Research Article A Predictive HQSAR Model for a Series of...

Research ArticleA Predictive HQSAR Model for a Series of Tricycle CoreContaining MMP-12 Inhibitors with Dibenzofuran Ring

Jamal Shamsara1 and Ahmad Shahir-Sadr2

1 Pharmaceutical Research Center School of Pharmacy Mashhad University of Medical Sciences Mashhad 91775-1365 Iran2Molecular and Cellular Biology Research Center School of Medicine Sabzevar University of Medical SciencesSabzevar 96138-73136 Iran

Correspondence should be addressed to Jamal Shamsara shamsarajmumsacir

Received 24 August 2014 Revised 26 October 2014 Accepted 5 November 2014 Published 7 December 2014

Academic Editor Rosaria Volpini

Copyright copy 2014 J Shamsara and A Shahir-Sadr This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

MMP-12 is a member of matrix metalloproteinases (MMPs) family involved in pathogenesis of some inflammatory based diseasesDesign of selectivematrixMMPs inhibitors is still challenging because of binding pocket similarities amongMMPs familyWe triedto generate a HQSAR (hologram quantitative structure activity relationship) model for a series of MMP-12 inhibitors Compoundsin the series of inhibitors with reported biological activity against MMP-12 were used to construct a predictive HQSAR model fortheir inhibitory activity against MMP-12 The HQSAR model had statistically excellent properties and possessed good predictiveability for test set compounds The HQSAR model was obtained for the 26 training set compounds showing cross-validated 1199022value of 0697 and conventional 1199032 value of 0986 The model was then externally validated using a test set of 9 compounds and thepredicted values were in good agreement with the experimental results (1199032pred = 08733)Then the external validity of themodel wasconfirmed by Golbraikh-Tropsha and 1199032

119898metrics The color code analysis based on the obtained HQSAR model provided useful

insights into the structural features of the training set for their bioactivity against MMP-12 and was useful for the design of somenew not yet synthesized MMP-12 inhibitors

1 Introduction

Matrix metalloproteinases (MMPs) family enzymes candegrade extracellular matrix components by their proteolyticactivity which depends on catalytic zinc ion [1]Themain roleof macrophage metalloelastase (MMP-12) is degradation ofelastin Furthermore MMP-12 is an interesting therapeutictarget overexpressed in inflammatory pathological condi-tions (such as respiratory system diseases including asthmaand chronic obstructive pulmonary disorder (COPD)) [2]Effectiveness ofMMP-12 inhibitors in reducing inflammationin respiratory system has been shown [3 4]

The active site is highly conserved amongMMPs with theexception of a loop region called S11015840 S11015840 pocket in MMPsactive sites varies slightly among MMPs in both sequenceand structure [5] Despite available structural informationstill the lack of selectivity remains as a main challenge for

successfulness of MMPs inhibitors in clinical trials Further-more intrinsic flexibility of MMPs active sites makes MMPsactive site analysis more complicated [6 7] Therefore in thisstudy a ligand based approach was used to modify the sidechain in a series of MMP-12 inhibitors HQSAR (hologramquantitative structure activity relationship) is a method forQSAR (quantitative structure activity relationship) studieswhose reliability has been established [8] In the presentstudy a HQSAR study on a series of tricycle cores containingMMP-12 inhibitors was carried out

2 Methods

21 Obtaining Biological Data and Generation of MolecularStructures The structures of 35 MMP-12 inhibitors and theirbiological activities for inhibition of MMP-12 were takenfrom the literatures (Figure 1 and Table 1) [9 10] As the

Hindawi Publishing CorporationInternational Journal of Medicinal ChemistryVolume 2014 Article ID 630807 9 pageshttpdxdoiorg1011552014630807

2 International Journal of Medicinal Chemistry

Table 1 Actual and predicted activities of the training and test sets based on the HQSAR model Activities were shown as pIC50 (120583M)

Name R Actual pIC50 values Predicted pIC50 values Residues Normalized meandistance score

10

OO

N2699 2594 0105 0066

11N

N N 18861 205 minus01639 0028

12 N

N18239 2144 minus03201 0022

13N

N

CF3

31549 2688 04669 0049

14 N NH 16383 1646 minus00077 0332

15a N N 17447 1754 minus00093 0065

16 N O 26576 2672 minus00144 0208

19

O

33979 3706 minus03081 0037

20O

4 4032 minus0032 0043

21O

Cl4 3778 0222 003

22S

3699 3647 0052 0033

23S

3699 3752 minus0053 0031

24N

NH 3 3049 minus0049 0005

International Journal of Medicinal Chemistry 3

Table 1 Continued


25a

NH

33979 317 02279 0085

26

N

S

3 2945 0055 0009

27

N

S

29208 2949 minus00282 0008

33 Methyl 20655 2341 minus02755 034 Ethyl 25376 2452 00856 00135 i-Propyl 23468 2423 minus00762 008736 t-Butyl 17696 1839 minus00694 055437 i-Butyl 22676 2203 00646 028438 CH2OCH3 27212 2571 01502 000739 CF3 26576 2543 01146 040 Cyclopropyl 27959 2767 00289 00841 Cyclobutyl 26383 2689 minus00507 037742 Cyclohexyl 21427 2126 00167 143 Phenyl 23979 2561 minus01631 0116

44 O 35229 3491 00319 0186

51aO

O

NH25441 2483 00611 0059

52a

O

O

NH 20969 2502 minus04051 0088

53a O

O

NH2173 2146 0027 0297

54a

O

ONH

F

25229 2526 minus00031 0049

55a

O

O

NH HN 21461 2305 minus01589 0324


Table 1 Continued


56a O

O

NH HN

S

28909 2616 02749

57a

O

O

NH HN

S

28037 2773 00307 0668

aTest set compounds

HOO

OO

O

NH

S

R

Figure 1 General structure for dataset

activity of compound 10 is determined in both studieswe normalized the IC

50based on the reported activity for

compound 10 The range of pIC50

(120583M) values for MMP-12spans around three orders ofmagnitude (min = 16383 max =4) in training setThe compounds were divided into two setstraining (119899 = 26) and test (119899 = 9) sets according to the main-taining of structural diversity and the uniform distributionof IC50 The pIC

50(minusLog IC

50) was employed as dependent

variable instead of IC50 The molecular structures were

built using PyMOL (httpwwwpymolorg The PyMOLMolecular Graphics System Version 12r3pre SchrodingerLLC) The HQSAR model was developed by SYBYL-X12molecularmodeling package (Tripos International St Louis)

22 HQSAR Model Generation and Validation HQSARtechnique explores the contribution of each fragment ofeach molecule under study to the biological activity Asinputs it needs datasets with their corresponding inhibitoryactivity in terms of pIC

50 Structures in the dataset were

fragmented and hashed into array bins Molecular hologramfingerprints were then generated Hologram was constructedby cutting the fingerprint into strings at various hologramlength parameters

After generation of descriptors partial least square (PLS)methodology was used to find the possible correlationbetween dependent variable (minuspIC

50) and independent vari-

able (descriptors generated by HQSAR structural features)LOO (leave-one-out) cross-validation method was used todetermine the predictive value of the model Optimum num-ber of components was found out using results from LOO

calculations At this step 1199022 and standard error obtained fromleave-one-out cross-validation roughly estimate the predic-tive ability of the model This cross-validated analysis wasfollowed by a non-cross-validated analysis with the calculatedoptimum number of principle components Conventionalcorrelation coefficient 1199032 and standard error of estimate (SEE)indicated the validity of themodelThe internal validity of themodel was also tested by 119884-randomization method [11] Inthis test the dependent variables are randomly shuffled whilethe independent variables (descriptors) are kept unchangedIt is expected that 1199022 and 1199032 calculated for these randomdatasets will be low Finally a set of compounds (which werenot present in model development process) with availableobserved activity were used for external validation of thegenerated model Predictive 1199032 (1199032pred) value was calculatedusing

1199032

pred = 1 minusPRESSSD (1)

PRESS sum of the squared deviation between pre-dicted and actual pIC

50for the test set compounds

SD sum of the squared deviation between the actualpIC50values of the compounds from the test set and

the mean pIC50value of the training set compounds

The external validity of the model was also evaluated byGolbraikh-Tropsha [12] method and 1199032

119898[13] metrics For an

acceptable QSAR model the value of ldquoaverage 1199032119898rdquo should

be gt05 and ldquodelta 1199032119898rdquo should be lt02 The applicability

domain of the generated model was evaluated for both testand prediction sets by Euclidean based method It calculatesa normalized mean distance score for each compound intraining set in range of 0 (least diverse) to 1 (most diverse)Then it calculates the normalized mean distance score forcompounds in an external set If a score is outside the 0to 1 range it will be considered outside of the applicabilitydomain The external validity tests (Golbraikh-Tropsha andRm2) and applicability domain test were done using toolsavailable at httpdtclabwebscomsoftware-tools


005

115

225

335

445

0 05 1 15 2 25 3 35 4 45

Pred

icte

d

Observed

Training setTest set

Figure 2 Plot of observed versus predicted activity obtained fromHQSAR model for training and test sets

23 Prediction Set (Design of New Compounds) The predic-tion set contained 5 new not yet synthesized compoundshaving unknown observed values of activity against MMP-12 They were designed based on the prediction ability ofdeveloped HQSAR model

24 Molecular Docking The molecular docking process wascarried out employing Glide (Glide version 57 SchrodingerLLC New York NY 2011) using default parameters Theprotein (3F17) was prepared using Protein Preparation Wiz-ard Hydrogens were added bond orders were assignedoverlapping hydrogens were corrected missing side chainswere added and water molecules were removed Finallythe protein structure was minimized by OPLS2005 forcefield The prepared protein structure containing inhibitormolecule was used for active site definition (within 13Afrom cocrystalized ligand) The 2D maps of ligands-receptorinteractions were generated by ligand interaction diagram(Schrodinger molecular modeling suite)

3 Results

31 HQSAR Model Predictivity The statistics for developedHQSAR model were shown in Table 2 The statistical param-eters 1199022 1199032 SEE and 1199032pred showed the validity of our modelThe best hologram model was generated using histogramlength of 199 having six optimum components Descrip-tors used for model generation were atoms connectionsand hydrogen atoms The best generated model had cross-validated 1199022 of 0697 and non-cross-validated 1199032 value of0986 with a standard error of 093The total collection of thegenerated models for various histogram lengths comprisesensemble and the ensemble value for 1199032 was found tobe 0528 The 119884-randomization results indicated that thecalculated 1199022 (minus1238 minus0303 minus0793 0081 and minus0146) and1199032 (0683 0088 0086 0241 and 0126) for five randommodels are very low which also confirm the internal validityof the generated HQSAR model The results of 1199032pred calcula-tion showed that the proposed HQSAR model was reliable

H

H

H

H

H HH

HH H

HH H

HH

HH

H

H

O

O O

O O

O

(a)

HH

H H

HH H

H

HH

H

HH H

HH

HH

H

H

H

O

O O

O

OO

(b)

Figure 3 Contribution plot obtained from hologram quantitativestructure activity relationship model for (a) compound 19 and (b)compound 20

and could successfully predict pIC50

for structurally relatedcompounds which were not included in development of themodels The 1199032

119898(after scaling) was 0825 and delta 1199032

119898was

0107 Additionally all four conditions of The Golbraikh-Tropsha method are satisfied (1199032 = 0876) Predictedvalues for the activity of molecules are shown in Table 1 andthe experimental pIC

50against the values predicted by the

HQSAR models are plotted (Figure 2)

32 HQSAR Atomic Contribution Plot The generated modelcan be accessed through atomic contribution plot The vari-ous colors of each atom correspond to various degrees of con-tribution towards the overall biological activity Red redorange and orange depicted that the color belonging atomswere contributing negatively to the generated HQSARmodelwhile colors reflecting yellow green and green blue werecontributing positively to the model Intermediate contribu-tions were reflected by gray atom The maximum commonsubstructure was shown in cyan Figure 3 depicts the contri-bution of the most potent compound 20 as well as compound19

33 Prediction Set (Design of New Virtual Compounds) Thiswork allowed prediction of the activity of a set shown inTable 3 (not yet synthesized molecules) Their inhibitoryactivities were calculated according to the HQSAR modelThey were designed based on compound 19 (one of themost active compounds) We had proposed a set of 5 newstructures some of them may show improved experimentalMMP-12 inhibitory activity in comparison with the parentcompound This hypothesis and their selectivity should beverified experimentally

34 Molecular Docking The molecular docking approachwas employed to further analyze the ability of designed


ALA PROLYS

LYS

PHE

234

ALA184

232

PROTHR

HIS

ZN

238

239

183

HIS222

HIS228ILE

180

ALA182LEU

181

HIS218

THR215

LEU214

TYR240

PHE248

VAL N S

O

O

O

O

O

SNH

235

GLU219

264

241

233

237

VAL243

Charged (negative)Charged (positive)PolarHydrophobic

GlycineMetal

WaterHydration site

Displaced hydration site

n-n stacking

n-cationH-bond (backbone)

