Package: rossmann-toolbox
Rossmann ToolboxThe Rossmann Toolbox provides two deep learning models for predicting the cofactor specificity of Rossmann enzymes based on either the sequence or the structure of the beta-alpha-beta cofactor binding motif.Table of contentsInstallationUsageSequence-based approachStructure-based approachEGATConv layerRemarksHow to citeContactFundingInstallationCreate a conda environment:condacreate--namertbpython=3.6.2
condaactivatertbInstall pip in the environment:condainstallpipInstall rtb usingrequirements.txt:pipinstall-rrequirements.txtUsageSequence-based approachThe input is a full-length sequence. The algorithm first detectsRossmann cores(i.e. the β-α-β motifs that interact with the cofactor) in the sequence and later evaluates their cofactor specificity:importmatplotlib.pylabaspltfromrossmann_toolboximportRossmannToolboxrtb=RossmannToolbox(use_gpu=True)# Eample 1# The b-a-b core is predicted in the full-length sequencedata={'3m6i_A':'MASSASKTNIGVFTNPQHDLWISEASPSLESVQKGEELKEGEVTVAVRSTGICGSDVHFWKHGCIGPMIVECDHVLGHESAGEVIAVHPSVKSIKVGDRVAIEPQVICNACEPCLTGRYNGCERVDFLSTPPVPGLLRRYVNHPAVWCHKIGNMSYENGAMLEPLSVALAGLQRAGVRLGDPVLICGAGPIGLITMLCAKAAGACPLVITDIDEGRLKFAKEICPEVVTHKVERLSAEESAKKIVESFGGIEPAVALECTGVESSIAAAIWAVKFGGKVFVIGVGKNEIQIPFMRASVREVDLQFQYRYCNTWPRAIRLVENGLVDLTRLVTHRFPLEDALKAFETASDPKTGAIKVQIQSLE'}preds=rtb.predict(data,mode='seq',core_detect_mode='dl',importance=False)# Eample 2# The b-a-b cores are provided by the user (WT vs mutant)data={'seq_wt':'AGVRLGDPVLICGAGPIGLITMLCAKAAGACPLVITDIDEGR',# WT, binds NAD'seq_mut':'AGVRLGDPVLICGAGPIGLITMLCAKAAGACPLVITSRDEGR'}# D211S, I212R mutant, binds NADPpreds,imps=rtb.predict(data,mode='core',importance=True)# Example 3# Which residues contributed most to the prediction of WT as NAD-binding?seq_len=len(data['seq_wt'])plt.errorbar(list(range(1,seq_len+1)),imps['seq_wt']['NAD'][0],yerr=imps['seq_wt']['NAD'][1],ecolor='grey')For more examples of how to use the sequence-based approach, seeexample_minimal.ipynb.Structure-based approachStructure-based predictions are not currently available. We are working on a new version that will not only provide predictions, but also the ability to make specificity-shifting mutations.EGATConv layerThe structure-based predictor includes an EGAT layer that deals with graph neural networks supporting edge features. The EGAT layer is available from DGL, and you can find more details about it in theDGL documentation. For a detailed description of the EGAT layer and its usage, please refer to the supplementary materials of theRossmann Toolbox paper.RemarksHow to cite?If you find therossmann-toolboxuseful, please cite the paper:Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteinsKamil Kamiński, Jan Ludwiczak, Maciej Jasiński, Adriana Bukala, Rafal Madaj, Krzysztof Szczepaniak, Stanisław Dunin-HorkawiczBriefings in Bioinformatics, Volume 23, Issue 1, January 2022,bbab371ContactIf you have any questions, problems or suggestions, please contactus.FundingThis work was supported by the First TEAM program of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.