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Small Molecule Interactions with Protein Tyrosine Phosphatase-1B

Jon Paul

4950/4960

May 16, 2014

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INTRODUCTION

Protein Tyrosine Phophatase-1B is a class of enzymes playing an important part in cell-

signaling among and between cells.1 Protein Tyrosine Kinases are necessary to the

functioning of proteins in the body. These PTPs work antagonistically with PTKs; this

alteration in protein function is accomplished through phosphorylation and

dephosphorylation of the phosphotyrosine residues by PTKs and PTPs respectively. As well

as its aid towards cell differentiation and proliferation, PTP-1B has shown involvement in

diabetes, hypertension, rheumatoid arthritis, and cancer.2 As a negative regulator of

insulin, PTP-1B inhibition proves critical in the development of drugs treating type two

diabetes. This examination of PTP-1B will begin with its structure and signature motifs, we

will then move towards a molecular modeling approach where we examine a ligand docking

to several of PTP-1B crystal structure – both wild type and mutant. A list of important

residues toward binding will be discussed - the RMSD of each ligand will be measured and

analyzed against each PTP-1B crystal structure. A qualitative structure activity-relationship

will be performed to validate the method and test whether future studies can be used with

such parameters. Finally, a pharmacophore model will then be performed on a NCBI list of

compounds containing carboxylate and amino bi-functional groups. Among these

compounds, 50 will be selected and docked to a wild type protein, 1PXH, in the NCBI

protein database. A further discussion will conclude with the docking score of the top two

ligands and their docking score; an analysis as to their usefulness in inhibiting PTP-1B will

encourage the development of further research. An outline of this paper can be seen below:

Structure

PTP-1B Proteins

Ligand Properties

1 Silva, Nathan, and David Marcey. "Protein Tyrosine Phosphatase." Protein Tyrosine Phosphatase. N.p., 1 Jan. 2001. Web. 12 May 2014. <http://www.callutheran.edu/BioDev/omm/ptp1b/molmast.htm>.2 http://ruchir.myweb.uga.edu/bcmb8010/Enzyme%20report.pdf

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Ligand Docking

QSAR Studies

Docking and QSAR Discussion

Pharmacophore Model

Selection of Ligands

Docking of Ligands

Conclusion

STRUCTURE

PTP-1B’s structure contains three loops proving important in catalysis; these are the WDP-

loop, the PTP-loop, and the recognition loop. The secondary structure of PTP-1B contains 9

alpha helices and 1 main beta sheet – 8 strands. The PTP-loop is highly conserved

containing the signature motif, H-C-X-X-G-X-X-R.3 This 1 letter code corresponds to their

respective amino acids. X stand for the stop codon typically addressed as TAA, TAG, or

TGA. Cys215 and Arg221 of the PTP-loop are the most crucial for catalysis. Val49 and

Tyr46 help aid the substrate into the active site. Ser216 forms a hydrogen bond with the

recognition loop further stabilizing the active site. The WDP-loop, Tryptophan, aspartic

acid, and proline residues account for the name of this loop. Asp181 and Gln262 are also

important in catalysis. Of the PTP-loop, Ser222 stabilizes the thiolate while arginine helps

bind and stabilize the transition state. The WDP-loop is commonly acid-base mechanics

with Asp181 activity relying on the closed conformation of this loop. Other loops include

the Q-loop and the lysine loop. The Q-loop contains Gln262 which serves in the second step

of catalysis. The lysine loop contains Lys120 – this interacts with the WDP-loop. Below is an

image marking the important residues and loops.

3 http://www.auburn.edu/academic/classes/biol/6190/CellSignalingBiology/csb005.pdf

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Figure 1 - Important Residues and Loops of PTP-1B

PTP-1B ProteinsThe crystal structure of several PTP-1B were looked at. The PDB contained all of these

structures and can be found using the following ID entries: 1I57, 1PXH, 1PA1, 2B4S, 3CV2,

3I80, 3A5K, and 2CM2. PTP-1B crystal structures were downloaded from the NCBI

database and imported to Maestro and MOE; this step reviewed missing residues and

water molecules; accomplished by fellow classmate, John Martinez. Several versions

without water interactions were studied; however, these were not extensively focused on

during the research. Of the proteins, 1PXH, 2B4S, 3I80, and 2CM2 are wild-type. 1I57 is a

mutant showing C215S – Cysteine to Serine. 1PA1 shows C215D – Cysteine to Aspartic

Acid. 3A5K shows a mutation at C121W – Cysteine to Tryptophan. 3ZV2 contains mutation

C215A – Cysteine to Alanine; S216A – Serine to Alanine. An image below shows the

proteins superposed. Mutations in the crystal structure account for the differences seen in

the loop structure next to the binding pocketing. One thing to note is that only 1PXH, 2B4S,

3A5K, 3I80, and 3ZV2 are shown in the image below.

