Virendra S. Gomase

Prediction of MHC Binding Peptides and Epitopes from Alfalfa mosaic virus

Peptide fragments from alfalfa mosaic virus involved multiple antigenic components directing and empowering the immune system to protect the host from infection. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class-I & II in response to almost all antigens. Coat protein of alfalfa mosaic virus contains 221 aa residues. Analysis found five MHC ligands in coat protein as 64-LSSFNGLGV-72; 86- RILEEDLIY-94; 96-MVFSITPSY-104; 100- ITPSYAGTF-108; 110- LTDDVTTED-118; having rescaled binding affinity and c-terminal cleavage affinity more than 0.5. The predicted binding affinity is normalized by the 1% fractil. The MHC peptide binding is predicted using neural networks trained on c-terminals of known epitopes. In analysis predicted MHC/peptide binding is a log transformed value related to the IC50 values in nM units. Total numbers of peptides found are 213. Predicted MHC binding regions act like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. The sequence analysis method allows potential drug targets to identify active sites against plant diseases. The method integrates prediction of peptide MHC class I binding; proteosomal c-terminal cleavage and TAP transport efficiency.

microRNA: human disease and development

microRNAs or miRNAs are an abundant class of highly conversed, small non-coding RNAs that present an entirely new theme of post-transcriptional gene regulation. miRNAs play a key role in diverse biological systems, such as virology, embryogenesis, differentiation, inflammation and cancer research. Research showed the importance of these non-coding small RNAs on immune system development and response. It plays important regulatory roles in various metabolic pathways in most eukaryotes. miRNAs are found to be involved in the regulation of immunity, including the development and differentiation of immune cells, antibody production and the inflammatory mediator release.

RNAi--a tool for target finding in new drug development.

RNAi (RNA interference) refers to the introduction of homologous double stranded RNA (dsRNA) to specifically target a genes product, resulting in null or hypomorphic phenotypes. Long double-stranded RNAs (dsRNAs; typically >200 nt) can be used to silence the expression of target genes in a variety of organisms and cell types (e.g., worms, fruit flies, and plants). The long dsRNAs enter a cellular pathway that is commonly referred to as the RNA interference (RNAi) pathway. RNAi is being considered as an important tool not only for functional genomics, but also for gene-specific therapeutic activities that target the mRNAs of disease-related genes. RNAi plays a very important role in endogenous cellular processes, such as heterochromatin formation, developmental control and serves as an antiviral defense mechanism. RNAi has shown great potential for use as a tool for target finding in new drug development, molecular biological discovery, analysis and therapeutics. RNAi pathway is involved in post-transcription silencing, transcriptional silencing and epigenetic silencing as well as its use as a tool for forward genetics and therapeutics.

Computer Aided Multi Parameter Antigen Design: Impact of Synthetic Peptide Vaccines from Soybean Mosaic Virus

The potyvirus coat protein (CP) is involved in aphid transmission, cell-to-cell movement and virus assembly, not only by binding to viral RNA, but also by self-interaction or interactions with other factors. Peptide fragments of genome coatprotein can be used to select nonamers for use in rational vaccine design and to increase the understanding of roles of the immune system in infectious diseases. For development of MHC binder prediction method, an elegant machine learning technique support vector machine (SVM) has been used. SVM has been trained on the binary input of single amino acid sequence. The MHC peptide binding is predicted using neural networks trained on C terminals of known epitopes. SVM has been trained on the binary input of single amino acid sequence. The average accuracy of SVM based method for 42 alleles is ~80%. In this analysis, we found the MHCII-IAb peptide regions, 880-YKTAKDLLT, 2577-PILAPDGTI, 1438-KVTKVDGRT, 2647- TWLYDTLST, (optimal score is 1.506); MHCII-IAd peptide regions 2079-GSFIITNGH, 1911-FIHLYGVEP, 1306-GSSNIVVMT, 695-AAYMLTVFH, (optimal score is 0.893); MHCII-IAg7 peptide regions 2962-SDAAEAYIE, 2891-WYNAVKDEY, 1544-FIATEAAFL, 1123-KIVAFMALL (optimal score is 1.915); MHCII-RT1.B peptide regions 1114-KTATQLQLE, 413-STAENASLQ, 162-TKERRATSQ, 1112-QAKTATQLQ, (optimal score is 1.807); which are represent predicted binders from genome polyprotein. Computer aided multi parameter antigen design was used to developed synthetic peptide vaccines from soybean mosaic virus.

Proteomics: Technologies for Protein Analysis

Proteomics technologies have produced an abundance of drug targets, which is creating a bottleneck in drug development process. There is an increasing need for better target validation for new drug development and proteomic technologies are contributing to it. Identifying a potential protein drug target within a cell is a major challenge in modern drug discovery; techniques for screening the proteome are, therefore, an important tool. Major difficulties for target identification include the separation of proteins and their detection. These technologies are compared to enable the selection of the one by matching the needs of a particular project. There are prospects for further improvement, and proteomics technologies will form an important addition to the existing genomic and chemical technologies for new target validation. Proteomics is applicable for protein analysis and bioinformatics based analysis gives the comprehensive molecular description of the actual protein component. Bioinformatics is being increasingly used to support target validation by providing functionally predictive information mined from databases and experimental datasets using a variety of computational tools. This review is focused on key technologies for proteomics strategy and their application in protein analysis.

Species scaling and extrapolation.

