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Original Article

Pharmacogenomics of drug resistance in BreastCancer Resistance Protein (BCRP) and its mutatedvariants

Sugunakar Vuree a, Nageswara Rao Dunna b, Imran Ali Khan c,Khalid K. Alharbi c, Satti Vishnupriya a, Divya Soni d, Pratik Shah d,Harshpreet Chandok d, Mukesh Yadav e, Anuraj Nayarisseri d,*aDepartment of Genetics & Biotechnology, Osmania University, Hyderabad, Indiab School of Chemical & Biotechnology, SASTRA University, Thanjavur 613401, Tamilnadu, IndiacClinical Laboratory Sciences Department, College of Applied Medical Sciences, King Saud University,

P.O. Box 10219, Riyadh 11433, Saudi Arabiad In silico Research Laboratory, Eminent Biosciences, Vijaynagar, Indore 452010, IndiaeDepartment of Pharmaceutical Chemistry, Softvision College, Vijaynagar, Indore 452010, India

a r t i c l e i n f o

Article history:

Received 21 June 2013

Accepted 29 June 2013

Available online 29 July 2013

Keywords:

Breast Cancer Resistance Protein

(BCRP)

Mutagenesis

Comparative modeling

Molecular docking

Pharmacogenomics

* Corresponding author. Tel.: þ91 9752295342E-mail address: anuraj@eminentbio.com

0974-6943/$ e see front matter Copyright ªhttp://dx.doi.org/10.1016/j.jopr.2013.06.020

a b s t r a c t

Aim: Drugs in breast cancer treatment suffer resistance and drug efflux from ATP-binding

cassette (ABC) efflux transporter protein. Drugs inhibiting BCRP suffer activity alteration

due to sequence variants. It is imperative to investigate role of mutant variants using

structure based aspects of drug binding.

Method: In presentwork,we included single nucleotide polymorphisms like F208S, S248P and

F431L in BCRP structure and evaluated their role in alteration of drug binding affinities using

computational approaches. Comparativemodeling of BCRP 3D structurewas achieved using

various tools available followed by structure validation. Mutagenesis and its impact by SNPs

was attained in 3D structure of BCRP. A set of selected and established BCRP inhibitors were

further docked into binding site to record the drug resistance in mutant variants.

Results: Nucleotidebinding (NB)domain (258AA)andtransmembrane (TM)domain (291AA)of

BCRP were modeled separately and assembled together to generate a single structure. Ram-

achandranPlot confirmedqualityofmodeled structuresalongwithmainchainandsidechain

parameters. Mutagenesis included threemain variants (F208S, S248P and F431L) using Triton

program.Moleculardocking results showed inhibitorCID_25223199bindingeffectively towild

andF431Lmutant structureofBCRPwhile inhibitorsCID_25223002 toF208SandCID_119373 to

S248P.

Conclusion: Distortion inspatial arrangementofaminoacids inBCRPproteindue tomutations

led to low efficacy in drug response with respect to wild isoform. Results of present work

demand to probe pharmacogenomic aspects in drug development efforts for breast cancer.

Copyright ª 2013, JPR Solutions; Published by Reed Elsevier India Pvt. Ltd. All rights

reserved.

.(A. Nayarisseri).2013, JPR Solutions; Published by Reed Elsevier India Pvt. Ltd. All rights reserved.

j o u rn a l o f p h a rma c y r e s e a r c h 6 ( 2 0 1 3 ) 7 9 1e7 9 8792

1. Introduction

2. Methods

Breast Cancer Resistance Protein (BCRP) is amembrane-bound

protein andbelongs to theATP-binding cassette family. BCRP is

also called as ABCG2 which is present in many normal tissues

and solid tumors including bloodebrain barrier, placenta, liver,

small intestine, adrenal gland, testis and stem cells.1 BCRP

deliberate drug resistance to many anti-cancer agents such as

irinotecan, topotecan, tyrosine kinase inhibitors and mitox-

antrone. BCRP is ATP-binding cassette (ABC) efflux transporter

that deliberatesmultidrug resistance in breast cancer and also

plays an important role in the absorption, distribution and

elimination of drugs.1,2 It is of elementary significance to

investigate the function and binding site of BCRP protein. BCRP

contains 655-amino acid with a single nucleotide binding

domain (NBD) and six transmembrane domains (TMD). BCRP is

a half-transporter, and thus requires at least two NBDs to

function as a drug efflux pump. Hence, functional BCRP exists

as either homodimers or homo-multimers.3,4 3D structure of

BCRP has not been solved in Protein Data Bank yet. Hence the

aimof thecurrent study is to construct the3Dstructureof BCRP

to investigate the interaction of ligands of BCRP in wild and

mutated models in order to define possible binding sites.

Fig. 1 e Multiple alignment between target sequence and templ

PSIPRED.

Protein sequence has been retrieved from UniProtKB/Swiss-

Prot.5 Present study used Homology modeling methods to

construct the 3D structure of Human BCRP. Human BCRP ho-

mology model was built using MODELLER (Figs. 2 and 3), a

Computational algorithm for Protein structural assessment.6

The template protein was searched through Delta Blast algo-

rithm against PDB Database which maintain by RCSB.7 High

resolution of Crystal Structure of the ATP-bound Escherichia

coli MalK (PBD ID: 1Q12)8 and Staphylococcus aureus permease

protein SAV1866 (PDB ID: 2HYD)9 were used as a template to

model nucleotide binding domain (NBD) and transmembrane

(TM) domains respectively. It is mandatory to convert the

target sequence into MODELLER format. MODELLER requires

the sequence in PIR format in order to be read. The FASTAwas

converted to PIR using Readseq, an algorithm developed by

EMBL.6 Structure similarity has been performed by using the

profile.build(), an in-built command in MODELLER.10 The

result has been then compared with Blast result. The build_-

profile.py has been used for the local dynamic algorithm to

identify homologous sequences against target BCRP sequence.

At the end of this process a log file has been generated which

ate sequence based on secondary structure using DSSP and

Fig. 2 e Solid ribbon view of human BCRPeNB domain and TM domain and assembled model generated by in silico

modeling using MODELLER.

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is named build_profile.log which contains errors and warn-

ings in log file.11e13

3. Results and discussions

3.1. BCRP structure modeling

The result generated here was the same templates 1Q12 and

2HYD, that was earlier obtained fromDelta blast alignment. In

order to ratify the conserved secondary structure profiles, a

multiple sequence alignment program DSSP14 and PSIPRED15

was utilized which identified the corresponding position of

amino acids in the query sequence of BCRP and template

Protein (Fig. 1). This is a confirmatory statement to build the

strong alignment in homology modeling.6

For a comparative investigation, Homology Modeling also

been performed using various softwares like SPDBV, MOD-

ELLER, CPH, Phyre, PS2, 3Djigsaw, Esypred3D etc. Structure

validation has been studies using Ramachandran Plot16 by

Procheck.17 Ramachandran Plot shows the MODELLER which

is the bettermodel have out of 428 obtained amino acids 90.1%

residues are in core region, 8.2 are in additional allowed re-

gion, 1.1 are in generous allowed region and 0.6% are in dis-

allowed region (Table 1).

After satisfactory validation using Ramachandran dia-

gram, it is mandatory to analyze main chain and side chain

parameters using Procheck tool for structure validation. In

retrieval and perusal of parametric values from main chain

validation, it was confirmed that the ratio of % of residues

(>90%) to resolution in angstrom (2.0) fits in the expected

Fig. 3 e Visualization of molecular surfaces of human BCRPeNB d

in silico modeling using MODELLER.

place. Standard deviation to resolution ratio touches the bot-

tom values of the region indicating acceptance of the model

(Fig. 4). Bad contacts in themodels structure remained below 5

per 100 residues which again add up to the better quality of

homologymodel. In addition, zeta angle standard deviation in

range and G-factor near 0 values suggests appreciable protein

structure quality (Fig. 5). Moving to side chain parameters,

Chi-1 gauche minus and Chi-1 Trans parameters fell below

required belt of optimal region and thus suggest improved

modeling efforts related to side chain minimization. Various

side chain parameters confirmed BETTER status in graphical

presentation view and thus the protein 3D structure model

retains good quality of main chain and side chain parameters

(Table 2).

