Architecture of database for MtbVeb platform.

<p>Architecture of database for MtbVeb platform.</p

Distribution of virulent proteins, secretion system proteins and proteins of genetic variance among three category of strains.

<p>Distribution of virulent proteins, secretion system proteins and proteins of genetic variance among three category of strains.</p

A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of <i>Mycobacterium tuberculosis</i>

<div><p>Development of an effective vaccine against drug-resistant <i>Mycobacterium tuberculosis</i> (Mtb) is crucial for saving millions of premature deaths every year due to tuberculosis. This paper describes a web portal developed for assisting researchers in designing vaccines against emerging Mtb strains using traditional and modern approaches. Firstly, we annotated 59 genomes of Mycobacterium species to understand similarity/dissimilarity between tuberculoid, non-tuberculoid and vaccine strains at genome level. Secondly, antigen-based vaccine candidates have been predicted in each Mtb strain. Thirdly, epitopes-based vaccine candidates were predicted/discovered in above antigen-based vaccine candidates that can stimulate all arms of immune system. Finally, a database of predicted vaccine candidates at epitopes as well at antigen level has been developed for above strains. In order to design vaccine against a newly sequenced genome of Mtb strain, server integrates three modules for identification of strain-, antigen-, epitope-specific vaccine candidates. We observed that 103522 unique peptides (9mers) had the potential to induce an antibody response and/or promiscuous binder to MHC alleles and/or have the capability to stimulate T lymphocytes. In summary, this web-portal will be useful for researchers working on designing vaccines against Mtb including drug-resistant strains. Availability: The database is available freely at <a href="http://crdd.osdd.net/raghava/mtbveb/" target="_blank">http://crdd.osdd.net/raghava/mtbveb/</a>.</p></div

The SMOreg based performance of QSAR models developed using selected descriptors calculated from mutant_whole datasets.

<p>The SMOreg based performance of QSAR models developed using selected descriptors calculated from mutant_whole datasets.</p

Flow chart showing training and validating datasets used in developing prediction models.

<p>Flow chart showing training and validating datasets used in developing prediction models.</p

Scatter plots between experimental versus predicted pIC50 values of wild types EGFR inhibitors.

<p>Scatter plots between experimental versus predicted pIC50 values of wild types EGFR inhibitors.</p

Performance of SMOreg based models developing for predicting inhibitors against wild, mutant and hybrid EGFR on the training and validation data set on PaDEL descriptors.

<p>Performance of SMOreg based models developing for predicting inhibitors against wild, mutant and hybrid EGFR on the training and validation data set on PaDEL descriptors.</p

List of descriptors showing positive and negative correlation with the wild EGFR inhibitory activity.

<p>List of descriptors showing positive and negative correlation with the wild EGFR inhibitory activity.</p

Comparative performance of existing method with our method developing for predicting inhibitors against wild type EGFR inhibitors.

<p>Comparative performance of existing method with our method developing for predicting inhibitors against wild type EGFR inhibitors.</p

A diagram demonstrating list of softwares used for computing chemical descriptors and types of different descriptors.

<p>A diagram demonstrating list of softwares used for computing chemical descriptors and types of different descriptors.</p