Molecular Simulation to build models for enzyme induced fit
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The document outlines the activities and research interests of the Bioinformatics & Molecular Design Research Center, focusing on molecular dynamics simulations, enzyme interactions, and drug design methodologies. It highlights computational techniques used for analyzing molecular behaviors, including various force fields and modeling systems, alongside the applications of high-performance computing in biological research. Additionally, it discusses specific case studies and advancements in molecular modeling and computer-aided drug development.
Molecular Simulation to build models for enzyme induced fit
- 1. 분자 설계 연구실 홈페이지 http://www.bmdrc.org/ 전화 02-393-9550, 이메일 [email protected] 연세대학교 세미나3 발표 - Molecular Simulation to build models for enzyme induced fit 2015년 5월 22일(금) 조교 : 김 민 성 (통 합) 지도 교수 : 노 경 태 교 수
- 2. Lab : 공학관B408 Phone : 02-2123-7739 Mail : [email protected] Homepage : http://www.csblab.or.kr Computational Systems Biology Lab. Professor : Kyoung Tai No Computational Chemistry Cheminformatics Solvation free energy, charge model, and forcefields; gives concrete understanding and analysis tool for further developments. Statistical analysis of multivariate chemical feature space via machine learning techniques. Spectral similarity Structural similarity Activity similarity Natural Product Profiling&Networking Profiling natural product/metabolite in high-throughput manner, revealing its biological activity. Commercial available screening Database (12 Millions) PPI Screening Library Development of PPI focused screening library (0.2 Millions) Target-focused Library Design Pharmacophore Based Screening Structure-based Pharmacophore screen Screen of protein Interaction surface Ligand-based pharmacophore screen Virtual screening Virtual hits Predicted binding mode •ASN159 Hot Spot region •GLU196 Hot Spot region ASN159 GLN160 LYS154 GLU196 “Hotspot” binding region: •Define binding site •Hydrogen bond region Biding Site Prediction Flora Genesis System In silico Drug Design RESEARCH INTEREST
- 5. What is molecular Dynamics • A computational microscope • An experiment on a computer • A simulation of the classical mechanics of atoms
- 6. GOOD Energy Calculation Function, Force Fields, for DGsystem GOOD Simulation Method for DGsystem t1 t2 t3 t4 t5 t6 t7 t8 tn tn+1 tn+2 tn+3 S1 S2 S3 S4 S5 S6 S7 S8 Sn Sn+1 Sn+2 Sn+3 G1 G2 G3 G4 G5 G6 G7 G8 Gn Gn+1 Gn+2 Gn+3 Energy/Mechanics Based Design Time Structure Free Energy
- 7. Systems in a Life System Atom 10-12 m Protein 10-9 m Cell 10-6 m Tissue 10-3 m Organ 100 m Organ System & Organism Physiology Gene Networks Pathway Models Stochastic Models Differential Equation Continuum Model Partial Diff. Eqn Systems Model 10-6s Molecular Events ion channel gating 10-3s Diffusion Cell signaling 100s Mobility 103s Mitosis 104s Protein Turnover 109s Human Lifetime Spatial and temporal levels encompassed by biological systems
- 8. Protein-Protein Interaction Electron carriers of the SQR complex. FADH2, iron-sulfur centers, heme b, and ubiquinone. We can Observe Protein-Protein Interaction with MD
- 9. Induced fit model of enzyme
- 10. Speed Isn’t Everything • How accurate are molecular mechanics force fields? - Clearly good enough for some biologically and pharmacologically important applications • Where are the weak points? - Polarizability? Hydrogen bonds? Combinning rules? • Can we Improve the accuracy of today’s force fields? - At what cost in execution speed? • Even negative results could provide biologically and pharmacologically relevant insights
- 11. Research Interest Areas Force Fields SBFF CHARM AMMBER MMFF Simulator Lammps Gaussian Schrödinger Application PPI Molecular Modeling Eco Engineering Appropriate Tech Consilience Approach
- 12. Nature Process Mimetic Inter Particle Interaction Analysis Modeling Prediction Commercial Product Inter Molecular PEF Solvation Models QM calculations Statistical PEF Scoring Function, ….. Molecular Mechanics MD, MC, FEP Regression methods ANN, GFA Statistical methods Bioinformatics Protein structure prediction Drug design ADME/Tox prediction PK prediction …………………
- 13. Force Field: Potential Energy Function: )(StructureE f Potential Energy Function :PEF
- 14. 2 0 )( ddkEstretch )( 0 1 dd estretch eDE 2 0 )( kEbend )cos(1 S S S kEtorsion Intra Molecular Motions and their PEF
- 15. Classification of Force Fields Classical FFs AMBER, CHARMm, CVFF, ECEPP/2, Homans’ FF, Pullman (DNA) Second-generation FFs CFF91, PCFF, CFF95, MMFF93, MM Water potential models (Flexible or non-flexible, inclusion of ploarization or not ST2, TIP3, TIP4, SPC, CVFF, OPLS COSMO, FDM, BEM, SMx, SFED Broadly applicable FFs UFF, Dreiding FF, ESFF (Extensible systematic FF), …….. Special-purpose FFs Glass, Zeolite, sorption, Morphology, ……..
