Computational Framework for High Copy-Number Probe Selection and Cross-Binding Reduction.

Area of Science:

• Biotechnology
• Genomics
• Biosensor Technology

Background:

• DNA probe design is crucial for biosensors in diagnostics and monitoring.
• Traditional probes targeting low-copy sequences limit detection sensitivity.
• Limited hybridization events in conventional methods hinder signal amplification.

Purpose of the Study:

• To develop a novel DNA probe design strategy for enhanced biosensor sensitivity.
• To leverage highly repetitive DNA sequences for signal amplification without PCR.
• To create a computational framework for designing sensitive and specific DNA probes.

Main Methods:

• Developed a custom bioinformatics tool to identify repetitive DNA sequences in the Mycobacterium tuberculosis genome.
• Utilized BLAST for cross-referencing identified sequences against the Homo sapiens genome to minimize host cross-reactivity.
• Analyzed a specific 23 bp repetitive sequence for its hybridization potential and specificity.

Main Results:

• Identified a 23 bp sequence repeated 39 times in M. tuberculosis.
• This sequence showed only 78% identity with human DNA and was present in two copies in the human genome.
• The selected probe demonstrated potential for significantly stronger hybridization signals for M. tuberculosis compared to human cfDNA.

Conclusions:

• The novel probe design strategy enhances biosensor sensitivity by targeting repetitive DNA sequences.
• This computational methodology offers a robust framework for developing high-sensitivity biosensors.
• The approach facilitates more effective infectious disease diagnostics, environmental monitoring, and point-of-care testing.