virtual library screening and molecular 
 modeling have been used 
 widely in the drug discovery process and have yielded experimentally 
 confirmed hits for various protein targets .
different virtual screening ( vs ) approaches have been used , including 
 structure - based docking and ligand - based mapping .
for example , reliable and 
 relevant structures of the target proteins are necessary for docking . 

in contrast ligand - based mapping only requires knowledge of known 
 ligands of the target .
often , a novel target of therapeutic interest 
 does not have a crystal structure .
for instance , a recent survey showed that there were crystal structures available 
 for only 155 individual kinases among the total 518 human kinases . 

the time needed to obtain such crystal structures varies considerably , 
 and the outcome is not guaranteed .
in addition , crystal structures 
 without bound ligands may not be relevant , especially for proteins 
 that undergo large conformational changes upon ligand - binding .
the 
 solution in such situations would be either to generate a model structure 
 ( either entirely or partially ) via homology modeling and/or molecular 
 dynamics ( md ) simulation or to apply a ligand - based mapping 
 approach , such as pharmacophore mapping and shape - based screening 
 of the ligand so the protein structures are not used .
pkr - like endoplasmic reticulum kinase ( perk ) , along with two 
 other 
 proteins ire1 ( inositol requiring enzyme 1 ) and atf6 ( activating transcription 
 factor 6 ) , are the three principle transducers of the unfolded protein 
 response ( upr ) .
the upr is activated in response to the accumulation 
 of unfolded or misfolded proteins in the endoplasmic reticulum ( er ) , 
 due to er stress arising from a number of conditions including glucose 
 deprivation , hypoxia , oxidative stress , viral infection , high cholesterol , 
 and protein mutations .
an active upr can restore homeostasis by increasing 
 the capacity of the er for protein folding and degradation while reducing 
 protein synthesis ; however , prolonged upr activity , implying an unresolved 
 er stress , may lead to cell apoptosis , thus protecting the organism 
 from the potential harmful consequences .
the perk arm of the upr regulates 
 protein levels entering the er by phosphorylating the translation 
 initiation factor eif2 , thereby reducing protein synthesis . 

perk is activated by autophosphorylation through a poorly understood 
 mechanism , which may involve oligomerization .
recent studies 
 have implicated the upr in several human diseases , 
 for example , protein - misfolding diseases , like retinitis pigmentosa and type ii diabetes , where apoptosis signals from the upr triggered by misfolded proteins 
 cause the death of normal cells .
certain types of cancer and viruses exploit the upr signal 
 to increase the er capacity in order to sustain the rapid growth of 
 cancer cells or viral replication . given the integral roles of perk 
 in the upr , an understanding of its interactions with other proteins 
 in the signaling pathways may inspire the development of potential 
 therapeutic strategies .
recently , glaxosmithkline reported their first - in - class 
 perk inhibitor ( gsk2606414 ) . here
we 
 discuss the discovery of novel inhibitors of perk utilizing virtual 
 library screening approaches in hopes of providing new scaffolds for 
 the development of perk inhibitors . in this paper
, we apply 
 both structure - based docking and ligand - based 
 screening approaches to identify potential novel inhibitors of perk . 

we first discuss how md simulations are necessary to refine a perk 
 crystal structure for docking - based virtual screening .
then we present 
 a ligand - based pharmacophore model generated from four hits derived 
 from high throughput screening ( hts ) .
both approaches are first validated 
 against the hts results of a screen against a library of about 27  000 
 compounds .
the initial vs results suggest that a consensus approach 
 by combining both pharmacophore modeling and docking are more effective 
 than either one alone , which is in accordance with previous retrospective 
 studies on vegfr-2 inhibitors using a number of 
 combinations of vs methods .
our vs protocol is then applied to screen 
 the zinc lead - like database containing more than 3 million compounds . 

