Computational approaches on PBP
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This document discusses using computational approaches to understand penicillin-binding proteins (PBPs) and their inhibitors. It summarizes docking and QSAR studies of hamamelitannin derivatives as potential PBP inhibitors. Docking identified compounds with highest binding affinity. QSAR models had good predictive ability. Docking of beta-lactam antibiotics suggested some may be potent against various PBPs. A PCM model analyzed how 50 PBP mutations affect antibiotic susceptibility, finding certain mutations decrease binding near the active site. Computational approaches provide insights to improve treatment of drug-resistant bacteria.
Computational approaches on PBP
- 1. Computational Approaches for Understanding Penicilling-binding Proteins and its Inhibitors Thet Su Win MTMT/D 5736940
- 2. Outline 2 Penicillin-binding Proteins Different computational approaches Objectives and Study Design Outcomes Discussion and Conclusion5 4 3 2 1
- 3. 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
- 4. 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
- 5. 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
- 7. Function of PBPs • PBPs are involved in the final stages of synthesis of peptidoglycan 7
- 8. Function of PBPs • Inhibition of PBPs lead to irregularities in cell wall structure, cell death and lysis 8
- 9. 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
- 10. PBPs and antibiotics Changing in PBP • Polymorphism in PBP sequence • formation of PBPs that have low affinity for penicillins 10 drug resistance
- 11. 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
- 12. Spreading Scourge of Drug resistance microbial infection 12 www.go.nature.com/c217ry
- 13. Drug Discovery and Computational approaches C. O’Driscoll. http://www.nature.com/horizon/chemicalspace/background/pdf/odyssey.pdf 13
- 14. 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)
- 15. 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
- 16. QSAR 16
- 18. Comparison of QSAR and PCM 18 QSAR PCM
- 19. A systematic overview of PCM 19
- 20. 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
- 21. Study Design PBPs New Compounds (Hamamelitannin derivatives) Β-lactam antibiotics Β-lactam antibiotics 21 PCM QSAR DOCKING
- 22. Docking and QSAR studies on Hamamelitannin derivatives and PBP4 22
- 23. Study Design - 1 PBP4 New Compounds (Hamamelitannin derivatives) Β-lactam antibiotics Β-lactam antibiotics 23 QSAR DOCKING Discovery Studio 2.5Hex 6.3 PBP4 is essential for beta-lactam struggle in MRSA
- 24. Hamamelitannin derivatives a natural product found in the bark and the leaves of Hamamelis virginiana (witch hazel) 24
- 25. 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
- 26. 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.
- 27. Interaction and binding energy 27 Compound 11 Compound 14 Compound 13 -268.84 -274.11 -317.38 Promising inhibitory activity
- 28. QSAR studies steric Physicochemical Constitutional Lipo philic electrostatic 28 electronic molecular Hydro phobic Discovery Studio 2.5 structural
- 29. 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
- 30. Docking study of 19 β–lactam antibiotics on different PBPs 30
- 31. Study Design - 2 PBPs New Compounds (Hamamelitannin derivatives) Β-lactam antibiotics Β-lactam antibiotics 31 DOCKING PBP1a, PBP1b, PBP2a, PBP2b, PBP2x, PBP3, PBP4, PBP5, PBP6 19 new generation beta-lactam antibiotics and penicillin derivatives
- 32. 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
- 33. • 3D structure of these PBPs were visualized through PyMOL viewer. 33
- 34. Virtual screening results of β-lactam antibiotics by iGEMDOCK 34The lower the energy scores represent better protein-ligand binding affinity.
- 35. 35 PBP2x PBP2b PBP2a PBP1b Docking results of Ceftobiprole Formation of more than 5 hydrogen bonds with PBPs
- 36. 36 PBP3 PBP6PBP4 PBP5 Docking results of Ceftaroline Formation of more than 5 hydrogen bonds with PBPs
- 37. • Molecular properties of Penicillin derivatives and Cephalosporins obtained from Molinspiration 37
- 38. PCM model for predicting Interaction of 50 amino acid mutations in PBP2 with 3 β- lactam antibiotics 38
- 39. Study Design PBP2 New Compounds (Hamamelitannin derivatives) Β-lactam antibiotics Β-lactam antibiotics 39 PCM Penicillin G Cefixime Cefriaxone 50 PBP2 mutant variants
- 40. Chemical Descriptors Chemical Descriptors Target chemical space Compound chemical space Interaction space 40 Performance of the PCM models Interactions Cross-terms Targets Compounds
- 41. Performance of the PCM models 41
- 42. Effects of amino acid mutations on susceptibility to the three β-lactam antibiotics as calculated from the PCM Model-7 42
- 44. Location of the mutation that associated with increased resistance to β-lactam 44 A B
- 45. 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
- 46. 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
- 47. 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
- 48. 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
- 49. 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
- 50. 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
- 51. Acknowledgement Assoc. Prof. Dr. Chanin Nantasenamat 51
- 52. 52