Abstract

COMPUTATIONAL PREDICTION OF POTENT ANTIGENIC PEPTIDES FROM BORRELIA BURGDORFERI PROTEOME FOR VACCINE DESIGNING WITH STRUCTURAL VIEWPOINT FOR ITS INHIBITION.

Riya Rane, Rahul Ravichandran, Pritam Kumar Panda*, Priyam Patel, Hetalkumar Panchal

ABSTRACT

Lyme disease is the most common tick-borne infectious disease found in North America and in Europe. Lyme is a Multi-Systemic Disease caused by the bacteria called Borrelia burgdorferi (Bb) which normally is a bacterial spirochete (spiral shape). It is zoonotic disease, which is also known as Lyme boreliosis. The scope of the current work is to predict and design a potent antigenic peptide of the bacteria Borrelia burgdorferi to develop a potential vaccine. The bacterial proteome is collected from HAMAP and processed for screening purpose based on the similarity against Human CDS using TFastaY. The proteins were subjected to fourfold screening process for the identification of most antigenic sites within them by using different servers like EMBOSS Antigen and Protein Variability Server. The selected antigenic peptides were designed using Chimera followed by the geometry optimization and energy evaluation. Since many commercial drugs have been available the molecular docking studies has been carried out to know the inhibitory efficiency of drugs to inhibit the disease causing protein. Targeting the surface antigenic peptides of Borrelia burgdorferi the In-silico studies has been carried out to identify the best antigenic peptides responsible for this. Based on the energy values the best antigenic peptides were selected, Protein-peptide docking was carried out to analyze the antigenic propensity with the protein receptor and thus can be further processed for the production of potential vaccine to eradicate the disease. Finally screened peptides after geometry optimization and energy evaluation are the best peptides for the Vaccine development. These peptides can be synthesized and attenuated for vaccination against the Lyme disease.

Keywords: Emboss Antigen, PVS Server, Peptide designing, Auto dock vina, Innova gen, Modeller 9v14, Hex6.0.