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Figure 2 - PTP-1B Superposition

LIGAND PROPERTIES63 ligands were used for the docking used in this research study. The ligands were

downloaded from the protein databank. Minimization was performed and superposition

before docking occurred. The ligand properties included the KD/IC50 (nM) and resolution.

Below is a list of the 63 ligands with their respective activity concentration and resolution.

These properties were all found from the RCSB protein data bank and their respective

values can be confirmed using these search engines. Any ligand that had an activity below

1000 nM was considered to be a strong binder. The ligands that exhibited this feature are

highlighted in yellow. The root mean squared distance was also evaluated and can be seen

using the following graphs. In addition, this shows the number of ligands that achieved a

distance of less than < 2.0 or 2.5 Å.

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<2.0 >2.0 >2.5 <2.50

10

20

30

40

50

RMSD For Ligands Binded to 1PXH

Å

Cou

nt

<2.0 >2.0 >2.5 <2.505

1015202530354045

RMSD For Ligands Binded to 2B4S

Å

Cou

nt

<2.0 >2.0 >2.5 <2.50

10

20

30

40

50

RMSD For Ligands Binded to 3CV2

Å

Cou

nt

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<2.0 >2.0 >2.5 <2.505

10152025303540

RMSD For Ligands Binded to 3A5K

Å

Cou

nt

<2.5 >2.5 <2 >2 05

10152025303540

RMSD For Ligands Binded to 1I57

Å

Cou

nt

<2.0 >2.0 >2.5 <2.505

1015202530354045

RMSD For Ligands Binded to 1PA1

Å

Cou

nt

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LigandResoluti

onKD/IC50

(nM) LigandResoluti

onKD/IC50

(nM)4I8N 2.5 N/A 2CNH 1.8 593EB1 2.4 20000 2CNG 1.9 1103EAX 1.9 12000 2CNF 2.2 2703D9C 2.3 N/A 2CNE 1.8 1.73CWE 1.6 120 2CMB 1.7 652ZN7 2.1 13 2CMA 2.3 1852ZMM 2.1 25 2CM8 2.1 13502VEY 2.2 43 2CM7 2.1 1900002VEX 2.2 180 2BGE 1.8 16080002VEW 2.0 64 2BGD 2.4 25002VEV 1.8 1300 2B07 2.1 3702VEU 2.4 2900 2AZR 2.0 2300002QBS 2.1 210 1XBO 2.5 9202QBR 2.3 470 1WAX 2.2 860002QBQ 2.1 36 1T4J 2.7 80002QBP 2.5 4 1T49 1.9 220002NTA 2.1 16000 1T48 2.2 3500002NT7 2.1 300 1QXK 2.3 90002HB1 2.0 160000 1Q6T 2.3 53402H4K 2.3 3200 1Q6S 2.2 2202H4G 2.5 300 1Q6P 2.3 2402FJM 2.1 142 1Q6N 2.1 232F71 1.6 2500 1Q6M 2.2 1202F70 2.1 33500 1Q6J 2.2 1202F6Z 1.7 4800 1Q1M 2.6 1100002F6Y 2.2 134800 1ONZ 2.4 80002F6W 2.2 >500000 1ONY 2.2 1702F6V 1.7 37000 1NWL 2.4 41000002F6T 1.7 42500 1NWE 3.1 N/A2CNI 2.0 21 1NO6 2.4 260001NL9 2.4 1100 1NNY 2.4 22

Figure 3 - Ligand Properties

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LIGAND DOCKINGDocking was carried out using sixty-two ligands from the NCBI database. The ligands were

incorporated to MOE with energy minimization. The above PTP-1B crystal structures were

used as the docking protein for these ligands – these are mutant/wild-type variants of PTP-

1B. A table of the sixty-two ligands with their entry id and their docking score can be found

in appendix A1. After the ligand docking was completed, images of the ligand interactions

with the proteins were analyzed to find similarities and differences. Below is an image of

the interactions between the top two ligands with the highest docking scores. Further

interactions of the top ten ligands can be found in appendix A2. The ligands below are

2CNF and 1Q6S with a docking score of -9.512 and -9.433 respectively. These were docked

to the IPXH wild-type protein.