H-bond (side chain)

Metal coordinationSalt bridge

Solvent exposure

H2O

(a)

PRO232

O

O

O

O

O

O

LYS241

ALA234

PHE237

PRO238

THR239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE180

THR215


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

TYR240

PHE248

LEU214

VAL235VAL

243

S

NH

H2O

(b)

PRO238 THR

239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

180

THR215

LYS241 PRO

232

ALA234

PHE237

VAL235

TYR240

LEU214

PHE248

LYS233

VAL243

O

O

O

O

O

O

S

NH

H2O

(c)

Figure 4 2D interaction diagram of 3 docked designed compounds (a) structure 26 (b) structure 20 and (c) structure n3


Figure 5 Compound 20 in the active site of MMP-12 (docked byGlide)

Table 2 Statistical characteristics of the developed HQSAR model

Parameter ValueNumber of compounds included in training set 26Optimum number of components used in the PLSanalysis 6

1199022 (cross-validated correlation coefficient) 0697SEEa (1199022) 04281199032 (non-cross-validated correlation coefficient) 0986SEE (1199032) 00931199032 (ensembleb) 0528SEE (ensemble) 0530Best hologram length 199

Used information

Atoms+ Connections+ Hydrogen

atoms1199032

pred 08733aSEE standard error of estimate bFor each hologram length a model couldbe established The collection of these models comprises the ensemble

compounds in inhibition of MMP-12 In Table 3 the dockingscores of the 3 new designed compounds and 3 moleculesfrom train set were reportedThe binding positions of all newcompounds were inspected for their binding conformationand interactions inMMP-12 active site For n3 2D diagram ofligand-receptor interaction was presented (Figure 4(c)) Thevarious heterocyclic rings substituted on the dibenzofuranscaffold do not seem to have strong interactions with thebinding pocket of MMP-12 as it was suggested previously byX-ray crystallography [9] However if they have undesiredproperties they cannot fit in the narrow deep S11015840 pocketof MMP-12 On the other hand they can induce sterichindrance that prevents other parts of the molecule to havestrong interactions with residues in the binding pocket Theconformation of the heterocyclic ring upon ligand binding isdemonstrated in Figure 5

4 Discussion

We successfully developed a HQSAR model for predictionof some MMP-12 inhibitors with good internal and externalvalidity Subsequently the model was used to predict theactivity of newMMP-12 inhibitorsThebinding energy of newnot yet synthesized molecules was evaluated by moleculardocking

Crystal structures have provided useful informationfor developing selective inhibitors toward particular MMPsincluding MMP-12 The segment 241ndash245 of MMPs (MMP-1numbering) has the highest sequence variability among thevarious MMP enzymes and could be a target for designingselective inhibitors However this segment is very flexiblewhich makes the molecular modeling predictions using 3Dstructures of MMPs inaccurate [14 15] Only some small dif-ferences in the sizes of hydrophobic side chainswere seen Forexample Val 235 inMMP-12 is replaced by Leu214 inMMP-13which makes the MMP-13 binding pocket smaller and morehydrophobic The series of MMP-12 inhibitors employed inthis study had carboxylic acid zinc binding group Changingthe R group that was placed in hydrophobic pocket of MMP-12 active site altered the potency and selectivity of theseinhibitors We modified this R group for fine tuning anddesigned new compound with promisingMMP-12 inhibitoryactivity

In the present study we used HQSAR approach for a setof MMP-12 inhibitors Contribution plot (Figure 3) showedthat the green aromatic carbon was contributing positivelyto the model Oxygen atom in furan ring was depictedby green or yellow and was contributing to the biologicalactivity Hydrogen molecules were rendered green yellow orwhite indicating that they showed intermediate contributionThe bulky group (methyl) was green indicating that it wascontributing positively to the generated model and it can beexplained from the example that compound 20 was showinghigh potency The developed model was used for the designof 5 new molecules Overall docked conformation of thetraining set of inhibitors and new compounds in MMP-12active site was similar to one determined by crystallographyAmong new designed compounds compounds n1 n2 and n3had low SEP and their predicted activities were more reliableFurthermore compound n3 has the lowest docked energy

5 Conclusion

In summary we have developed a reliable HQSARmodel fora series of tricycle cores containing MMP-12 inhibitors withdibenzofuran ring using activity data reported earlier [9 10]We used HQSAR analysis to design new not yet synthesizedpotent MMP-12 inhibitors Their binding energies were eval-uated by docking studies but for further validation it needssynthesize of the proposed new compounds and subsequentenzyme inhibition study

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper


Table 3 Predicted activities for the molecules based on the HQSAR model Activities were shown as pIC50 (120583M)

Name R PredictedpIC50 values

SE of prediction Normalized meandistance score Docking score

n1 O 4182 0158579 0161 minus12038972

n2O

389 0097741 0076 minus1392

n3O

3942 0093502 0072 minus1387

n4O

3846 0308627 0291 mdasha

n5 O 4003 0325568 0315 mdasha

20 (observed = 4)O

4032 mdash 0043 minus137908

19 (observed = 33979)

O

3706 mdash 0037 minus12555956

26 (observed = 3)

N

O

2945 mdash 0009 minus11781243

aDocking was not performed for these compounds


Acknowledgment

This work has been partially supported financially by Sabze-var University of Medical Sciences

References

[1] V Vargova M Pytliak and V Mechırova ldquoMatrix metallopro-teinasesrdquo EXS vol 103 pp 1ndash33 2012

[2] V Lagente C Le Quement and E Boichot ldquoMacrophage met-alloelastase (MMP-12) as a target for inflammatory respiratorydiseasesrdquo Expert Opinion on Therapeutic Targets vol 13 no 3pp 287ndash295 2009

[3] C le Quement I Guenon J-Y Gillon et al ldquoThe selectiveMMP-12 inhibitor AS111793 reduces airway inflammation inmice exposed to cigarette smokerdquo British Journal of Pharmacol-ogy vol 154 no 6 pp 1206ndash1215 2008

[4] P Norman ldquoSelective MMP-12 inhibitors WO-2008057254rdquoExpert Opinion on Therapeutic Patents vol 19 no 7 pp 1029ndash1034 2009

[5] S P Gupta and V M Patil ldquoSpecificity of binding with matrixmetalloproteinasesrdquo EXS vol 103 pp 35ndash56 2012

[6] K-C Tsai and T-H Lin ldquoA ligand-based molecular modelingstudy on some matrix metalloproteinase-1 inhibitors usingseveral 3D QSAR techniquesrdquo Journal of Chemical Informationand Computer Sciences vol 44 no 5 pp 1857ndash1871 2004

[7] B Pirard ldquoInsight into the structural determinants for selectiveinhibition ofmatrixmetalloproteinasesrdquoDrug Discovery Todayvol 12 no 15-16 pp 640ndash646 2007

[8] HQSAR Manual SYBYL-X 10 Tripos St Louis Mo USA2009

[9] Y Wu J Li J Wu et al ldquoDiscovery of potent and selectivematrix metalloprotease 12 inhibitors for the potential treatmentof chronic obstructive pulmonary disease (COPD)rdquo Bioorganicamp Medicinal Chemistry Letters vol 22 no 1 pp 138ndash143 2012

[10] L Wei L Jianchang W Yuchuan et al ldquoA selective matrixmetalloprotease 12 inhibitor for potential treatment of chronicobstructive pulmonary disease (COPD) discovery of (119878)-2-(8-(Methoxycarbonylamino)dibenzo[119887119889]furan-3-sulfonamido)-3-methylbutanoic acid (MMP408)rdquo Journal ofMedicinal Chem-istry vol 52 no 7 pp 1799ndash1802 2009

[11] K Roy and I Mitra ldquoOn various metrics used for validation ofpredictive QSAR models with applications in virtual screeningand focused library designrdquo Combinatorial Chemistry and HighThroughput Screening vol 14 no 6 pp 450ndash474 2011

[12] A Golbraikh and A Tropsha ldquoBeware of q2rdquo Journal of Mole-cular Graphics and Modelling vol 20 no 4 pp 269ndash276 2002

[13] K Roy P Chakraborty IMitra P KOjha S Kar andRNDasldquoSome case studies on application of ldquorm2rdquo metrics for judg-ing quality of quantitative structure-activity relationship pre-dictions emphasis on scaling of response datardquo Journal ofComputational Chemistry vol 34 no 12 pp 1071ndash1082 2013

