in 
 the postgenomic era , data generation in the pharmaceutical sciences 
 has massively accelerated and new analytical escience approaches are 
 needed to adequately exploit this new chemical and biological information .
open source cheminformatics tools are available to generate , annotate , 
 and visualize structures of small molecules and calculate chemical 
 descriptors and fingerprints for their comparison and the identification 
 of structure  property or structure  activity relationships .
these tools are available in various forms , often as libraries or 
 extensions to widely used environments such as r , python , or java .
data analytics platforms such as knime allow the combination of bioinformatics and 
 cheminformatics tools and integration of the growing 
 amount of publically available chemical , structural , and biological 
 data from chembl , pubchem , bindingdb , and 
 pdb .
knime has emerged as a widely used 
 open source data mining tool , and the knime repository contains configurable 
 nodes to perform a wide variety of functions that can be combined 
 in customizable data analytics workflows .
the standard 
 knime nodes , together with those supplied by the user community , allow access to the functionality of several 
 cheminformatics tools including rdkit , cdk , chemaxon , erlwood , indigo , and openbabel .
the embl - ebi and 
 vernalis nodes , provide access to chembl 
 and pdb , respectively , and the openphacts ( chembionavigator , pharmatrek ) nodes allow the mining of yet more heterogeneous 
 data .
we have developed new cheminformatics and 
 bioinformatics tools that provide detailed information on the structural 
 interactions between small molecule ligands and their biological macromolecular 
 targets ( http://3d-e-chem.github.io ) and incorporated these tools in an open source virtual machine , 
 3d - e - chem - vm , that makes use of the knime infrastructure .
3d - e - chem - vm 
 consists of software libraries , workflow tools , and databases that 
 allow interoperability of different chemical and biological data formats , 
 enabling the analysis and integration of small molecule and protein 
 structural information in the graphical programming environment of 
 knime .
the new cheminformatics 
 tools , knime nodes , and data analytics workflows enable efficient 
 data mining from established structural ( pdb ) and bioactivity ( chembl ) databases 
 as well as customized g protein - coupled receptor ( gpcrdb ) and protein kinase ( klifs ) focused data resources .
the cheminformatics toolbox allows the 
 design of customizable workflows for virtual screening , off - target 
 prediction , and ligand design , including bioisostere detection based 
 on protein 
ligand interaction pharmacophore features ( kripo ) and consideration of ligand - based metabolite 
 prediction ( sygma ) .
the integrated structural 
 cheminformatics infrastructure enables large - scale structural chemogenomics 
 studies , where protein  ligand binding interaction and bioactivity 
 data are considered across multiple ligands and targets .
knime , postgresql , and chemistry - aware 
 open source tools were integrated to 
 become the backbone of a desktop cheminformatics infrastructure ( supporting information , figure s1 ) .
ligand 
 interaction data from kripo , gpcrdb , and klifs databases and has been 
 made publically available on github ( http://3d-e-chem.github.io ) .
the previously reported mychembl vm provided a useful template to design the 3d - e - chem - vm and a local 
 copy of the chembl database can optionally 
 be incorporated into the vm ( https://github.com/3d-e-chem/3d-e-chem-vm/wiki/datasets#chembl ) .
the 3d - e - chem - vm is available in the vagrant box catalog of hashicorp called atlas .
the vagrant box is automatically constructed using packer , which creates a virtualbox machine image , installs lubuntu , and finally executes our 
 ansible playbooks to install all the 
 additional software and enhancements ( supporting information , figure s1 ) . to obtain a copy of the 3d - e - chem - vm 
 on a local pc
, the user installs virtualbox and vagrant , then downloads 
 the vagrant box , and starts the vm by running two vagrant commands : 
  vagrant init nlesc/3d - e - chem  then  vagrant 
 up  .
new functionalities implemented in later 3d - e - chem - vm 
 releases can be installed using the command  sudo vagrant_upgrade  
 from a terminal inside the vm .
the gpcrdb , klifs , kripodb , and sygma 
 knime nodes included in the 3d - e - chem - vm are built and tested automatically 
 on the continuous integration platform travis - ci every time a change is pushed to the github code repository .
the knime node development procedure to generate a skeleton , write the code , run 
 tests , and deploy the nodes via the eclipse user interface was automated 
 using tycho based eclipse plug - ins .
the 
 3d - e - chem knime nodes are tested for knime version compatibility ( specified 
 in the node config file ) and if necessary will be adapted to comply 
 with future knime releases .
the 3d - e - chem - vm requires at least 2 gb 
 ram memory to run , 16 gb of disk space , and the cpu must have virtualization 
 support .
the 3d - e - chem tools and workflows are available for use in 
 any environment as long as the dependencies and prerequisites are 
 correctly installed and configured .
the 3d - e - chem - vm further facilitates 
