Motivation Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming, and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery. Results A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77, and sensitivity of 0.93, when ten-fold cross-validation was performed. The model was further evaluated using 74 central nerve system (CNS) compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85, and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models. Availability The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.
Background: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-ago go related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates. Result: In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when tenfold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models. Conclusion: The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.
Differentiation of oligodendrocytes (ODs) presents a challenge in regenerative medicine due to their role in various neurological diseases associated with dysmyelination and demyelination. Here, we designed a peptide derived from vitronectin (VN) using in silico docking simulation and examined its use as a synthetic substrate to support the differentiation of ODs derived from human pluripotent stem cells. The designed peptide, named VNP2, promoted OD differentiation induced by the overexpression of SOX10 in OD precursor cells compared with Matrigel and full-length VN. ODs differentiated on VNP2 exhibited greater contact with axon-mimicking nanofibers than those differentiated on Matrigel. Transcriptomic analysis revealed that the genes associated with morphogenesis, cytoskeleton remodeling, and OD differentiation were upregulated in cells grown on VNP2 compared with cells grown on Matrigel. This new synthetic VN-derived peptide can be used to develop a culture environment for efficient OD differentiation.
Background As methane is 84 times more potent than carbon dioxide in exacerbating the greenhouse effect, there is an increasing interest in the utilization of methanotrophic bacteria that can convert harmful methane into various value-added compounds. A recently isolated methanotroph, Methylomonas sp. DH-1, is a promising biofactory platform because of its relatively fast growth. However, the lack of genetic engineering tools hampers its wide use in the bioindustry. Results Through three different approaches, we constructed a tunable promoter library comprising 33 promoters that can be used for the metabolic engineering of Methylomonas sp. DH-1. The library had an expression level of 0.24–410% when compared with the strength of the lac promoter. For practical application of the promoter library, we fine-tuned the expressions of cadA and cadB genes, required for cadaverine synthesis and export, respectively. The strain with PrpmB-cadA and PDnaA-cadB produced the highest cadaverine titre (18.12 ± 1.06 mg/L) in Methylomonas sp. DH-1, which was up to 2.8-fold higher than that obtained from a non-optimized strain. In addition, cell growth and lysine (a precursor of cadaverine) production assays suggested that gene expression optimization through transcription tuning can afford a balance between the growth and precursor supply. Conclusions The tunable promoter library provides standard and tunable components for gene expression, thereby facilitating the use of methanotrophs, specifically Methylomonas sp. DH-1, as a sustainable cell factory. Graphical Abstract
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