finally , about 50 commercially available compounds from virtual screening 
 were tested in biochemical kinase assays , confirming activities of 
 10 .
we first trained 
 our protocol against previous high - throughput screening data ( the green and brown blocks in figure 1 ) . from the known active compounds obtained in the 
 hts ,
a ligand - based pharmacophore was generated and used to screen 
 other potential compounds .
alternatively , we also performed protein 
 structure - based docking to screen the compounds .
the performance of 
 both pharmacophore and docking were evaluated by comparing with the 
 hts result . on the basis of this ,
a protocol was proposed and applied 
 to a vs of the zinc database , which is the lower portion of the triangle 
 shown in figure 1 .
the available apo mouse perk 
 structure ( pdb code : 3qd2 ) shows a closed g - loop when it is superimposed 
 with a structurally similar kinase pkr ( pdb code : 2a19 ) .
it can be seen that the g - loop region in 3qd2 clashes with the 
 atp in 2a19 ( figure 2 ) . with such clashing ,
the 3qd2 structure is not 
 meaningful for docking . to obtain a perk structure with an 
open  
 active site , we first raised the g - loop region artificially by modeling 
 after 2a19 , 
 then manually docked the atp and two mg ions into the 
 atp - binding site of 3qd2 ( using 2a19 as the template ) .
the mouse perk - atp complex was then solvated in 
 an octahedral box of tip3p water , with 
 a minimum buffer distance of 14  from the protein surface to 
 the box edge .
structural 
 minimization was applied before running molecular dynamics simulation 
 in order to remove any bad contacts between atoms . during the md simulation , 

the system was heated up from 0 to 300 k in 200 ps with nvt ensemble 
 and then switched to npt ensemble for 10 ns .
positional restraints 
 were applied to the two mg ions , each with a weight of 
 5.0 kcal / mol /  in minimization and 2.0 kcal / mol /  in md simulations .
a representative structure of 
 perk used in docking was obtained 
 using the pairwise average linkage clustering method provided in the 
 maxcluster program .
a total of 450 snapshots 
 ( 20 ps apart ) were taken from the last 9 ns of md simulation .
rmsd 
 of the protein structure was used as the measure of distance between 
 two nodes in clustering , with a threshold value of 1.2  . a total 
 of eight clusters
are generated , and the median structure of the most 
 populated cluster was chosen as the final model structure and used 
 in subsequent docking work .
a hit was 
 defined as a compound 
 demonstrating more than 50% inhibition at 1 m concentration 
 in the perk kinase assay among a small library of 875 known kinase 
 inhibitors .
the remaining 
 860 compounds along with a larger library of 26  365 compounds 
 were then considered as decoys or inactive compounds .
therefore , a 
 library of 27  240 compounds was used in training the virtual 
 screening .
a library of 27  240 
 compounds , including a known kinase - focused library , was processed 
 by docking , using gold5.0.1 and the goldscore scoring function . for each compound , 10 ga runs were performed 
 with a docking efficiency of 100% .
it took approximately 3 days to 
 dock the whole library on 20 2.4 ghz amd opteron cores for each target .
a ligand - based pharmacophore was generated and utilized in pharmacophore 
 mapping using discoverystudio3.5 .
a 3d database of the library was 
 built first , with 255 conformations generated for each compound .
the 
 pharmacophore was generated based on the four most potent hits we 
 found in the experimental high - throughput screening assay .
a number 
 of pharmacophores were first generated based on each of the compounds . 