[14] P Cuniasse L Devel A Makaritis et al ldquoFuture challengesfacing the development of specific active-site-directed syntheticinhibitors of MMPsrdquo Biochimie vol 87 no 3-4 pp 393ndash4022005

[15] J A Jacobsen J L Major Jourden M T Miller and S MCohen ldquoTo bind zinc or not to bind zinc an examination ofinnovative approaches to improved metalloproteinase inhibi-tionrdquo Biochimica et Biophysica ActamdashMolecular Cell Researchvol 1803 no 1 pp 72ndash94 2010

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

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Carbohydrate Chemistry

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Chemistry


Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of


Chromatography Research International


Applied ChemistryJournal of



Theoretical ChemistryJournal of


Journal of

Spectroscopy

Analytical ChemistryInternational Journal of


Journal of


Quantum Chemistry


Organic Chemistry International

ElectrochemistryInternational Journal of



CatalystsJournal of


Table 1 Actual and predicted activities of the training and test sets based on the HQSAR model Activities were shown as pIC50 (120583M)


10

OO

N2699 2594 0105 0066

11N

N N 18861 205 minus01639 0028

12 N

N18239 2144 minus03201 0022

13N

N

CF3

31549 2688 04669 0049

14 N NH 16383 1646 minus00077 0332

15a N N 17447 1754 minus00093 0065

16 N O 26576 2672 minus00144 0208

19

O

33979 3706 minus03081 0037

20O

4 4032 minus0032 0043

21O

Cl4 3778 0222 003

22S

3699 3647 0052 0033

23S

3699 3752 minus0053 0031

24N

NH 3 3049 minus0049 0005


Table 1 Continued


25a

NH

33979 317 02279 0085

26

N

S

3 2945 0055 0009

27

N

S

29208 2949 minus00282 0008


44 O 35229 3491 00319 0186

51aO

O

NH25441 2483 00611 0059

52a

O

O

NH 20969 2502 minus04051 0088

53a O

O

NH2173 2146 0027 0297

54a

O

ONH

F

25229 2526 minus00031 0049

55a

O

O

NH HN 21461 2305 minus01589 0324


Table 1 Continued


56a O

O

NH HN

S

28909 2616 02749

57a

O

O

NH HN

S

28037 2773 00307 0668

aTest set compounds

HOO

OO

O

NH

S

R






50(minusLog IC











1199032












005

115

225

335

445

0 05 1 15 2 25 3 35 4 45

Pred

icte

d

Observed





3 Results


H

H

H

H

H HH

HH H

HH H

HH

HH

H

H

O

O O

O O

O

(a)

HH

H H

HH H

H

HH

H

HH H

HH

HH

H

H

H

O

O O

O

OO

(b)





119898was








ALA PROLYS

LYS

PHE

234

ALA184

232

PROTHR

HIS

ZN

238

239

183

HIS222

HIS228ILE

180

ALA182LEU

181

HIS218

THR215

LEU214

TYR240

PHE248

VAL N S

O

O

O

O

O

SNH

235

GLU219

264

241

233

237

VAL243


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

H2O

(a)

PRO232

O

O

O

O

O

O

LYS241

ALA234

PHE237

PRO238

THR239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE180

THR215


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

TYR240

PHE248

LEU214

VAL235VAL

243

S

NH

H2O

(b)

PRO238 THR

239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

180

THR215

LYS241 PRO

232

ALA234

PHE237

VAL235

TYR240

LEU214

PHE248

LYS233

VAL243

O

O

O

O

O

O

S

NH

H2O

(c)







Used information


atoms1199032



4 Discussion




5 Conclusion








n1 O 4182 0158579 0161 minus12038972

n2O

389 0097741 0076 minus1392

n3O

3942 0093502 0072 minus1387

n4O

3846 0308627 0291 mdasha

n5 O 4003 0325568 0315 mdasha

20 (observed = 4)O

4032 mdash 0043 minus137908

19 (observed = 33979)

O

3706 mdash 0037 minus12555956

26 (observed = 3)

