Computational Science and Engineering Field of Specialisation: Chemistry and Biology. Contact Person: Prof. Philippe Hünenberger
|
|
|
- Alexandrina Goodwin
- 6 years ago
- Views:
Transcription
1 Computational Science and Engineering Field of Specialisation: Chemistry and Biology Contact Person: Prof. Philippe Hünenberger
2 MOLECULAR MODELING/SIMULATION IN CHEMISTRY & BIOLOGY Understand (and predict) the workings of (bio)molecular systems using physics-based models Simulation can replace of experiment when: Simulation can complement experiment when: the process cannot be studied experimentally e.g. interior of a star, weather forecast (experiment is too late!) the process is dangerous to study experimentally e.g. flight simulators, explosion of a nuclear bomb, fighting ability of the Swiss army the process is expensive to study experimentally e.g. volcanism on Venus, aerodynamics in aircraft design approximate simulations reduce the number of experiments to be performed or/and increase their likelihood of success e.g. modeling in industry: drug design, protein engineering, stock market predictions (banks), risk assessment (insurances) a simulation reproducing an experiment provides additional insight e.g. modeling in academia: quantum chemistry, molecular simulations The equations governing the model may be: e.g. experimentally inaccessible resolution in time/space/energy very simple analytical treatment (e.g. perfect gas, harmonic crystal) moderately complex but numerous computer simulation (numerical solution) not known or too complex small-scale simulation (e.g. avalanches)
3 FOUR BASIC CHOICES DEFINING A MOLECULAR MODEL degrees of freedom elementary particles of the model interaction boundary conditions MOLECULAR MODEL Hamiltonian operator or function system size and shape, thermodynamical constraints, experimentally-derived information generation of configurations number of configurations, properties of the configuration sequence
4 MOLECULAR MODELING/SIMULATION IN CHEMISTRY & BIOLOGY Levels of modelling, resolution and degrees of freedom QUANTUM MODELS CLASSICAL MODELS MESOSCOPIC MODELS highest resolution IMPLICIT nucleons IMPLICIT nuclei ( atoms), all photons = IMPLICIT atom groups ( residues) QUANTUM MECHANICS MOLECULAR MECHANICS RESIDUE- BASED MODELS rel. TDSE (Dirac) core electrons, high energy photons MD (non-polar) hydrogen ( united atoms) SD intramolecular dof ( molecules) QUANTUM MECHANICS MOLECULAR MECHANICS (UNITED ATOM) RIGID- MOLECULE MODELS TDSE all electrons, medium energy photons (Born-Oppenheimer) MD atom groups ( beads) MD, SD intramolecular dof ( "particles") TISE (elec.) TDSE (nucl.) QUANTUM CHEMISTRY solvent MD COARSE- GRAINED MODELS solvent BD, DPD MESOSCOPIC MODELS granularity of matter ( densities, fluxes and fields) QUANTUM CHEMISTRY (IMPL. SVT.) MOLECULAR MECHANICS (IMPL. SVT.) CONTINUUM MODELS TISE (elec.) TDSE (nucl.) SD lowest resolution FE (conserv. + transp.) freely inspired from "Simulating the physical world" by Herman Berendsen (2007)
5 DEGREES OF FREEDOM: FROM QUANTUM TO MESOSCOPIC MODELS Levels of resolution: the tradeoff increasing system size and number of configurations MESOSCOPIC MODELS CLASSICAL MODELS FASTER COMPUTERS currently not feasible QUANTUM MODELS increasing resolution and Hamiltonian cost Computing power: Moore's law log[flop] 12 9 FUJITSU VP 200 NEC SX 2 CRAY X MP CRAY 2 CRAY 1 CYBER 205 FUJITSU VPP CRAY T3D SX3 teraflop gigaflop 1 flop = 1 floating-point operation (14 digit precision) per second milliflop 6 IBM 7090 CDC 6000 IBM 360/195 CDC year megaflop The computing power has increased till now on average by a factor 10 every 6 years
6 EXAMPLE: CLASSICAL SIMULATION Gel (GL) liquid crystal (LC) phase transition in GMP bilayers Glycerol monopalmitate (GMP) Tm GL LC Experimentally, these lipids evidence the usual Tm increase upon dehydration Can we calculate (bracket) their Tm as a function of hydration? Experimental phase diagram of GMP
7 EXAMPLE: CLASSICAL SIMULATION Simulations of a 2 32 bilayer patch using GROMOS 53A OXY and SPC water Full (F), half (H) or quarter (Q) hydration Starting from liquid-crystal (LC) or gel (GL) phase At various temperatures differing by 4K Horta, de Vries & Hünenberger J. Chem. Theory Comput (2008) [+ Laner & Hünenberger, Mol. Simul., in press] F H Sim: 51, 59 and 63 o C for F, H and Q (±2 o C) Q Exp: 50, 53 and 58 o C for F, H and Q (?±4 o C?) investigate further: effect of lipid type & chirality, effect of added cosolutes such as alcohols (anesthesia) or sugars (bioprotection)
8 EXAMPLE: QUANTUM-MECHANICAL/CLASSICAL SIMULATION QM/MM simulations of HIV-protease in aqueous solution Liu, Müller-Plathe & van Gunsteren J. Mol. Biol. 261 (1996)
9 EXAMPLE: QUANTUM-MECHANICAL/CLASSICAL SIMULATION U ( r) = F = mɺɺ r Hˆ Ψ ( r) = EΨ( r) MD: Newton s equations of motion at 300K Computing Effort proportional to N 1-2 atoms ASP-25 MM CH 2 QM H 2 C ASP-25 QM: Semi-empirical PM3- model at each time point Computing Effort proportional to N 3-5 electrons protein COO - H C N H H 2 O substrate O C active site H C COOH water dimer, 2*99 residues 2000 atoms GROMOS force field periodic box 5.1*5.3*7.2 nm degrees of freedom 5427 SPC model
10 CG Coarse-grained model 4 atoms EXAMPLE: COARSE-GRAINED SIMULATION liquid alkanes: hexadecane AL(FG) All-atom model (non-hydrogen) 16 (CH 2 or CH 3 ) atoms MAP mapped all-atom configurations simulation + analysis simulation analysis A Centre of mass A 1 A 4 B Centre of mass B 1 B 4 C Centre of mass C 1 C 4 D Centre of mass D 1 D 4 W Marrink et al., J. Phys.Chem.B 108 (2004) 750 Compare: - structural characteristics - energetic / entropic characteristics
11 EXAMPLE: COARSE-GRAINED SIMULATION Christen & van Gunsteren J. Chem. Phys, 124 (2006) Multi-grained simulation of 25 hexadecanes in water CG + FG CG CG CG + FG Time: 0 ps 8ps 25ps 100ps FG 8.5ps FG 25.5ps CG level simulation with occasional switching to FG level enhances exploration of FG conformational space Interactions at CG and FG levels should be thermodynamically consistent
12 Vertiefungsgebiet-Vorlesungen: Chemie und Biologie Bachelor Studium Vorlesung SWS Semester Departement KP - Computer Simulation in Chemistry, Biology and Physics 3G HS (7-th) CHAB 7 - Quantum Chemistry 3G FS (5-th) CHAB 6 Master Studium Vorlesung SWS Semester Departement KP - Computer simulation in Chemistry, Biology and Physics 3G HS (7-th) CHAB 7 - Quantum Chemistry 3G FS (5-th) CHAB 6 - Advanced Quantum Chemistry 3G HS (7-th) CHAB Hünenberger Reiher Hünenberger Reiher Reiher - Computational Biology 3V 2U HS INFK 6 Gonnet - Computer Applications: Finite Elements in Solids and Structures 2V 2U FS MATL 4 Gusev - Seminar in Chemistry and Biology HS/FS RW 4 Hünenberger
Lecture 11: Potential Energy Functions
Lecture 11: Potential Energy Functions Dr. Ronald M. Levy ronlevy@temple.edu Originally contributed by Lauren Wickstrom (2011) Microscopic/Macroscopic Connection The connection between microscopic interactions
Hamiltonian Replica Exchange Molecular Dynamics Using Soft-Core Interactions to Enhance Conformational Sampling
John von Neumann Institute for Computing Hamiltonian Replica Exchange Molecular Dynamics Using Soft-Core Interactions to Enhance Conformational Sampling J. Hritz, Ch. Oostenbrink published in From Computational
Van Gunsteren et al. Angew. Chem. Int. Ed. 45, 4064 (2006)
Van Gunsteren et al. Angew. Chem. Int. Ed. 45, 4064 (2006) Martini Workshop 2015 Coarse Graining Basics Alex de Vries Every word or concept, clear as it may seem to be, has only a limited range of applicability
Peptide folding in non-aqueous environments investigated with molecular dynamics simulations Soto Becerra, Patricia
University of Groningen Peptide folding in non-aqueous environments investigated with molecular dynamics simulations Soto Becerra, Patricia IMPORTANT NOTE: You are advised to consult the publisher's version
Some Accelerated/Enhanced Sampling Techniques
Some Accelerated/Enhanced Sampling Techniques The Sampling Problem Accelerated sampling can be achieved by addressing one or more of the MD ingredients Back to free energy differences... Examples of accelerated
An introduction to Molecular Dynamics. EMBO, June 2016
An introduction to Molecular Dynamics EMBO, June 2016 What is MD? everything that living things do can be understood in terms of the jiggling and wiggling of atoms. The Feynman Lectures in Physics vol.
Summary of the new Modelling Vocabulary
Summary of the new Modelling Vocabulary These two pages attempts to summarise in a concise manner the Modelling Vocabulary. What are Models? What are Simulations? Materials Models consist of Physics or
Structural Bioinformatics (C3210) Molecular Mechanics
Structural Bioinformatics (C3210) Molecular Mechanics How to Calculate Energies Calculation of molecular energies is of key importance in protein folding, molecular modelling etc. There are two main computational
Molecular Mechanics. I. Quantum mechanical treatment of molecular systems
Molecular Mechanics I. Quantum mechanical treatment of molecular systems The first principle approach for describing the properties of molecules, including proteins, involves quantum mechanics. For example,
All-atom Molecular Mechanics. Trent E. Balius AMS 535 / CHE /27/2010
All-atom Molecular Mechanics Trent E. Balius AMS 535 / CHE 535 09/27/2010 Outline Molecular models Molecular mechanics Force Fields Potential energy function functional form parameters and parameterization
From Dynamics to Thermodynamics using Molecular Simulation
From Dynamics to Thermodynamics using Molecular Simulation David van der Spoel Computational Chemistry Physical models to describe molecules Software to evaluate models and do predictions - GROMACS Model
Methods for searching and sampling configurational space
International Spring School Statistical Thermodynamics, Santiago de Chile Tuesday, ovember 28, 2017 Lecture 18 Methods for searching and sampling configurational space Prof. Dr. Wilfred F. van Gunsteren
The present and future role of computers in chemical research
The present and future role of computers in chemical research Laboratorium für Physikalische Chemie, ETH Zurich, Switzerland http://www.reiher.ethz.ch June 24, 2011 Overview Aims of modeling in chemistry
Introduction to Computer Simulations of Soft Matter Methodologies and Applications Boulder July, 19-20, 2012
Introduction to Computer Simulations of Soft Matter Methodologies and Applications Boulder July, 19-20, 2012 K. Kremer Max Planck Institute for Polymer Research, Mainz Overview Simulations, general considerations
CE 530 Molecular Simulation
1 CE 530 Molecular Simulation Lecture 14 Molecular Models David A. Kofke Department of Chemical Engineering SUNY Buffalo kofke@eng.buffalo.edu 2 Review Monte Carlo ensemble averaging, no dynamics easy
Fondamenti di Chimica Farmaceutica. Computer Chemistry in Drug Research: Introduction
Fondamenti di Chimica Farmaceutica Computer Chemistry in Drug Research: Introduction Introduction Introduction Introduction Computer Chemistry in Drug Design Drug Discovery: Target identification Lead
Nuggets on coarse- graining and mul,scale computa,onal schemes Maria Fyta Ins,tut für Computerphysik, Universität Stu<gart Stu<gart, Germany
Nuggets on coarse- graining and mul,scale computa,onal schemes Maria Fyta Ins,tut für Computerphysik, Universität Stu
Intro/Review of Quantum
Intro/Review of Quantum QM-1 So you might be thinking I thought I could avoid Quantum Mechanics?!? Well we will focus on thermodynamics and kinetics, but we will consider this topic with reference to the
Simulation Methods II
Simulation Methods II Maria Fyta Institute for Computational Physics Universität Stuttgart Summer Term 2018 SM II - contents First principles methods Hartree-Fock and beyond Density-funtional-theory Ab
Fernanda C Bononi 1, Ted Hullar 2, Cort Anastasio 2, Davide Donadio 1
Fernanda C Bononi 1, Ted Hullar 2, Cort Anastasio 2, Davide Donadio 1 1 Department of Chemistry 2 Department of Air, Land and Water Resources UC Davis Ice surface in climate modeling: sea water ice polar
Martini Basics. Hands on: how to prepare a Martini
Martini Basics Hands on: how to prepare a Martini gin and vermouth are combined at a ratio of 2:1, stirred in a mixing glass with ice cubes, then strained into a chilled cocktail glass and garnished with
The Molecular Dynamics Simulation Process
The Molecular Dynamics Simulation Process For textbooks see: M.P. Allen and D.J. Tildesley. Computer Simulation of Liquids.Oxford University Press, New York, 1987. D. Frenkel and B. Smit. Understanding
4 th Advanced in silico Drug Design KFC/ADD Molecular Modelling Intro. Karel Berka, Ph.D.
4 th Advanced in silico Drug Design KFC/ADD Molecular Modelling Intro Karel Berka, Ph.D. UP Olomouc, 21.1.-25.1. 2019 Motto A theory is something nobody believes, except the person who made it An experiment
3rd Advanced in silico Drug Design KFC/ADD Molecular mechanics intro Karel Berka, Ph.D. Martin Lepšík, Ph.D. Pavel Polishchuk, Ph.D.
3rd Advanced in silico Drug Design KFC/ADD Molecular mechanics intro Karel Berka, Ph.D. Martin Lepšík, Ph.D. Pavel Polishchuk, Ph.D. Thierry Langer, Ph.D. Jana Vrbková, Ph.D. UP Olomouc, 23.1.-26.1. 2018
ICCP Project 2 - Advanced Monte Carlo Methods Choose one of the three options below
ICCP Project 2 - Advanced Monte Carlo Methods Choose one of the three options below Introduction In statistical physics Monte Carlo methods are considered to have started in the Manhattan project (1940
Michael W. Mahoney Department of Physics, Yale University, New Haven, Connecticut 06520
JOURNAL OF CHEMICAL PHYSICS VOLUME 115, NUMBER 23 15 DECEMBER 2001 Quantum, intramolecular flexibility, and polarizability effects on the reproduction of the density anomaly of liquid water by simple potential
MARTINI simulation details
S1 Appendix MARTINI simulation details MARTINI simulation initialization and equilibration In this section, we describe the initialization of simulations from Main Text section Residue-based coarsegrained
Marcus Theory for Electron Transfer a short introduction
Marcus Theory for Electron Transfer a short introduction Minoia Andrea MPIP - Journal Club -Mainz - January 29, 2008 1 Contents 1 Intro 1 2 History and Concepts 2 2.1 Frank-Condon principle applied to
The Molecular Dynamics Method
The Molecular Dynamics Method Thermal motion of a lipid bilayer Water permeation through channels Selective sugar transport Potential Energy (hyper)surface What is Force? Energy U(x) F = d dx U(x) Conformation
The GROMOS Software for (Bio)Molecular Simulation
The GROMOS Software for (Bio)Molecular Simulation Volume 2: Algorithms and Formulae for Modelling of Molecular Systems September 28, 2017 Contents Chapter 1. Introduction 2-1 Chapter 2. Basic choices
Colloids as nucleons
Colloids as nucleons Willem Kegel & Jan Groenewold Van t Hoff Laboratory Utrecht University The Netherlands Finite-size equilibrium structures macroscopic phase separation Equilibrium clusters & periodic
Water models in classical simulations
Water models in classical simulations Maria Fyta Institut für Computerphysik, Universität Stuttgart Stuttgart, Germany Water transparent, odorless, tasteless and ubiquitous really simple: two H atoms attached
Intro/Review of Quantum
Intro/Review of Quantum QM-1 So you might be thinking I thought I could avoid Quantum Mechanics?!? Well we will focus on thermodynamics and kinetics, but we will consider this topic with reference to the
Advanced Molecular Dynamics
Advanced Molecular Dynamics Introduction May 2, 2017 Who am I? I am an associate professor at Theoretical Physics Topics I work on: Algorithms for (parallel) molecular simulations including GPU acceleration
ProteinMolecularDynamicsWithElectrostaticForce EntirelyDeterminedbyaSinglePoisson-Boltzmann Calculation
PROTEINS: Structure, Function, and Genetics 48:497 504 (2002) ProteinMolecularDynamicsWithElectrostaticForce EntirelyDeterminedbyaSinglePoisson-Boltzmann Calculation BenZhuoLu, 1,2 WeiZuChen, 2 CunXinWang,
Molecular dynamics simulation of Aquaporin-1. 4 nm
Molecular dynamics simulation of Aquaporin-1 4 nm Molecular Dynamics Simulations Schrödinger equation i~@ t (r, R) =H (r, R) Born-Oppenheimer approximation H e e(r; R) =E e (R) e(r; R) Nucleic motion described
Supporting Information. for. Influence of Cononsolvency on the. Aggregation of Tertiary Butyl Alcohol in. Methanol-Water Mixtures
Supporting Information for Influence of Cononsolvency on the Aggregation of Tertiary Butyl Alcohol in Methanol-Water Mixtures Kenji Mochizuki,, Shannon R. Pattenaude, and Dor Ben-Amotz Research Institute
Molecular-Level Description of Reaction Equilibrium
Molecular-Level Description of Reaction Equilibrium CBE 450: Reactor Engineering Fundamentals Fall, 2009 Prof. David Keffer dkeffer@utk.edu Thermodynamic Properties Quantum Mechanics can be combined with