 the use of the 3d - e - chem tools and other resources ( supporting information , figure s1 ) by taking care of these 
 dependencies and prerequisites , including the preconfiguration of 
 ( i ) python and r packages to facilitate the use of knime nodes and workflows , ( ii ) 
 scripts to set up infrastructures that allow data mining of locally 
 installed databases like the postgresql and rdkit postgresql cartridge to exploit 
 a local copy of chembldb , ( iii ) additional 
 cheminformatics modeling and visualization software ( e.g. , pymol , camb , and fpocket ) , and ( iv ) openphacts knime functionalities and the new gpcrdb , klifs , and kripo knime nodes 
 to interact with local files and web servers .
gpcrs 
 are the largest group of signal 
 transducing membrane proteins and hence one of the most important 
 target family for drugs that can stimulate , reduce , or block endogenous 
 gpcr activity .
gpcr structural chemogenomic analyses require the integration 
 of phylogenetic , sequence , and structure similarity and ligand binding 
 information .
gpcrdb ( http://gpcrdb.org , accessed 25 august 
 2016 ) is an online repository of the accumulated knowledge on gpcrs 
 including structure - based annotation of protein sequence alignments 
 of 18  787 sequences of 421 receptor subtypes and of 3096 species , 
 analysis of 142 gpcr crystal structures and gpcr - ligand interactions , 
 and 14  099 mutational data points .
for the integration of this data in customizable workflows for systematic 
 structural chemogenomics analyses we have developed seven knime nodes 
 that interface with gpcrdb via a web service client generated with 
 swagger code generator .
an example workflow 
 utilizing these nodes is shown in figure 1.gpcrdb 
 protein families : extraction of protein family 
 information , including the protein names and classifications of all 
 gpcrs in the four - level hierarchy defined by gpcrdb ( class , ligand 
 type , subfamily , subtype).gpcrdb protein 
 information : retrieval of source , species , 
 and sequence data from uniprot identifiers or protein family identifier.gpcrdb protein residues : retrieval of residues 
 and numbering 
 schemes .
this node retrieves all residues of the specified protein 
 with secondary structure annotation , uniprot numbering , and gpcr residue 
 numbering.gpcrdb structures of a protein : retrieval of experimental 
 gpcr structures with literature references , pdb codes , and ligands.gpcrdb mutations of a protein : retrieval 
 of single point 
 mutations in gpcrs , including the sequence position , mutation , ligand , 
 assay type , mutation effect , protein expression information , and publication 
 reference.gpcrdb structure 
ligand 
 interactions : returns 
 the sequence numbers of amino acid residues interacting with ligands 
 in the specified pdb entry .
the interaction type is annotated in the 
 output table.gpcrdb protein similarity : 
 returns the sequence identity 
 and similarity of a query receptor versus a set of receptors , based 
 on the full sequence or a specified set of residues .
gpcrdb 
 protein families : extraction of protein family 
 information , including the protein names and classifications of all 
 gpcrs in the four - level hierarchy defined by gpcrdb ( class , ligand 
 type , subfamily , subtype ) .
gpcrdb protein 
 information : retrieval of source , species , 
 and sequence data from uniprot identifiers or protein family identifier .
this node retrieves all residues of the specified protein 
 with secondary structure annotation , uniprot numbering , and gpcr residue 
 numbering .
gpcrdb structures of a protein : retrieval of experimental 
 gpcr structures with literature references , pdb codes , and ligands .
gpcrdb mutations of a protein : retrieval 
 of single point 
 mutations in gpcrs , including the sequence position , mutation , ligand , 
 assay type , mutation effect , protein expression information , and publication 
 reference .
ligand 
 interactions : returns 
 the sequence numbers of amino acid residues interacting with ligands 
 in the specified pdb entry .
gpcrdb protein similarity : 
 returns the sequence identity 
 and similarity of a query receptor versus a set of receptors , based 
 on the full sequence or a specified set of residues .
knime workflows to exploit cheminformatics and bioinformatics information 
 on gpcrs ( gpcrdb nodes ) and protein kinases ( klifs nodes ) . in the 
 gpcrdb workflow ,
knime nodes are used to enable the extraction and 
 combination of protein information , sequence , alternative numbering 
 schemes , mutagenesis data , and experimental structures for a selected 
 receptor from gpcrdb .
the lower branch of the workflow returns all 
 sequence identities and similarities of the tm domain for the selected 
 receptors and can be used for further structural chemogenomics analyses using , e.g. , structural and structure - based 
 sequence alignments of the ligand binding site residues of crystallized 
 aminergic receptors ( available in the vm as a pymol session ) . in the 
 klifs workflow ,
knime nodes enable the integrated analysis of structural 
 kinase  ligand interactions from all structures for a specific 
 kinase in klifs ( human mapk in the example ) .
ligand 
 complexes with a specific hydrogen bond interaction pattern between 
 the ligand and residues in the hinge region of the kinase ( stacked 
 bar chart ) are selected for an all - against - all comparison of their 
 structural kinase  ligand interactions fingerprints ( heat map ) . 