A table was compiled to list the ligand and their corresponding residue interactions. It

should be noted that Arg221 is a common trend among most of the ligands, followed by

Ser216. The least occurring interaction was Arg24.

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The common Arg221 is seen in these ligands as it is crucial in catalysis. The interaction is

formed generally from the deprotonated sulfonamide or phosphate group. As noted earlier

Ser216 will form a hydrogen bond to stabilize the active site; a common feature in the

ligands above. Gln262 is a characteristic amide amino acid and will interact with the

hydrogens attached to the amino groups. Arginine is present at residue 24. Only one

interaction with Arg24 is seen in 1Q6S; this is likely due to the fact that there are no

deprotonated carboxyl groups present in the given radius. Alanine is a small amino acid

that interacts with a free ketone usually located on a phosphate group; this is not seen in

2CNF as the orientation of the ligand to the active site does not allow for Arg217 to

interact – instead, Arg221 is more important in catalysis than Arg217. 2NTA has the third

highest docking score but contains only Arg221 as an interaction. The molecule is small in

nature with only a sulfonamide and a ketone. 2CNH has the fourth highest docking score

featuring a benzene ring that interacts with Tyr46 and a sulfonamide group interacting

with Asp48. A benefit of adding more benzene rings along with other sulfonamide groups

may allow for a better docking score for 2CNF and 1Q6S as this would allow for more

interactions. Among all of these ligands we see characteristic function groups of ketones,

sulfonamides, amines, phosphates, and some ions such as chlorine or fluorine. We could

further expand this study in the future by incorporating more ligands and running a

quantitative structure-activity relationship (QSAR) study with these compounds; however,

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this is out of the aim of this current research analysis. A list of the docking score of the ten

ligands can be seen below:

2CNF -9.5121Q6S -9.4332NTA -9.4042BGD -9.1892CNH -9.1342VEW -9.0771Q6N -8.8672FJM -8.8212VEV -8.8171Q6J -8.791

The above ligands were selected for pharmacophore modeling based on their docking

score. The selection was chosen based on the fact that 1PXH is a wild-type crystal structure

of PTP-1B. Other wild-type proteins could have been used. A similar study would

incorporate pharmacophore modeling of the ligands docked to other PTP-1B protein

structures. Protein 2B4s was able to obtain the highest docking scores with the ligands.

Ligands 2CMB and 2CNI had docking scores of -12.509 and -12.381 respectively. An image

of their interactions can be seen below. These two ligands contained the notable Arg221

and Asp48. Other interactions included Arg45, Arg47, and Phe182. A list of the top ten

ligands docked to 2B4S and their interactions can be seen below:

2CMB

-12.50

9

ASP48, ARG45, ARG221

2CNI -12.38

1

ARG24, ASP48, PHE182, GLN266, ALA217

2CNH

-12.37

6

ARG47, ASP48, ALA217, GLY220, GLN266, PHE182, ARG221

2VEY -11.51

4

ALA217, ARG47, PHE182, ILE219, ASP48, GLN266, ARG221, GLY220

2CM8

-11.44

1

ARG221, GLN262

1Q6S - ARG24, SER216, ALA217, GLY218, ILE219, GLY220, ARG221

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11.401

2CMA

-11.16

1

GLN266, PHE182, ARG47, ASP48, I:LE219, GLY220, GLY218, ALA217, ARG24

2VEU

-10.85

7

ALA217, GLY220, GLN266, PHE182, ARG221

2VEV -10.74

3

GLN266, PHE182, ALA217, GLY220, ASP48

2VEW

-10.73

9

ARG47, ASP48, ALA217, GLY220, ARG221, PHE182, GLN266

2CMB Interactions

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Another wild-type protein 3I80 was examined. The top two ligands had a docking score of -

10.832 and -9.223. A list of the docking scores and interactions can be found below. Two

images of 1NWE and 1Q6T interactions will follow.

