computational methodologies utilized for 
 in silico high throughput 
 screening ( hts ) are a critical component of drug discovery approaches . within the available in silico hts approaches , methodologies that 
 combine ligand- and structure - based screening procedures find the 
 widest application . the challenge in any hts virtual 
 screening ( vs ) platform is to develop an algorithm that is sufficiently 
 fast and robust to evaluate many compounds while maintaining sufficient 
 accuracy to identify a subset of biological active compounds ( i.e. , 
 hits ) that have diverse structural scaffolds ( i.e. , scaffold - hopping ) . we sought to employ in silico screening to evaluate the repurposing 
 of current drugs for a new therapeutic target . drug - repurposing 
 maximizes the potential value of each hit by screening well - known 
 compounds that have minimal toxicity and/or few side - effects . comparative studies of well - established ligand- and docking - based 
 approaches concluded that shape - based ligand screening yielded markedly 
 better outcomes than protein docking schemes . a ligand - based computational 
 method involved two essential elements :   an efficient similarity 
 measure and   a reliable scoring method . the similarity measure 
 varied among different methods and focused on three factors : pharmacophores , 
 molecular shapes , and molecular fields . the molecular - shape approaches 
 maximized the overlap of shapes and determined a similarity value 
 based on the degree of shape overlap . over the years , despite the 
 investment made in developing scoring functions for molecular - shape 
 approaches , none possessed accuracy and general applicability . consequently , 
 investigators turned to the consensus - scoring technique that improved 
 the probability of finding solutions by combining the scores from 
 multiple scoring functions or using different reference molecules . we recently developed an efficient 3d shape - based similarity 
 algorithm 
 encoding the consensus molecular shape pattern of a set of active 
 ligands into one descriptor , called the 4d fingerprint ( figure 1 ) . the 4d fingerprint formalism was originally proposed 
 by hopfinger and co - workers and developed the quantitative structure the 4d - qsar 
 model estimates molecular similarity measures as a function of conformation , 
 alignment , and atom type . the resulting 
 descriptors values were the occupancy measures for the atoms in the 
 investigated set of bioactive molecules . while the similarity measures 
 achieved excellent predictions for a variety of enzyme inhibitors , the weakness of this approach lies with the occupancy measures for 
 the atoms ( or pharmacophoric groups ) which may also be present in 
 similar , the resulting 4d fingerprint encoded in the 3d shape of the candidate 
 ligand b is docked and ranked using the hwk scoring function . the application of the 4d fingerprint 
 to the ligand b decreases the interaction 
 ( purple arrow ) with the receptor . the 4d fingerprint approach implemented in the shape - approach - based 
 routines enhanced or sabre program possessed a number of 
 attractive advantages over other vs methods . first , it depended explicitly on 3d shape , not on the underlying 
 chemical structure , and thus it excelled in identifying novel chemical 
 scaffolds based on a set of known active ligands ( scaffold - hopping ) . the iterative 4d fingerprint approach was particularly robust for 
 several reasons : ( i ) the 4d fingerprint descriptors were very sensitive 
 to the details of molecular shape of active ligands , reducing the 
 need to use multiple conformers of multiple query structures ; ( ii ) 
 the method excel by the incorporation of the spatial distributions 
 of chemical features of similar inactive ligands during the optimization 
 and screening procedures ; ( iii ) the algorithm was fast and had the 
 ability to scan a library of millions of compounds in a matter of 
 hours . the method unified ligand- and structure - based 4d fingerprint 
 vs approaches by docking the shape filtered ligand structures into 
 the receptor - binding cavity . finally , running searches using this 
 methodology was remarkably easy and required only that the end - user 
 supply a query structure and runtime parameters to control the number 
 of hits that were returned . despite these advantages , the 4d fingerprint 
 method , as previously reported , suffered from a weakness in the empirical hwz scoring function for ranking 
 and selecting the active ligands from large databases . to remedy this 
 deficiency , we modified the shape - based vs algorithm of the sabre 
 program and implemented a new , robust scoring function that accommodated 
 the diversity of ligand scaffolds with an accuracy that exceeded our 
 prior efforts . tuberculosis ( tb ) is a chronic and complex disease 
 resulting from 
 infection with the bacterium mycobacterium tuberculosis . tb remains an important public health problem worldwide , with 8.6 
 million estimated cases and 1.3 million deaths attributed to the disease 
 in 2012 . in order to combat the spread 
 of tb it is necessary to identify new molecular targets for tb 
 drugs and develop new , more efficient , methods for screening ligands 
 as potential drug candidates than methods used in the past . historically , 
 high - throughput screening ( hts ) approaches coupled with in vitro testing 
 served to identify promising hits with anti - tb activity . while successful 
 in some cases , the hts approach frequently failed in the antibacterial 
 drug discovery area due to the poor admet properties and insufficient 
 or improper molecular diversity of the compounds screened . the mycobacterial esx secretion system , also referred to as the 
 type vii secretion system , represents a promising , new target for 
 tb drug development . the esx secretion system is a 
 specialized system unique to mycobacteria that secrete a large number 
 of proteins necessary for m. tuberculosis virulence . each esx secretion system includes a membrane - associated 
 subtilisin - like protease , called the mycosin : mycp1mycp5 ( numbered according to gene cluster ) . mycp1 from 
 the esx-1 system hydrolyzes the esx associated protein b ( espb ) during 
 secretion , and this processing affects virulence in a mouse 
 model of tb infection . the recent description 
 of the molecular structure and substrate specificity of mycp1 prompted interest in mycp1 as a promising target for 
 structure - based drug design . recently , we applied the combined 
 ligand- and structure - based virtual 
 screening procedure and 4d fingerprint algorithm to identify new inhibitors 
 for mycp1 protease . the study 
 reported here extends our previous work and reports a rational approach 
 for ranking the ligand databases and demonstrates the performance 
 of the novel hwk scoring function using 81 targets 
 from the dekois database . validation 
 of the efficiency of the vs method for scaffold - hopping and drug repurposing 
 involved the application of this methodology for the identification 
 of diverse inhibitor scaffolds against mycp1 protease and 
 experimental testing of some of these scaffolds in an in vitro enzyme 
 assay . we have recently developed a ligand and structure 
 shape - based vs 
 algorithm implemented within the sabre program . unlike other ligand - based shape overlapping methods , our approach efficiently detected the key pharmacophore groups of 
 the active ligands responsible for binding to the target . the main 
 advance of our methodology resided in the consideration of  virtual  
 but similar inactive structures ( decoys ) during the consensus molecular - shape 
 detection process ( figure 1 ) . after similarity 
 scoring , the selected structures were ranked according to their shape 
 complementarity in the receptor - binding site . this report highlights 
 the major steps of the algorithm and describes the approach used for 
 the development of this scoring function . the molecular 
 shape density function (r ) of a ligand is expressed in terms of the shape density 
 functions of individual atoms and their overlap1 in which each atom i with 
 coordinates ri = ( xi , yi , zi ) is described by a spherical gaussian:2where 
 i is the van der waals radius of 
 the atom i. the 
 molecular volume v of the ligand is defined as3the volume vi of an atom i is4the intersection volume of atom pairs is defined 
 as5the overlap volume of two molecules a and 
 b is defined as6 in the sabre algorithm , the shape - density 
 model is enhanced and defined as a linear combination of weighted 
 atomic gaussian functions . thus , the molecular shape - density is the sum of all individual weighted 
 pharmacophore densities , and the molecular volume is defined as7where vkpharm  k = idr ki(r ) is 
 the partial volume of the pharmacophore group k and 
 is defined as a linear combination of atomic gaussian functions . the 
 optimal coefficients { ck } are determined by iteratively adjusting the coefficients of the 
 set of known active ligands { a } in the 
 presence of virtual decoy structures { b } ( virtual decoys are inactive similar compounds that are not necessarily 
 synthetically feasible or identified in the previous vs rounds ) until 
 they satisfy these two criteria:7athis 
 algorithm quickly builds a consensus 
 molecular - shape pattern in which the optimal coefficients { ck } define a 4d fingerprint 
 of the entire set of active ligands that also effectively excludes 
 structurally similar , but inactive ligands ( decoys ) ( figure 1 ) . given a set of known active ligands { ai } with volumes v{ai } and a query structure a with the volume va , we effect the shape - filtering and ranking 
 the candidate molecule b with volume vb . during 
 the vs process , we observe two trends : ( i ) vbi  va or ( ii ) vbi  va . as a result , we can rank the structure b according to either the condition ( i ) , ( ii ) , 
 or the combination of ( i ) and ( ii ) for two different ligands b and b. thus , we have two possible outcomes:(1)the volume of the 
 candidate structure 
 b is smaller than the volume of the query a : vbi  va . the maximal overlap volume vabi  for the two structures is restricted 
 as vabi  vbi and rewritten as8(2)the volume of the structure b is larger than the volume of the query a : vbi  va and vabi  va is rewritten as 9(3)we illustrate this case by considering 
 two candidate structures with vbi  va and vbj  va . for clarity we consider that they have the same overlap volume 
 with the query structure and consequently , vabi  vabj . adding eqs 8 and 9 givesand we obtain 
 the tanimoto scoring function:10we rewrote eqs 8 and 9 as smooth gaussian distributions 
 and defined the 
 scoring function hwk ( hamza  wei  korotkov ) 
 that converges to one for optimal similarity:1112the tanimoto function and eqs 11 and 12 clearly reveal that the ranking 
 of the candidate structure b is determined 
 by several inhomogeneous criteria . for a fixed overlap volume , eq 11 gives the highest score for the ligand b with a smallest size even if it possess less similar 
 chemical features than other ligands with larger volume sizes . second , 
 the vabi term takes into 
 account the overlap volume of the full ligand size instead the volume 
 of the key chemical features present in both the query a , and candidate ligand b. this result 
 in a higher ranking for the ligand b with 
 largest size since the overlap volume varies with the full size of 
 the ligand . third , we recently demonstrated the weakness of the tanimoto 
 scoring function when used for filtering the 3d shape of the ligands 
 and found that the tanimoto function only efficiently ranks the ligands 
 with comparable volume size to the query . the volume of the 
 candidate structure 
 b is smaller than the volume of the query a : vbi  va . the maximal overlap volume vabi  for the two structures is restricted 
 as vabi  vbi and rewritten as8 the volume of the structure b is larger than the volume of the query a : vbi  va and vabi  va is rewritten as 9 we illustrate this case by considering 
 two candidate structures with vbi  va and vbj  va . for clarity we consider that they have the same overlap volume 
 with the query structure and consequently , vabi  vabj . adding eqs 8 and 9 gives these drawbacks can be overcome by taking into account the 
 specific 
 atom - type information , such as the consensus molecular shape pattern 
 or according to the 4d fingerprint approach ( eq 7 ) , the volume of the query a and the set of active 
 ligands { ai } are defined as13the optimization of the 
 coefficients { ck } leads 
 to the residual volume v  vapharm , and the value 
 of the vapharm term in eq 7 ranges 
 across the interval [ min v{a}pharm ; 
 maxv{a}pharm ] . in the following 
 demonstration , we assumed that the candidate b is an active ligand and similar to the query a in the set of active ligands { ai } , since 
 if b is dissimilar the 
 overlap volume converges to a small value . we haveusing the 
 computed 4d fingerprint coefficients , 
 the volume of b is written as14the volume of b is defined by eq 7 and is equals to 
 the sum 
 of the weighted partial volumes of the key pharmacophore groups and 
 its volume size is in the interval of the set of active ligand volumes v{a}*. three possible scenarios exist:(1)vb *  va*in this case , vb *  
 [ min(v{a } * ) ; va * ] and the optimal 
 similarity value is reached for(2)vb *  va*in this 
 case , vb *  
 [ va * ; max(v{a } * ) ] and the optimal similarity is reached 
 for(3)according to the eqs 13 and 14 and the 4d fingerprint coefficients , 
 the tanimoto scoring function is written as t = vab/(vb * + va *  vab ) withthe optimal similarity is reached for t = vb*/va *  t = va*/vb *  1 . in this case , vb *  
 [ min(v{a } * ) ; va * ] and the optimal 
 similarity value is reached for in this 
 case , vb *  
 [ va * ; max(v{a } * ) ] and the optimal similarity is reached 
 for according to the eqs 13 and 14 and the 4d fingerprint coefficients , 
 the tanimoto scoring function is written as t = vab/(vb * + va *  vab ) with as a result , 