Computational Studies of the Photoreceptor Rhodopsin. Scott E. Feller Wabash College
Computational Studies of the Photoreceptor Rhodopsin Scott E. Feller Wabash College Rhodopsin Photocycle Dark-adapted Rhodopsin hn Isomerize retinal Photorhodopsin ~200 fs Bathorhodopsin Meta-II ms timescale
Understanding Chemical Reactions through Computer Modeling. Tyler R. Josephson University of Delaware 4/21/16
Understanding Chemical Reactions through Computer Modeling Tyler R. Josephson University of Delaware 4/21/16 A little about me B.S. in Chem E from U of M, 2011 Currently, Ph.D. student at University of
Larger Molecules / Longer Time Scales. Todd J. Martinez
Larger Molecules / Longer Time Scales Todd J. Martinez Extending Quantum Chemistry Extend accuracy and/or size range of quantum chemistry? Remember the canon! Right Answer Electron Correlation Minimal
XV 74. Flouorescence-Polarization-Circular-Dichroism- Jablonski diagram Where does the energy go?
XV 74 Flouorescence-Polarization-Circular-Dichroism- Jablonski diagram Where does the energy go? 1) Excite system through A Absorbance S 0 S n Excite from ground excited singlet S = 0 could be any of them
Example questions for Molecular modelling (Level 4) Dr. Adrian Mulholland
Example questions for Molecular modelling (Level 4) Dr. Adrian Mulholland 1) Question. Two methods which are widely used for the optimization of molecular geometies are the Steepest descents and Newton-Raphson
Limitations of temperature replica exchange (T-REMD) for protein folding simulations
Limitations of temperature replica exchange (T-REMD) for protein folding simulations Jed W. Pitera, William C. Swope IBM Research pitera@us.ibm.com Anomalies in protein folding kinetic thermodynamic 322K
Effects of bundling on the properties of the SPC water model
Theor Chem Acc (21) 125:335 344 DOI 1.17/s214-9-59-4 REGULAR ARTICLE Effects of bundling on the properties of the SPC water model Marc Fuhrmans Æ Barbara P. Sanders Æ Siewert-Jan Marrink Æ Alex H. de Vries
QM/MM study on inhibitor of HIV-1 protease. Graduate School of Science, Kyoto University Masahiko Taguchi, Masahiko Kaneso, Shigehiko Hayashi
QM/MM study on inhibitor of HIV-1 protease Graduate School of Science, Kyoto University Masahiko Taguchi, Masahiko Kaneso, Shigehiko Hayashi HIV Human Immunodeficiency Virus Origin of AIDS HIV appearing
Protein structure forces, and folding
Harvard-MIT Division of Health Sciences and Technology HST.508: Quantitative Genomics, Fall 2005 Instructors: Leonid Mirny, Robert Berwick, Alvin Kho, Isaac Kohane Protein structure forces, and folding
Molecular Dynamics Simulation of Methanol-Water Mixture
Molecular Dynamics Simulation of Methanol-Water Mixture Palazzo Mancini, Mara Cantoni University of Urbino Carlo Bo Abstract In this study some properties of the methanol-water mixture such as diffusivity,
Protein Simulations in Confined Environments
Critical Review Lecture Protein Simulations in Confined Environments Murat Cetinkaya 1, Jorge Sofo 2, Melik C. Demirel 1 1. 2. College of Engineering, Pennsylvania State University, University Park, 16802,
Exp. 4. Quantum Chemical calculation: The potential energy curves and the orbitals of H2 +
Exp. 4. Quantum Chemical calculation: The potential energy curves and the orbitals of H2 + 1. Objectives Quantum chemical solvers are used to obtain the energy and the orbitals of the simplest molecules
This semester. Books
Models mostly proteins from detailed to more abstract models Some simulation methods This semester Books None necessary for my group and Prof Rarey Molecular Modelling: Principles and Applications Leach,
Presentation Microcalorimetry for Life Science Research
Presentation Microcalorimetry for Life Science Research MicroCalorimetry The Universal Detector Heat is either generated or absorbed in every chemical process Capable of thermal measurements over a wide
Many-Body Coarse-Grained Interactions using Gaussian Approximation Potentials
Many-Body Coarse-Grained Interactions using Gaussian Approximation Potentials arxiv:1611.09123v2 [cond-mat.soft] 28 Sep 2017 S.T. John Peterhouse September 2016 This dissertation is submitted for the degree
