Recombinant enzyme expression in Escherichia coli is one of the most popular methods to produce bulk concentrations of protein product. However, this method is often limited by the inadvertent formation of inclusion bodies. Our analysis systematically reviews literature from 2010 to 2021 and details the methods and strategies researchers have utilized for expression of difficult to express (DtE), industrially relevant recombinant enzymes in E. coli expression strains. Our review identifies an absence of a coherent strategy with disparate practices being used to promote solubility. We discuss the potential to approach recombinant expression systematically, with the aid of modern bioinformatics, modelling, and ‘omics’ based systems-level analysis techniques to provide a structured, holistic approach. Our analysis also identifies potential gaps in the methods used to report metadata in publications and the impact on the reproducibility and growth of the research in this field.

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

The biologics sector has amassed a wealth of data in the past three decades, in line with the bioprocess development and manufacturing guidelines, and analysis of these data with precision is expected to reveal behavioural patterns in cell populations that can be used for making predictions on how future culture processes might behave. The historical bioprocessing data likely comprise experiments conducted using different cell lines, to produce different products and may be years apart; the situation causing inter-batch variability and missing data points to human- and instrument-associated technical oversights. These unavoidable complications necessitate the introduction of a pre-processing step prior to data mining. This study investigated the efficiency of mean imputation and multivariate regression for filling in the missing information in historical bio-manufacturing datasets, and evaluated their performance by symbolic regression models and Bayesian non-parametric models in subsequent data processing. Mean substitution was shown to be a simple and efficient imputation method for relatively smooth, non-dynamical datasets, and regression imputation was effective whilst maintaining the existing standard deviation and shape of the distribution in dynamical datasets with less than 30% missing data. The nature of the missing information, whether Missing Completely At Random, Missing At Random or Missing Not At Random, emerged as the key feature for selecting the imputation method.