  Protein solubility plays a critical role in improving production yield of
recombinant proteins in biocatalyst and pharmaceutical field. To some extent,
protein solubility can represent the function and activity of biocatalysts
which are mainly composed of recombinant proteins. Highly soluble proteins are
more effective in biocatalytic processes and can reduce the cost of
biocatalysts. Screening proteins by experiments in vivo is time-consuming and
expensive. In literature, large amounts of machine learning models have been
investigated, whereas parameters of those models are underdetermined with
insufficient data of protein solubility. A data augmentation algorithm that can
enlarge the dataset of protein solubility and improve the performance of
prediction model is highly desired, which can alleviate the common issue of
insufficient data in biotechnology applications for developing machine learning
models. We first implemented a novel approach that a data augmentation
algorithm, conditional WGAN was used to improve prediction performance of DNN
for protein solubility from protein sequence by generating artificial data.
After adding mimic data produced from conditional WGAN, the prediction
performance represented by $R^{2}$ was improved compared with the $R^{2}$
without data augmentation. After tuning the hyperparameters of two algorithms
and organizing the dataset, we achieved a $R^{2}$ value of $45.04\%$, which
enhanced $R^{2}$ about $10\%$ compared with the previous study using the same
dataset. Data augmentation opens the door to applications of machine learning
models on biological data, as machine learning models always fail to be well
trained by small datasets.