the ligands from the selected structures are compared and the ligand 
 pair with the lowest chemical similarity and a high interaction fingerprint 
 similarity are retrieved from klifs for binding mode comparison .
meta 
 nodes in the workflows in panels a and b are indicated with a star 
 ( * ) .
protein kinases are important signal pathway 
 regulators and comprise one of the largest protein families that are 
 encoded within the human genome .
the klifs database ( http://klifs.vu-compmedchem.nl , accessed 25 august 2016 ) contains detailed structural 
 kinase  ligand interaction information derived from 3354 structures 
 of catalytic domains of human and mouse protein kinases deposited 
 in the pdb in order to map the structural determinants of kinase 
to leverage this information for structural 
 chemogenomics analyses we have developed nine knime nodes that interface 
 with klifs via a web service client generated with swagger code generator .
i d mapper : maps a user - supplied set of kinase 
 names ( names according to manning et al . ) , hgnc gene symbols , or uniprot accession codes to a klifs kinase 
 i d .
the output also contains all related kinase information present 
 within klifs ( see  kinase information retriever).kinase information retriever : returns a 
 table comprising 
 the klifs kinase i d , kinase name , hgnc symbol , kinase group , kinase 
 family , kinase class , species , full name , uniprot accession code , 
 iuphar i d , and the amino acid sequence of the pocket based on the 
 klifs pocket definition using a consistent alignment of 85 residues .
kinase i d mapper : maps a user - supplied set of kinase 
 names ( names according to manning et al . ) , hgnc gene symbols , or uniprot accession codes to a klifs kinase 

the output also contains all related kinase information present 
 within klifs ( see  kinase information retriever  ) .
kinase information retriever : returns a 
 table comprising 
 the klifs kinase i d , kinase name , hgnc symbol , kinase group , kinase 
 family , kinase class , species , full name , uniprot accession code , 
 iuphar i d , and the amino acid sequence of the pocket based on the 
 klifs pocket definition using a consistent alignment of 85 residues .
ligand 
 interaction fingerprint ( ifp ) into a 
 human - readable table with annotated interactions for each structure . 

this node can optionally add the sequence number and the klifs residue 
 position for each pocket residue to the 
 table.interaction fingerprint retriever : 
 retrieval of the 
 interaction fingerprint of specific kinase - ligand complexes from klifs . 

the fingerprint has been corrected for gaps / missing residues within 
 the klifs pocket thereby enabling all - against - all comparisons.interaction types retriever : retrieves the 
 different 
 interaction types for each bit position of the interaction fingerprint 
 method and can be used in combination with the interaction fingerprint 
 decomposer to identify which kinase  ligand interactions are 
 present in a given set of kinase structures .
ligand 
 interaction fingerprint ( ifp ) into a 
 human - readable table with annotated interactions for each structure . 

this node can optionally add the sequence number and the klifs residue 
 position for each pocket residue to the 
 table .
interaction fingerprint retriever : 
 retrieval of the 
 interaction fingerprint of specific kinase - ligand complexes from klifs . 

the fingerprint has been corrected for gaps / missing residues within 
 the klifs pocket thereby enabling all - against - all comparisons . interaction types retriever : retrieves the 
 different 
 interaction types for each bit position of the interaction fingerprint 
 method and can be used in combination with the interaction fingerprint 
 decomposer to identify which kinase  ligand interactions are 
 present in a given set of kinase structures . ligands overview retriever : retrieval of ligand ids , 
 three - letter pdb - codes , names , molecular structures ( smiles ) , and 
 inchikeys for all ligands from ( a specific set of ) kinase - ligand complexes 
 present within klifs .
structures overview retriever : 
 retrieves a list of all 
 corresponding structures within klifs based on a user - supplied set 
 of klifs kinase or ligand ids ( e.g. , from a specific kinase family ) . 

the node returns the structure i d , kinase name , kinase i d , pdb - code , 
 and all other structural annotation data within klifs ( e.g. , pocket 
 sequence , resolution , quality , ligands , dfg conformation , targeted 
 subpockets , waters).structures pdb mapper : maps a set of pdb - codes to structure 
 ids from klifs and provides all related structural information from 
 klifs.structures retriever ( mol2 ) : retrieves 
 from klifs a 
 set of structures , ( optionally the full complex , the protein , the 
 pocket , or the ligand ) in mol2 format , based on a user - supplied set 
 of structure ids . as output
the node provides a table of aligned structures 
 based on the klifs pocket definition .
structures overview retriever : 
 retrieves a list of all 
 corresponding structures within klifs based on a user - supplied set 
 of klifs kinase or ligand ids ( e.g. , from a specific kinase family ) . 

the node returns the structure i d , kinase name , kinase i d , pdb - code , 
 and all other structural annotation data within klifs ( e.g. , pocket 
 sequence , resolution , quality , ligands , dfg conformation , targeted 
 subpockets , waters ) .
structures pdb mapper : maps a set of pdb - codes to structure 
 ids from klifs and provides all related structural information from 
 klifs .
structures retriever ( mol2 ) : retrieves 
 from klifs a 
 set of structures , ( optionally the full complex , the protein , the 
 pocket , or the ligand ) in mol2 format , based on a user - supplied set 
 of structure ids . as output
the node provides a table of aligned structures 
 based on the klifs pocket definition .
the kripodb includes an sqlite 
 database with more than 2.3  10 pairwise ligand 
 binding site similarity scores based on kripo pharmacophore fingerprints of 483  083 subpockets associated with 
 the substructures ( fragments ) of small - molecule ligands identified 
 in the binding sites of all pdb entries released until 29 june 2016 . 

the full similarity matrix is available as a web service ( http://3d-e-chem.vu-compmedchem.nl/kripodb/ui/ ) , whereas a similarity matrix calculated between all crystallized 
 gpcrs and the whole pdb above a similarity threshold of 0.45 ( calculated 
 as a modified tanimoto similarity score ) is included in the 3d - e - chem - vm as compact hdf5 file .
the kripo 
 python library with a command line interface is provided inside the 
 vm to extract and manipulate fragment structural data in kripodb . 

we have developed the following two knime nodes to efficiently extract 
 and integrate the information in kripodb.similar fragments : retrieval of ligand fragments that 
 share a similar subpocket with the query fragment , based on a specified 
 similarity matrix ( local hdf5 file or web service url ) , similarity 
 threshold , and maximum number of fragment hits.fragment information : retrieval of the chemical structures 
 of the fragment , the full ligand , and the associated pdb based on 
 the fragment identifier .
similar fragments : retrieval of ligand fragments that 
 share a similar subpocket with the query fragment , based on a specified 
 similarity matrix ( local hdf5 file or web service url ) , similarity 
 threshold , and maximum number of fragment hits .
fragment information : retrieval of the chemical structures 
 of the fragment , the full ligand , and the associated pdb based on 
 the fragment identifier .
figure 2 presents an example kripo 
 knime workflow to identify similar ligand 
 binding sites ( for e.g. off - target prediction ) and search for bioisosteric 
 replacements based on ligand binding site similarity .
ligands in kripodb that share 
 a chemical ( sub)structure with a specified molecule ( doxepin in the 
 example ) are identified and defined as query fragment(s ) .
ligand ( fragment ) 
 binding site hits that share pharmacophore fingerprint similarity 
 with the binding site(s ) associated with the query fragment(s ) ( e.g. , 
 the doxepin binding site of the histamine h1 receptor ) 
 are identified and ranked according to tanimoto similarity score . 