Multiscale Materials Modeling
Multiscale Materials Modeling Lecture 10 Adaptive Resolution Fundamentals of Sustainable Technology These notes created by David Keffer, University of Tennessee, Knoxville, 2012. Reminder In the previous
Computational Molecular Biophysics. Computational Biophysics, GRS Jülich SS 2013
Computational Molecular Biophysics Computational Biophysics, GRS Jülich SS 2013 Computational Considerations Number of terms for different energy contributions / E el ~N ~N ~N ~N 2 Computational cost for
Temperature and Pressure Controls
Ensembles Temperature and Pressure Controls 1. (E, V, N) microcanonical (constant energy) 2. (T, V, N) canonical, constant volume 3. (T, P N) constant pressure 4. (T, V, µ) grand canonical #2, 3 or 4 are
Ab Ini'o Molecular Dynamics (MD) Simula?ons
Ab Ini'o Molecular Dynamics (MD) Simula?ons Rick Remsing ICMS, CCDM, Temple University, Philadelphia, PA What are Molecular Dynamics (MD) Simulations? Technique to compute statistical and transport properties
On the calculation of solvation free energy from Kirkwood- Buff integrals: A large scale molecular dynamics study
On the calculation of solvation free energy from Kirkwood- Buff integrals: A large scale molecular dynamics study Wynand Dednam and André E. Botha Department of Physics, University of South Africa, P.O.
SERS and NMR Studies of Typical Aggregation-induced. Emission Molecules
Supplemental information SERS and NMR Studies of Typical Aggregation-induced Emission Molecules Cheng Fang, Yujun Xie, Martin R. Johnston, Yinlan Ruan, Ben Zhong Tang 5, *, Qian Peng, *, Youhong Tang 6,
PHYSICS. Course Syllabus. Section 1: Mathematical Physics. Subject Code: PH. Course Structure. Electromagnetic Theory
PHYSICS Subject Code: PH Course Structure Sections/Units Topics Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8 Mathematical Physics Classical Mechanics Electromagnetic
Molecular Dynamics. Park City June 2005 Tully
Molecular Dynamics John Lance Natasa Vinod Xiaosong Dufie Priya Sharani Hongzhi Group: August, 2004 Prelude: Classical Mechanics Newton s equations: F = ma = mq = p Force is the gradient of the potential:
Molecular Dynamics Simulations of Biomolecular Systems
Molecular Dynamics Simulations of Biomolecular Systems Tadeusz Kuliński Institute of Bioorganic Chemistry, Polish Academy of Sciences Noskowskiego 12-14, Poznan, Poland E-mail: tadkul@ibch.poznan.pl Introduction
Molecular simulation and structure prediction using CHARMM and the MMTSB Tool Set Free Energy Methods
Molecular simulation and structure prediction using CHARMM and the MMTSB Tool Set Free Energy Methods Charles L. Brooks III MMTSB/CTBP 2006 Summer Workshop CHARMM Simulations The flow of data and information
Advanced in silico drug design
Advanced in silico drug design RNDr. Martin Lepšík, Ph.D. Lecture: Advanced scoring Palacky University, Olomouc 2016 1 Outline 1. Scoring Definition, Types 2. Physics-based Scoring: Master Equation Terms
Microcalorimetry for the Life Sciences
Microcalorimetry for the Life Sciences Why Microcalorimetry? Microcalorimetry is universal detector Heat is generated or absorbed in every chemical process In-solution No molecular weight limitations Label-free
A generalized Born formalism for heterogeneous dielectric environments: Application to the implicit modeling of biological membranes
THE JOURNAL OF CHEMICAL PHYSICS 122, 124706 2005 A generalized Born formalism for heterogeneous dielectric environments: Application to the implicit modeling of biological membranes Seiichiro Tanizaki
Diatomic Molecules. 7th May Hydrogen Molecule: Born-Oppenheimer Approximation
Diatomic Molecules 7th May 2009 1 Hydrogen Molecule: Born-Oppenheimer Approximation In this discussion, we consider the formulation of the Schrodinger equation for diatomic molecules; this can be extended
Ab initio Molecular Dynamics Born Oppenheimer and beyond
Ab initio Molecular Dynamics Born Oppenheimer and beyond Reminder, reliability of MD MD trajectories are chaotic (exponential divergence with respect to initial conditions), BUT... With a good integrator
Scientific Computing II
