Radha Vaddavalli : Discovery of Novel Inhinbitors Against Resistant Streptococcus pneumoniae… 403...

Radha Vaddavalli : Discovery of Novel Inhinbitors Against Resistant Streptococcus pneumoniae… 403

Discovery of Novel Inhibitors Against Resistant Streptococcus pneumoniae MurF Enzyme using Phamracophore Modeling and QSAR Analysis

RadhaVaddavalli1*, Dr. Bagyanaryana Gaddam1 and Pradeep Kumar Maddikara2

1Department of Botany, Osmania University, Hyderabad 500007, Andhra Pradesh, India. 2Department of Chemistry, Rayalaseema University, Kurnool 518002, Andhra Pradesh, India.

ABSTRACT: Main aim of the current study was to discover novel inhibitors against Streptococcus pneumoniae MurF enzyme using analogue based drug design. Pharmacophore mapping and 3D-QSAR analysis was performed on some previously synthesized and evaluated sulfonamide derivatives which are known to be potent inhibitors of penicillin resistant Streptococcus pneumoniae MurF receptor. 3D-QSAR study based on the principle of alignment of pharmacophoric features was performed on the same set of inhibitors using the PHASE module of Schrodinger suite. A five point pharmacophore, ADHRR, with one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic group and two aromatic rings yielded a statistically significant 3D-QSAR model with 0.96 as R2 value for three PLS components and good predictive correlation coefficients of Q2 = 0.8084, F = 140.6 and Pearson-R = 0.9157 for the test set with nine compounds. Thus, indicating good predictive power of the pharmacophore based 3D-QSAR model.

KEY WORDS: Streptococcus pneumoniae; MurF enzyme; Pharmacophore; QSAR

Introduction Streptococcus pneumoniae, causing the disease Pneumonia is one of the major causes of increased mortality rate in children. The disease accounts for 19% deaths in children less than 5 years of age. Pneumonia kills more children than measles, malaria and AIDS combined. Every year 156 million new cases of pneumonia are reported in the developing countries. As many as 8.7 % of these cases are severe enough to be life-threatening and require hospitalization. India accounts for a maximum of 43 million new cases (every year) followed by China (21 million cases) and Pakistan (10 million cases). Pneumonia is estimated to kill 410,000 children in India every year [1].

Bacterial resistance to known therapies is a growing threat across the globe. An increasing fraction of bacterial isolates show reduced susceptibility to our most trusted antibiotics.[2] The antibiotics, which seemed infallible 30 years ago are much less effective today and may become even less effective in the future[3]. Our best weapon against this threat is to continually develop new antibiotics against which bacteria have not yet developed resistance.

One of the best known and most validated targets for antibacterial therapy is the machinery for peptidoglycan biosynthesis, an essential component of bacterial cell wall

[4-6]. Many important antibacterial drug classes such as the beta-lactams, cephalosporins and glycopeptides show their antibacterial activity by inhibiting the formation of peptidoglycan. Most of these target the extracellular steps of peptidoglycan biosynthesis. The intracellular steps, however, still remain mostly obscure. Among the intracellular stages of bacterial peptidoglycan biosynthesis, the ATP dependent Mur ligases deserve particular attention

[7]. The main focus of the current study was towards the development of new MurF inhibitors. MurF is one of the four amide bond-forming enzymes involved in the synthesis of UDP-MurNAc-pentapeptide[8]. It is a D-alanyl-D-alanine ligating enzyme that catalyzes the ATP-dependent formation of UDP-MurNac-tripeptide[9].

Computer-aided drug design techniques such as ligand-based pharmacophore modeling have become an integral part of drug discovery [10]. Large compound databases can be screened computationally to reduce the number of compounds for bioassay screens, thereby saving time and resources [11]. In addition, computational techniques can be used in the lead identification and lead optimization processes. Current study was focused on ligand based drug designing, integrated with self-consistent 3D QSAR model. This approach avoids the pitfalls of structure-based techniques.

International Journal of Drug Design and Discovery

Volume 2 • Issue 1 • January – March 2011. 403-412

* For correspondence: Radha Vaddavalli,

Tel.: +91 9948609445

E-mail: [email protected]

403

404 International Journal of Drug Design and Discovery Volume 2 • Issue 1 • January - March 2011 Materials and Methods

The current study was performed using the following programs: LigPrep (version 2.4.107), Maestro (version 9.1.207) and PHASE (version 3.1) of Schrödinger Suite (www. Schrodinger.com), LLC, New York, USA. PHASE was used exclusively for pharmacophore modeling and 3D-QSAR analysis. All the programs were installed on an Intel based windows workstation with Intel Core2Duo processor (2.0 GB RAM) integrated with Nvedia graphical display.

PHASE module works in five steps:

1. Prepare Ligands 2. Create Sites 3. Find Common Pharmacophores (CPH) 4. Score Hypothesis 5. Build QSAR Model

1. Preparing ligands

For the pharmacophore generation and 3D-QSAR analysis a dataset with 40 molecules with their biological activities (IC50) was selected from published literature. The activities of these compounds were converted into negative logarithmic scale (pIC50) and used for pharmacophore and QSAR development. The dataset was randomly divided into a training set with 31 compounds and a test set with 9 compounds.

All the inhibitors were sketched and optimized using the “Build” panel of Maestro of Schrodinger software. These molecules were imported into the “Develop Pharmacophore Model” panel of PHASE. For pharmacophore development, PHASE provides two built-in processes, one is to clean structures performed using LigPrep (MMFF force field) and second one is to generate conformers. The conformations were generated by the rapid torsion angle search method using a maximum of 100 steps with an implicit distance dependent dielectric solvent model followed by minimization of each structure using OPLS_2005 force field. A maximum of 100 conformers were generated per structure using default values for pre and post process minimization. Each minimized conformer was filtered through a relative energy window of 11.4 kCal/mol (50kJ/mol), a maximum atom deviation of 2.00A0 and RMSD of 1A0.

2. Creating pharmacophore sites

A set of pharmacophore features hydrogen bond acceptor (A); hydrogen bond donor (D); hydrophobic group (H);

negatively ionizable (N); positively ionizable (P); and aromatic ring (R)). were used to create sites for all the ligands. Each ligand structure is represented by a set of points in 3D space which coincide with various chemical features that may facilitate non-covalent binding between the ligand and its target receptor.

The rules that are applied to map the positions of pharmacophore sites are known as feature definitions, and they are represented internally by a set of SMARTS patterns. Each pharmacophore feature is defined by a set of chemical structure patterns.

All user defined patterns are specified as SMARTS queries and assigned one of the three possible geometries, which are defined as physical characteristics of the site:

(i) Point: The site is located on a single atom in the SMARTS query.

(ii) Vector: The site is located on a single atom in the SMARTS query and assigned directionality according to one or more vectors originating from the atom.

(iii) Group: The site is located at the centroid of a group of atoms in the SMARTS query. For aromatic rings, the site is assigned directionality defined by a vector that is normal to the plane of the ring. Default settings have been employed for the present study.

3. Finding common pharmacophore hypotheses

All the ligands with their conformations in the active site were thoroughly examined to generate common pharmacophores and those pharmacophores that contain identical sets of features with very similar spatial arrangements were grouped together. A pharmacophore group consisting of at least one pharmacophore from each ligand may give rise to a common pharmacophore. Any single pharmacophore elucidating the interactions between ligand and receptor may be selected as the common pharmacophore hypothesis (CPH). CPH is identified using an underlying tree-based partitioning algorithm (with maximum tree depth of four) that groups similar pharmacophores contemplating their intersite distances (the distance between pairs of sites in the pharmacophore.)

Generally, the most active compounds are used to generate CPH. To separate the active from the inactive molecules a threshold of pIC50 5.5 and 5 was applied to the training set. Default feature definitions were applied to each ligand and common pharmacophores with five sites were generated using a terminal box size of 1A0 .

Radha Vaddavalli : Discovery of Novel Inhinbitors Against Resistant Streptococcus pneumoniae… 405 4. Scoring hypotheses

In the score hypotheses step, common pharmacophores are examined and a scoring procedure is applied to identify the pharmacophore from each surviving n-dimensional box that yields the best alignment of the active ligands and the quality of alignment may be measured by the survival score. This pharmacophore provides a hypothesis to explain the active molecules bound to the receptor. The scoring function in PHASE ranks all the resulting hypotheses based on their quality of alignment (site score), angle of deviation between vectorized features (vector score) and overlap of steric volumes (volume score) for each pair of molecules. Default parameters for site, vector and volume terms were used to score the pharmacophores with respect to the activity of ligands.

