Finding Bicliques in Digraphs: Application into Viral-host Protein Interactome

We provide the first formalization true to the best of our knowledge to the problem of finding bicliques in a directed graph. The problem is addressed employing a two-stage approach based on an existing biclustering algorithm. This novel problem is useful in several biological applications of which we focus only on analyzing the viral-host protein interaction graphs. Strong and significant bicliques of HIV-1 and human proteins are derived using the proposed methodology, which provides insights into some novel regulatory functionalities in case of the acute immunodeficiency syndrome in human

A new approach to ward off Error Propagation Effect of AES – Redundancy Based Technique Redefined

Advanced Encryption Standard (AES) [1, 2] is a great research challenge. It has been developed to replace the Data Encryption Standard (DES). AES suffers from a major limitation of Error propagation effect. To tackle this limitation, two methods are available. One is Redundancy Based Technique and the other one is Bite Based Parity Technique. The first one has a significant advantage of correcting any error on definite term over the second one but at the cost of higher level of overhead and hence lowering the processing speed. In this paper we have proposed a new approach based on the Redundancy Based Technique that would certainly speed up the process of reliable encryption and hence the secured communication. Keywords Advanced Encryption Standard, Error Propagation Effect, Redundancy Based Technique, Longitudinal Redundancy Check Cod

Three dimensional structure prediction and ligand-protein interaction study of expansin protein ATEXPA23 from Arabidopsis thaliana L.

Arabidopsis thaliana L. is a small flowering plant that is widely used as a model organism in plant biology. In the present study, we study the peripheral membrane protein ATEXPA23 from Arabidopsis thaliana L. using homology modelling and molecular docking. The microarray analysis shows expression of ATEXPA23 (AT5G39280) protein, which leads to loosening and extension of plant cell walls. This protein is differentially expressed during different stages of plant embryogenesis. It contains one expansin-like CBD domain and one expansin-like EG45 domain. ATEXPA23 belongs to the expansin family in expansin a subfamily. The 3D model after refinement is used to explore the xyloglucan binding characteristics of ATEXPA23 using AutoDock. The docking analysis shows that the surface exposed aromatic amino acid residues Phe 193 and Phe 265 interact with ligand xyloglucan through CH-л interaction. The binding energy values of docking reflect a stable conformation of the docked complex. The interaction of expansin protein with carbohydrate xyloglucan, present in hemicellulose structures of plant cell wall, is thoroughly analysed with cellotetrose and xyloglucan heptasaccharide using electrostatic potential calculation. This CH-л non-covalent interaction predominates on the cellulose-xyloglucan interaction in plant cell wall during cell growth

PPIcons: identification of protein-protein interaction sites in selected organisms

The physico-chemical properties of interaction interfaces have a crucial role in characterization of protein–protein interactions (PPI). In silico prediction of participating amino acids helps to identify interface residues for further experimental verification using mutational analysis, or inhibition studies by screening library of ligands against given protein. Given the unbound structure of a protein and the fact that it forms a complex with another known protein, the objective of this work is to identify the residues that are involved in the interaction. We attempt to predict interaction sites in protein complexes using local composition of amino acids together with their physico-chemical characteristics. The local sequence segments (LSS) are dissected from the protein sequences using a sliding window of 21 amino acids. The list of LSSs is passed to the support vector machine (SVM) predictor, which identifies interacting residue pairs considering their inter-atom distances. We have analyzed three different model organisms of Escherichia coli, Saccharomyces Cerevisiae and Homo sapiens, where the numbers of considered hetero-complexes are equal to 40, 123 and 33 respectively. Moreover, the unified multi-organism PPI meta-predictor is also developed under the current work by combining the training databases of above organisms. The PPIcons interface residues prediction method is measured by the area under ROC curve (AUC) equal to 0.82, 0.75, 0.72 and 0.76 for the aforementioned organisms and the meta-predictor respectively. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00894-013-1886-9) contains supplementary material, which is available to authorized users

Economical Analysis of Flexibility in Micro Grids

International audienceAs energy demand increased and production means diversified, conventional approaches of looking into distri- bution grids need to evolve. The Smart Grid paradigm introduces new possibilities of real-time market sensing and interaction models between producers and consumers. In particular, by understanding the types of con- sumers and their potential willingness to adapt their energy demand with price incentives, innovative pricing strategies in the Smart Grid are expected to lead to better production management, profit maximization and end consumers satisfaction levels. In this work we propose a novel framework and a simulation scenario of a global energy network with heterogeneous types of producers and consumers from which different types of behaviors and interactions can be studied

Economical Analysis of Flexibility in Micro Grids

International audienceAs energy demand increased and production means diversified, conventional approaches of looking into distri- bution grids need to evolve. The Smart Grid paradigm introduces new possibilities of real-time market sensing and interaction models between producers and consumers. In particular, by understanding the types of con- sumers and their potential willingness to adapt their energy demand with price incentives, innovative pricing strategies in the Smart Grid are expected to lead to better production management, profit maximization and end consumers satisfaction levels. In this work we propose a novel framework and a simulation scenario of a global energy network with heterogeneous types of producers and consumers from which different types of behaviors and interactions can be studied

Machine Learning and Rule Mining Techniques in the Study of Gene Inactivation and RNA Interference

RNA interference (RNAi) and gene inactivation are extensively used biological terms in biomedical research. Two categories of small ribonucleic acid (RNA) molecules, viz., microRNA (miRNA) and small interfering RNA (siRNA) are central to the RNAi. There are various kinds of algorithms developed related to RNAi and gene silencing. In this book chapter, we provided a comprehensive review of various machine learning and association rule mining algorithms developed to handle different biological problems such as detection of gene signature, biomarker, gene module, potentially disordered protein, differentially methylated region and many more. We also provided a comparative study of different well-known classifiers along with other used methods. In addition, we demonstrated the brief biological information regarding the immense biological challenges for gene activation as well as their advantages, disadvantages and possible therapeutic strategies. Finally, our study helps the bioinformaticians to understand the overall immense idea in different research dimensions including several learning algorithms for the benevolent of the disease discovery

Application of RotaSVM for HLA class II Protein-Peptide Interaction Prediction

In this article, the recently developed RotaSVM is used for accurate prediction of binding peptides to Human Leukocyte Antigens class II (HLA class II) proteins. The HLA II - peptide complexes are generated in the antigen presenting cells (APC) and transported to the cell membrane to elicit an immune response via T-cell activation. The understanding of HLA class II protein-peptide binding interaction facilitates the design of peptide-based vaccine, where the high rate of polymorphisms in HLA class II molecules poses a big challenge. To determine the binding activity of 636 non-redundant peptides, a set of 27 HLA class II proteins are considered in the present study. The prediction of HLA class II - peptide binding is carried out by an ensemble classifier called RotaSVM. In RotaSVM, the feature selection scheme generates bootstrap samples that are further used to create a diverse set of features using Principal Component Analysis. Thereafter, Support Vector Machines are trained with th ese bootstrap samples with the integration of their original feature values. The effectiveness of the RotaSVM for HLA class II protein-peptide binding prediction is demonstrated in comparison with other traditional classifiers by evaluating several validity measures with the visual plot of ROC curves. Finally, Friedman test is conducted to judge the statistical significance of RotaSVM in prediction of peptides binding to HLA class II proteins

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed