Assays

ITC binding (BIND) experiments to determine the binding parameters of NADP+ to Gre2p in 100 mM KPi Buffer.

Build the chemical defensome gene list for 5 fish: Zebrafish (Danio rerio), Atlantic cod (Gadus morhua), medaka (Oryzias latipes), Atlantic killifish (Fundulus heteroclitus) and stickleback (Gasterosteus aculeatus). Source code and relevant files can be found on GitHub: https://github.com/zhxiaokang/fishDefensome/tree/main/defensomeGenes

To study the defensome genes' expression in early developmental stages of zebrafish and stickleback. Souce code and relevant files can be found on GitHub: https://github.com/zhxiaokang/fishDefensome/tree/main/developmentalStages

Compound data and computational prediction of physicochemical properties

This section contains the links to the tools used for reproducing the computational results presented in U2019. This is required because SloppyCell is under the risk of becoming rotting code. Using Docker we can assure some persistence for the computational environment that allows to run SloppyCell.

The associated git repository can be found in https://github.com/jurquiza/Urquiza2019a.git which can be cloned.

The docker image can either be pulled from the docker hub site

docker pull ...

this assay include the hub genes of modules from different mapping schemes with highly functional similarities.

A Weighted Gene Co-Expression Network Analysis (WGCNA) of breast cancer prognostic genes (derived from transcriptome data from the TCGA Genomics Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/)), and cancer hallmark genes.

A Jaccard Index of the overlap between prognostic and hallmark genes for 17 cancer types across different mapping schemes. The impact of selecting different mapping schemes was assessed by pairwise comparisons where there were 5 or more shared genes.

Model files for FMv1.5. The model is based on FMv1 of Chew et al. PNAS 2014, which is also in FAIRDOMHub and linked to the Model record as an 'Attribution'. FMv1 was extended in this work by Hannah Kinmonth-Schultz and Daniel Seaton, in Matlab.

Exposing zebrafish to benzo(a)pyrene (B(a)P) (gene counts from NCBI GEO: GSE64198, previously published by Fang, et al. 2015. Souce code and relevant files can be found on GitHub: https://github.com/zhxiaokang/fishDefensome/tree/main/exposureResponse

please add your model here - associated to this assay

Motivated by an increasing population and the desire to grow plants more efficiently, attention has turned to the use of Light Emitting Diodes (LEDs) to illuminate plants which are grown indoors. Indoor growing facilities enable closely controlled and mon- itored environmental conditions. More and more of these facilities exchange High Pressure Sodium (HPS) lamps for LED lighting since they provide more efficient lighting and the possibility to control light intensity and quality in order to ...

Differential expression analysis (using R package edgeR) on liver gene expression between salmon with all four fads2 genes knockout, only fads2d6b & fads2d6c knockout and wildtype. All salmon was given either a low-PUFA diet or a high-PUFA diet

Here, we conduct a proof of principle by comparing a 2D and 3D fluorescent image analysis based approach on unlabeled cardiomyocytes. Based on the CellProfiler software, we extracted high-dimensional features of individual cells and nuclei, which are subsequently down-sampled and clustered. These clusters are furthermore benchmarked via different machine learning classifiers (e.g., AdaBoost, Gradient Boosting, Random Forest) as the ground truth for our proposed approach.

Docking results of pteridine-based compounds in different target PTR1 and DHFR receptors and the off-target human DHFR when treating ligands as flexible and the protein receptors as a rigid-body.

Docking results of pteridine-based compounds in different target PTR1 and DHFR receptors and the off-target human DHFR when using an induced fit docking routine with an initial crude ligand placement step, subsequent receptor optimization in response to ligand binding and another docking step into the optimized receptor.

Computational prediction of physicochemical and advanced descriptors related to ADME-Tox.

Assessment of possible linear correlations between predicted ADMET descriptors from QikProp (Schrödinger, LLC, New York, NY) runs and experimentally determined activities against T. brucei brucei bloodstream forms with the help of a Python script.

Assessment of the possible multiple correlation between experimentally determined TbPTR1 and TbDHFR inhibition values and corresponding anti-parasitic activities against T. brucei brucei bloodstream forms using a Python script.

In silico check and filtering for potential Pan-assay interference compounds.

Compound data, library construction schemes and preparation routine for small drug-like molecules as ligands in docking and for further analysis.

Prepared target (parasite PTR1, DHFR) and off-target (human DHFR) protein receptors for docking studies, in part with conserved structural water sets, and the corresponding preparation routine.