two model species (Yeast and Human) show that HashGO not only more accurately predicts functions than other related approaches, but also runs faster than them.     

Predicting protein disorder and induced folding: from theoretical principles to practical applications

In the last years there has been an increasing amount of experimental evidence pointing out that a large number of proteins are either fully or partially disordered (unstructured). Intrinsically disordered proteins are ubiquitary proteins that fulfil essential biological functions while lacking highly populated and uniform secondary and tertiary structure under physiological conditions. Despite the large abundance of disorder, disordered regions are still poorly detected. Recognition of disordered regions in a protein is instrumental for reducing spurious sequence similarity between disordered regions and ordered ones, and for delineating boundaries of protein domains amenable to crystallization. As presently none of the available automated methods for prediction of protein disorder can be taken as fully reliable on its own, we present a brief overview of the methods currently employed highlighting their philosophy. We show a few practical examples of how they can be combined to avoid pitfalls and to achieve more reliable predictions. We also describe the currently available methods for the identification of regions involved in induced folding and provide a few practical examples in which the accuracy of predictions was experimentally confirmed.     

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.     

Probing the water coordination of protein-targeted MRI contrast agents by pulsed ENDOR spectroscopy.

A novel methodology based on electron-nuclear double resonance (ENDOR) spectroscopy is used for the direct determination of the water coordination number (q) of gadolinium-based magnetic resonance imaging (MRI) contrast agents. Proton ENDOR spectra can be obtained at approximately physiological concentrations for metal complexes in frozen aqueous solutions either in the presence or absence of protein targets. It is shown that, depending on the structure of the co-ligand, the water hydration number of a complex in aqueous solution can be significantly different to when the complex is noncovalently bound to a protein. From the ENDOR spectra of the exchangeable protons, precise information on the metal-proton distance can be derived as well. These essential parameters directly correlate with the efficacy of MRI contrast agents and should therefore aid the development of novel, highly efficient compounds targeted to various proteins.