The Development of a Universal In Silico Predictor of Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.

High functional coherence in k-partite protein cliques of protein interaction networks

We introduce a new topological concept called k-partite protein cliques to study protein interaction (PPI) networks. In particular, we examine functional coherence of proteins in k-partite protein cliques. A k-partite protein clique is a k-partite maximal clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI’s k-partite maximal cliques, we propose to transform PPI networks into induced K-partite graphs with proteins as vertices where edges only exist among the graph’s partites. Then, we present a k-partite maximal clique mining (MaCMik) algorithm to enumerate k-partite maximal cliques from K-partite graphs. Our MaCMik algorithm is applied to a yeast PPI network. We observe that there does exist interesting and unusually high functional coherence in k-partite protein cliques—most proteins in k-partite protein cliques, especially those in the same partites, share the same functions. Therefore, the idea of k-partite protein cliques suggests a novel approach to characterizing PPI networks, and may help function prediction for unknown proteins.

Antifungal activity of flavonoids from Heteropterys byrsonimifolia and a commercial source against Aspergillus ochraceus: In silico interactions of these compounds with a protein kinase

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

In silico identification of novel G protein coupled receptors

G-protein coupled receptors (GPCRs) are a superfamily of membrane integral proteins responsible for a large number of physiological functions. Approximately 50% of marketed drugs are targeted toward a GPCR. Despite showing a high degree of structural homology, there is a large variance in sequence within the GPCR superfamily which has lead to difficulties in identifying and classifying potential new GPCR proteins. Here the various computational techniques that can be used to characterize a novel GPCR protein are discussed, including both alignment-based and alignment-free approaches. In addition, the application of homology modeling to building the three-dimensional structures of GPCRs is described.

Protein expression of Myt272-3 recombinant clone and in silico prediction of a possible vaccine candidate against Mycobacterium tuberculosis

Purpose: To investigate the expression of Myt272-3 recombinant protein and also to predict a possible protein vaccine candidate against Mycobacterium tuberculosis . Methods: Myt272-3 protein was expressed in pET30a+-Myt272-3 clone. The purity of the protein was determined using Dynabeads® His-Tag Isolation & Pulldown. Protein sequence was analysed in silico using bioinformatics software for the prediction of allergenicity, antigenicity, MHC-I and MHC-II binding, and B-cell epitope binding. Results: The candidate protein was a non-allergen with 15.19 % positive predictive value. It was also predicted to be antigenic, with binding affinity to MHC-I and MHC-II, as well as B-cell epitope binding. Conclusion: The predicted results obtained in this study provide a guide for practical design of a new tuberculosis vaccine.

Identification of protein interaction regions of VINC/NEAT1/Men epsilon RNA

The virus inducible non-coding RNA (VINC) was detected initially in the brain of mice infected with Japanese encephalitis virus (JEV) and rabies virus. VINC is also known as NEAT1 or Men epsilon RNA. It is localized in the nuclear paraspeckles of several murine as well as human cell lines and is essential for paraspeckle formation. We demonstrate that VINC interacts with the paraspeckle protein, P54nrb through three different protein interaction regions (PIRs) one of which (PIR-1) is localized near the 50 end while the other two (PIR-2, PIR-3) are localized near the 30 region of VINC. Our studies suggest that VINC may interact with P54nrb through a novel mechanism which is different from that reported for protein coding RNAs. (C) 2010 Federation of European Biochemical Societies. Published by Elsevier B. V. All rights reserved.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial `driver' mutations in oncogenesis. They also provide the foundation toward the design of protein protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview. (C) 2014 Elsevier Ltd. All rights reserved.

A transcriptional activating region with two contrasting modes of protein interaction

A C-terminal segment of the yeast activator Gal4 manifests two functions: When tethered to DNA, it elicits gene activation, and it binds the inhibitor Gal80. Here we examine the effects on these two functions of cysteine and proline substitutions. We find that, although certain cysteine substitutions diminish interaction with Gal80, those substitutions have little effect on the activating function in vivo and interaction with TATA box-binding protein (TBP) in vitro. Proline substitutions introduced near residues critical for Gal80 binding abolish that interaction but once again have no effect on the activating function. Crosslinking experiments show that a defined position in the activating peptide is in close proximity to TBP and Gal80 in the two separate reactions and show that binding of the inhibitor blocks binding to TBP. Thus, the same stretch of amino acids are involved in two quite different protein–protein interactions: binding to Gal80, which depends on a precise sequence and the formation of a defined secondary structure, or interactions with the transcriptional machinery in vivo, which are not impaired by perturbations of either sequence or structure.