the pharmacophore overlay underlying the similarity value of an example 
 hit ( histamine methyltransferase , pdb i d : 2aot ; available in the vm as a pymol session ) . 

the full workflow is provided in the supporting information ( figure s4 ) . in the sygma
workflow smiles strings 
 of clozapine and dasatinib are converted into rdkit molecules for 
 the prediction of metabolites using the sygma metabolites node , filtered 
 based on a sygma_score threshold of 0.1 .
the two tables are subsections 
 of the resulting table , showing the top ranked metabolites of clozapine 
 and dasatinib , consistent with experimental metabolism data .
meta nodes are indicated with a star ( * ) . for the assessment or prediction of a complete 
 pharmacological profile ,
we have developed a 
 sygma knime node thin wrapper around the sygma python library that enables straightforward generation 
 of the structures of possible metabolites of a specified molecule . 

the sygma metabolites node generates putative metabolites 
 based on the 2d coordinates of molecules in rdkit format , and the 
 definition of the number of phase 1 and phase 2 metabolism cycles 
 in the node dialogue .
the sygma_metabolite output column contains 
 the resulting metabolite structures , including the parent , ordered 
 by decreasing probability score .
the generated 2d chemical structures 
 are aligned to atomic coordinates of the parent , which facilitates 
 visual inspection of the metabolic modifications .
the sygma_pathway 
 column lists the metabolic reaction rules that were applied to result 
 in the given metabolite structure .
the sygma_score column lists the 
 probability score , which can be used to filter the results .
figure 2 shows a simple workflow 
 to predict the metabolites for the gpcr antagonist clozapine and kinase 
 inhibitor dasatinib . in the klifs workflow ( figure 1 ) information on all 14 human 
 mapk kinases with crystal structure data
subsequently , for each mapk 
 kinase  ligand complex the interaction fingerprints ( ifps ) , 
 describing the interactions between the residues in the binding site 
 of the enzyme and the ligand , are downloaded . from these ifps
the 
 h - bond donor and acceptor interaction frequency with the hinge region 
 of the kinases are summarized in a stacked bar chart .
ligand complexes 
 in which the ligand has an h - bond donor for residue hinge.46 ( gatekeeper 
 + 1 ) and an h - bond acceptor for residue hinge.48 ( gatekeeper + 3 ) . 
 in 98 of the 478 monomers
( 58 unique pdb structures ) , this interaction 
 pattern with the hinge region is observed .
the interaction pattern 
 similarity for these monomers is calculated using the tanimoto coefficient 
 ( tc ) on the ifps as visualized in a heat map , showing that overall 
 ifp similarity is relatively low despite their shared hinge interaction 
 pattern .
finally , this group of monomers is used to identify structures 
 with a high ifp similarity but low structural similarity of the ligands . 

to this end , the molecular structures of the ligands are obtained 
 and compared to each other using the ecfp-4 fingerprint and the tanimoto coefficient .
subsequently , the ifp 
 and ligand similarity matrices are combined to select the structure 
 pair with a high ifp similarity ( tc  
 0.75 ) and the lowest chemical similarity ( pdb ids 3pze and 4qp4 , ecfp-4 similarity : 
 0.07 , ifp similarity : 0.76 ) .
the 3d ligand binding modes are downloaded 
 from klifs and shown in the 3d - viewer marvinspace .
this workflow can , 
 among others , be used for scaffold hopping purposes by identifying 
 ligands with a high ifp similarity , but a relatively low chemical 
 similarity .
for example , the structures with pdb ids 3gc8 ( mapk11 ) and 3fl4 ( mapk14 ) contain 
 ligands that are chemically different ( ecfp-4 similarity : 0.2 ) but 
 share similar binding modes ( ifp similarity : 0.76 ) , identifying the 
 pyrazolopyrimidine ( 3fl4 ) to dihydroquinazolinone ( 3gc8 ) scaffold hop as an interesting design strategy to 
 obtain kinase inhibitors with similar structural interaction patterns .
a workflow combining different 3d - e - chem functionalities 
 was created to illustrate their integration and applicability for 
 structural chemogenomics studies across different protein families . 

