Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples.

Many high-performance DTA deep learning models have been proposed, but they are mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models more trustworthy, and allows to distill biological knowledge from the models. Counterfactual explanation is one popular approach to explaining the behaviour of a deep neural network, which works by systematically answering the question "How would the model output change if the inputs were changed in this way?".

Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring.

Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem. However, the machine learning model faces the cold-start problem where the model performance drops when predicting the interaction of a novel drug or target.

An age-stratified serosurvey against purified Salmonella enterica serovar Typhi antigens in the Lao People´s Democratic Republic.

The epidemiology of typhoid fever in Lao People`s Democratic Republic is poorly defined. Estimating the burden of typhoid fever in endemic countries is complex due to the cost and limitations of population-based surveillance; serological approaches may be a more cost-effective alternative. ELISAs were performed on 937 serum samples (317 children and 620 adults) from across Lao PDR to measure IgG antibody titers against Vi polysaccharide and the experimental protein antigens, CdtB and HlyE.

GEFA: Early Fusion Approach in Drug-Target Affinity Prediction.

Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues.

Using the IPTG-Inducible Pgrac212 Promoter for Overexpression of Human Rhinovirus 3C Protease Fusions in the Cytoplasm of Bacillus subtilis Cells.

Expression and secretion of recombinant proteins in the endotoxin-free bacterium, Bacillus subtilis, has been thoroughly studied, but overexpression in the cytoplasm has been limited to only a few proteins. Here, we used the robust IPTG-inducible promoter, Pgrac212, to overexpress human rhinovirus 3C protease (HRV3C) in the cytoplasm of B. subtilis cells.

Development of inducer-free expression plasmids based on IPTG-inducible promoters for Bacillus subtilis.

Background: Besides Escherichia coli, Bacillus subtilis is an important bacterial species for the production of recombinant proteins. Recombinant genes are inserted into shuttle expression vectors which replicate in both E. coli and in B.