  N-terminal coding sequence (NCS) influences gene expression by impacting the
translation initiation rate. The NCS optimization problem is to find an NCS
that maximizes gene expression. The problem is important in genetic
engineering. However, current methods for NCS optimization such as rational
design and statistics-guided approaches are labor-intensive yield only
relatively small improvements. This paper introduces a deep learning/synthetic
biology co-designed few-shot training workflow for NCS optimization. Our method
utilizes k-nearest encoding followed by word2vec to encode the NCS, then
performs feature extraction using attention mechanisms, before constructing a
time-series network for predicting gene expression intensity, and finally a
direct search algorithm identifies the optimal NCS with limited training data.
We took green fluorescent protein (GFP) expressed by Bacillus subtilis as a
reporting protein of NCSs, and employed the fluorescence enhancement factor as
the metric of NCS optimization. Within just six iterative experiments, our
model generated an NCS (MLD62) that increased average GFP expression by
5.41-fold, outperforming the state-of-the-art NCS designs. Extending our
findings beyond GFP, we showed that our engineered NCS (MLD62) can effectively
boost the production of N-acetylneuraminic acid by enhancing the expression of
the crucial rate-limiting GNA1 gene, demonstrating its practical utility. We
have open-sourced our NCS expression database and experimental procedures for
public use.