this 
 led to a five - feature pharmacophore , including two hydrogen - acceptor 
 features and three hydrophobic features .
an additional aromatic ring 
 feature was added manually afterward to mimic the adenine ring of 
 atp .
the most time - consuming part of pharmacophore mapping is the 
 conformation - building step .
mapping the whole 3d database of 27  240 
 compounds ( 255 conformation each ) took only 4 min on a workstation 
 of four 2.4 ghz intel xeon cores .
enrichment is defined as1where percentage of hits means the percentage 
 of the 15 true hits found by docking , while percentage of library 
 indicates the percentage of the total number of compounds in the library .
two other measures , true positive rate and false positive rate , 
 used in the receiver operating characteristic ( roc ) plot in this study , 
 are defined as23 after virtual screening of the zinc database , 50 
 commercially available compounds were purchased and assessed .
perk 
 kinase activity assay was performed in 96-well microplates ( optiplate-96 , 
 perkinelmer las , inc . ) .
the reaction had a total volume of 100 l , 
 containing 25 mm hepes ( ph 7.5 ) , 10 mm mgcl2 , 50 mm kcl , 
 2 mm dtt , 0.1 mm egta , 0.1 mm edta , 0.03% brij 35 , 5% dmso , and 10 
 g / ml bsa .
each reaction mixture was incubated in a 96 well plate 
 at room temperature for 30 min .
the reaction was initiated by the 
 addition of 10 l [ -p ] atp , adjusting the 
 final atp concentration to 10 m .
the reaction was incubated 
 at room temperature for 10 min and then quenched by transferring 80 
 l of reaction mixture to each well of a p81 96-well filter 
 plate ( unifilter , whatman ) containing 0.1 m phosphoric acid .
the p81 
 filter plate was washed with 0.1 m phosphoric acid thoroughly , followed 
 by the addition of a scintillation cocktail . a microbeta trilux liquid 
 scintillation counter ( perkinelmer )
the inhibition of perk activity 
 was determined by measuring initial velocities in the presence of 
 varying concentrations of four compounds .
as noted 
 earlier , the apo perk structure shows a closed g - loop region ( figure 2 ) . superimposing
the md - refined perk structure with 
 the original crystal structure clearly shows that residues gly18 and 
 phe19 in the apo structure block the gate of the atp - binding pocket 
 ( figure 3a ) , and that they were lifted away 
 in the md refined structure .
the rmsd plot of non - hydrogen atoms of 
 phe19 over time ( aligned by backbone atoms ) , for example , also indicates 
 a notable rearrangement of not only the backbone but also the side 
 chain of the residue ( figure 3b ) .
plots of perk : 
 ( a ) comparison of md - refined perk ( yellow surface ) 
 with the crystal structure ( blue ) reveals that residues gly18 and 
 phe19 blocked the gate of the atp - binding pocket .
( b ) rmsd plot of 
 the non - hydrogen atoms in arg16 ( dark ) , gly18 ( green ) , and phe19 ( red ) 
 from the md simulation .
this closure , observed in the apo perk structure , blocks 
 the binding 
 of compounds at the atp - binding site .
a simple illustration is shown 
 in figure 4a , where a number of top ranked 
 compounds from the docking with the apo and model structures are shown . 

the docked compounds in the apo structure appear to be more randomly 
 distributed around the binding site in comparison with those docked 
  deeply  into the model structure as shown in figure 4b .
this simple visual comparison between the docking 
 results of the apo and model structure demonstrates how unreliable 
 the results could be using the apo structure .
a previous study on 
 cyclin - dependent kinase 2 using goldscore and chemscore has demonstrated 
 that the enrichment in docking - based virtual screening is related 
 to the quality of the binding poses predicted , thus it is important to ensure that the docked poses are 
 reasonable in vs. docked compounds in ( a ) the apo perk structure and ( b ) 
 the modeled 
  open  structure .
compounds in apo structure show a 
 more random likely distribution around the atp - binding pocket ( red 
 cycle ) due to the closure of the g- loop region , while the md - refined 
 model structure gives more meaningful docking poses in the pocket 
 in general . to achieve a quantitative 
 understanding of the docking results , 

statistical measures like enrichment factor and roc plot were calculated 
 by comparing the in silico results with the in vitro results .
presumably , if the same docking protocol 
 was used in docking , a better kinase structure would give a better 
 prediction .
thus , we ranked the docked compounds , and at select rankings 
 we calculated the ratio of identified hits to total ( 15 ) hits .
we also calculated the ratio of the decoys to the total number 
 of decoys to obtain the false positive rate ( eq 3 ) .
plotting the true positive rate against the false positive rate 
 gives the so - called roc plot ( figure 5 ) .
a 
 steeper curve in the roc plot indicates a better prediction of the 
 true hits against the decoys .
generally , the modeled perk structure 
 gives a line that is significantly above the line of the apo structure . 

when 50% of the hits are found from the top ranked compounds , only 
 6% of the decoys are picked up using the model structure .
in contrast , 
 22% of the decoys are picked up by the docking using apo structure 
 when 50% of the hits are found , resulting in a 4-fold performance 
 boost with the model structure .
another indicator of the predictive 
 power called area under the curve ( auc ) was also calculated .
the auc 
 was measured to be 0.90 and 0.75 for the model and apo perk , respectively . 

