Mugdha Srivastava

In silico CD4+ T-cell epitope prediction and HLA distribution analysis for the potential proteins of Neisseria meningitidis Serogroup B--a clue for vaccine development.

Neisseria meningitidis, an exclusive human pathogen, is a major cause of mortality due to meningococcal meningitis and sepsis in many developing countries. Three meningococcal serogroup B proteins, i.e. T-cell stimulating protein A (TspA), autotransporter A (AutA), and IgA-specific serine endopeptidase (IGA1) elicits CD4+ T-cell response and may enhance the effectiveness of meningococcal vaccines by acting as protective immunogens. A very limited data on T-helper cell epitopes in MenB proteins is available. Hence, in silico prediction of peptide sequences which may act as helper T lymphocyte epitopes in MenB proteins was carried out by NetMHCIIpan web server. HLA distribution analysis was done by using the population coverage tool of Immune Epitope Database to determine the fraction of individuals in various populations expected to respond to a given set of predicted T-cell epitopes based on HLA genotype frequencies. Six epitopic core sequences, two from each MenB proteins, i.e. AutA, TspA and IgA1 protease were predicted to associate with a large number of HLA-DR alleles. These six peptides may act as T-cell epitope in more than 95% of populations in 8 out of 12 populations considered. The T-cell stimulation potential of these predicted peptides containing the core epitopic sequences is to be validated by using laboratory experiments for their efficient use as peptide vaccine candidates against N. meningitidis serogroup B.

Identification of immunogenic consensus T-cell epitopes in globally distributed influenza-A H1N1 neuraminidase

Antigenic drift is the ability of the swine influenza virus to undergo continuous and progressive changes in response to the host immune system. These changes dictate influenza vaccine updates annually to ensure inclusion of antigens of the most current strains. The identification of those peptides that stimulate T-cell responses, termed T-cell epitopes, is essential for the development of successful vaccines. In this study, the highly conserved and specific epitopes from neuraminidase of globally distributed H1N1 strains were predicted so that these potential vaccine candidates may escape with antigenic drift. A total of nine novel CD8(+) T-cell epitopes for MHC class-I and eight novel CD4(+) T-cell epitopes for MHC class-II alleles were proposed as novel epitope based vaccine candidates. Additionally, the epitope FSYKYGNGV was identified as a highly conserved, immunogenic and potential vaccine candidate, capable for generating both CD8(+) and CD4(+) responses.

In silico DNA vaccine designing against human papillomavirus (HPV) causing cervical cancer.

HPV vaccines available in the market are not effective against different strains of papillomavirus, therefore, there is a need to develop a new prophylactic DNA vaccine which can work against different strains of HPVs and may lead to protection of cervical cancer against new pandemic viruses. We designed a potential prophylactic DNA vaccine by using all the consensus epitopic sequences of HPVs L2 capsid protein and performed in silico cloning of multiepitopic antigenic DNA sequence in pVAX-1 vector. Immunogenicity of vaccine has been enhanced by techniques like codon optimization, engineering CpG motifs, introducing promoters and co-injection with plasmids expressing immune-stimulatory molecules.

In silico designing and optimization of anti-breast cancer antibody mimetic oligopeptide targeting HER-2 in women.

Overexpression of HER-2 is of frequent (20-30%) occurrence in breast cancer. Therapeutic targeting of HER-2 with humanized antibody derived oligopeptide may be a promising approach to the treatment of breast cancer. HER-2 gene is part of a family of genes that play critical roles in regulating transmembrane growth of breast cancer cells. Pertuzumab, a recombinant humanized monoclonal antibody (2C4), binds to extracellular domain II of the HER-2 receptor and inhibits its ability to dimerize with other HER receptors blocking the cell growth, signaling and apoptosis induction. The unique binding pocket on HER-2 for pertuzumab provides an important target domain for creation of new anticancer drugs. In the present work an efficient oligopeptide was designed by our computational method that interacts with pertuzumab binding sites of HER-2. In silico docking study demonstrated the best specific interaction of RASPADREV oligopeptide with the dimerization domain in the HER-2 molecule among various screened oligopeptides. ADMET and SAR properties prove the drug likeness of designed oligopeptide as having value 0.98.

Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach

New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.

In silico accelerated identification of structurally conserved CD8+ and CD4+ T-cell epitopes in high-risk HPV types.

