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Software

Software

Overview

We saw that there were no algorithms available that could predict the best length of the antisense mRNA for sense-antisense binding to achieve the best population control output from our designed Quorum Sensing Circuit.

We, therefore, developed ‘AntiRNA,’ an algorithm that incorporates deep learning, structure prediction, and molecular docking software such as DegScore, Seqfold, and IntaRNA. Our modified Quorum Sensing mathematical model determines the best antisense mRNA length for sense-antisense binding to achieve the desired population regulation.

Teams performing cell cycle arrest mediated Quorum Sensing of bacterial populations can use ‘AntiRNA’ to determine the best antisense mRNA length by inputting the sense mRNA sequence. The software will automatically return the optimal antisense length to achieve the desired target based on the user-set circuit parameters.

Backbone Code Snippet

Development of AntiRNA

AntiRNA is designed to take the input sense mRNA sequence, synthesize antisense mRNAs of various lengths, predict their secondary structures, determine degradation rates, and compute the optimal antisense length for population control.

Functions/Softwares Used

  • Seqfold: Python module for predicting secondary structure.
  • DegScore: Python module for predicting RNA degradation.
  • IntaRNA: Software for RNA-RNA interaction prediction.

AntiRNA Functionality

AntiRNA follows a structured process, which includes listing model ODEs, parameter customization, mRNA input, antisense synthesis, structure prediction, and degradation rate calculation.

Some important steps include:

  • Listing ODEs and parameters for customization.
  • Inputting the sense mRNA sequence.
  • Synthesizing antisense mRNAs from 50 to 600 bps.
  • Predicting secondary structures using Seqfold.
  • Computing half-lives and degradation rates using DegScore.
  • Determining ΔG of binding using IntaRNA and calculating the optimal antisense length.
AntiRNA Output Example

Future Developments

Due to time constraints, further development is required to refine AntiRNA. Our future goals include defining a Python module to call IntaRNA directly from the algorithm and making AntiRNA more generalized for predicting the best antisense mRNA length for any sense mRNA sequence.

Project Phases Flowchart

Conclusion

AntiRNA is a promising tool for predicting optimal antisense mRNA length to regulate bacterial populations. This software will assist researchers in controlling bacterial populations while minimizing toxic by-products.

Acknowledgements