The various scaling methodologies and molecular features analysis were applied to new dataset to predict human pharmacokinetics studies. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from species continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Also, extrapolation is the method for constructing new data points given a set of discrete data points. Methods estimate is reasonably reliable for short times, but for longer times, the estimate is liable to become less accurate. Species Scaling and Extrapolation are useful for acquiring toxicological data- epidemiological and experimental study. Animal studies help us to understand toxicity characteristics of a chemical before human exposure is allowed, whereas the epidemiological method generally does not. Species scaling and extrapolation from animals is necessary in many cases which helps in dealing with the so-called human risks more properly.

Prediction of antigenic epitopes of neurotoxin Bmbktx1 from Mesobuthus martensii.

Gene therapy or recombinant DNA vaccines targeting multiple antigenic components to direct empower the immune system. Antigenic epitopes on neurotoxin Mesobuthus martensii (Buthus martensii) are important determinant of protection against cardiovascular disorder. Small segments 4-YSSDCRVKCVAM-15, 18-SSGKCINSKC-27 of neuro-toxin protein called the antigenic epitopes is sufficient for eliciting the desired immune response. In analysis predicted antigenic epitopes neurotoxin protein is seen. Immunization cassettes should be capable of immunizing of broad immunity against both humoral and cellular epitope thus giving vaccines the maximum ability to deal with neurotoxin protein of M. martensii. We have predicted a successful immunization.

Prediction of Brugia malayi Antigenic Peptides: Candidates for Synthetic Vaccine Design Against Lymphatic Filariasis

Brugia malayi is a threadlike nematode causes swelling of lymphatic organs, condition well known as lymphatic filariasis; till date no invention made to effectively address lymphatic filariasis. In this analysis we a have predicted suitable antigenic peptides from Brugia malayi antigen protein for peptide vaccine design against lymphatic filariasis based on cross protection phenomenon as, an ample immune response can be generated with a single protein subunit. We found MHC class II binding peptides of Brugia malayi antigen protein are important determinant against the diseased condition. The analysis shows Brugia malayi antigen protein having 505 amino acids, which shows 497 nonamers. In this assay, we have predicted MHC-I binding peptides for 8mer_H2_Db (optimal score- 15.966), 9mer_H2_Db (optimal score- 15.595), 10mer_H2_Db (optimal score- 19.405), 11mer_H2_Dballeles (optimal score- 23.801). We also predicted the SVM based MHCII-IAb nonamers, 51-FQQIDPLDA, 442-FAAIACLVH, 206-YLNPFGHQF, 167-WYVIMAACY, 367-YAMIVIRLL, 434- LVITTAANF, 176-LDSYCLWKP, 435-VITTAANFA, 364-WPGYAMIVI (optimal score- 13.963); MHCII-IAd nonamers, 52-QQIDPLDAE, 171-MAACYLDSY, 239-QWRSVILCN, 168-YVIMAACYL, 3-QYLSVHSLS, 322-EILLHAKVV, 417- LGIIASFVS, 396-KAIFLAHFG, 167-WYVIMAACY, 269-LALHCINVI, 93-FINKAAPKQ, 259-NCIIVLKAF, 79- QGVLLIIPR, 22-TILQRSQAI, 63-RGFVYGNVS, 109-NISSLAFET,(optimal score- 16.748); and MHCII-IAg7 nonamers 171-MAACYLDSY, 73-KIVNGAQGV, 259-NCIIVLKAF, 209-PFGHQFSFE, 102-SCDTLLKNI, 25-QRSQAIRIV, 444- AIACLVHLF, 88-SLVNGFINK, 252-FPRHQLLNC, 471-RFVLANDNE, 52-QQIDPLDAE, 469-HRRFVLAND, 457- SNRHYFLAD, 362-KSWPGYAMI, 476-NDNEGEDFE, 370-IVIRLLQAL (optimal score- 19.847) which represents potential binders from Brugia malayi antigen protein. The method integrates prediction of MHC class I binding proteasomal C-terminal cleavage peptides and Eighteen potential antigenic peptides at average propensity 1.063 having highest local hydrophilicity. Thus a small antigen fragment can induce immune response against whole antigen. This approach can be applied for designing subunit and synthetic peptide vaccines.

Phylogenomics: evolution and genomics intersection

Phylogenomics is the analysis of genomes of a group of closely related species. Almost all functional prediction methods rely on the identification, characterisation and quantification of sequence similarity between the gene of interest and genes for which functional information is available. This is the new evolved branch that is developed from the ongoing genome sequencing projects that have led to a phylogenetic approach based on genome-scale data. The use of large data sets in phylogenomic analysis results in a global increase in resolution owing to a decrease in sampling error.

Immunoproteomics Approach for Development of Synthetic Peptide Vaccine from Thioredoxin Glutathione Reductase

Schistosomiasis is the second most widespread human parasitic disease. It is principally treated with one drug, praziquantel, which is administered to 100 million people each year; less sensitive strains of schistosomes are emerging. One of the most appealing drug targets against schistosomiasis is thioredoxin glutathione reductase (TGR). This natural chimeric enzyme is a peculiar fusion of a glutaredoxin domain with a thioredoxin selenocysteine (U)-containing reductase domain. Selenocysteine is located on a flexible C-terminal arm that is usually disordered in the available structures of the protein and is essential for the full catalytic activity of TGR. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class in response to almost all antigens and it give effects on specific sites. Predicted MHC binding regions acts like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. In this study, we analyzed thioredoxin glutathione reductase of Schistosoma mansoni and is allows potential drug targets to identify active sites, which form antibodies against or infection. The method integrates prediction of peptide MHC class binding; proteosomal C terminal cleavage and TAP transport efficiency. Antigenic epitopes of thioredoxin glutathione reductase are important antigenic determinants against the various toxic reactions and infections.