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Molecular Modeling & Drug Design

Molecular Modeling & Drug Design. Xin Chen xinchen@zju.edu.cn. Molecular Modeling and Drug Design. Homology modeling Docking QSAR/QSPR. Homology modeling. Why Molecular modeling?.

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Molecular Modeling & Drug Design

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  1. Molecular Modeling & Drug Design Xin Chen xinchen@zju.edu.cn

  2. Molecular Modeling and Drug Design • Homology modeling • Docking • QSAR/QSPR

  3. Homology modeling

  4. Why Molecular modeling? • The expression, purification, crystallization, and structure determination of some kinds of proteins remain difficult, such as membrane-bound proteins. • Functional characterization of a protein is one of the most fundamental problems in biology.

  5. Laws • The laws of physics - de novo / ab initio • The theory of evolution – threading and homology modeling

  6. What is Homology modeling? • Homology modeling (comparative modeling) of protein refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein (the "template").

  7. Theory • A small change in the protein sequence usually results in a small change in its 3D structure. • The 3D structure of proteins from the same family is more conserved than their primary sequences. • Proteins that share low or even non-detectable sequence similarity many times also have similar structures.

  8. Necessary conditions The necessary requirements for getting a useful model : • detectable similarity between the target sequence and the template sequences. • availability of a correct alignment between them. The quality of the homology model is dependent on the quality of the sequence alignment and template structure.

  9. Steps in homology modeling

  10. Errors in comparative models • Errors due to an incorrect template. • Errors due to mis-alignments. A misalignment by only one residue position will result in an error of approximately 4Å in the model because the current modeling methods generally cannot recover from errors in the alignment. • Errors in regions without a template. • Errors in side-chain packing.

  11. Summary Homology modeling remains the only method that can reliably predict the 3D structure of a protein with an accuracy comparable to a low-resolution experimentally determined structure.

  12. Useful Softwares and Servers • Identify related template: eg. Blast… • Multiple sequence alignment: eg. ClustalX, MUSCLE, etc. • Modeling: • modeller http://www.salilab.org/modeller/ • Swiss-Model: http://swissmodel.expasy.org/ • M4T Server: http://manaslu.aecom.yu.edu/M4T/ • Simulation: • GROMACS http://www.gromacs.org/ • MDynaMix http://www.fos.su.se/~sasha/mdynamix/ • Evaluate the model: eg. PROCHECK, What If, ProSA… • Visualization tools: eg. Swiss-PdbViewer, VMD, PyMol, Rasmol…

  13. Molecular Docking

  14. What is Docking? • Docking is a method which predicts the preferred orientation of one molecule to a second one when bound to each other forming a stable complex. Schematic diagram illustrating the docking of a small molecule ligand (brown) to a protein receptor (green) to produce a complex.

  15. Docking approaches (1) • Geometric matching / shape complementarity. • Fast and robust, but cannot usually model the movements or dynamic changes in the ligand / protein conformations accurately. • Scalable to protein-protein interactions. • More amenable to pharmacophore based approaches.

  16. Docking approaches (2) • Simulation • Calculate the pairwase interaction energy. • More amenable to incorporate ligand flexibility. • Physically closer to what happens in reality. • Takes longer time to evaluate the optimal pose of binding. • Grid-based techniques.

  17. Mechanics of docking (1) • Search algorithm Several strategies for sampling the search space: • coarse-grained molecular dynamics simulation • linear combination • genetic algorithm

  18. Mechanics of docking (2) • Scoring function • The scoring function takes a pose as input and returns a number indicating the likelihood that the pose represents a favorable binding interaction. • Most scoring functions are physics-based molecular mechanics force fields that estimate the energy of the pose. • A low (negative) energy indicates a stable system and thus a likely binding interaction. • Recalculate the energy of the top scoring poses using more accurate but computationally more intensive techniques could reduce the number of false positives (such as Generalized Born or Poisson-Boltzmann methods).

  19. Applications • Docking is frequently used to predict the binding orientation of small molecule drug candidates to their protein targets, in order to, in turn, predict the affinity and activity of the small molecule. It plays an important role in the rational design of drugs. • Applications • Virtual screening • Lead optimization • Bioremediation

  20. Useful Softwares and Servers • AutoDock http://autodock.scripps.edu/ • DOCK http://dock.compbio.ucsf.edu/ • GLIDE http://www.schrodinger.com/ProductDescription.php?mID=6&sID=6 • GOLD http://www.ccdc.cam.ac.uk/products/life_sciences/gold/ • PATCHDOCK http://bioinfo3d.cs.tau.ac.il/PatchDock/ • DockingServer http://www.dockingserver.com/web • ParDOCK http://www.scfbio-iitd.res.in/dock/pardock.jsp

  21. PATCHDOCK PatchDock is an algorithm for molecular docking. The input is two molecules of any type: proteins, DNA, peptides, drugs. The output is a list of potential complexes sorted by shape complementarity criteria.

  22. Result example

  23. QSAR / QSPR

  24. Why QSAR? • The number of compounds required for synthesis in order to place 10 different groups in 4 positions of benzene ring is 104 . • Solution: synthesize a small number of compounds, and from their activity data, derive rules to predict the biological activity of other compounds.