- 16. For Small Organic molecules MM2 - For structure determination of small organic molecules - Developed by Allinger at U. Georgia - FF parameters are determined with spectroscopic data MM3 - Accurate vibrational frequency than MM2 MMFF93: Merck Molecular FF - Using QM calculation as constraints for FF parameters fitting Tripos force field - For small organic molecules Classification of FFs; Small Organic Molecules
- 17. CFF : Consistant FF (CFF91, PCFF, CFF95) - Contains both Anharmonic term and cross terms CFF91- Hydrocarbons, Proteins, protein-ligand PCFF- Polymer, organic materials CFF95-Biomolecules, organic polymers - for small organics and liquid and solid simulations Shortcomings of above force fields - inadequate for inter molecular interaction - does not include electrostatic interaction - van der Waals radii are too small Classification of FFs; Small Organic Molecules
- 18. ECEPP, ECEPP/2 SBFF (Self Balance Force Field) Protein structure, in torsional space (no stretching & bending) Harold Scheraga at Cornell U, Kyoung Tai No at Yonsei U AMBER (Assisted Model Building with Energy Refinement) Protein / Nucleic Acids, Peter Kollman at UCSF CHARMM (Chemistry at HARvard using Molecular Mechanics) Mainly for Protein, Martin Karplus at Havard GROMOS (GROenigen MOlecular Simulation) van Gunsteren and Berendsen at ETH Zurich. CVFF(Consistent-valence force field ) Dauber-Osguthorpe, out-of-plane energy calculation included For amino acids, water, and a variety of other functional groups Classification of FFs; Biomolecules
- 19. Dreiding force field, 1st and 2nd period elements Goddard at Caltec / Mayo at Biodesign / Olafson UFF (Universal Force Field) Include most of elements in Periodic table Rappe at Colorado State U. / Casewit at Calleo Scientific / Skiff at Shell Research Classification of FFs; Broadly Used
- 20. Self-Balanced Force Field (SBFF) • 1) accurate intra- and inter-molecular Potential Energy Function (PEF), and • 2) good simulation algorithm that describes nature of the molecular worlds.
- 21. Bioinformatics & Molecular Design Research Center (사) 분자설계연구소
- 22. Bioinformatics & Molecular Design Research Center (사) 분자설계연구소
- 23. 23 Lammps code is Object oriented which is very similar to JAVA Lammps has a huge diversity of force-fields you can use, and also you can define new force-fields. Which makes it seems good candidate for BMDRC Object Oriented Lammps
- 25. 25 Practice 4 : Aluminum Uniaxial Tension
- 26. 26 This example script shows how to run an atomistic simulation of uniaxial tensile loading of an aluminum single crystal oriented in the <100> direction. Practice 4 : Aluminum Uniaxial Tension Data retrieval was denied due to Dr. Raju’s Calculation
- 28. 28 Result - Peptide solvation Lammps with Charmn force field
- 30. PotentialE Conformational Space PotentialE Conformational Space PotentialE Conformational Space PotentialE Conformational Space Energy Minimization Normal Mode Analysis Molecular Dynamics Monte Carlo Simulation Illustration Credit: M. Levitt 0.5kx2 X=X(t)
- 31. Length & Tome Scale of Molecular Motions Motion Length (in A) Time (in fs) Bond Vibration 0.1 10 Water Hindered Rotation 0.5 1000 Surface Sidechain Rotation 5 105 Water Diffusion Motion 4 105 Buried Sidechain Libration 0.5 105 Hinge Bending of Chain 3 106 Buried Sidechain Rotation 5 1013 Allosteric Transition 3 1013 Local Denaturation 7 1014 Values from McCammon & Harvey (1987) & Eisenberg & Kauzmann
- 33. 33 Result - Peptide solvation 72.105 74.11 1 THREAD 4 THREAD timesteps/s Comparison of serial & parallel calc Loop time of 346.715 on 1 procs for 25000 steps with 2004 atoms 99.0% CPU use with 1 MPI tasks x 1 OpenMP threads Performance: 0.019 ns/day 1284.129 hours/ns 72.105 timesteps/s Loop time of 337.334 on 4 procs for 25000 steps with 2004 atoms 99.2% CPU use with 1 MPI tasks x 4 OpenMP threads Performance: 0.019 ns/day 1249.386 hours/ns 74.110 timesteps/s
- 34. 34 High Performance Computing(HPC) Cloud Platform • Key Cloud Properties • Cloud HPC: Good & Evil • Success Stories • Features & Opportunities
- 35. 35 High Performance Computing(HPC) Cloud Platform What differentiates the Cloud from non-Cloud? Cloud is “awesome” Cloud is OSSM
- 36. 36 High Performance Computing(HPC) Cloud Platform
- 37. 37 High Performance Computing(HPC) Cloud Platform What kinds of clouds are there?
