Package: pymCADRE
pymCADREAuthors:Nantia LeonidouPublicationWhen using pymCADRE in a research work, please cite the following work:Leonidou, N., Renz, A., Mostolizadeh, R., & Dräger, A. (2023). New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells. PLOS Computational Biology, 19(3), e1010903.Overview.ThepymCADREtool is an advanced re-implementation of the metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE) algorithm in Python. It constructs tissue-specific metabolic models by leveraging gene expression data and literature-based evidence, along with network topology information.The reactions within the generic global model are being ranked, and the ones with the lowest supporting evidence for the tissue
of interest are given the highest priority for removal:GM, C, NC, P, Z, model_C = rank_reactions(model, G, U, confidence_scores, C_H_genes, method)If the generic functionality test is passed, the model undergoes pruning, which results in a context-specific reconstruction:PM, cRes = prune_model(GM, P, C, Z, eta, precursorMets, salvage_check, C_H_genes, method)Installationpip install pymcadrePrerequisitesThis tool has the following dependencies:python >=3.8.5Packages:pandasnumpycobrarequestsosInput datamodel: COBRA model structure for the metabolic model of interestprecursorMets: list of precursor, key, metabolites in form of .txt fileconfidence_scores: literature/experimental-based confidence assigned to reactions inmodelTissue-specific expression evidence:G: list of Entrez IDs for all genes inmodelU: list of ubiquity scores calculated for all genes inmodelOptional InputssalvageCheck: flag whether to perform a functional check for the nucleotide salvage pathway (1) or not (0)C_H_genes: list with Entrez IDs for genes with particularly strong evidence of activity in the tissue of interestmethod: method to use internal optimizations, (1) flux variability analysis or (2) fastccOutputsPM: pruned COBRA tissue-specific modelGM: COBRA model after removing blocked reactions from the input global modelC: core reactions inGMNC: non-core reactions inGMZ: reactions with zero expression across all samples after binarizationmodel_C: core reactions in the generic model (including blocked reactions)pruneTime: total reaction pruning timecRes: result of model checks (consistency/function) during pruningUsageTo run pymCADRE, execute the notebook named main_pymcadre.ipynb or the python script named pymcadre.py. The scripts can be modified to the preferred parameters and input files. Jupyter notebooks with test runs and test scripts are also provided as reference points.Additional materialPREDICATE (Prediction ofAntiviralTargets):Steps:introduction of mutations in the reference sequence based on the protein sequencescalculation of the necessary stoichiometric coefficients for the final virus biomass functionstarget detection using two approaches: reaction knock-outs and the host-derived enforcementvisualizations that could give insights into the dataset and a better understanding of the results.The tool can be applied to either one or more nucleotide sequences and all existing RNA viruses. This makes it particularly advantageous and time-saving when studying multiple variants of a single virus. The number of genomic input sequences equals the number of the calculated VBOF.To run the tool, set the constant variables to the file pathways where the desired files are stored.