Chemical specificity and physical properties of the lipid bilayer in the regulation of protein kinase C by anionic phospholipids: evidence for the lack of a specific binding site for phosphatidylserine.

The association of protein kinase C (PKC) with membranes was found not to be specific for phosphatidyl-L-serine (PS). In particular, a synthetic phospholipid, dansyl-phosphatidylethanolamine, proved to be fully functional in the association of PKC with lipid bilayers and in mediating the interaction of this enzyme with diacylglycerol. Dansyl-phosphatidylethanolamine was also able to activate the enzyme in a Ca2+-dependent fashion. Differences in the ability to bind and activate PKC observed for an array of anionic lipids were not larger than alterations caused by changes in acyl chain composition. Thus, although different lipids interact to different extents with PKC, there are no specific binding sites for the PS headgroup on the enzyme. We found that lipids with a greater tendency to form inverted phases increased the binding of PKC to bilayers. However, these changes in lipid structure cannot be considered separately from the miscibility of lipid components in the membrane. For pairs of lipids with similar acyl chains, the dependence on PS concentration is sigmoidal, while for dissimilar acyl chains there is much less dependence of binding on PS concentration. The results can be explained in terms of differences in the lateral distribution of components in the membrane.

Negative activation enthalpies in the kinetics of protein folding.

Although the rates of chemical reactions become faster with increasing temperature, the converse may be observed with protein-folding reactions. The rate constant for folding initially increases with temperature, goes through a maximum, and then decreases. The activation enthalpy is thus highly temperature dependent because of a large change in specific heat (delta Cp). Such a delta Cp term is usually presumed to be a consequence of a large decrease in exposure of hydrophobic surfaces to water as the reaction proceeds from the denatured state to the transition state for folding: the hydrophobic side chains are surrounded by "icebergs" of water that melt with increasing temperature, thus making a large contribution to the Cp of the denatured state and a smaller one to the more compact transition state. The rate could also be affected by temperature-induced changes in the conformational population of the ground state: the heat required for the progressive melting of residual structure in the denatured state will contribute to delta Cp. By examining two proteins with different refolding mechanisms, we are able to find both of these two processes; barley chymotrypsin inhibitor 2, which refolds from a highly unfolded state, fits well to a hydrophobic interaction model with a constant delta Cp of activation, whereas barnase, which refolds from a more structured denatured state, deviates from this ideal behavior.

Reconstruction and in silico analysis of the MAPK signaling pathways in the human blood fluke, Schistosoma japonicum

At present, little is known about signal transduction mechanisms in schistosomes, which cause the disease of schistosomiasis. The mitogen-activated protein kinase (MAPK) signaling pathways, which are evolutionarily conserved from yeast to Homo sapiens, play key roles in multiple cellular processes. Here, we reconstructed the hypothetical MAPK signaling pathways in Schistosoma japonicum and compared the schistosome pathways with those of model eukaryote species. We identified 60 homologous components in the S. japoncium MAPK signaling pathways. Among these, 27 were predicted to be full-length sequences. Phylogenetic analysis of these proteins confirmed the evolutionary conservation of the MAPK signaling pathways. Remarkably, we identified S. japonicum homologues of GTP-binding protein beta and alpha-I subunits in the yeast mating pathway, which might be involved in the regulation of different life stages and female sexual maturation processes as well in schistosomes. In addition, several pathway member genes, including ERK, JNK, Sja-DSP, MRAS and RAS, were determined through quantitative PCR analysis to be expressed in a stage-specific manner, with ERK, JNK and their inhibitor Sja-DSP markedly upregulated in adult female schistosomes. (c) 2006 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions

Protein-protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%-80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

An algorithm to find distant repeats in a pair of protein sequences

Distant repeats between a pair of protein sequences can be exploited to study the various aspects of proteins such as structure-function relationship, disorders due to protein malfunction, evolutionary analysis, etc. An in-depth analysis of the distant repeats would facilitate to establish a stable evolutionary relation of the repeats with respect to their three-dimensional structure. To this effect, an algorithm has been devised to identify the distant repeats in a pair of protein sequences by essentially using the scores of PAM (Percent Accepted Mutation) matrices. The proposed algorithm will be of much use to researchers involved in the comparative study of various organisms based on the amino-acid repeats in protein sequences. (C) 2010 Elsevier B.V. All rights reserved.