Scientific Computing II Molecular Dynamics Simulation Michael Bader SCCS Summer Term 2015 Molecular Dynamics Simulation, Summer Term 2015 1 Continuum Mechanics for Fluid Mechanics? Molecular Dynamics the
Adaptive resolution molecular-dynamics simulation: Changing the degrees of freedom on the fly
THE JOURNAL OF CHEMICAL PHYSICS 123, 224106 2005 Adaptive resolution molecular-dynamics simulation: Changing the degrees of freedom on the fly Matej Praprotnik, a Luigi Delle Site, and Kurt Kremer b Max-Planck-Institut
This is a simple input file for the calculation of NMR chemical shieldings for a given molecule using the B3LYP functional and def2-tzvpp basis set:
Computing NMR parameters using ORCA This practical comes with a short lecture on the basics of the computation of NMR parameters using standard electronic structure theory methods. By now you should have
MD Thermodynamics. Lecture 12 3/26/18. Harvard SEAS AP 275 Atomistic Modeling of Materials Boris Kozinsky
MD Thermodynamics Lecture 1 3/6/18 1 Molecular dynamics The force depends on positions only (not velocities) Total energy is conserved (micro canonical evolution) Newton s equations of motion (second order
PH 548 Atomistic Simulation Techniques
PH 548 Atomistic Simulation Techniques Lectures: Lab: 2-0-2-6 Tuesday 12-1 PM Thursday 12-1 PM Monday 2-5 PM P. K. Padmanabhan PH548: Two Excellent Books How to do it? 1 2 F 12 F 23 3 F i = F ij j i F
Large Scale Electronic Structure Calculations
Large Scale Electronic Structure Calculations Jürg Hutter University of Zurich 8. September, 2008 / Speedup08 CP2K Program System GNU General Public License Community Developers Platform on "Berlios" (cp2k.berlios.de)
Biomolecular modeling III
2016, January 5 Déjà vu Enhanced sampling Biomolecular simulation Each atom x, y, z coordinates Déjà vu Enhanced sampling Expression for energy the force field = 1 2 + N i i k i (r i r 0 i ) 2 + 1 2 N
Introduction to Computational Chemistry
Introduction to Computational Chemistry Vesa Hänninen Laboratory of Physical Chemistry room B430, Chemicum 4th floor vesa.hanninen@helsinki.fi September 3, 2013 Introduction and theoretical backround September
1. What is the difference between intermolecular forces and intramolecular bonds? Variations in the Boiling Point of Noble Gases
NAME: DATE: Chemical Bonding Forces Assignment 1. What is the difference between intermolecular forces and intramolecular bonds? 2. Use your data booklet to fill in the following chart Variations in the
Multiscale Materials Modeling
Multiscale Materials Modeling Lecture 09 Quantum Mechanics/Molecular Mechanics (QM/MM) Techniques Fundamentals of Sustainable Technology These notes created by David Keffer, University of Tennessee, Knoxville,
Statistical Mechanics for Proteins
The Partition Function From Q all relevant thermodynamic properties can be obtained by differentiation of the free energy F: = kt q p E q pd d h T V Q ), ( exp 1! 1 ),, ( 3 3 3 ),, ( ln ),, ( T V Q kt
Supporting Information
Supporting Information Constant ph molecular dynamics reveals ph-modulated binding of two small-molecule BACE1 inhibitors Christopher R. Ellis 1,, Cheng-Chieh Tsai 1,, Xinjun Hou 2, and Jana Shen 1, 1
Potential Energy (hyper)surface
The Molecular Dynamics Method Thermal motion of a lipid bilayer Water permeation through channels Selective sugar transport Potential Energy (hyper)surface What is Force? Energy U(x) F = " d dx U(x) Conformation
Temperature and Pressure Controls
Ensembles Temperature and Pressure Controls 1. (E, V, N) microcanonical (constant energy) 2. (T, V, N) canonical, constant volume 3. (T, P N) constant pressure 4. (T, V, µ) grand canonical #2, 3 or 4 are
The Life, Loves and Death of Hg 2+ Jeremy C. Smith
The Life, Loves and Death of Hg 2+ Jeremy C. Smith Experimental ligand-binding free energies in aqueous solution (kcal/mol) -52.7-30.0-25.0-19.1-14.5-10.0 Hg(SH) 2 > Hg(OH) 2 > Hg(OH)Cl > Hg(Cl) 2 > HgOH
PCCP Accepted Manuscript
PCCP Accepted Manuscript This is an Accepted Manuscript, which has been through the Royal Society of Chemistry peer review process and has been accepted for publication. Accepted Manuscripts are published