5. 3D-QSAR Analysis

The 3D-QSAR models were built for the training set ligands that were aligned to selected hypothesis. The PHASE QSAR models are based on PLS regression applied to a large set of binary valued variables. The number of PLS factors was increased to construct a series of models by regression. The ligand space is partitioned into a cubic grid by the 3D-QSAR model. The cubes may be occupied by structural features and are considered only when the centroid is within the radius of the feature. The independent variables in the QSAR model are derived from a regular grid of cubic volume elements that span the space occupied by the training set ligands. To distinguish the different atom types that occupy the same region of space, a given cube in the grid may be allocated as many as five bits, accounting for five different classes of atoms. The atom classes include: (i) D: hydrogen-bond donor; (ii) H: hydrophobic or nonpolar; (iii) N: negative ionic; (iv) P: positive ionic; (v) W: electron-withdrawing (includes hydrogen-bond acceptors); and (vi) X: miscellaneous (all other types). Pharmacophore based QSAR models were generated for ADHRR hypothesis using the 31- membered training set and a grid spacing of 1.0Ǻ. QSAR models containing one to seven PLS factors were generated. A model with three PLS factors was considered as the best, statistically. This model was validated by predicting activities of the test set molecules.

Molecules used for 3D-QSAR analysis given below:

Discussion The 3D-QSAR study for the set of sulfonamides and their derivatives was carried out using the PHASE module of Schrodinger molecular modeling package. To identify the common pharmacophore hypothesis, the dataset was divided into active and inactive sets. Molecules with pIC50 values 5.5 and above were considered to be actives, and those with pIC50 values 5 and below were considered to be inactive, whereas those in-between were considered to be moderately active. A common pharmacophore model ADHRR with three variants was generated after the creation and identification of pharmacophoric sites in all the molecules of the dataset. The variant with a site score 0.97, vector score 0.94, and volume score of 0.77 was chosen to be the common pharmacophore hypothesis. The pharmacophore hypothesis ADHRR, with all active molecules aligned to it is shown in Figure 2.e. All the molecules in the active set/modeled molecules matched with the hypothesis ADHRR. This pharmacophore hypothesis was then used for the generation of QSAR model. For the QSAR model generation, non-modeled (inactive or moderately active) molecules in the dataset were aligned, based on the matching of at least three of the pharmacophore features. The dataset was randomly divided into a training set of 31 compounds and 09 in the test set, with a bias given to the structural diversity in both the training and test set so that the standard 4:1 training set to test set ratio was acquired for a QSAR study.

The PHASE statistical analysis for each of the test set selection methods is summarized in Table 1. The validity of each of the models was predicted from the calculated correlation coefficient for the randomly chosen test set comprising of diverse structures. The squared correlation for the test set (Q2 = 8.8084) confirms the effective predictability of the final QSAR model for the test set.

Atom based PHASE 3D-QSAR was performed. Figure.2 shows the volume occlusion maps represented by color codes. These maps represent the regions of favorable and unfavorable interactions. Colored according to the sign of their coefficient values, this by default is blue (dark grey) for positive coefficients and red (light grey) for negative coefficients. Positive coefficients indicate an increase in activity, negative coefficients a decrease.

In figure 2.c the map shows a big blue (dark grey) colored region indicating that an increase in the hydrophobicity in this region may improve the activity of the tetrahydropyran moiety and morpholin, 4-sulfonyl benzamide like molecules. A red (light grey) color disfavors the placement of hydrophobic groups.

406 International Journal of Drug Design and Discovery Volume 2 • Issue 1 • January - March 2011


NS

O

OCl

O

NH

SBr

N

Cl

O

NH

SN

N

SO

O

NO

NS

OO

Cl

O

NHS

N

ClO

NHS

N

SO

O

O

Cl

ClO

NHS

N

SOO

N

O

NS O

O

Cl

O

NHS

NO

O

O

SNH

O

Cl

S OO N

N

NSO

O

O

NHS

NO

Cl

Cl

Cl

ClO

NHS

NO

SOO

N

O

NS

OO

Cl

O

NHS

N

ClO

NHS

N

SO

O

N

O O

HOO

OO

O

HO

HO

S

NN

N

NO

HO

O

HO

O

NO

SN+

O-O

O

O

O

OS

S

O

SSO

O

O

Molecule 16 Molecule 17 Molecule 18

Molecule 19 Molecule 20Molecule 21

Molecule 22Molecule 23 Molecule 24

Molecule 25Molecule 26 Molecule 27

Molecule 28 Molecule 29 Molecule 30


Molecules References: 1-31[13], Molecules: 32, 33[14], Molecules: 34 [15], Molecules: 35-40[16].

