Package: faltwerk
Faltwerkfaltwerkis a library for spatial exploratory data analysis of protein structures. It helps parse them, select items of interest, generate and visualise various protein annotations, and then provide convenient interfaces for downstream tools to run, for example, spatial regression. The most convenient way to runfaltwerkis in ajupyter notebook. This REPL-like execution gives rapid feedback and can help form hypotheses. However,faltwerkalso integrates nicely into workflows (no point and click required).The curious thing about proteins is that they come as linear strings of residues, but then fold up into 3D structures, that represent the functionally active form of the molecule (the thing that "does stuff"). Correspondingly, nature most often selects on structure. However, many analyses (positive selection etc.) require the linear sequence.faltwerkbridges this gap (see specific use cases below).PRs and suggestions welcome! The awesomeAnviohas astructure module, should you be dissatisfied withfaltwerk.Usage# TL;DR: Quickly visualize a zinc-finger and annotate the zinc-binding residues# wget 'http://www.pdb.org/pdb/download/downloadFile.do?fileFormat=pdb&compression=NO&structureId=1MEY' -O 1mey.pdbfromfaltwerkimportFold,Binding,Layoutmodel=Fold('1mey.pdb')hmms='path/to/pfam_v31/Pfam-A.hmm'b=Binding(model,option='confident')b.predict_binding_(hmms)# inplace operationmodel.annotate_('binding',b.get_binding('PF13912.5','ZN'))Layout(model).geom_surface('binding').render()We assume that all protein structures contain a single structure, and if you are not analysing protein complexes, then you want the structures to only contain a single chain, also. A simple way to clean a query pdb file is throughpdb-tools(http://www.bonvinlab.org/pdb-tools/).AlphaFold2predictions as generated byColabFoldare ideal.More information can be found in thedocumentation. Additionally, we cover several use cases withjupyternotebooks:jupyternotebookexamples/example_local.ipynbBut let's step through areal world exampleof the amazing assymetric selection that can be observed in human transferrin as a consequence of bacterial iron piracy, arr!# Load datafromfaltwerk.modelsimportFold,AlphaFold# ... (for details see notebook)fp='data/alphafold2/transferrin/test_08df6_unrelaxed_rank_1_model_3.pdb'model=Fold(fp)# orfp='data/alphafold2/transferrin/'af=AlphaFold(fp)model=af.best# Visualise pLDDT (how good is AF2 the prediction)ly=Layout(model).geom_ribbon('plddt',palette='rainbow_r')# Predict ligand binding sites using the "InteracDome" approachb=Binding(model,'representable')b.predict_binding_(pfam)binding=b.get_binding('PF00405.16','FE')fe=[ifori,jinenumerate(binding)ifj>.5]ly=Layout(model)# selectfe_=ly.select(residues=fe)# stylely.geom_ribbon(color='#ffffff')ly.geom_ribbon(selection=fe_,color='red')ly.render().show()# Test for spatial signal in residues that are e. g. mutated or under positive# natural selection. Here we use residues from Barber et al., Science, 2014# (https://www.science.org/doi/10.1126/science.1259329) that are under positive# selection.original=[153,253,382,434,435,436,439,558,574,575,576,591,592,593,614,617,619,625]# -1 bc/ positions from manuscript are 1-basedbarber2014=[i-1foriinoriginal]selection=[1ifiinbarber2014else0foriinrange(len(model))]# (1) Spatial autocorrelation, i. e. "hotspots" in selected features, here using# the Getis-Ord metric.FDR=0.05hotspots=find_hotspots(model,selection,method='getis_ord',angstrom=8,false_discovery_rate=FDR,test_two_sided=False)# (2) Point density analysis, here using HDBSCAN.clusters=cluster(model,hotspots,min_cluster_size=5)# Annotate modelmodel.annotate_many_({'selection':selection,'hotspots':hotspots,'clusters':clusters})# Build figure like a layer cakely=Layout(model,panel_size=(200,200),grid=(1,3),linked=True)pos=ly.select(residues=barber2014,elements=['CA'],chain='A')ly.geom_ribbon(color='#ffffff')ly.geom_sphere(selection=pos,color='black')ly.geom_surface('hotspots',palette='binary',panel=(0,1))ly.geom_surface('clusters',palette='Set2_r',panel=(0,2))ly.render().show()From here,faltwerkallows to easily interact with downstream tools likealtairfor visualisation of features orpysalfor spatial regression (see notebookexample.ipynb)df=pd.DataFrame.from_dict(flatten(model.annotation,expected_track_length=len(model)))Use casesAnnotate solvent access, active centers and moreSome regions in the amino acid sequence are more conserved than others across species. Which regions in the 3D structure do they correspond to?Are there any significant spatial hotspots where residues experience positive selection?Do observed mutations cluster in any part of the protein, for example in regions that interface other proteins (protein binding sites) or active sites of the protein?A hotspot/ cluster has been identified; which protein features if any can explain this occurance. For example, is the cluster associated with known protein-binding sites?What's the spatial relationship between mutations deemed pathogenic and the annotated functional domains of a protein?InstallIf you want to programmatically search for hotspots, the easiest way to get started is to use the image we provide through docker:dockerpullnanozoo/faltwerk:3.8--08dc798Quick start using thecolab notebook.# Dependencies!pipinstallfaltwerk==0.3# Install tools from upstream and downstream tasks!pipinstall-qpdb-toolsaltairvega_datasetsspreggeopandas# More dependencies!mambainstall-q-y-cconda-forge-cbiocondafoldseek=3.915ef7dhmmer# For local execution in a jupyter notebookpipinstallnotebook
jupyternotebookexamples/example_local.ipynbIt might be necessary to install ajupyterextension to visualize the proteins (you notice if they don't render). Please refer to thepy3Dmolinstall documentationandrepoin this case. A typical setup that renders correctly looks like this (Linux and Mac tested):# ubuntu 20.04. LTS# Python 3.10.4node--version# v10.19.0jupyter--version# IPython          : 8.4.0# ipykernel        : 6.15.1# ipywidgets       : not installed# jupyter_client   : 7.3.4# jupyter_core     : 4.11.1# jupyter_server   : not installed# jupyterlab       : not installed# nbclient         : 0.6.6# nbconvert        : 6.5.3# nbformat         : 5.4.0# notebook         : 6.4.12# qtconsole        : not installed# traitlets        : 5.3.0