- 38. 38 High Performance Computing(HPC) Cloud Platform Cloud gives an illusion of unlimited capacity Sounds useful for HPC!
- 39. 39 High Performance Computing(HPC) Cloud Platform • Key Cloud Properties • Cloud HPC: Good & Evil • Success Stories • Features & Opportunitie
- 40. 40 High Performance Computing(HPC) Cloud Platform • 1. Works even with limited budget. • 2. Perfect for infrequent “monster” jobs. • 3. Helps to reduce Time to Market. • 4. Enables disaster-resiliency. • 5. Reduces IT complexity. The Bright Side • 1. Performance and latency issues. • 2. Data volume issues. • 3. Vendor-related issues. • 4. Security concerns. • 5. Cost-effectiveness concerns. The Dark Side
- 41. 41 High Performance Computing(HPC) Cloud Platform Efficient workload patterns?
- 42. 42 High Performance Computing(HPC) Cloud Platform We probably don’t want to use the Cloud if we have this*: * however, the costs might not be our primary concern
- 43. 43 High Performance Computing(HPC) Cloud Platform • Key Cloud Properties • Cloud HPC: Good & Evil • Success Stories • Features & Opportunitie
- 44. 44 High Performance Computing(HPC) Cloud Platform • Batch Processing: New York Times and MapReduce - 4 TB raw images, 11M PDFs, 100 Hadoop workers = $240 • Data Processing: Morgridge Institute for Research, gene indexing - 1M core-hours, high-memory EC2 Spot instances: < $20K paid • Simulations and Analysis: Schrödinger (drug research) - 50K cores, 21M chemical compounds: < $5K paid - (Amazon infrastructure value estimated at $20~40M) HPC Cloud Case Studies
- 45. 45 • Simulations and Analysis: Schrödinger (drug research) High Performance Computing(HPC) Cloud Platform
- 46. 46 High Performance Computing(HPC) Cloud Platform
- 47. 47 High Performance Computing(HPC) Cloud Platform
- 48. 48 Novartis Uses AWS to Conduct 39 Years of Computational Chemistry In 9 Hours High Performance Computing(HPC) Cloud Platform http://youtu.be/oa-M9GcaDN0
- 49. Molecular modeling is the study of the geometry and properties of molecules by computer-aided techniques. Molecular modeling is a growing area in science & technology to explain the phenomena at molecular level. visualize molecules study the structure of molecules study the properties of a molecule compare the structure and properties of molecules study interactions between molecules study reaction mechanisms predict the structure of molecules predict the properties of molecules predict reaction mechanisms Molecular Modeling (Design)
- 50. Computer Aided Drug Design Receptor Structure Unkonwn Receptor Structure Konwn Ligand Structure Unknown Combinatorial Chemistry 3D Structure Generation De novo Design Receptor Based 3D Searching Ligand Structure Known Pharmacophore Define QSAR Structure Based Optimization Affinity Calculation
- 51. Computer Added Molecular Design Energy – Mechanics Based Design Research Subject Mechanics Based Model Simulation X, T, t, E Expiments Analysis Prediction QM, E-FF, TD / MM, MC, MD Knowledge Based Design DATA Analysis RULEs Prediction QSAR 13.
- 52. Plan Conclusion
- 53. Thank you
Editor's Notes
- #7: You can get atomistic insight We can predict and understand molecular behavior We have total control of molecular forces, structure, and conditions