3.2. Mutagenesis and mutational assessment of HumanBCRP

Mutational investigation of BCRP has been carried out from

various literature. Natural variants and Non-natural variants

have been obtained from literature and experimental infor-

mation. The transport activity of Q141K would be expected to

be lesser as compared to BCRP wild-type. BCRP Wild-type

generally had lower plasma levels of BCRP substrate drugs

than Q141 variant.18 A systematic study of 16 natural variants

of BCRP showed that the variants Q126stop, F208S, S248P,

E334stop, and S441N were defective in porphyrin transport,

whereas F489L displayed approximately 10% of the transport

activity of wild-type BCRP19 (Fig. 6). PolyPhen-2 software has

been used for selecting the effective mutagenesis for the

present study.20,21

omain and TM domain and assembledmodel generated by

Table 1 e Structure validation using Ramachandran Plot by Procheck.

Modelingtool

Model No ofresidues

Totalresidues

Most favoredregion

Additionalallowed regions

Generouslyallowed

Disallowedregion

MODELLER NB 213 428 90.1% 8.2% 1.1% 0.6%

TM 215 81.7% 15.3% 2.4% 0.5%

SPDBV NB 206 378 86.9% 11.7% 2.1% 0.5%

TM 172 84.3% 9.1% 4.3% 2.3%

CPH NB 195 362 82.1% 13.1% 3.1% 1.3%

TM 167 85.4% 11.1% 2.3% 1.1%

Phyre NB 183 348 86.8% 11.7% 1.0% 0.5%

TM 165 80.1% 14.6% 4.3% 2.1%

Note: NB domain total length 258 amino acid and TM domain total length 291 amino acids.

j o u rn a l o f p h a rma c y r e s e a r c h 6 ( 2 0 1 3 ) 7 9 1e7 9 8794

PolyPhen-2 reports that out of all the 16 SNPs, G51C, F208S,

S248P, R482G, R482T and F431L are probably and possibly

damaging with an average score of 0.630 (sensitivity: 0.64;

specificity: 0.63). HenceMutagenesis has been carried out only

for the above mentioned Variants. Mutagenesis model was

constructed using TRITON,22 a Linux based graphic software

package for In silico construction of protein mutants (Fig. 7).

Mutagenesis has been carried out only for F208S, S248P and

F431L as the remaining mutants are not covered in the

sequence of homology model.

Fig. 4 e Shows the main chain parameters of BC

Fig. 5 e Shows the main chain parameters

3.3. Molecular docking studies of BCRP

Flexible molecular docking studies using Molegro Virtual

Docker (MVD) produced appreciable results in terms of se-

lective interactions with wild BCRP and its mutant (F208S,

S248P and F431L) variants. 26 Inhibitors, selected by simi-

larity structure search from BindingDB and subsequently

from Pubchem database, were docked in the inhibitor bind-

ing site of BCRP inhibitors. Results of molecular docking are

presented in Table 3. Results showed different magnitudes of

RP, % of residues in most favored regions.

of BCRP, Standard deviation (kcal/mol).

Table 2 e Side chain parameters of BCRP plot statistics.

Stereochemicalparameter

No. ofdata pts

Parametervalue

Comparison values No. of band widthsfrom mean

Typical value Band width

a. Chi-1 gauche minus st dev 65 6.8 18.1 6.5 �1.7 BETTER

b. Chi-1 trans st dev 135 7.3 19.0 5.3 �2.2 BETTER

c. Chi-1 gauche plus st dev 151 7.7 17.5 4.9 �2.0 BETTER

d. Chi-1 pooled st dev 351 7.5 18.2 4.8 �2.2 BETTER

e. Chi-2 trans st dev 99 10.3 20.4 5.0 �2.0 BETTER

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interactions and energy scores in terms of MolDock score,

rerank score and RMSD values. Inhibitors are found to show

profound impact of mutation isoforms BCRP protein. Inhibi-

tor (CID_25223199) binding strongly wild isoform (rerank

�162.89) of BCRP was also found to act equally on F431L

Fig. 6 e Results obtaine

Fig. 7 e Mutagenesis models

(rerank �145.18) but was found non-effective in F208S and

S248P mutated isoforms, as showed in Table 3. Other two

inhibitors which appeared in the top list are CID_25223002

against F208S with rerank score (�145.703) and CID_119373

against S248P with rerank score (�139.266) respectively.

d from PolyPhen-2.

constructed in TRITON.