 the candidate ligands with a volume size slightly 
 smaller or larger than the query volume are ranked equivalently when 
 using the 4d fingerprint method with the tanimoto equation . finally , 
 we observe that the weighting coefficients of the 4d fingerprint adjust 
 and group the unknown  active  candidate 
 structures with miscellaneous volume sizes and scaffolds into three 
 classes relative to the query size . this confers the advantage of 
 ranking more effectively these three types of shape using the hwk scoring function ( hwk , hwk , hwk ) . the docking approach described 
 in our previous work combined the performance and speed of the ligand - based 
 4d fingerprint method with the shape characteristic of the receptor 
 binding site . the current sabre docking 
 algorithm encodes both the 4d fingerprint and the novel hwk scoring function , and it generates alignments where patterns with 
 similar binding character are oriented in a similar fashion in the 
 binding site of the receptor ( figure 1 ) . during 
 the rigid docking process , the sabre program takes into account only 
 the pharmacophore groups present in the vb(vb * ) that interact in designated ways with key receptor atoms . five important chemical features were assigned to an atom type : hydrogen 
 bond donor , hydrogen bond acceptor , acidic center ( negatively charged 
 at physiological ph 7 ) , basic center ( positively charged at ph 7 ) , 
 and metal - chelation . the main novelty of the sabre docking approach 
 is that the pairwise interaction between the key pharmacophoric groups 
 ( defined by the 4d fingerprint , figure 1 ) of 
 the ligand and the receptor atoms are calculated using the gaussian 
 function gij . the pairwise 
 interaction of atoms i ( ligand atoms ) and j ( receptor atoms ) is defined as15where deqtype is the standard 
 distance 
 between the heavy atoms i and j for 
 each  type  of interaction ( i.e. , hydrogen bond interaction , 
 electrostatic interactions ) and di , j is the distance between the two atom centers . the parameter 
  is a freely adjustable parameter and controls 
 the distribution of the gaussian function . the parameter  controls the weight of the interaction type and depends 
 on the 4d fingerprint coefficients . the pairwise interaction 
 is attractive for  > 0 and repulsive for 
  < 0 . the total of n pairwise 
 interactions 
 between the pharmacophoric groups of the ligand and the key receptor 
 atoms is defined by the geometric mean gi , jtotal(di , j ) as16thus , the combination of the total pairwise 
 interactions gi , jtotal(di , j ) and eqs 1012 takes into account both the 4d fingerprint and 
 the key interacting pharmacophoric groups of the ligand and leads 
 to improved enrichment of the vs process . the hwkdock scoring function of the sabre docking method is summarized 
 by the three equations:171819it is interesting 
 to note that a ligand with 
 high similarity score ( eqs 1012 ) is reranked with a lower score if its chemical 
 features are close to repulsive receptor atoms . therefore , our scoring 
 strategy developed in the docking method combines the fast and efficient 
 ligand - shape - based 4d fingerprint vs with an extremely quick calculation 
 of the interactions between the ligand pharmacophoric groups and the 
 key receptor atoms . in addition , we observe in figure 1 that the interaction ( purple arrow ) involving the pharmacophore 
 groups present in both active and decoy structures become negligible . analysis of the hwkdock function ( eqs 1719 ) highlights a new 
 strategy for improving scaffold - hopping and drug repurposing performances . 
 during the vs campaign , the shape size of the query is fundamental 
 and orients the choice of the scoring equations . thus , if the shape 
 of the query is small and does not completely fill the receptor binding 
 cavity , the hwkdock+ is appropriate to identify structural hits 
 with either comparable or larger volume sizes than the query volume . however , the hits with volume sizes ranging in the interval [ min(v{a } * ) ; va * ) ] of the set of active ligands 
 ( eq 14 ) are also ranked with a high hwkdock+ score . in contrast , if the shape of the query complement the receptor 
 binding cavity , the hwkdock is better suited to identify 
 hits with smaller volume sizes while keeping high overlap volume with 
 the pharmacophore groups of the query . finally , the hwkdocktanimoto is effective to identify hits with comparable query shape ( i.e. , rather smaller or larger structural sizes that fit 
 into the receptor binding cavity while retaining structural diversity ) . 
 consequently , three different classes of hits emerge based on the 
 equations of the hwk function that selected them . 
 in each list , the compounds are first ranked according to query similarity 
 using the 4d fingerprint approach , and the diversity is achieved by 
 selecting compounds ranked highly using one of these scoring equations . the dekois ( version 2.0 ) database 
 of annotated active compounds and decoys was used to validate the hwk scoring function . the dekois 
 database is a publicly available vs test database consisting of 81 
 targets . for our purposes , the ratio of the number of decoys to the 
 number of active ligands was fixed at 30 . we used the dekois database 
 instead of the 40 targets of the dud database for our vs test in order 
 to measure the robustness of the scoring function when screening a 
 large number of targets . this is one of the most commonly encountered 
 measures for estimating prediction accuracy of vs algorithms . the effectiveness of the sabre program was evaluated using the enrichment 
 factor ( ef ) metric at a given percentage of the database screened , and the area under the roc ( receiver operator characteristics ) . 
 to test the efficiency of the hwk scoring function 
 defined by eqs 1012 , we screened each target 10 times using a different set of five 
 randomly selected active ligands ( as templates ) and reported both 
 the highest performance ( roc auc value ) and enrichment factor ef at 1% for the 81 targets . screening results using the 
 empirical hwz scoring function were reported for 
 comparison . for each screening test , the five template structures where first removed from the list of 
 active ligands . the detailed vs procedure was described 
 in our previous work and summarized in 
 the supporting 
 information ( supplementary figure si-1 ) . briefly , the 4d fingerprint 
 algorithm defined in the 3d - shape - based similarity method of sabre 
 was used as the first filter of the nci ( national cancer institute ) 
 from the nci open database compounds ( release 4 , 265  000 
 structures ) and fda ( food and drug administration , 1217 compounds ) 
 database downloaded from the zinc database . it is important to note that the 4d fingerprint was generated using 
 both the previously identified leads ( active ligands ) and inactive 
 compounds ( considered as decoys ) . the 
 multiple conformation states of each ligand in the database were generated 
 using omega ( openeye scientific software ) . thereafter , 
 we utilized the  docking option  of the sabre program 
 to place the filtered conformations of each ligand into the active 
 site of the mycp1 ( pdb i d : 4hvl ) and ranked them using the hwk scoring function . according to eq 14 , the candidate b structure is similar to the set of known active 
 ligands { ai } if vb *  [ min(v{a } * ) ; max(v{a } * ) ] and vb * the volume vapharm is defined by the 4d fingerprint coefficients { ck } that encoded the chemical features 
 of the consensus molecular shape pattern of known active ligands ( eq 7 ) for the specific binding target . therefore , this 
 suggests that the best fitted and most highly ranked ligands from 
 the vs of the database have similar 4d fingerprint coefficients and 
 thus should interact with the receptor of the known active ligands 
 ( concept of the drug - target profile ) . it is important to note that 
 this approach considers only the 4d fingerprint and the fast - fitting 
 method implemented in the sabre program . to validate the effectiveness 
 of sabre for drug repurposing , we conducted a ligand- and structure - based 
 vs procedure using the fda database . recombinant mycobacterium 
 thermoresistibile mycp1 was 
 expressed and purified as reported previously . a quenched , fluorescent peptide assay was used to measure 
 the activity of mycp1 in the presence of inhibitors . mycp1 was used to digest 20 m of the fluorescent substrate , 
 abzavkaaslgk(dnp)oh ( genscript inc . ) . potential mycp1 inhibitors 
 identified by sabre were diluted to a concentration of 150 m , 
 and assays were measured in 96-well format . compounds that were considered 
 hits showed less than 50% activity compared to controls ( dmso - buffer 
 blank ) . the same in vitro assay was used to measure the inhibitory 
 concentration 50% ( ic50 ) of the most promising hits . for 
 ic50 measurements , inhibitors were added at 0 , 5 , 10 , 50 , 
 100 , 200 , 350 , and 500 m concentrations . we evaluated 
 the accuracy of the hwk scoring function 
 and compared the current values to those obtained with the empirical hwz scoring function using 
 the multi conformational states of the decoys and active ligands of 
 the 81 targets from the dekois database . the merits of scoring function 
 became clear as it accurately ranked compounds with subtle structural 
 changes . in the present vs trial , we selected query molecules according 
 to the procedures presented in kirchmair et al . and used to describe the performance of the algorithms . as shown in figure 2 , we evaluated 
 the auc for each target of the dekois database and the average auc 
 using the sabre and sabre scoring functions . the average auc value of the best performing query for the 81 dekois 
 targets using hwk and hwz scoring 
 functions was 0.875  0.054 and 0.851  0.054 , respectively . the two scoring functions had similar overall performance for some 
 of the 81 targets based on the auc metric ; however , an analysis of 
 the complete set of targets revealed that sabre performed 
 more consistently in terms of auc with an average auc  0.9 
 for 26 targets and 0.9 > auc  0.8 for 51 targets than sabre . moreover , sabre did not fail for any of 
 the 81 targets screened . in comparison , sabre ranked 
 the screening results with an auc > 0.9 for only 17 of the 81 dekois 
 targets ( 11 targets out of 81 have auc  0.8 ) . one of the advantages of the sabre approach 
 is that the vs performance combining the 4d fingerprint and the novel hwk scoring function depended less on the screened targets , 
 as already observed in our previous benchmark tests using the hwz function with the 40 dud targets . comparison of the areas under the roc curves 
 ( auc ) of the 81 dekois 
 databases using the sabre and sabre scoring 
 functions . the 
 efficiency of the sabre scoring function was evaluated 
 using the enrichment factor at 1% 
 ( ef ) , and the results were also compared to those using 
 the sabre function ( figure 3 and table si-1 ) . the average ef values 
 for the 81 targets using the novel hwk and empirical hwz score - based virtual screening were 21.8  5.0 and 
 15.5  5.9 , respectively . the sabre method performed 
 more consistently resulting in an ef less than 10% for 
 only two targets , whereas the results using the sabre method provided enrichment factors below 10% for 24 targets . thus , 
 the enrichments achieved with sabre are considerably 
 better than those obtained with the empirical hwz scoring function , indicating that the novel scoring function was 
 more efficient in identifying hits with notably different scaffolds 
 compared to the query structure . therefore , on the basis of the auc 
 and enrichment factor ef values , these results indicated that the 
 novel hwk score demonstrated an improved and robust 
 vs performance , albeit with the caveat that we used only 81 targets 
 in this study . comparison of the enrichment factor ef at 1% of the 81 
 dekois databases 
 using the sabre and sabre scoring functions . the sabre program 
 was generally applicable for ranking any bioactive 
 scaffold classes with the exception of inactive decoys . the recognition 
 of a wide variety of structurally different ligand classes was an 
 important goal of our virtual screening strategy . the mycp1 protease represented a challenge for both ligand- and structure - based 
 virtual - screening approaches . indeed , only the crystal structure of 
 the apo form of the enzyme was available , and the protein active site 
 is relatively large , which decreased the probability of successfully 
 identifying and ranking the correct pose of the screened ligands . the structures of mycp1 inhibitors that we previously reported 
 were available , and visual analysis of their putative poses in the 
 active site revealed that the binding mode of compound 1 was reasonable . these compounds have 
 chemical features that enabled sar ( structure  activity relationship ) 
 studies and generated a novel 4d fingerprint . for the purpose of sar , an intuitive strategy for scaffold - hopping used the hit compound 1 as query and the hwkdock+ scoring function to rank hits 
 with larger volume sizes from the nci database . we constructed such 
 a ranking of the best 1000 structures ( top-1000 ) according to the 
 docking - score function . the pharmacophore model reduced the number 
 of these structures to a small subset of promising mycp1 lead candidates . the 135 hits derived from the nci database were 
 superimposed with the binding query ( compound 1 ) and 
 visually inspected . forty molecules were selected from the hits and 
 tested in vitro for inhibitory activity against mycp1 . notably , 9 compounds out of the 40 were able to inhibit mycp1 by more than 50% when added at 150 m ( table 1 and figure 4 ) and one compound showed 
 an ic50 less than 100 m . compound 2 inhibited mycp1 activity in the low micromolar range 
 with an ic50 of 76.8 m and does not includes substructures 
 described as pan assay interference compounds ( pains ) . structural scaffold of mycp1 inhibitors identified 
 during 
 the vs of the nci database . pan assay interference compounds 
 ( pains ) remover , see ref ( 64 ) . the vs procedure 
 identified these compounds based on their common 
 chemical features ( 4d fingerprint ) present in the subset of known 
 active ligand structures and their fit in the binding pocket . the 