N

O

2945 mdash 0009 minus11781243



Acknowledgment


References

























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Table 1 Continued


25a

NH

33979 317 02279 0085

26

N

S

3 2945 0055 0009

27

N

S

29208 2949 minus00282 0008


44 O 35229 3491 00319 0186

51aO

O

NH25441 2483 00611 0059

52a

O

O

NH 20969 2502 minus04051 0088

53a O

O

NH2173 2146 0027 0297

54a

O

ONH

F

25229 2526 minus00031 0049

55a

O

O

NH HN 21461 2305 minus01589 0324


Table 1 Continued


56a O

O

NH HN

S

28909 2616 02749

57a

O

O

NH HN

S

28037 2773 00307 0668

aTest set compounds

HOO

OO

O

NH

S

R






50(minusLog IC











1199032












005

115

225

335

445

0 05 1 15 2 25 3 35 4 45

Pred

icte

d

Observed





3 Results


H

H

H

H

H HH

HH H

HH H

HH

HH

H

H

O

O O

O O

O

(a)

HH

H H

HH H

H

HH

H

HH H

HH

HH

H

H

H

O

O O

O

OO

(b)





119898was








ALA PROLYS

LYS

PHE

234

ALA184

232

PROTHR

HIS

ZN

238

239

183

HIS222

HIS228ILE

180

ALA182LEU

181

HIS218

THR215

LEU214

TYR240

PHE248

VAL N S

O

O

O

O

O

SNH

235

GLU219

264

241

233

237

VAL243


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

H2O

(a)

PRO232

O

O

O

O

O

O

LYS241

ALA234

PHE237

PRO238

THR239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE180

THR215


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

TYR240

PHE248

LEU214

VAL235VAL

243

S

NH

H2O

(b)

PRO238 THR

239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

180

THR215

LYS241 PRO

232

ALA234

PHE237

VAL235

TYR240

LEU214

PHE248

LYS233

VAL243

O

O

O

O

O

O

S

NH

H2O

(c)







Used information


atoms1199032



4 Discussion




5 Conclusion








n1 O 4182 0158579 0161 minus12038972

n2O

389 0097741 0076 minus1392

n3O

3942 0093502 0072 minus1387

n4O

3846 0308627 0291 mdasha

n5 O 4003 0325568 0315 mdasha

20 (observed = 4)O

4032 mdash 0043 minus137908

19 (observed = 33979)

O

3706 mdash 0037 minus12555956

26 (observed = 3)

N

O

2945 mdash 0009 minus11781243



Acknowledgment


References

























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Table 1 Continued


56a O

O

NH HN

S

28909 2616 02749

57a

O

O

NH HN

S

28037 2773 00307 0668

aTest set compounds

HOO

OO

O

NH

S

R






50(minusLog IC











1199032












005

115

225

335

445

0 05 1 15 2 25 3 35 4 45

Pred

icte

d

Observed





3 Results


H

H

H

H

H HH

HH H

HH H

HH

HH

H

H

O

O O

O O

O

(a)

HH

H H

HH H

H

HH

H

HH H

HH

HH

H

H

H

O

O O

O

OO

(b)





119898was








ALA PROLYS

LYS

PHE

234

ALA184

232

PROTHR

HIS

ZN

238

239

183

HIS222

HIS228ILE

180

ALA182LEU

181

HIS218

THR215

LEU214

TYR240

PHE248

VAL N S

O

O

O

O

O

SNH

235

GLU219

264

241

233

237

VAL243


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

H2O

(a)

PRO232

O

O

O

O

O

O

LYS241

ALA234

PHE237

PRO238

THR239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE180

THR215


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

TYR240

PHE248

LEU214

VAL235VAL

243

S

NH

H2O

(b)

PRO238 THR

239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

180

THR215

LYS241 PRO

232

ALA234

PHE237

VAL235

TYR240

LEU214

PHE248

LYS233

VAL243

O

O

O

O

O

O

S

NH

H2O

(c)







Used information


atoms1199032



4 Discussion




5 Conclusion








n1 O 4182 0158579 0161 minus12038972

n2O

389 0097741 0076 minus1392

n3O

3942 0093502 0072 minus1387

n4O

3846 0308627 0291 mdasha

n5 O 4003 0325568 0315 mdasha

20 (observed = 4)O

4032 mdash 0043 minus137908

19 (observed = 33979)

O

3706 mdash 0037 minus12555956

26 (observed = 3)

N

O

2945 mdash 0009 minus11781243



Acknowledgment


References

























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


005

115

225

335

445

0 05 1 15 2 25 3 35 4 45

Pred

icte

d

Observed





3 Results


H

H

H

H

H HH

HH H

HH H

HH

HH

H

H

O

O O

O O

O

(a)