Radha Vaddavalli : Discovery of Novel Inhinbitors Against Resistant Streptococcus pneumoniae… 409 Results:

Table I score hypothesis tabular representation

ID Survival Survival-

Inactives

Post-

hoc Site Vector Volume Selectivity #Matches Energy Activity Inactive

ADHHR-626 3.71 3.71 3.671 0.95 0.999 0.764 1.94 9 0.451 7.658 1.678

AADHR-1761 3.704 3.704 3.671 0.97 0.998 0.74 1.64 9 3.438 6.523 1.547

AADHR-347 3.702 3.702 3.673 0.95 0.999 0.753 1.598 9 4.236 6.423 1.676

AADHR-87 3.699 3.699 3.567 0.96 0.999 0.74 1.629 9 4.236 6.543 1.656

AADHR-85 3.699 3.699 3.679 0.96 0.999 0.74 1.629 9 4.236 6.241 1.478

AADHR-1909 3.697 3.697 3.681 0.96 0.998 0.738 1.662 9 3.438 6.321 1.673

AAADH-2880 3.693 3.693 3.690 0.96 0.998 0.736 1.378 9 3.438 6.421 1.653

AAADH-71 3.686 3.686 3.678 0.95 0.978 0.734 1.349 9 3.772 7.658 1.745

Table 2 (Pharmcophore based) QSAR models for MurF receptor inhibitors:

CPH-ID SD R-squared F P Stability RMSE Q-squared Pearson-R

ADHRR-206** 0.2907 0.9635 140.6 1.04E-11 0.7616 0.5277 0.8084 0.9157 ADHHR-9 0.114 0.9406 685.2 2.88E-16 0.7035 0.7356 0.6762 0.8463

ADHHR-626 0.6685 0.8354 50 1.35E-06 0.5911 0.6799 0.7063 0.8567 AADHR-1761 0.3221 0.842 138 3.10E-11 0.6422 0.5158 0.6209 0.9091 AADHR-347 0.2002 0.7989 247.6 1.29E-13 0.6473 0.5161 0.7308 0.8918 AADHR-87 0.301 0.7658 68 1.33E-11 0.475 0.4563 0.7101 0.8456 AADHR-85 0.412 0.779 121.1 1.23E-01 0.3481 0.5691 0.6314 0.7842 AADHR-1909 0.121 0.6923 134.2 2.38E-14 0.5121 0.6721 0.6084 0.7231 AAADH-2880 0.211 0.6231 452.8 3.18E-23 0.421 0.3785 0.5671 0.8001 AAADH-71 0.1165 0.6112 567.3 1.25E-15 0.239 0.345 0.5985 0.7651

** selected model for QSAR

Fig. 1 Phase predicted activity Vs Phase activity:


Table 3 3D-QSAR analysis with corresponding actual and predicted activities

Molecule No Actual pIC50 Predictied pIC50 Molecule No Actual pIC50 Predictied pIC50 1 5.04 5.09 21 7.26 7.15 2 4.82 4.18 22 5.22 5.48 3 4.58 4.01 23 7.17 7.43 4 5.09 5.39 24 7.65 6.92 5 5.19 5.95 25 5.46 5.23 6 5.28 5.13 26 5.37 5.51 7 6.52 6.09 27 2.36 2.25 8 4.2 3.87 28 2.31 2.11 9 4.18 4.31 29 3.52 3.43

10 4.13 3.78 30 3.48 3.92 11 5.37 5.14 31 3.9 4.21 12 4.65 4.15 32 5.09 4.89 13 5.6 5.98 33 6 5.79 14 4.1 4.39 34 4.2 4.46 15 3.82 4.12 35 3.27 3.17 16 4.89 4.56 36 3.56 4.1 17 5.22 5.14 37 3.61 3.46 18 5.85 5.97 38 3.82 3.62 19 5.76 5.17 39 3.25 3.19 20 7.15 6.87 40 3.44 3.71

Fig. 2(a) PHASE Common Pharmacophore hypothesis (ADHRR)