 coefficients of the 4d fingerprint efficiently encoded the spatial 
 distributions of pharmacophoric points providing the alignment of 
 compounds relative to the binding site surfaces . each point accounted 
 for an important chemical feature such as hydrogen bond donors / acceptors 
 and negative / positive charged groups . the basic physicochemical features 
 of the known mycp1 compounds included the potential to 
 establish hydrogen bonds as donors with thr156 and ser202 , glu203 
 and thr333 residues . furthermore , 
 analysis of the docking results revealed that the lead compound 2 fit well within the binding site cavity . the compound 2 formed hydrogen bonds with the thr156 , thr333 , ser202 , and 
 glu203 residues of mycp1 . the results were in agreement 
 with our previous docking studies pointing out ser202 and thr156 as 
 key residues to stabilize the ligand scaffold in the mycp1 catalytic binding site . a detailed 
 analysis of the docking mode of the 11 compounds ( figure 4 ) revealed a close match between the pattern of 
 hydrophobic and hydrogen bond donor pharmacophoric points of these 
 hits compared to the pharmacophore model defined in our previous study . stick view of the binding compound 2 ( nsc-67021 ) 
 and 11 ( hydroxystilbamidine ) in the mycp1 active 
 site . as shown in table 2 , the vs procedure ranked 
 9 lead compounds at different cut - offs among the initial 1000 docked 
 structures . among the top-30 , one lead compound was present , and this 
 outcome corresponded to 11% coverage . furthermore , the 9 lead compounds 
 were among the top-300 of the filtered nci database . these results 
 highlight the merits of our 4d fingerprint vs approach when combined 
 with the novel hwk scoring function . we also compared 
 this simple approach to a complex approach including other likely 
 query conformations . we modeled three plausible binding poses of the 
 compound 1 ( query ) with different conformations in mycp1 cavity and redocked the top-1000 ligands using these three 
 conformations , as shown in table 2 . the fusion 
 approach markedly improved the percentage of retrieved lead compounds 
 in the top-75 and further underscored the potential of the 4d fingerprint 
 and hwkdock scoring vs procedure in the 
 identification of lead compounds using the structure of unliganded 
 receptor . % coverage of leads = ( number of 
 leads in the top / total lead )  100 . the integration of this newly 
 generated computational method , which combined the 4d fingerprint 
 and the hwk scoring function with in vitro enzyme 
 inhibition studies , was a useful approach for evaluating current drugs , 
 already on the market for a particular therapeutic purpose as potential 
 agents for treating tb . to demonstrate the applicability of this integrated 
 virtual and experimental screening for drug - repurposing , we undertook 
 the virtual screening of the fda - approved drug database consisting 
 of 1,217 compounds ( corresponding to 3358 structures including tautomers ) 
 using the 4d fingerprints previously generated during nci database 
 screening . hits were evaluated using the aforementioned mycp1 enzyme assay . in order to increase the structural diversity of the 
 compounds identified by this process , we conducted the vs procedure 
 three times using compounds 1 , 8 , and 9 as query for each vs round . the choice of the structural 
 query was critical to the success of this approach . as described in methods , the scaffold diversity depended on the 
 selected hwk or hwk or hwk ( tanimoto ) scoring 
 equations , which also depended on the query size . thus , compound 1 , discovered in our previous work , was used as query since 
 it has the highest affinity to mycp1 . the compounds 8 ( larger volume than that of compound 1 ) and 9 ( smaller volume than that of compound 1 ) were 
 selected based on their differential volumes and structural diversity 
 compared to the structure of 1 . the goal of our screen 
 was to find hits with diverse structural scaffolds and comparable 
 volume sizes to the queries . thus , the resulting docked ligands of 
 the fda database were ranked using the hwkdocktanimoto scoring 
 function ( eq 19 ) . since the screening 
 process of the nci database and the benchmark test using the 4d fingerprint 
 of sabre program demonstrated high enrichment factor at 1% of the 
 screened database , we visually inspected the binding mode of the best 
 30 structures ( 1% ) identified within the fda database . we 
 focused , in particular , on four compounds based on their high hwkdocktanimoto docking score and their interactions with the key residues ( thr156 
 and ser202 ) of the mycp1 binding cavity . these four compounds were 
 chosen for in vitro inhibition assays , and three out of the four selected 
 compounds exhibited more than 50% inhibition of mycp1 when 
 used at 150 m ( table 1 ) , which validated 
