Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony OptimizationThe Luxembourg database of trichothecene type B F. graminearum and F. culmorum producers.

Hepatitis is an emerging global threat to public health due to associated mortality, morbidity, cancer and HIV co-infection. Available diagnostics and therapeutics are inadequate to intercept the course and transmission of the disease. Antimicrobial peptides (AMP) are widely studied and broad-spectrum host defense peptides are investigated as a targeted anti-viral. Therefore, it is of interest to describe the supervised identification of anti-hepatitis peptides. We used a hybrid Support Vector Machine (SVM) with Ant Colony Optimization (ACO) algorithm for simultaneous classification and domain feature selection. The described model shows a 10 fold cross-validation accuracy of 94 percent. This is a reliable and a useful tool for the prediction and identification of hepatitis specific drug activity.

Identification of Glucose-Binding Pockets in Human Serum Albumin Using Support Vector Machine and Molecular Dynamics Simulations.

Human Serum Albumin (HSA) has been suggested to be an alternate biomarker to the existing Hemoglobin-A1c (HbA1c) marker for glycemic monitoring. Development and usage of HSA as an alternate biomarker requires the identification of glycation sites, or equivalently, glucose-binding pockets. In this work, we combine molecular dynamics simulations of HSA and the state-of-art machine learning method Support Vector Machine (SVM) to predict glucose-binding pockets in HSA. SVM uses the three dimensional arrangement of atoms and their chemical properties to predict glucose-binding ability of a pocket. Feature selection reveals that the arrangement of atoms and their chemical properties within the first 4Å from the centroid of the pocket play an important role in the binding of glucose. With a 10-fold cross validation accuracy of 84 percent, our SVM model reveals seven new potential glucose-binding sites in HSA of which two are exposed only during the dynamics of HSA. The predictions are further corroborated using docking studies. These findings can complement studies directed towards the development of HSA as an alternate biomarker for glycemic monitoring.

Computational identification of novel microRNAs and their targets in the malarial vector, Anopheles stephensi.

MicroRNAs are a ~22 nucleotide small non-coding RNAs found in animals, plants and viruses. They regulate key cellular processes by enhancing, degrading or silencing protein coding targets. Currently most of the data on miRNA is available from Drosophila . Given their important post-transcriptional role in several organisms, there is a need to understand the miRNA mediated processes in normal and abnormal conditions. Here we report four novel microRNAs ast - mir - 2502, ast - mir - 2559, ast - mir - 3868 and ast - mir - 9891 in Anopheles stephensi identified from a set of 3,052 transcriptome sequences, showing average minimum free energy of -31.8 kcal/mol of duplex formation with mRNA indicating their functional relevance. Phylogenetic study shows conservation of sequence signatures within the Class Insecta. Furthermore, 26 potential targets of these four miRNAs have been predicted that play an important role in the mosquito life-cycle. This work leads to novel leads and experimental possibilities for improved understanding of gene regulatory processes in mosquito.