Primary approach to prevent cervical cancer includes the development of human papillomavirus (HPV) vaccines. Currently available HPV vaccines (Gardasil and Cervarix) predominantly consider HPV16 and HPV18 strains. However, due to ignorance of the other high-risk strains aside from HPV16 and HPV18 during vaccine development, the critical need is to synthesize a vaccine with possible protection against all the high-risk HPV types. One feasible approach is to design a vaccine containing conserved immunogenic peptides that represent the genotypic diversity of all the current and future high-risk HPV types. While the epitopes derived from sequentially conserved regions may undergo mutations, it is worthwhile to explore the structurally conserved regions as a new dimension for epitope prediction. In the present study, 81 structurally conserved peptides were predicted to have immune relevance as T-cell epitopes of all the reported high-risk HPV proteins studied. A small dataset of three epitopes was also recognized as potential vaccine candidates generating both CD8+ and CD4+ responses.

Virtual screening of specific chemical compounds by exploring E.coli NAD+-dependent DNA ligase as a target for antibacterial drug discovery

Unique substrate specificity compared with ATP-dependent human DNA ligases recommends E.coli NAD+-ligases as potential targets. A plausible strategy is to identify the structural components of bacterial DNA ligase that interact with NAD+ and then to isolate small molecules that recognize these components and thereby block the binding of NAD+ to the ligase. This work describes a molecular modeling approach to detect the 3D structure of NAD+-dependent DNA ligase in E. coli whose partial structure was determined by wet lab experiments and rest structure was left as such on the road for repairment. We applied protein-drug docking approach to detect the binding affinity of this enzyme with Quinacrine and some of its virtual derivatives. In silico docking results predict that the virtual derivative of Quinacrine (C21H26ClN3O2) has greater binding affinity than Quinacrine. Drug likeness value of 0.833 was observed for this derivative without showing any toxicity risk.

Computational analysis and modeling the effectiveness of 'Zanamivir' targeting neuraminidase protein in pandemic H1N1 strains

Antigenic drift causes number of mutations in neuraminidase protein of H1N1 swine influenza virus. We analyzed neuraminidase mutations in H1N1 strains distributed over six continents, at both the sequence and structural level. Mutations in the nearby residues of the drug binding site play crucial role in the binding affinity of the drug with the protein. For this purpose, mutant models were generated for the neuraminidase protein from 34 pandemic H1N1 isolates and docking were performed with zanamivir drug. Multiple sequence alignment (MSA) and variations in docking score suggest that there are considerable changes in the binding affinity of neuraminidase with zanamivir, which leads to probable ineffectiveness of zanamivir in the isolated samples of pandemic H1N1 collected from quite a few countries. To further evaluate the effectiveness of the antiviral drugs, we derived, calibrated and analyzed an ordinary differential equations based mathematical model for H1N1 infection dynamics and drug mediated virus deactivation.

A Staphylococcus aureus Proteome Overview: Shared and Specific Proteins and Protein Complexes from Representative Strains of All Three Clades

Staphylococcus aureus is an important model organism and pathogen. This S. aureus proteome overview details shared and specific proteins and selected virulence-relevant protein complexes from representative strains of all three major clades. To determine the strain distribution and major clades we used a refined strain comparison combining ribosomal RNA, MLST markers, and looking at highly-conserved regions shared between strains. This analysis shows three sub-clades (A–C) for S. aureus. As calculations are complex and strain annotation is quite time consuming we compare here key representatives of each clade with each other: model strains COL, USA300, Newman, and HG001 (clade A), model strain N315 and Mu50 (clade B) and ED133 and MRSA252 (clade C). We look at these individual proteomes and compare them to a background of 64 S. aureus strains. There are overall 13,284 S. aureus proteins not part of the core proteome which are involved in different strain-specific or more general complexes requiring detailed annotation and new experimental data to be accurately delineated. By comparison of the eight representative strains, we identify strain-specific proteins (e.g., 18 in COL, 105 in N315 and 44 in Newman) that characterize each strain and analyze pathogenicity islands if they contain such strain-specific proteins. We identify strain-specific protein repertoires involved in virulence, in cell wall metabolism, and phosphorylation. Finally we compare and analyze protein complexes conserved and well-characterized among S. aureus (a total of 103 complexes), as well as predict and analyze several individual protein complexes, including structure modeling in the three clades.

Improving Re-annotation of Annotated Eukaryotic Genomes

In the age of post-genomics, the task of improving existing annotation is one of the major challenge. The sequenced transcriptome allows to revisit the annotated sequenced genome of the corresponding organism and improve the existing gene models. In addition, misleading annotations propagate in multiple databases by comparative approaches of annotation, automatic annotation, and lack of curating power in the face of large data volume. In this pursuit, re-annotated improved gene models can prevent misleading structural and functional annotation of genes and proteins. In this chapter, we will highlight annotation and re-annotation procedures and will explain how annotations can be improved using computational methods. Our integrative workflow can be used to re-annotate genomes of any sequenced eukaryotic organism. We describe the annotation of splice sites, open reading frames, encoded proteins and peptides, hints for functional annotation including phylogenetic and domain analysis as well as critical evaluation of data transfer procedures, and the genome annotation process.