Bacterial Defense System Identification via Genomic Proximity
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Solution Overview
Problem
Current methods for identifying novel bacterial defense systems are inefficient and lack high-throughput capabilities, hindering their adoption in biotechnology and therapeutic applications.
Innovation Solution
An engineered system comprising ATPases, adenosine deaminases, reverse transcriptases, retrons, and other proteins, along with a method to identify defense systems by analyzing genes in bacterial genomes and selecting homologs based on sequence identity and proximity to known defense systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to identify bacterial defense systems, then the identification process is simple, but the productivity and throughput are low
Solution Approach 1:
The identification method is divided into distinct sequential steps: (1) identifying genes of known defense systems in multiple bacterial genomes, (2) recording candidate genes within 10 kb or 10 open reading frames from known defense genes, (3) identifying homologs of candidate genes across genomes, and (4) selecting candidate genes based on homology thresholds. This segmentation enables systematic high-throughput processing while maintaining analytical rigor.
Solution Approach 2:
The method performs preliminary identification of known defense system genes across multiple genomes before searching for novel systems. By first establishing a database of known defense genes and their genomic contexts, the system prepares reference data that accelerates subsequent novel system identification, improving overall throughput without sacrificing accuracy.
2Measurement precision
If genome-wide analysis is performed to identify novel defense systems, then the measurement precision improves, but the loss of time increases
Solution Approach 1:
The search for novel defense systems is focused on specific genomic regions with high probability of containing defense genes: within 10 kb or 10 open reading frames of known defense system genes. This localized approach maintains high identification accuracy by concentrating analysis where defense systems are most likely to be found, while reducing overall analysis time by excluding irrelevant genomic regions.
Solution Approach 2:
The method employs adjustable homology thresholds (e.g., at least 10% of homologs within 5000 nucleotides or 5 genes) and distance parameters (10 kb or 10 open reading frames) to optimize the balance between identification sensitivity and computational efficiency. These parameter adjustments allow the system to maintain precision while adapting to different analysis requirements and resource constraints.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the identification and validation of novel bacterial defense systems, enhancing their utility in biotechnology and therapeutic applications by providing efficient resistance to phage infections and foreign nucleic acid invasions.
Implementation Method 1
the adenosine deaminase comprises a sequence of WP_012906048.1 or WP_064360593.1... the modification of the target nucleic acid comprises causing an A to G mutation in the target nucleic acid
Implementation Method 2
one or more reverse transcriptases comprising one or more UG1, UG2, UG3, UG8, UG15, or UG16 reverse transcriptase
Implementation Method 3
an ATPase and an adenosine deaminase... the ATPase comprises a sequence of WP_012906049.1 or WP_155731552.1
Data Source
AI summary
Engineered systems comprising components of defense systems identified in prokaryotes are provided.