HH

H H

HH H

H

HH

H

HH H

HH

HH

H

H

H

O

O O

O

OO

(b)





119898was








ALA PROLYS

LYS

PHE

234

ALA184

232

PROTHR

HIS

ZN

238

239

183

HIS222

HIS228ILE

180

ALA182LEU

181

HIS218

THR215

LEU214

TYR240

PHE248

VAL N S

O

O

O

O

O

SNH

235

GLU219

264

241

233

237

VAL243


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

H2O

(a)

PRO232

O

O

O

O

O

O

LYS241

ALA234

PHE237

PRO238

THR239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE180

THR215


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

TYR240

PHE248

LEU214

VAL235VAL

243

S

NH

H2O

(b)

PRO238 THR

239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

180

THR215

LYS241 PRO

232

ALA234

PHE237

VAL235

TYR240

LEU214

PHE248

LYS233

VAL243

O

O

O

O

O

O

S

NH

H2O

(c)







Used information


atoms1199032



4 Discussion




5 Conclusion








n1 O 4182 0158579 0161 minus12038972

n2O

389 0097741 0076 minus1392

n3O

3942 0093502 0072 minus1387

n4O

3846 0308627 0291 mdasha

n5 O 4003 0325568 0315 mdasha

20 (observed = 4)O

4032 mdash 0043 minus137908

19 (observed = 33979)

O

3706 mdash 0037 minus12555956

26 (observed = 3)

N

O

2945 mdash 0009 minus11781243



Acknowledgment


References

























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


ALA PROLYS

LYS

PHE

234

ALA184

232

PROTHR

HIS

ZN

238

239

183

HIS222

HIS228ILE

180

ALA182LEU

181

HIS218

THR215

LEU214

TYR240

PHE248

VAL N S

O

O

O

O

O

SNH

235

GLU219

264

241

233

237

VAL243


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

H2O

(a)

PRO232

O

O

O

O

O

O

LYS241

ALA234

PHE237

PRO238

THR239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE180

THR215


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

TYR240

PHE248

LEU214

VAL235VAL

243

S

NH

H2O

(b)

PRO238 THR

239

HIS183

ALA184

GLU219

ZN264

HIS222

HIS228

ALA182LEU

181

HIS218

ILE


GlycineMetal

WaterHydration site


n-n stacking


H-bond (side chain)


Solvent exposure

180

THR215

LYS241 PRO

232

ALA234

PHE237

VAL235

TYR240

LEU214

PHE248

LYS233

VAL243

O

O

O

O

O

O

S

NH

H2O

(c)







Used information


atoms1199032



4 Discussion




5 Conclusion








n1 O 4182 0158579 0161 minus12038972

n2O

389 0097741 0076 minus1392

n3O

3942 0093502 0072 minus1387

n4O

3846 0308627 0291 mdasha

n5 O 4003 0325568 0315 mdasha

20 (observed = 4)O

4032 mdash 0043 minus137908

19 (observed = 33979)

O

3706 mdash 0037 minus12555956

26 (observed = 3)

N

O

2945 mdash 0009 minus11781243



Acknowledgment


References

























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of






Used information


atoms1199032



4 Discussion




5 Conclusion








n1 O 4182 0158579 0161 minus12038972

n2O

389 0097741 0076 minus1392

n3O

3942 0093502 0072 minus1387

n4O

3846 0308627 0291 mdasha

n5 O 4003 0325568 0315 mdasha

20 (observed = 4)O

4032 mdash 0043 minus137908

19 (observed = 33979)

O

3706 mdash 0037 minus12555956

26 (observed = 3)

N

O

2945 mdash 0009 minus11781243



Acknowledgment


References

























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of





n1 O 4182 0158579 0161 minus12038972

n2O

389 0097741 0076 minus1392

n3O

3942 0093502 0072 minus1387

n4O

3846 0308627 0291 mdasha

n5 O 4003 0325568 0315 mdasha

20 (observed = 4)O

4032 mdash 0043 minus137908

19 (observed = 33979)

O

3706 mdash 0037 minus12555956

26 (observed = 3)

N

O

2945 mdash 0009 minus11781243



Acknowledgment


References

























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Acknowledgment


References

























Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of










Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

Research Article A Predictive HQSAR Model for a Series of...

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