Table 3 e Molecular drug binding studies on wild and mutated BCRP using Molegro Virtual Docker.

S.N. CID MolDock score Rerank score RMSD

Wild F208S S248P F431L Wild F208S S248P F431L Wild F208S S248P F431L

1 CID_119373 �167.574 �161.97 �187.307 �147.16 �117.25 �106.967 �139.266 �117.003 0.5459 0.3857 4.454 3.96

2 CID_104842 �134.962 �130.27 �138.82 �119.01 �97.7431 �102.433 �108.008 �94.299 0.0435 0.5221 1.429 4.237

3 CID_123631 �169.267 �141.31 �122.98 �167.76 �119.833 �96.390 �91.020 �95.001 0.7995 1.20 0.860 2.560

4 CID_403923 �132.687 �121.10 �136.09 �119.62 �99.753 �110.19 �121.10 �109.63 0.3199 1.211 3.100 0.966

5 CID_698956 �123.338 �118.56 �125.10 113.91 �94.9286 �103.75 �96.11 �96.29 0.6628 0.878 0.792 1.020

6 CID_1553837 �125.657 �107.20 �119.30 �122.10 �92.6223 �107.24 �98.16 �91.88 0.2935 1.982 1.781 0.686

7 CID_5280682 �125.76 �114.91 �129.17 �102.36 �95.042 �102.14 �122.22 �98.10 0.8064 1.677 1.102 2.199

8 CID_5352005 �123.128 �134.19 �102.87 �120.14 �68.017 �111.37 �119.49 �114.38 0.6553 1.011 0.8889 0.926

9 CID_9880195 �122.711 �119.11 �114.99 �101.17 �92.075 �117.07 �91.88 �121.62 2.8226 0.619 0.644 1.411

10 CID_10322450 �129.122 �126.86 �126.01 �122.68 �95.7735 �105.69 �123.19 �120.70 2.72 1.771 1.316 1.901

11 CID_14034811 �109.801 �111.189 �97.562 �103.34 �76.708 �104.26 �96.28 �124.33 3.66 1.671 0.315 0.671

12 CID_20239593 �27.659 �22.390 �27.622 �27.289 �94.948 �109.15 �97.20 �127.14 1.342 1.278 0.254 0.691

13 CID_24827935 �118.02 �112.01 �117.10 �120.85 �81.463 �102.12 �126.10 �119.25 1.719 1.289 0.915 0.591

14 CID_25195356 �133.192 �120.73 �111.29 �129.39 �110.42 �116.10 �94.92 �120.87 1.665 1.274 0.912 0.281

15 CID_25222999 �178.525 �171.19 �166.61 �152.10 �113.723 �101.39 �103.19 �117.22 1.957 1.090 1.271 1.061

16 CID_25223000 �170.343 �142.20 �121.87 �121.10 �78.991 �114.17 �98.24 �112.20 1.898 0.610 0.781 1.276

17 CID_25223002 �179.151 �197.165 �163.576 NA �133.96 �145.703 �112.56 NA 1.792 2.479 0.859 NA

18 CID_25223199 �164.46 �128.17 �132.10 �96.10 �162.89 �136.19 �135.15 �145.18 3.898 0.758 0.891 0.289

19 CID_25223200 �153.309 �130.96 �147.19 �126.97 �97.503 �127.29 �92.19 �136.27 3.730 1.520 1.803 0.750

20 CID_54754510 �137.98 �117.29 �129.891 �116.28 �107.02 �137.29 �135.29 �133.38 0.645 2.901 3.189 0.619

21 CID_54754512 �135.59 �124.19 �126.109 �129.16 �89.203 �129.17 �127.18 �131.17 1.862 1.892 0.671 0.381

22 CID_56681840 �124.497 �134.16 �127.201 �120.48 �85.734 �133.18 �125.92 �119.68 0.5250 0.456 0.293 2.180

23 CID_56924700 �137.55 �146.19 �129.109 �96.129 �110.006 �135.15 �128.49 �131.14 1.6169 0.901 3.189 0.129

24 CID_56924701 �157.819 �131.79 �142.49 �129.39 �156.84 �93.18 �137.17 �135.17 2.991 1.671 1.670 0.318

25 CID_56951137 �161.44 �117.18 �139.28 �159.20 �112.117 �110.88 �96.57 �96.74 2.931 0.891 0.419 0.618

26 CID_56951138 �141.21 �135.96 �126.17 �135.28 �107.556 �134.37 �139.19 �124.10 5.723 3.116 2.179 0.328

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Detailed report comprising MolDock score, rerank score and

RMSD values of docked inhibitors have been produced in

Table 3 below.

Docking scores are mathematical calculations to quantify

force-fields between binding site of receptors and interacting

