computational approaches on pbp

Computational Approaches for

Understanding Penicilling-binding Proteins

and its Inhibitors

Thet Su Win

MTMT/D 5736940

Outline

2

Penicillin-binding Proteins

Different computational approaches

Objectives and Study Design

Outcomes

Discussion and Conclusion 5

4

3

2

1

Penicillin-Binding Proteins (PBPs)

• PBPs are a group of

proteins that are

characterized by their

affinity for and binding

to penicillin

• normal constituent of

many bacteria

3

PBPs

• They are the membrane proteins with molecular

weights ranging from 40,000 to 120,000.

• Found on several Gram-positive and Gram-

negative bacteria.

• Bacteria possess a variable number of PBPs.

• A wide variation in both number and patterns of

PBPs in different bacteria.

• These patterns often correlate with the affinity of

PBPs to penicillin and β-lactam antibiotics.

4

Classification of PBPs

5

PBPs

High molecular weight (HMW)

PBPs

Class A Class B

Low molecular weight (LMW)

PBPs

Class C

Sauvage et al, FEMS Microbiology Rev 32(2008) 234-258.

Examples

6

Exam

ple

s o

f ea

ch

cla

ss

of

PB

Ps

Function of PBPs

• PBPs are involved in the final stages of synthesis of

peptidoglycan

7

Function of PBPs

• Inhibition of PBPs lead to irregularities in cell wall

structure, cell death and lysis

8

PBPs and antibiotics

• PBPs bind β-lactam antibiotics

(penicillin, cephalosporin,

ceftazidine)

• They are similar in chemical

structure to the modular pieces

that form the peptidoglycan.

• This is an irreversible reaction

and inactivates the enzyme

• So, it is a very good target for

drugs.

9

PBPs and antibiotics

Changing in PBP

• Polymorphism in PBP

sequence

• formation of PBPs that

have low affinity for

penicillins 10

drug resistance

PDPs and Drug resistance

Four main mechanisms of drug resistance by

microorganisms

1.Drug inactivation or modification

Deactivation of penicillin G through the production of

Beta-lactamases

2.Alteration of target site

Alteration of target site in PBP (e.g. – by encoded mecA

gene in MRSA)

3.Alteration of metabolic pathway

4.Reduction of drug accumulation 11

Spreading Scourge of Drug resistance microbial infection

12

www.go.nature.com/c217ry

Drug Discovery and Computational approaches

C. O’Driscoll. http://www.nature.com/horizon/chemicalspace/background/pdf/odyssey.pdf

13

Computational approaches on Drug Targets

• Computational approaches are instrumental for drug

discovery and design efforts.

• These approaches in the context of drug discovery/design

can be divided into 3 major classes:

14

Computational

Approach example

1. Ligand-based Quantitative Structure-Activity

Relationships (QSAR)

2. Structure-based Molecular Docking

3. Systems-based Proteochemometric modeling (PCM)

Molecular Docking

A Structure-Based Drug Design (SBDD)

means “using protein structure”

Computational method that predicts the binding affinity or a score representing the strength of binding

15

QSAR

16

Proteochemometrics Modeling (PCM)

17

Comparison of QSAR and PCM

18

QSAR PCM

A systematic overview of PCM

19

Objectives

• To study the inhibitory effects of the new

compounds as potential PBP inhibitors

• To study the interaction of the existing β-lactam

antibiotics to suggest the further modification to

increase the efficacy of the drug

• To study the effects of PBP mutations on β-

lactam antibiotics

20

Study Design

PBPs

New Compounds

(Hamamelitannin derivatives)

Β-lactam antibiotics


21

PCM

QSAR DOCKING

Docking and QSAR studies on

Hamamelitannin derivatives and PBP4

22

Study Design - 1

PBP4

New Compounds




23

QSAR DOCKING

Discovery Studio 2.5 Hex 6.3 PBP4 is essential

for beta-lactam

struggle in MRSA

Hamamelitannin derivatives

a natural product found in

the bark and the leaves of

Hamamelis virginiana

(witch hazel)

24

Methods Program used

1. structure of PBP4 receptor RCSB protein data bank

2. structure of 14

Hamamelitannin derivatives

Pub Chem database

MDL ISIS draw 2.5

Module Chem3D Ultra 8.0

3. Docking Hux (version 6.3)

4. QSAR Discovery Studio v.2.5

25

Summary of Docking Study

Outcome of Docking studies

26

• Energy values were computed using docking software,

Hex (v6.3)

• Cpd 11, 13, 14 exhibited highest inhibitory activity with

lowest energy score.