the full gpcr - kinase cross - reactivity prediction workflow for off - target 
 identification , ligand repurposing , or the discovery of ligands with 
 a desired gpcr - kinase polypharmacological profile is shown in supporting information figure s5 . in this workflow 
 the gpcrdb and klifs nodes
are used to fetch all experimentally determined 
 structures of ligand - protein complexes in the two drug target families . 

the kripo nodes are subsequently used to assess the structure - based 
 pharmacophore similarity between all gpcr and kinase binding sites , 
 yielding 1428 similar gpcr - kinase pairs ( modified tanimoto coefficient > 0.5 ) .
the analysis for example identified 
 the 
 similar ergotamine bound serotonin 5-ht2b receptor ( pdb : 4ib4 ) and sorafenib bound 
 mapk14 ( pdb : 3heg , ic50 = 57 nm ) binding site pair ( modified tc = 0.55 ) , 
 which is consistent with the recent experimental identification of 
 sorafenib as a high affinity 5-ht2b ligand ( ki = 56 nm ) .
combination 
 of the kripo pharmacophore similarity assessment and a systematic 
 chembl database search indicated for 
 example that the 5-ht2b receptor also shares a similar 
 binding site and experimentally evaluated ligands with several other 
 kinases , including cdk8 , abl1 , ddr1 , fgfr1 , kit , hck , vgfr2 , and b - raf . 

the mapk14 kinase furthermore shares high binding site similarity 
 and experimentally validated ligands with the adenosine a2a and smoothened ( smor ) g protein - coupled receptors , amongst others .
the computationally 
 predicted kinase - gpcr pairs offer opportunities for the rational identification 
 and design of ligands with well - defined polypharmacological profiles .
the kinase - gpcr cross - reactivity workflow can 
 for example be complemented by the chemdb4vs workflow for the evaluation 
 and optimization of virtual screening strategies to identify selective 
 or multitarget ligands ( figure 3 ) .
in addition , the sygma metabolite predictor node can be 
 used to enumerate potential metabolites of ligands identified for 
 drug repurposing or of hits identified in virtual screening ( figure 3 ) .
the 3d - e - chem - vm provides 
 preconfigured starting points that can be easily adapted to construct 
 flexible structural chemogenomics analysis and drug design workflows 
 using the 3d - e - chem structural cheminformatics research tools .
schematic diagram 
 of possible interactions of the 3d - e - chem - vm 
 virtual machine elements : klifs and gpcrdb web service connector nodes , 
 kripodb , kripo , and sygma nodes , and the chemdb4vs workflow ( full 
 workflow presented in the supporting information , figure s6 ) integrated in a gpcr - kinase cross - reactivity prediction 
 workflow .
 3d - e - chem - vm is an open source , freely 
 available virtual machine 
 ( http://3d-e-chem.github.io/3d-e-chem-vm/ ) that integrates cheminformatics and bioinformatics tools for the 
 analysis of protein  ligand interaction data . 
 3d - e - chem - vm consists 
 of software libraries , and database and workflow tools that can analyze 
 and combine small molecule and protein structural information in a 
 graphical programming environment . new chemical and biological data 
 analytics tools and workflows have been developed for the efficient 
 exploitation of structural and pharmacological protein  
 ligand 
 interaction data from proteomewide databases ( e.g. , chembldb and pdb ) , 
 as well as customized information systems focused on , e.g. , g protein - coupled 
 receptors ( gpcrdb ) and protein kinases ( klifs ) . 
 the integrated structural 
 cheminformatics research infrastructure compiled in the 3d - e - chem - vm 
 enables the design of new approaches in virtual ligand screening ( chemdb4vs ) , 
 ligand - based metabolism prediction ( sygma ) , and structure - based protein 
 binding site comparison and bioisosteric replacement for ligand design 
 ( kripodb ) . 
Introduction