given that a value of 0.5 means a random result with no selectivity 
 and a value of 1.0 for a perfect model , the docking result using our 
 model structure is indeed noticeably better than that of the apo structure .
the roc 
 plot of the docking results using the apo ( red dashed line ) 
 and model ( dark solid line ) structures . the steeper the line , the 
 better predictive power the model has ( a perfect line should have 
 an auc value of 1.00 ) , and the diagonal dark dash dot line indicates 
 random results ( with an auc value of 0.50 ) .
the calculated auc values 
 are 0.90 and 0.75 for the model and apo structures , respectively .
other than the roc plot , the enrichment 
 of the top 20% of docking 
 results is also examined .
the model structure generally doubles the 
 enrichment of the apo structure in the top 20% of the ranked library , 
 i.e. , the chance of finding one of the 15 hits in the top 20% prediction 
 increased twice from the apo to the model structure ( table 1 ) . using the apo structure ,
in contrast , the first hit was captured 
 in the top 60 compounds , and a total of two hits were captured in 
 the top 100 . for comparison , in an earlier virtual screening study using the cocrystal structure of fgfr1 kinase , 
 an enrichment of about 8 was reported when the top 1000 compounds 
 were selected from the docked library of about 40  000 compounds , 
 including 41 actives , which is comparable to our enrichment of 7.3 
 and 9.1 for apo and model structures , respectively , when the same 
 number of compounds are selected , respectively .
our docking results 
 are also comparable with another bench mark study using the dud data set with 
 different scoring functions , including chemscore , chemgauss , and plp , 
 which can identify about 2033% of the true hits within the 
 top 5% of the ranked libraries against kinase targets like abl , egfr , 
 p38 , and vegfr2 .
the docking finds about 27% and 33% of the true hits 
 when about 4% of the ranked compounds in the library are selected 
 using the apo and model structures respectively in our study ( table 1 ) .
the four hits used 
 to generate the 
 pharmacophore model are excluded in statistics . as an alternative 
 to docking ,
a ligand - based pharmacophore model was also generated 
 by using discoverystudio3.5 based on four hits identified in the biochemical 
 assay .
the automated process generated a five - feature pharmacophore 
 with two hydrogen acceptor features and three hydrophobic features . 

an additional aromatic ring feature was added manually for the purpose 
 of mimicking the adenine region of atp , making it a six - feature pharmacophore 
 ( figure 6 ) .
this is similar to a generic kinase 
 inhibitor pharmacophore reported , which 
 divided the atp - binding site into five regions , the adenine region , 
 the sugar pocket , the phosphate binding region , and the hydrophobic 
 regions i and ii .
for each screened compound , the highest fit value 
 among the 255 conformations was assigned as the final fit value .
compounds 
 with fit values of less than zero were considered as inactive , thus 
 were ignored .
this collected 5138 compounds , which represents roughly 
 20% of the whole training library .
it is noted that among the 5138 compounds , 
 9 out of 11 hits ( the four hits used to generate the pharmacophore 
 are excluded ) were found , which is comparable with the docking result 
 ( table 1 ) .
the corresponding roc plot is shown 
 in figure 7 as  pharmacophore mapping ;  
 the top 20% of docking results using our model structure is also presented 
 in the same plot as  docking using model structure .
the auc value was 
 measured as 0.09 and 0.09 for curves of pharmacophore and docking , 
 respectively .
note that the x - axis in this plot was 
 truncated at 0.2 , thus an auc value of 0.2 corresponds to a perfect 
 model while a value of 0.04 represents random results .
this suggests 
 that pharmacophore mapping and docking performed equally well in this 
 particular study .
a ligand - based six - feature pharmacophore manually overlaid 
 within 
 the perk active site ( cartoon in yellow ) .
blue spheres represent hydrophobic 
 features , and a brown arrow with a green base means an aromatic ring 
 feature .
note that the 
 four hits employed to generate the pharmacophore model are removed 
 in all data .
the green dotted lines represents the consensus model of pharmacophore 
 mapping and docking , and the dark dotted dash line is the random reference . 

