The role of electrostatic energy in prediction of obligate protein-protein interactions

Background

Prediction and analysis of protein-protein interactions (PPI) and specifically types of PPIs is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. In addition, electrostatic interactions are important in understanding inter-molecular interactions, since they are long-range, and because of their influence in charged molecules. This is the main motivation for using electrostatic energy for prediction of PPI types.

Results

We propose a prediction model to analyze protein interaction types, namely obligate and non-obligate, using electrostatic energy values as properties. The prediction approach uses electrostatic energy values for pairs of atoms and amino acids present in interfaces where the interaction occurs. The main features of the complexes are found and then the prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). For an in-depth analysis of classification results, some other experiments were performed by varying the distance cutoffs between atom pairs of interacting chains, ranging from 5Å to 13Å. Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative pairs of atom types and amino acid types as features for prediction.

Conclusions

Our results on two well-known datasets of obligate and non-obligate complexes confirm that electrostatic energy is an important property to predict obligate and non-obligate protein interaction types on the basis of all the experimental results, achieving accuracies of over 98%. Furthermore, a comparison performed by changing the distance cutoff demonstrates that the best values for prediction of PPI types using electrostatic energy range from 9Å to 12Å, which show that electrostatic interactions are long-range and cover a broader area in the interface. In addition, the results on using feature selection before prediction confirm that (a) a few pairs of atoms and amino acids are appropriate for prediction, and (b) prediction performance can be improved by eliminating irrelevant and noisy features and selecting the most discriminative ones.

     

"Hot cores" in proteins: Comparative analysis of the apolar contact area in structures from hyper/thermophilic and mesophilic organisms

Background  

A wide variety of stabilizing factors have been invoked so far to elucidate the structural basis of protein thermostability. These include, amongst the others, a higher number of ion-pairs interactions and hydrogen bonds, together with a better packing of hydrophobic residues. It has been frequently observed that packing of hydrophobic side chains is improved in hyperthermophilic proteins, when compared to their mesophilic counterparts. In this work, protein crystal structures from hyper/thermophilic organisms and their mesophilic homologs have been compared, in order to quantify the difference of apolar contact area and to assess the role played by the hydrophobic contacts in the stabilization of the protein core, at high temperatures.     

Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification

Background

Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems.

Results

In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on “pair-properties” of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA).

Conclusion

In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier.

     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Prediction of protein-protein interaction types using association rule based classification

Background  

Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches.     

Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling

Background  

Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis.     

Ethylene Receptors Function as Components of High-Molecular-Mass Protein Complexes in Arabidopsis

Background

The gaseous plant hormone ethylene is perceived in Arabidopsis thaliana by a five-member receptor family composed of ETR1, ERS1, ETR2, ERS2, and EIN4.

Methodology/Principal Findings

Gel-filtration analysis of ethylene receptors solubilized from Arabidopsis membranes demonstrates that the receptors exist as components of high-molecular-mass protein complexes. The ERS1 protein complex exhibits an ethylene-induced change in size consistent with ligand-mediated nucleation of protein-protein interactions. Deletion analysis supports the participation of multiple domains from ETR1 in formation of the protein complex, and also demonstrates that targeting to and retention of ETR1 at the endoplasmic reticulum only requires the first 147 amino acids of the receptor. A role for disulfide bonds in stabilizing the ETR1 protein complex was demonstrated by use of reducing agents and mutation of Cys4 and Cys6 of ETR1. Expression and analysis of ETR1 in a transgenic yeast system demonstrates the importance of Cys4 and Cys6 of ETR1 in stabilizing the receptor for ethylene binding.

Conclusions/Significance

These data support the participation of ethylene receptors in obligate as well as ligand-dependent non-obligate protein interactions. These data also suggest that different protein complexes may allow for tailoring of the ethylene signal to specific cellular environments and responses.     

Instance-based concept learning from multiclass DNA microarray data

Background  

Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as nearest neighbor (NN) approaches perform remarkably well in comparison to more complex models, and are currently experiencing a renaissance in the analysis of data sets from biology and biotechnology. While binary classification of microarray data has been extensively investigated, studies involving multiclass data are rare. The question remains open whether there exists a significant difference in performance between NN approaches and more complex multiclass methods. Comparative studies in this field commonly assess different models based on their classification accuracy only; however, this approach lacks the rigor needed to draw reliable conclusions and is inadequate for testing the null hypothesis of equal performance. Comparing novel classification models to existing approaches requires focusing on the significance of differences in performance.     

Transformation of Anaplasma phagocytophilum

Background  

Tick-borne pathogens cause emerging zoonoses, and include fastidious organisms such as Anaplasma phagocytophilum. Because of their obligate intracellular nature, methods for mutagenesis and transformation have not been available.     

Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction

Background  

Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.     

FlexSnap: Flexible Non-sequential Protein Structure Alignment

Background  

Proteins have evolved subject to energetic selection pressure for stability and flexibility. Structural similarity between proteins that have gone through conformational changes can be captured effectively if flexibility is considered. Topologically unrelated proteins that preserve secondary structure packing interactions can be detected if both flexibility and Sequential permutations are considered. We propose the FlexSnap algorithm for flexible non-topological protein structural alignment.     

Oligomeric protein structure networks: insights into protein-protein interactions

Background  

Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues) with special emphasis to protein interfaces.     

Application of methods of identifying receptor binding models and analysis of parameters

Background  

Possible methods for distinguishing receptor binding models and analysing their parameters are considered.     

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method

Background  

Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction.     

<Emphasis Type="Italic">Chlamydia trachomatis</Emphasis>responds to heat shock,penicillin induced persistence,and IFN-gamma persistence by altering levels of the extracytoplasmic stress response protease HtrA

Background  

Chlamydia trachomatis, an obligate intracellular human pathogen, is the most prevalent bacterial sexually transmitted infection worldwide and a leading cause of preventable blindness. HtrA is a virulence and stress response periplasmic serine protease and molecular chaperone found in many bacteria. Recombinant purified C. trachomatis HtrA has been previously shown to have both activities. This investigation examined the physiological role of Chlamydia trachomatis HtrA.     

Application of a pig ligated intestinal loop model for early <Emphasis Type="Italic">Lawsonia intracellularis</Emphasis> infection

Background  

Porcine proliferative enteropathy in pigs is caused by the obligate, intracellular bacterium Lawsonia intracellularis. In vitro studies have shown close bacterium-cell interaction followed by cellular uptake of the bacterium within 3 h post inoculation (PI). However, knowledge of the initial in vivo interaction between porcine intestinal epithelium and the bacterium is limited. The aims of the present study were to evaluate the usefulness of a ligated small intestinal loop model to study L. intracellularis infections and to obtain information on the very early L. intracellularis-enterocyte interactions.     

Identification and characterization of subfamily-specific signatures in a large protein superfamily by a hidden Markov model approach

Background  

Most profile and motif databases strive to classify protein sequences into a broad spectrum of protein families. The next step of such database studies should include the development of classification systems capable of distinguishing between subfamilies within a structurally and functionally diverse superfamily. This would be helpful in elucidating sequence-structure-function relationships of proteins.