 the merits of our vs approach . finally , the active compounds were 
 filtered for pan assay interference compounds ( pains ) ( table 1 ) and showed that the hydroxystilbamidine scaffold 
 may be used as starting structure for further optimization . an analysis 
 of the three ranked fda approved drugs showed that the compounds 11 , 12 , and 13 were ranked by sabre 
 near the top at positions 2 , 24 , and 3 out of 1217 . the high score 
 for compounds 11 and 13 was attributable 
 to the hwkdocktanimoto scoring function , which took into account 
 the ligand similarity as well as the optimal ligand / receptor pharmacophore 
 model ( eq 19 ) . out of the three , compound 11 had the greatest effect , with an ic50 of 85.6 
 m , whereas the two other leads had ic50 > 100 
 m . in addition , we noted the low structural similarity between the three 
 identified fda compounds ( figure 6 ) . as observed 
 for the other leads , compound 11 formed hydrogen bonds 
 with the thr156 , glu203 , and thr333 residues of the mycp1 active site ( figure 5 ) . interestingly , compound 11 is typically used as a histochemical stain to understand 
 the distribution and localization of biomarkers , and these results suggested that it or its analogs may 
 be repurposed for inhibition of mycp1 . more importantly , 
 these preliminary findings show that the sabre algorithm with hwk scoring provides an efficient means for the identification 
 of new uses for current drugs and encourages us to pursue the applicability 
 of methodology in drug repurposing strategy for other medically relevant 
 drug targets . structural scaffolds of mycp1 inhibitors identified 
 during the vs of the fda database . the percentage of inhibition and 
 ic50 are displayed . published data 
 suggested counting hits only when the chemotype of a molecule is not 
 equal to a template chemotype or any other chemotype that already 
 exists in the hit list . this approach 
 resulted in a chemotype enrichment that emphasizes discovery of ligands 
 with different chemotype properties . we assessed the novelty of the 
 confirmed 12 hits by comparing their structural similarities with 
 a we computed the pairwise similarity index using the molecular access 
 system maccs structural keys ( maccs , 166 bits ) of our 13 compounds 
 ( query + 12 leads ) and represented the structural diversity using the 
 heat map ( figure 7 ) . the 
 map visualizes 15  15 = 225 pairwise comparisons and was color - coded 
 by similarity values ranging from red ( low similarity value ) to dark 
 blue ( high similarity value ) . we observed only two lobes in dark blue 
 consistent with high similarity between the compounds ( maccs index 
 > 0.8 ) and most of the compounds were dissimilar . this result supported 
 the increased structural diversity ( maccs index < 0.6 ) of the new 
 lead compounds using the combined 4d fingerprint and hwk scoring function . considering the high degree of substructure encoded 
 in each vs round , it was not surprising that the 4d fingerprint algorithm 
 performed well at finding diverse chemotypes . heat map of the maccs 
 similarity index for the 13 compounds ( 12 
 leads + query ) . we 
 report a rational method for the design of novel scoring function hwk and validated its performance using a large number of 
 targets from the dekois database . the vs approach test using the 4d 
 fingerprint and the hwk scoring function provided 
 high enrichment factors in detecting active compounds at early stage 
 of the 81 screened databases . we validated the efficiency of the combined 
 4d fingerprint and hwk scoring function in scaffold - hopping 
 strategy through the identification of nine novel lead compounds in 
 a short hit list from the vs of the nci database . the result of the 
 vs round ranked these compounds in the top-300 of the database , and 
 one of them displayed an ic50 comparable to that of the 
 reference structure . in the absence of new drugs for infectious 
 diseases like tb , it 
 made sense to develop a vs strategy capable of exploring databases 
 of current drugs used to treat diseases other than infectious diseases 
 and potentially repurpose some of them for tb treatment . the merit of this approach lies in the obvious point that these 
 commercially available drugs lack significant toxicity or side - effects . to test this notion , the screening of the fda database using our 
 screening approach identified three fda - approved compounds as potential 
 lead structures . the distributions 
 of pairwise structural similarities presented in the heat map revealed 
 that the 13 lead compounds resulting from the vs of nci and fda databases 
 were structurally diverse . in summary , this study represents the comprehensive 
 quantification of vs approach for scaffold - hopping and drug repurposing 
 and provides a solid strategy for the discovery of new classes of 
 mycp1 inhibitors .