the respective auc value for each line is 0.09 , 0.09 , 0.11 , and 0.04 
 while a value of 0.20 is for a perfect model and 0.04 for random selections .
it has been suggested that utilizing 
 the ligand - biased receptor - based 
 virtual screening could lead to better enrichment if a cocrystal structure 
 is known .
other studies also suggested 
 that using combinations of docking and similarity - based approaches 
 can increase the enrichment of vs. thus , we explored 
 the potential of a pharmacophore - based approach to facilitate a receptor - structure - dependent 
 docking method .
this combination yielded 
 a slightly better result than using either docking or pharmacophore 
 mapping alone .
this is supported by a bigger auc value of 0.11 , as 
 well as a roc curve that is always above the curves of either docking 
 or pharmacophore mapping ( figure 7 ) . to capture 
 50% of the hits ,
only 3% of the decoys were picked up in the combined 
 approach while 6% of decoys were picked to obtain the same amount 
 of hits in structure - based docking .
additionally the first captured 
 hit was among the top 40 compounds while the first hit in docking 
 was found in the top 60 compounds . comparing the consensus approach 
 with the pharmacophore mapping
, 
 it is noted that the former has significantly moved the 15 true hits 
 higher in ranking .
for instance , the enrichment obtained in the consensus 
 approach is generally 50100% more than that obtained in pharmacophore 
 mapping ( table 1 ) .
however , since the pharmacophore 
 model was generated based on four hits , there is likely a bias toward 
 these compounds in pharmacophore - based screening . in order to make 
 a fair comparison , we thus removed all four hits in the result and 
 calculated the enrichment again . not surprisingly ,
even if we excluded 
 the four hits from the result of the consensus approach , there was 
 still one hit left in the top 50 compounds .
however , neither docking 
 nor pharmacophore model happen to predict any hit at this range .
furthermore , 
 if we look at the top 1000 , 2000 , or 4000 ranked compounds by vs , 
 the consensus approach always returns more hits than the other two 
 approaches .
therefore , the consensus approach of combining a pharmacophore 
 model with structure - based docking can be a better choice than using 
 either approach alone , and a better enrichment can be expected .
the 
 possible reason behind this may be due to the fact that docking and 
 ligand - based pharmacophore approaches explore different chemical / physical 
 spaces , i.e. docking depends on the structure of a receptor as well 
 as the ligands while the pharmacophore is solely dependent on the 
 ligand .
if designed carefully , the two approaches could complement 
 each other in a consensus scheme .
on the basis of the 
 success of our virtual screening protocol in the training library , 
 we then applied it to screen the zinc lead - like database , which includes 
 about 3 million lead - like compounds .
we first imported the database 
 into discoverystudio , which then generated a conformational library 
 consisting of 255 conformations for each compound in the database . 

subsequently , all conformations of the 3 million compounds were screened 
 by mapping to the pharmacophore .
the highest scored conformation for 
 each compound was selected and then used to rank the 3 million compounds . 

the top 10  000 compounds were then selected and subjected to 
 structure - based docking .
the 
 top 10% of the docked compounds , i.e. the top 1000 , were then further 
 profiled by clustering them into 100 clusters in order to filter out 
 similar compounds in the library .
we anticipate this may increase 
 the chemical diversity in a smaller pool of selections .
the center 
 compound of each cluster was selected as the representative compound 
 for that cluster .
another possible advantage of this is that if any 
 center compound shows promising activity , we could come back and investigate 
 more compounds in that cluster .
we purchased 50 commercially 
 available compounds out of the 100 representative compounds and then 
 tested them in a kinase assay .
for each 50 l well , there were 
 20 nm of active perk , 5 m of eif2 , 25 m of the 
 compounds , and 10 m of atp .
the initial assay shows 10 active 
 compounds exhibiting more than 50% inhibition ( table 2 ) .
all 10 compounds fit our pharmacopore well . as an example , 
 the overlay of compound 6 with the pharmacophore is shown in figure 8 .
the ic50 of two of the compounds is less 
 than 10 m ( 2.6 and 8.7 m , respectively ) .
the assay condition 
 is set to 
 have , for each 100 l well , 20 nm of active perk , 5 m 
 of eif2 , 25 m of the compounds , and 10 m of radiolabeled 
 atp .
the 
 docking - based virtual screening method normally requires a 
 quality crystal structure , which may not always be available . in the 
 case of perk ,
the only available crystal structure , at the time of 
 our study , was an apo structure .
the structure presents a closed g - loop 
 region at the atp - binding site of the kinase , which hinders the docking 
 of the inhibitors into the atp - binding site .
we examined two approaches 
 that can potentially resolve the issue . in our first approach , by 
 using another kinase pkr as a template
, we artificially lifted the 
 g - loop so that the atp - binding site could accommodate an atp molecule . 

