Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary. The existing optimization methods are based on biological indexes. In this paper, we propose a novel codon optimization method based on deep learning. First, we introduce the concept of codon boxes, via which DNA sequences can be recoded into codon box sequences while ignoring the order of bases. Then, the problem of codon optimization can be converted to sequence annotation of corresponding amino acids with codon boxes. The codon optimization models for Escherichia Coli were trained by the Bidirectional Long-Short-Term Memory Conditional Random Field. Theoretically, deep learning is a good method to obtain the distribution characteristics of DNA. In addition to the comparison of the codon adaptation index, protein expression experiments for plasmodium falciparum candidate vaccine and polymerase acidic protein were implemented for comparison with the original sequences and the optimized sequences from Genewiz and ThermoFisher. The results show that our method for enhancing protein expression is efficient and competitive.

Coronary artery disease (CAD) is a leading cause of death, and microRNAs (miRNAs) are widely involved in physiological and pathological processes of CAD. We chose the targetscore method calculated via the variational Bayesian Gaussian mixture model (VB-GMM) as the prediction method of target genes. By observing the density overlap, we selected the thresholds of miRNA-1 and miRNA-155. In total, 18 target genes of miRNA-1, and 19 target genes of miRNA-155 were identified. The threshold of miRNA-146a was selected using the |logFC| value, and 16 target genes were screened out. In this study, our major contribution was to predict the target messenger RNAs (mRNAs) of the chosen miRNAs with the gene expression profiles, which can effectively reduce the workload of screening. Although the validated genes constituted only a small part in the final prediction results, it is a good sign for research in the future. It means that we could provide new research aims for future studies focusing on miRNA regulatory mechanisms.

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