1NWE

-10.832 ARG47,45, ASP48, GLN262, PHE182, ARG221, ALA217

1Q6T -9.223 ARG47, GLY2592CNI -8.339 ARG47, ARG453EB1 -8.337 TYR46, ARG47, GLN262, ARG221, ALA217,

PHE1821Q6N -8.195 ARG471Q1M

-8.120 TYR46, LYS120, PHE182, ALA217, ARG221, ARG45

1T49 -7.979 ASP48, TYR46, ARG472CNH

-7.932 N/A

3EAX -7.845 LYS41, ASP48, ARG47, TYR461Q6S -7.844 PHE182, ARG47

2CNI Interactions

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INWE Interactions 1Q6T Interactions

QSAR STUDIESThe qualitative structure-activity relationship was performed on this using a training set

and test set. The ligands were chosen at random and to achieve a better RMSD the highest

ranges were removed. When using four descriptors, partition coefficient, SMR, TPSA, and

weight, a RMSE of 0.86344 and A R2 of 0.67188 was obtained. Using three descriptors:

partition coefficient, SMR, and Weight, achieved a RMSE of 0.86537 and a R2 of 0.67041.

Using two descriptors: partition coefficient and SMR gave RMSE 1.13430 and R2 0.43373.

It is important to note that when we removed weight as an important descriptor we found

that the QSAR study was not as reproducible; this means weight is an important descriptor.

The best model was found using one descriptor, the partition coefficient which achieved an

RMSE of 0.63531 and a R2 of 7.4466. While I did not include graphs that show the line

equation or outliers, the general purpose of the QSAR study was accomplished.

DOCKING AND QSAR DISCUSSION

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Successful completion of the docking and ligand properties was performed. A careful

analysis shows that these ligands contain many of the important residues interactions

necessary for catalysis. From the QSAR study it would be important to look at other ligands

with different logP(o/w) coefficients. A further research would include a set of compounds

that have low/high logP(o/w) and low/high weight. Since these were the most important

descriptors it is possible to obtain a better method for testing these compounds if we can

perform docking over a broader range of ligands.

PHARMACOPHORE MODELPharmacophore modeling allows the user to select different areas of a molecule and search

them against a known NCBI database. The ten ligands that were docked to the 1PXH

crystal structure were superimposed and a pharmacophore model was selected. A list of

20,465 molecules were found fitting the 7 descriptors ranging from aromatic hydrogen

acceptor to hydrogen donor. Running the list with 6 of the 7 descriptors found 728,754

molecules. A final run finding 5 out of the 7 gave 10,747,393 molecules; a file that was 8

gigabytes in size. It is pertinent when performing a pharmacophore model to select as

many fields as possible to limit your query results. A limit of choosing the necessary parts

of the ligands is important so as not to incorporate unnecessary interactions. An image of

the pharmacophore model is shown below:

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SELECTION OF LIGANDSAfter the pharmacophore model had finished running, the database was opened using

MOE. 50 ligands were selected based on their characteristic bi-functional groups of

carboxylate and amino groups.

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DOCKING OF LIGANDSDocking of the 10 ligands was performed using MOE and Maestro. The top two ligands had

a docking score of -8.67 and -7.95. The lowest docking score was 2.56. Interactions of

Asp48, Arg221, Gly220, Cys215, Ala217, Lys120, Arg45, His25, and Gly259. These ligands

were able to obtain some of the important residues in the docking but it is likely that many

were hindered by the rings that flow throughout the molecule. A necessary study would be

to look at smaller ring systems vs. larger ring systems to see if there is a substantial

difference in the binding between these differences. A common feature that gave rise to

many interactions was the deprotonated carboxyl groups; a functional group that was

important in our selection did prove successful therefore it would be important to choose

ligands with these groups in future docking studies. A list of the ligands and their

interactions can be seen below:

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CONCLUSIONDocking of these carboxylate/amino ligands proved somewhat successful; however, the

docking score was not where I would have liked. Further expansion would include a much

broader, larger group of ligands containing these functional groups. A better analysis can

be concluded when using a larger sample because you are looking at more diversity in the

docking to the 1PXH protein. It would also be interesting to see how these ligands dock to

a mutant type PTP-1B such as 3A5K which has a mutation at C121W. Another area of

interest would be to develop a QSAR study of these ligands to determine if there is any

correlation; if the method is good, it’s possible it can be used for future compounds of

similarity. I would also like to thank Dr. Haizhen Zhong for his guiding hand and

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involvement in this

research as well as his

approachable character

and nice personality.

It is always a pleasure

working with him and I

plan to continue

doing research

with him.

APPENDIX A1

Figure 2 - 2CNF Docking Score -9.512

Figure 3 - IQ6S Docking Score -9.433

Figure 4 - 2NTA Docking Score -9.404

Figure 5 - 2BGD Docking Score -9.189

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APPENDIX A2

2NTA

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2BGD

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2CNH

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2VEW

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1Q6N

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2FJM

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2VEV

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1Q6J