Intelligent Design of Escherichia coli Terminators by Coupling Prediction and Generation Models

Terminators are specific nucleotide
sequences located at the 3′
end of a gene and contain transcription termination information. As
a fundamental genetic regulatory element, terminators play a crucial
role in the design of gene circuits. Accurately characterizing terminator
strength is essential for improving the precision of gene circuit
designs. Experimental characterization of terminator strength is time-consuming
and labor-intensive; therefore, there is a need to develop computational
tools capable of accurately predicting terminator strength. Current
prediction methods do not fully consider sequence or thermodynamic
information related to terminators, lacking robust models for accurate
prediction. Meanwhile, deep generative models have demonstrated tremendous
potential in the design of biological sequences and are expected to
be applied to terminator sequence design. This study focuses on intelligent
design of Escherichia coli terminators
and primarily conducts the following research: (1) to construct an
intrinsic terminator strength prediction model for E. coli, this study extracts sequence features and
thermodynamic features from E. coli intrinsic terminators. Machine learning models based on the selected
features achieved a prediction performance of R2 = 0.72. (2) This study employs a generative adversarial network
(GAN) to learn from intrinsic terminator sequence training data and
generate terminator sequences. Evaluation reveals that the generated
terminators exhibit similar data distributions to intrinsic terminators,
demonstrating the reliability of GAN-generated terminator sequences.
(3) This study uses the constructed terminator strength prediction
model to screen for strong terminators from the generated set. Experimental
verification shows that among the 18 selected terminators, 72% exhibit
termination efficiencies greater than 90%, confirming the reliability
of the intelligent design approach for E. coli terminators. In sum, this study constructs a terminator strength
prediction model and a terminator generation model for E. coli, providing model support for terminator design
in gene circuits. This enhances the modularity of biological component
design and promotes the development of synthetic biology.