md simulation was applied to relax the system to obtain a model cocrystal 
 structure of perk with an 
applying 
 the model structure in virtual library docking yielded a significantly 
 improved enrichment , generally twice the enrichment of using the apo 
 crystal structure .
this in turn suggests that a well - modeled protein 
 structure can be a better target than an inadequate crystal structure 
 in structure - based docking .
the other approach we investigated 
 was the ligand - based pharmacophore 
 mapping . on the basis of the four inhibitory compounds found in experimental 
 kinase assays ,
a six - feature pharmacophore was built using the ligand - based 
 pharmacophore generation module in discoverystudio3.5 . using the pharmacophore 
 mapping method to screen the same library shows a similar performance 
 to the docking method in terms of enrichment .
however , in regards 
 to efficiency , pharmacophore mapping is 1000 fold faster than docking , 
 given the fact that docking of about 27  000 compounds took 
 about 3 days using 20 2.4 ghz amd opteron cores , while pharmacophore 
 mapping ( with a prebuilt conformational database of all the compounds ) 
 only took 4 min on four 2.4 ghz intel xeon cores . reranking the pharmacophore 
 mapping results using the docking scores
the reranked result 
 generally predicts more hits in the upper region of the ranking list , 
 thus showing a higher probability of finding a hit in a smaller number 
 of the ranked compounds .
the improvement though is not enormous , yet 
 notably enough to validate our argument .
the limitation of the pharmacophore 
 approach , however , is that some known inhibitors must be available . 

this may be achievable by acquiring information from the literature 
 or by performing experiments , but within an affordable scale . with 
 the huge saving in resources and the competitive accuracy ,
the pharmacophore 
 approach can serve as a cost - effective predocking filter for virtual 
 library screening . upon our preliminary study of the combination 
 of docking and pharmacophore 
 modeling
, we proposed a consensus virtual screening approach which 
 uses pharmacophore mapping as a fast filter to generate a much reduced 
 compound pool for docking , then makes the final decision based on 
 the docking result ( figure 1 ) .
this consensus 
 approach was then applied to screen the zinc lead - like database , which includes about 3 million compounds . on the basis of the vs 
 using the consensus approach
ten out of 50 compounds show activity while two exhibit 
 an ic50 of less than 10 m , which further provides 
 validity of this consensus approach .
we anticipate that more potent 
 compounds , i.e. subnanomolar ic50 , may be found if a more 
 kinase specific library was screened .
 perk , as one of the principle unfolded 
 protein response signal 
 transducers , is believed to be associated with many human diseases , 
 such as cancer and type - ii diabetes . 
 there has been increasing effort 
 to discover potent perk inhibitors due to its potential therapeutic 
 interest . in this study , 
 a computer - based virtual screening approach 
 is employed to discover novel perk inhibitors , followed by experimental 
 validation . using a focused library , 
 we show that a consensus approach , 
 combining pharmacophore modeling and docking , can be more cost - effective 
 than using either approach alone . 
 it is also demonstrated that the 
 conformational flexibility near the active site is an important consideration 
 in structure - based docking and can be addressed by using molecular 
 dynamics . 
 the consensus approach has further been applied to screen 
 the zinc lead - like database , resulting in the identification of 10 
 active compounds , two of which show ic50 values that are 
 less than 10 m in a dose  response assay . 
Introduction
Method
Result
Conclusion