  25. Compounds + biological activity QSAR New compounds with improved biological activity QSAR and Drug Design

  26. What is QSAR? • QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics. • QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate / predict the activity of new compounds.

  27. Statistical Concepts Input: n descriptors P1,..Pn and the value of biological activity (EC50 for example) for m compounds.

  28. Statistical Concepts • The problem of QSAR is to find coefficients C0,C1,...Cn such that: Biological activity = C0+(C1*P1)+...+(Cn*Pn) and the prediction error is minimized for a list of given m compounds. • Partial least squares (PLS) is a technique used for computation of the coefficients of structural descriptors. • More sophisticated model, e.g. SVM, ANN, can also be used.

  29. Types • Fragment based (group contribution) • 3D-QSAR • Pharmacophore

  30. Pharmacophore (1) • Pharmacophore is an ensemble of steric and electronic features that is necessary to ensure the optimal drug interactions with a specific biological target and to trigger (or block) its biological response. • In modern computational chemistry, pharmacophores are used to define the essential features of one or more molecules with the same biological activity.

  31. Pharmacophore (2) • Typical pharmacophore features • Hydrophobic • Aromatic • Hydrogen bond acceptor • Hydrogen bond donor • Polar positive • Polar negative

  32. Catalyst CYP3A4 substrates pharmacophore Hydrophobic area, h-bond donor, 2 h-bond acceptors Saquinavir (most active compound) fitted to pharmacophore Ekins et al., Three-Dimensional Quantative Structure Activity Relationship Analyses of Substrates for CYP2B6, J. Pharmacology and Experimental Therapeutics, 1999, 288:21-29

  33. Judging the quality of QSAR models • Internal validation or cross-validation. • Validation by dividing the data set into training and test compounds. • True external validation by application of model on external data. • Data randomization or Y-scrambling. The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model.

  34. Applications • Chemical: • Predict boiling points, pKa… • Biological: • Predict IC50, Kd, toxicity, logP… • Study the interactions between the structural domains of proteins

  35. Softwares • GERM • GOLPE • GRID • Almond • Strike • CoMFA • VolSurf • Catalyst • HASL • Quasar • Serius

  36. Exercise • Homology modeling of human C-C chemokine receptor type 5 (CCR5).(Swiss-Prot Accession Number: P51681 ) • Dock compound No. 38 and compound No. 103 into your CCR5 model. • Check for potential atomic interactions visually, such as below: • Hydrophobic contact • Electrostatic interaction • Hydrogen bond • Pi-Pi interaction

  37. Exercise • QSAR/QSPR studies on the key structure features of CCR5 inhibitors. (CCR5_antagonists.sdf) You may find the bioactivity data from “Generation of predictive pharmacophore models for CCR5 antagonists Study with piperidine- and piperazine-based compounds as a new class of HIV-1 entry inhibitors”. • Scan up for literature support. • Critical assess your Docking result and QSAR model . • Write up your report.

  38. Report • Scientific article structure: Abstract, Introduction, Methods, Results, Discussions, Conclusions. • Compare of your findings with existing reports of CCR5 antagonist mechanisms. • Advantages and limitations of your study. • Ideas on potential modifications of CCR5 antagonist to achieve better inhibition. • Include snapshots to backup your claim of atomic interactions, with the hotspot atoms clearly labeled. • Submit your complex structure together with your report.

  39. References • QM Sun: Homology Modeling (Knowledge-Based Structure Modeling ) • Maeda K, Das D, Ogata-Aoki H, Nakata H, Miyakawa T, Tojo Y, Norman R, Takaoka Y, Ding J, Arnold GF et al: Structural and molecular interactions of CCR5 inhibitors with CCR5. The Journal of biological chemistry 2006, 281(18):12688-12698 • Asim Kumar Debnath: Generation of predictive pharmacophore models for CCR5 antagonists Study with piperidine- and piperazine-based compounds as a new class of HIV-1 entry inhibitors. Journal of medicinal chemistry  2003, vol. 46, no21, pp. 4501-4515 • http://www.ebi.ac.uk/Tools/clustalw2/ • http://salilab.org/modeller/download_installation.html • http://robetta.bakerlab.org/ • http://swissmodel.expasy.org/SWISS-MODEL.html • https://prosa.services.came.sbg.ac.at/prosa.php • http://accelrys.com/products/discovery-studio/visualization/discovery-studio-visualizer.html • http://manaslu.aecom.yu.edu/M4T/ • http://www.gromacs.org/ • http://www.fos.su.se/~sasha/mdynamix/ • http://dock.compbio.ucsf.edu/ • http://bioinfo3d.cs.tau.ac.il/PatchDock/ • http://www.dockingserver.com/web • http://www.scfbio-iitd.res.in/dock/pardock.jsp • Some other sources: • http://www.ebi.ac.uk/Tools/ • http://www.vls3d.com/links.html#section4 • http://en.wikipedia.org/wiki/List_of_software_for_molecular_mechanics_modeling • http://www.expasy.org/tools/

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