ligands. For qualitative discussion, we should identify partic-

ipation of atoms and groups of ligand with those

Fig. 8 e (a) Best inhibitors with wild-type (b) best inhibitors with

interactions (Maroon color) of best inhibitors interacting with w

complimenting atoms and groups of receptor amino acids. In

order tomap qualitative aspects of molecular docking studies,

we have noted various types of atomic and molecular in-

teractions which are reproduced in Fig. 8(a and b) and

Fig. 9(a and b). Blue dotted lines depicts H-bond while maroon

dotted lines quote steric interactions. Electrostatic in-

teractions are found absent in current docking studies.

F208S mutation. (a) and (b) H-bonds (Blue color) and steric

ild and F208S mutation in BCRP protein.

Fig. 9 e (a) Best inhibitors with S248P type (b) best inhibitors with F431L mutation. (a) and (b) e H-bonds (Blue color) and

steric interactions (Maroon color) of best inhibitors interacting with S248P and F431L mutation in BCRP protein.

j o u r n a l o f p h a rm a c y r e s e a r c h 6 ( 2 0 1 3 ) 7 9 1e7 9 8 797

Effect of mutagenesis in BCRP and drug response can be

clearly recorded from below interactions and binding affinity

scores of inhibitors with respect to wild and mutant isoforms.

Alteration of a single amino acid via mutagenesis introduces

major changes in spatial arrangement of amino acid in 3D

structure, thereafter, leading to response variation in different

genotypes. It is clear from Figs. 8 and 9 that single nucleotide

polymorphism (SNP) in BCRP has completely altered the in-

teractions among binding site and ligand atoms. There are

very few amino acids repeated in wild and mutated isoforms

to get involved in H-bond and steric interactions.

4. Conclusion

Extensive computational approaches resulted in successful

molecular modeling of BCRP structure using a set of compar-

ativemodeling tools. Satisfactory structure validation allowed

BCRP submission to mutagenesis including F208S, S248P and

F431Lmutant variation in its wild structure. A set of inhibitors

was docked subsequently with wild-type and all threemutant

isoforms to record impact of mutagenesis on drug binding

response. Present work clearly indicates profound role of

genotypic variants of BCRP responsible for altered drug ac-

tivity in different patients. We suggest an imperative and

extensive laboratory research on BCRP and its variants

developing drug resistance against established drugs in pa-

tients. Present work confers relation of mutant variants with

drug resistance in breast cancer patients.

Conflicts of interest

All authors have none to declare.

Acknowledgment

The financial support from T.R.R - Research scheme Feb 2012,

School of Chemical &Biotechnology, SASTRA University,

Thanjavur, India isgratefullyacknowledged.Theauthorswould

like to extend their sincere appreciation to the Deanship of

ScientificResearchatKingSaudUniversity for its fundingof this

research through the Research Group Project no RGP-VPP-244.

We thank Eminent Biosciences, Indore, India for providing the

necessaryComputational biology facility and technical support.

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