Interaction and binding energy

27

Compound 11

Compound 14

Compound 13

-268.84

-274.11

-317.38

Promising inhibitory activity

QSAR studies

steric Physicochemical

Constitutional

Lipo philic

electrostatic

28

electronic

molecular

Hydro

phobic

Discovery Studio 2.5

structural

Fitness plot using Grid based and

PLS based models

29

• QSAR models have Good R2 values

• Good predictive ability

Rsquare=0.958 Rsquare=0.975

Docking study of 19 β–lactam antibiotics

on different PBPs

30

Study Design - 2

PBPs

New Compounds




31

DOCKING

PBP1a, PBP1b, PBP2a,

PBP2b, PBP2x, PBP3,

PBP4, PBP5, PBP6

19 new generation beta-lactam antibiotics

and penicillin derivatives

Methods Program used

1. 3D structure of PBPs Protein Data Bank (PDB)

2. 3D structure of β-lactam

antibiotics NCBI PubChem Compound database

3. Active site identification Qsite finder

4. Virtual screening iGEMDOCK

5. Docking Auto-Dock version 4.0

32

Summary of Docking Study

• 3D structure of these PBPs were

visualized through PyMOL viewer.

33

Virtual screening results of β-lactam

antibiotics by iGEMDOCK

34 The lower the energy scores represent better protein-ligand binding affinity.

35

PB

P 2

x

PB

P 2

b

PB

P 2

a

PB

P 1

b

Docking results of Ceftobiprole

Formation of more than 5 hydrogen bonds with PBPs

36

PB

P 3

PB

P 6

P

BP

4

PB

P 5

Docking results of Ceftaroline

Formation of more than 5 hydrogen bonds with PBPs

• Molecular properties

of Penicillin

derivatives and

Cephalosporins

obtained from

Molinspiration

37

PCM model for predicting Interaction of 50

amino acid mutations in PBP2 with 3 β-

lactam antibiotics

38

Study Design

PBP2

New Compounds




39

PCM

Penicillin G

Cefixime

Cefriaxone

50 PBP2 mutant variants

Chemical

Descriptors Chemical

Descriptors

Target

chemical

space

Compound

chemical

space

Interaction

space

40

Performance of the PCM models

Interactions

Cross-terms

Targets Compounds

Performance of the PCM models

41

Effects of amino acid mutations on susceptibility to the three

β-lactam antibiotics as calculated from the PCM Model-7

42

Goodness-of-fit for model-7

43

Location of the mutation that associated with

increased resistance to β-lactam

44 A

B

Discussion - 1

• From the Docking study of 14 Hamamelitannin

derivatives, three compounds (Comp11, 13 and

14 exhibited minimum energy value.

• And also have good R2 values in QSAR models.

• The results of QSAR and Docking studies were

supportive to the possibilities of novel PBP

inhibitors.

45

Discussion - 2

• Virtual screening and docking results suggested

that Ceftobiprole and Ceftaroline can be potent

inhibitors for all types of PBPs.

• But their molecular weight (more than 500)

decreases their permeability and bioavailability.

• Further modification should be done on these

drugs.

46

Discussion - 3

• PCM models provided that;

• Mutation at amino acid position 501 and 551

showed important inter-dependencies with many

ligand descriptors

• This mutation is located near the active site

which markedly decreased the pMIC values.

• Mutation at amino acid position 541 and 541 can

change the hydrogen bonding network.

• Also lead to decrease drug susceptibility.

47

Conclusion - 1

• Results from different computational approaches

on PBPs are useful in nature on interaction

between PBP and its inhibitors such as β-lactam

antibiotics and Hamamelitannin derivatives

• Docking studies allows discerning the molecular

details on how compounds interact with PBPs.

48

Conclusion - 2

• QSAR allows discerning the relative importance

and contributions of each and every functional

groups present in the molecular structure of

compounds.

• Such knowledge from docking and QSAR can

use to modify the molecular of compounds to be

use as the more effective drug.

49

Conclusion - 3

• PCM supports the big and also molecular details

of how a series of compounds interact with a

series of proteins.

• PCM can assess the important mutation

positions conferring change in antimicrobial

susceptibility by analyzing the cross-terms

between mutated amino acids and antibiotics.

• Computational approaches can provide the

effective suggestion for improving the treatment

on bacterial infection especially for drug

resistance strains.

50

Acknowledgement

Assoc. Prof. Dr. Chanin Nantasenamat

51

computational approaches on pbp

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