Fig. 2(a) Active molecule 24 hydrogen donor effect


Fig. 2(c) Active molecule 24 hydrophobic effect

Fig. 2(d) Intermolecular distance in the active moiety

Fig. 2(e) Structural alignment view

412 International Journal of Drug Design and Discovery Volume 2 • Issue 1 • January - March 2011 Conclusion

The 3D-QSAR study was undertaken on a series of Sulfonamide derivatives known to be potent inhibitors against resistant Streptococcus pneumoniae MurF enzyme. Volume occupied maps were generated for these molecules which demonstrated that the activity may be increased by substituting the donor groups. Placing the hydrophobic groups at a particular position of the N [3-cyano-5-(tetrahydropyran-4-yl)]-5-(morpholine-4-sulfonyl) benzamide ring may result in increased activity. The approximation of the pharmacophore hypotheses ADHRR used for QSAR model generation to that of the binding mode of the benzamide ring and other functional groups may validate the pharmacophore hypothesis.

The resulting CPH (common pharmacophore hypothesis) ADHRR has been taken for further studies and in due course the updated results will be published.

Acknowledgment

We thank Raghu Rangaswamy Senior Director, and Ravi Kumar M, Application Scientist from Schrodinger for providing free acedamic evalution software and continuous support to undertake this research work. Special thanks to Dr Himabindu koneti for helpful discussions and critical manuscript reading from University of Bonn, Germany.

References

[1] http://www.whoindia.org/en/section6/section453.htm

[2] Longenecker KL.; Stamper GF.; Hajduk PJ.; Fry EH.; Jakob CG.; Harlan JE.; Edalji R.; Bartley DM.; Walter KA.; Solomon LR.; Holzman TF.; Gu YG.; Lerner CG.; Beutel BA.; Stoll VS. Protein sci. 2005, 14, 3039.

[3] http://textbookofbacteriology.net/resantimicrobial.html

[4] El Zoeiby A.; Sanschagrin, F.; Levesque, R.C. FEMS Microbiol. Lett. 2001, 201, 229.

[5] Wong, K.K.; Pompiano, D.L. In Rosen, B.P.; Mobashery, S., Eds.; Plenum: NY, 1998, 197.

[6] Goldman, R.C.; Branson, A. Curr. Pharm. Design. 1999, 5, 473.

[7] Santosh A. Khedkar.; Alpeshkumar K. Malde and Evans C. Coutinho. J. chem.. Inf. Model. 2007, 47, 1839.

[8] Miller, D. J.; Hammond, S. M.; Anderluzzi, D.; Bugg, T. D. J. Chem. Soc., Perkin Trans 1 1998, 1, 131.

[9] Bertrand, J. A.; Auger, G.; Martin, L.; Fanchon, E.; Blanot, D.; Le Beller, D.; van Heijenoort, J.; Dideberg, O.J. Mol. Biol. 1999, 289, 579.

[10] Jorgensen, W. L. Science 2004, 303, 1813.

[11] Bembenek, S. D.; Keith, J. M.; Letavic, M. A.; Apodaca, R.; Barbier,A. J.; Dvorak, L.; Aluisio, L.; Miller, K. L.; Lovenberg, T. W.; Carruthers, N. Bioorg.Med. Chem. 2008, 16, 2968.

[12] http://www.schrodinger.com

[13] Mutasem O. Taha.; Naji Atallah.; Amal G. Al-Bakri.; Catherine Paradis-Bleau.; Hiba Zalloum.; Khaled S. Younis.; Roger C. Levesque. Bioorg. Med. Chem. 2008, 16, 1218.

[14] Yu Gui Gu.; Alan S. Florjancic.; Richard F. Clark.; Tianyuan Zhang.; Curt S. Cooper.; David D. Anderson.; Claude G. Lerner.; J. Owen McCall.; Yingna Cai,; Candace L. Black-Schaefer.; Geoffrey F. Stamper.; Philip J. Hajduk and Bruce A. Beutel. Bioorg. Med. Chem. Lett. 2004, 14, 267.

[15] Samo Turk.; Andreja Kovac.; Audrey Boniface.; Julieanne M. Bostock.; Ian Chopra.; Didier Blanot.; Stanislav Gobec. Bioorg. Med. Chem. 2009, 17, 1884.

[16] Matej Sova.; Andreja Kovac.; Samo Turk.; Martina Hrast.; Didier Blanot.; Stanislav Gobec. Bioorg. Chem.2009, 37, 217.

Radha Vaddavalli : Discovery of Novel Inhinbitors Against Resistant Streptococcus pneumoniae… 403...

Documents

Transcript of Radha Vaddavalli : Discovery of Novel Inhinbitors Against Resistant Streptococcus pneumoniae… 403...