Abstract: The present invention relates to targets for Human microRNAs in Avian Influenza Virus (H5N1) Genome and provides specific miRNA targets against H5N1 virus. Existing therapies for Avian flu are of limited use primarily due to genetic re-assortment of the viral genome, generating novel proteins, and thus escaping immune response. In animal models, baculovirus-derived recombinant H5 vaccines were immunogenic and protective, but results in humans were disappointing even when using high doses. Currently, two classes of drugs are available with antiviral activity against influenza viruses: inhibitors of the M2 ion channel, amantadine and rimantadine, and inhibitors of neuraminidase, oseltamivir, and zanamivir. There is paucity of information regarding effectiveness of these drugs in H5N1 infection. These drugs are also well known to have side effects like neurotoxicity. Thus there exists a need to develop alternate therapy for targeting the Avian flu virus (H5N1).

Abstract: The present invention relates to human microRNA targets in HIV genome and a method of identification thereof. Using multiple software targets to six human microRNAs [miRNAs] were discovered in the net, vpr, env, and I vif genes. The miRNAs were identified as hsa-miR-29a, hsa-miR-29b, hsa-miR-29c, hsa-mir-149, hsa-mir-324-5p, hsa-mir-378. These miRNAs or its homologues can be used as therapeutics against HIV infection. The invention further relates to a novel strategy to target genes of HIV-1 by human microRNAs, or its homologues, to inactivate or block HIV activity. The computational approach towards identification of human miRNA targets in the HIV genome and the variation in the microRNA levels was further validated experimentally.

Abstract: A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of a neural network software wherein the attributes are (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv),charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (Pad) as ?0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins.

Abstract: Present invention relates to a method for predicting an individual's bronchodilatory response to a ? agonist. Present invention particularly relates to the detection of specific allelic variants of the ?2AR gene and their use as pharmacogenetic markers towards response to ? agonist.

Abstract: The present invention relates to a versatile method of identifying protein coding DNA sequences (genes) useful as drug targets in a genome using specially developed software GeneDecipher, said method comprising steps of generating peptide libraries from the known genomes with peptide of length ‘N’ computationally arranged in an alphabetical order, artificially translating the test genome to obtain a polypeptide corresponding to each reading frame, converting each polypeptide sequence into an alphanumeric sequence one corresponding to each reading frame on the basis of overlappings with the peptide libraries, training Artificial Neural Network (ANN) with sigmoidal learning function to the alphanumeric sequence, deciphering the protein coding regions in the test genome, thus, identifying longer streches of peptides mapping to large number of known genes and their corresponding proteins and lastly, a method of the management of the diseases caused by the pathogenic organisms comprising a step of evaluation of th