Small regulatory RNAs (sRNAs) are short noncoding RNAs that can fine-control the expression of target genes in trans at the post-transcriptional level in prokaryotes. Since there is a big challenge in constructing gene-knockout libraries, synthetic sRNAs have attracted considerable interest in synthetic biology and metabolic engineering, as they enable targeted gene knockdown without requiring chromosomal modifications. However, the development of high-efficiency synthetic sRNAs remains a demanding task that requires careful consideration of multiple design factors. Here, we provide a detailed protocol for the design and construction of synthetic sRNAs, detailing key design principles and critical optimization factors, including scaffold selection, target mRNA binding affinity, target mRNA secondary structure, and Hfq expression levels. This strategy can be broadly applied across E. coli and other bacterial hosts to modulate gene expression, thereby supporting versatile applications in synthetic biology and metabolic engineering.

Protein solubility is a critical factor in the production of recombinant proteins, which are widely used in various industries, including pharmaceuticals, diagnostics, and biotechnology. Predicting protein solubility remains a challenging task due to the complexity of protein structures and the multitude of factors influencing solubility. Recent advances in computational methods, particularly those based on machine learning, have provided powerful tools for predicting protein solubility, thereby reducing the need for extensive experimental trials. This review provides an overview of current computational approaches to predict protein solubility. We discuss the datasets, features, and algorithms employed in these models. The review aims to bridge the gap between computational predictions and experimental validations, fostering the development of more accurate and reliable solubility prediction models that can significantly enhance recombinant protein production.