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Motivation The heterologous expression of recombinant protein requires host cells, such as Escherichiacoli , and the solubility of protein greatly affects the protein yield. A novel and highly accurate solubility predictor that concurrently improves the production yield and minimizes production cost, and that forecasts protein solubility in an E.coli expression system before the actual experimental work is highly sought. Results In this article, EPSOL, a novel deep learning architecture for the prediction of protein solubility in an E.coli expression system, which automatically obtains comprehensive protein feature representations using multidimensional embedding, is presented. EPSOL outperformed all existing sequence-based solubility predictors and achieved 0.79 in accuracy and 0.58 in Matthew's correlation coefficient. The higher performance of EPSOL permits large-scale screening for sequence variants with enhanced manufacturability and predicts the solubility of new recombinant proteins in an E.coli expression system with greater reliability. Availability and implementation EPSOL's best model and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/EPSOL). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI. [ABSTRACT FROM AUTHOR]