Structural and mechanistic insight into the substrate. binding from the conformational dynamics in apo. and substrate-bound DapE enzyme
Electronic Supplementary Material (ESI) for Physical Chemistry Chemical Physics. This journal is the Owner Societies 215 Structural and mechanistic insight into the substrate binding from the conformational
High Energy D 2 Bond from Feynman s Integral Wave Equation
Applying the Scientific Method to Understanding Anomalous Heat Effects: Opportunities and Challenges High Energy D Bond from Feynman s Integral Wave Equation By: Thomas Barnard Sponsored by: Coolesence
CE 530 Molecular Simulation
1 CE 530 Molecular Simulation Lecture 1 David A. Kofke Department of Chemical Engineering SUNY Buffalo kofke@eng.buffalo.edu 2 Time/s Multi-Scale Modeling Based on SDSC Blue Horizon (SP3) 1.728 Tflops
Molecular modeling and simulation in chemistry
Molecular modeling and simulation in chemistry 1/20 Elements of modeling: Nuclei + electrons + photons: QED Nuclei + electrons: Schrödinger equation Auxiliary interaction centers (sites) Atoms classical
Tools for QM studies of large systems
Tools for QM studies of large systems Automated, hessian-free saddle point search & characterization QM/MM implementation for zeolites Shaama Mallikarjun Sharada Advisors: Prof. Alexis T Bell, Prof. Martin
Free energy, electrostatics, and the hydrophobic effect
Protein Physics 2016 Lecture 3, January 26 Free energy, electrostatics, and the hydrophobic effect Magnus Andersson magnus.andersson@scilifelab.se Theoretical & Computational Biophysics Recap Protein structure
New Perspective on structure and bonding in water using XAS and XRS
New Perspective on structure and bonding in water using XAS and XRS Anders Nilsson Stanford Synchrotron Radiation Laboratory (SSRL) and Stockholm University, Sweden R. Ludwig Angew. Chem. 40, 1808 (2001)
Can a continuum solvent model reproduce the free energy landscape of a β-hairpin folding in water?
Can a continuum solvent model reproduce the free energy landscape of a β-hairpin folding in water? Ruhong Zhou 1 and Bruce J. Berne 2 1 IBM Thomas J. Watson Research Center; and 2 Department of Chemistry,
The Computational Microscope
The Computational Microscope Computational microscope views at atomic resolution... Rs SER RER C E M RER N GA L... how living cells maintain health and battle disease John Stone Our Microscope is Made
Introduction to Solving the Time- Dependent Schrödinger Equation. Tom Penfold
Introduction to Solving the Time- Dependent Schrödinger Equation Tom Penfold Outline 1 Introduction to Solving the Time-Dependent Schrödinger Equation What problems are we trying to solve? How can we use
Computational Chemistry. An Introduction to Molecular Dynamic Simulations
Computational Chemistry An Introduction to Molecular Dynamic Simulations Computational chemistry simulates chemical structures and reactions numerically, based in full or in part on the fundamental laws
Free Energy Simulation Methods
Free Energy Simulation Methods Free energy simulation methods Many methods have been developed to compute (relative) free energies on the basis of statistical mechanics Free energy perturbation Thermodynamic
Molecular dynamics (MD) simulations are widely used in the
Comparative study of generalized Born models: Protein dynamics Hao Fan*, Alan E. Mark*, Jiang Zhu, and Barry Honig *Groningen Biomolecular Sciences and Biotechnology Institute, Department of Biophysical
Physical Chemistry I Fall 2016 Second Hour Exam (100 points) Name:
Physical Chemistry I Fall 2016 Second Hour Exam (100 points) Name: (20 points) 1. Quantum calculations suggest that the molecule U 2 H 2 is planar and has symmetry D 2h. D 2h E C 2 (z) C 2 (y) C 2 (x)
Protein-Ligand Docking Methods
Review Goal: Given a protein structure, predict its ligand bindings Protein-Ligand Docking Methods Applications: Function prediction Drug discovery etc. Thomas Funkhouser Princeton University S597A, Fall
AP Chemistry A Review of Analytical Chemistry
AP Chemistry A Review of Analytical Chemistry AP Chemistry Ch 1 (Prentice Hall) What Temperature Do You Read? A measurement always has some amount of uncertainty To indicate the uncertainty of a single