Molecular Beacon Based Biosensing for Detection of Pathogenic Water Borne Multiple Fungal Strains: An In-Silico Approach

Title:Molecular Beacon Based Biosensing for Detection of Pathogenic Water Borne Multiple Fungal Strains: An In-Silico Approach

Volume: 12 Issue: 6

Author(s): Sonali Mishra and Krishna Misra*

Affiliation:

• Department of Applied Sciences, Indian Institute of Information Technology, Allahabad-211012,India

Keywords: Water born fungi, pathogens, molecular beacons, in-silico designing, biosensors, fungal strains.

Abstract: Background: The water borne pathogenic fungi have recently become a big threat and lead cause of many hazardous infectious diseases in immuno challenged people.

Objective: A universal standardized method has to be developed for instant, specific and easier detection and diagnosis of water borne pathogenic fungi.

Method: None of the methods so far known for the detection of pathogenic microorganisms is as handy, economic, specific and sensitive as molecular beacons. Computational approach has been employed in the present work to detect the conserved and oft repeat sequence pattern in the r-RNA sequences of twenty three water borne pathogenic fungal species. These species were classified in three groups and models of probes containing secondary stem loop structures were designed for each group. Finally a common model capable of specifically detecting all species in one hit has been designed. Molecular beacons have been proposed by attaching donor and acceptor dyes to the designed probes

Results and Conclusion: In the present work a molecular beacon based probe has been modelled which is capable of detecting 23 pathogenic and water borne fungal strains. This approach of designing molecular beacons with specific sequences of rRNA can prove to be a sensitive diagnostic technique for detecting water borne pathogenic, fungal strains in miniscule amounts.

Cite this article as:

Mishra Sonali and Misra Krishna*, Molecular Beacon Based Biosensing for Detection of Pathogenic Water Borne Multiple Fungal Strains: An In-Silico Approach, Current Bioinformatics 2017; 12 (6) . https://dx.doi.org/10.2174/1574893611666160922153324