August 13 2009 MOBIL Summer School Lea Thøgersen.

Slides:



Advertisements
Similar presentations
Simulazione di Biomolecole: metodi e applicazioni giorgio colombo
Advertisements

The Kinetic Theory of Gases
Pressure and Kinetic Energy
Molecular Bonds Molecular Spectra Molecules and Solids CHAPTER 10 Molecules and Solids Johannes Diderik van der Waals (1837 – 1923) “You little molecule!”
Transfer FAS UAS SAINT-PETERSBURG STATE UNIVERSITY COMPUTATIONAL PHYSICS Introduction Physical basis Molecular dynamics Temperature and thermostat Numerical.
Molecular Dynamics: Review. Molecular Simulations NMR or X-ray structure refinements Protein structure prediction Protein folding kinetics and mechanics.
A Digital Laboratory “In the real world, this could eventually mean that most chemical experiments are conducted inside the silicon of chips instead of.
Survey of Molecular Dynamics Simulations By Will Welch For Jan Kubelka CHEM 4560/5560 Fall, 2014 University of Wyoming.
Hybridization A Way of Explaining VSEPR Theory. Covalent bonding Modern methods for describing bonding make use of quantum mechanical methods and describe.
Potential Energy Surface. The Potential Energy Surface Captures the idea that each structure— that is, geometry—has associated with it a unique energy.
Molecular Mechanics Force Fields Basic Premise If we want to study a protein, piece of DNA, biological membranes, polysaccharide, crystal lattice, nanomaterials,
Computational Chemistry
Applications and integration with experimental data Checking your results Validating your results Structure determination from powder data calculations.
Lawrence Hunter, Ph.D. Director, Computational Bioscience Program University of Colorado School of Medicine
Molecular Dynamics Simulation (a brief introduction)
Molecular Modeling of Crystal Structures molecules surfaces crystals.
Lecture 3 – 4. October 2010 Molecular force field 1.
Molecular Simulation. Molecular Simluation Introduction: Introduction: Prerequisition: Prerequisition: A powerful computer, fast graphics card, A powerful.
2. Modeling of small systems Building the model What is the optimal conformation of a molecule? What is the relative energy of a given conformation? What.
Protein Tertiary Structure Prediction. Protein Structure Prediction & Alignment Protein structure Secondary structure Tertiary structure Structure prediction.
Introduction to Statistical Thermodynamics of Soft and Biological Matter Lecture 4 Diffusion Random walk. Diffusion. Einstein relation. Diffusion equation.
Molecular Mechanics, Molecular Dynamics, and Docking Michael Strong, PhD National Jewish Health 11/23/2010.
Joo Chul Yoon with Prof. Scott T. Dunham Electrical Engineering University of Washington Molecular Dynamics Simulations.
Molecular Dynamics Simulations Modeling Domain Movements of P-type ATPases Pumpkin Annual Meeting September 19, 2008.
22/5/2006 EMBIO Meeting 1 EMBIO Meeting Vienna, 2006 Heidelberg Group IWR, Computational Molecular Biophysics, University of Heidelberg Kei Moritsugu MD.
Lecture 10 Energy production. Summary We have now established three important equations: Hydrostatic equilibrium: Mass conservation: Equation of state:
Bioinf. Data Analysis & Tools Molecular Simulations & Sampling Techniques117 Jan 2006 Bioinformatics Data Analysis & Tools Molecular simulations & sampling.
Molecular Modeling Part I Molecular Mechanics and Conformational Analysis ORG I Lab William Kelly.
Molecular Mechanics, Molecular Dynamics, and Docking
Molecular Modeling Part I. A Brief Introduction to Molecular Mechanics.
Molecular Dynamics Simulation Solid-Liquid Phase Diagram of Argon ZCE 111 Computational Physics Semester Project by Gan Sik Hong (105513) Hwang Hsien Shiung.
Reaction Rate How Fast Does the Reaction Go Collision Theory l In order to react molecules and atoms must touch each other. l They must hit each other.
RNA Secondary Structure Prediction Spring Objectives  Can we predict the structure of an RNA?  Can we predict the structure of a protein?
By: Debbie Schwagerman January 31, Atomic Bonds and Molecular Interactions Each atom has a defined number and geometry of covalent bonds. Each atom.
CZ5225 Methods in Computational Biology Lecture 4-5: Protein Structure and Structural Modeling Prof. Chen Yu Zong Tel:
Lecture 11: Potential Energy Functions Dr. Ronald M. Levy Originally contributed by Lauren Wickstrom (2011) Statistical Thermodynamics.
Computational Chemistry Molecular Mechanics/Dynamics F = Ma Quantum Chemistry Schr Ö dinger Equation H  = E 
Rates of Reactions Why study rates?
Computer Simulation of Biomolecules and the Interpretation of NMR Measurements generates ensemble of molecular configurations all atomic quantities Problems.
Conformational Entropy Entropy is an essential component in ΔG and must be considered in order to model many chemical processes, including protein folding,
Potential energy surface, Force field & Molecular Mechanics 3N (or 3N-6 or 3N-5) Dimension PES for N-atom system x E’ =  k i (l i  l 0,i ) +  k i ’
Common Potential Energy Functions of Separation Distance The Potential Energy function describes the energy of a particular state. When given as a function.
Molecular Mechanics Studies involving covalent interactions (enzyme reaction): quantum mechanics; extremely slow Studies involving noncovalent interactions.
 We just discussed statistical mechanical principles which allow us to calculate the properties of a complex macroscopic system from its microscopic characteristics.
Molecular Dynamics Simulations of Compressional Metalloprotein Deformation Andrew Hung 1, Jianwei Zhao 2, Jason J. Davis 2, Mark S. P. Sansom 1 1 Department.
MODELING MATTER AT NANOSCALES 3. Empirical classical PES and typical procedures of optimization Classical potentials.
Metals I: Free Electron Model
Molecular Dynamics Simulation of Membrane Channels Part III. Nanotubes Theory, Methodology Summer School on Theoretical and Computational Biophysics June.
Molecular Modelling - Lecture 2 Techniques for Conformational Sampling Uses CHARMM force field Written in C++
NIH Resource for Biomolecular Modeling and Bioinformatics Beckman Institute, UIUC Molecular Dynamics Method 2 Justin Gullingsrud.
ChE 452 Lecture 25 Non-linear Collisions 1. Background: Collision Theory Key equation Method Use molecular dynamics to simulate the collisions Integrate.
EEE 3394 Electronic Materials Chris Ferekides SPRING 2014 WEEK 2.
LSM3241: Bioinformatics and Biocomputing Lecture 6: Fundamentals of Molecular Modeling Prof. Chen Yu Zong Tel:
Review Session BS123A/MB223 UC-Irvine Ray Luo, MBB, BS.
VSEPR model for geometry of a molecule or an ion
Developing a Force Field Molecular Mechanics. Experimental One Dimensional PES Quantum mechanics tells us that vibrational energy levels are quantized,
Molecular Dynamics Arjan van der Vaart PSF346 Center for Biological Physics Department of Chemistry and Biochemistry Arizona State.
Molecular Mechanics (Molecular Force Fields). Each atom moves by Newton’s 2 nd Law: F = ma E = … x Y Principles of M olecular Dynamics (MD): F =
Structural classification of Proteins SCOP Classification: consists of a database Family Evolutionarily related with a significant sequence identity Superfamily.
Molecular dynamics (MD) simulations  A deterministic method based on the solution of Newton’s equation of motion F i = m i a i for the ith particle; the.
Lecture 7: Molecular Mechanics: Empirical Force Field Model Nanjie Deng Structural Bioinformatics II.
March 21, 2008 Christopher Bruns
Molecular Modelling - Lecture 3
BY JHERUDDEN PGT (CHEMISTRY) KV SECL,NOWROZABAD
Large Time Scale Molecular Paths Using Least Action.
Intro to Molecular Dynamics (MD) Simulation using CHARMM
CZ5225 Methods in Computational Biology Lecture 7: Protein Structure and Structural Modeling Prof. Chen Yu Zong Tel:
Steered Molecular Dynamics Simulations on the “Tail Helix Latch” Hypothesis in the Gelsolin Activation Process  Feng Cheng, Jianhua Shen, Xiaomin Luo,
The Selectivity of K+ Ion Channels: Testing the Hypotheses
Atomic Detail Peptide-Membrane Interactions: Molecular Dynamics Simulation of Gramicidin S in a DMPC Bilayer  Dan Mihailescu, Jeremy C. Smith  Biophysical.
Presentation transcript:

August MOBIL Summer School Lea Thøgersen

 Model based on observations and theory. Used to predict and explain new observations  Molecular Modeling  Use the computer as a laboratory  Do you know any methods?  What are they used for?  Today: Molecular Dynamics  Experimental observations and simple physical rules combined to simulate how different atoms move wrt each other.

 Topics: Conformational energy, force field and molecular dynamics  Literature: “Part 3” (Chap. 8 Diffraction and Simulation) p (first 4 lines), p , p  Goal: Obtain basic feeling for the possibilities and limitations of molecular dynamics  Means: active participation from you  First session “Conformational Energy and Force Fields”  ends with an exercise  Second session “Molecular Dynamics”  includes discussion of a current research study ?

 E tot =  E kin for a molecule  e.g. vibration, diffusion  coupled with temperature and atom velocities, but independent of atom positions  E pot for molecule  atoms affect each other dependent on atom type and distance => E pot coupled with atom positions  conformational energy E kin + E pot {½mv 2 } {mgh (gravity) ; ½kx 2 (spring)} ? ? ? ?

C 4 H 10

 Atoms  nuclei (protons+neutrons)  electrons  Quantum Mechanics:  when chemical bonds are formed electrons redistribute on all atoms in the molecule  a carbon (e.g.) would be different from molecule to molecule  the distribution of both the electrons and the nuclei in a molecule determines the conformational energy  Experimentally:  atoms of particular type and in particular functional groups behave similar independent of the molecule  IR wave lengths and NMR chemical shifts have characteristic values for certain atom types and groups independent of which molecule they are a part of  Molecular Mechanics:  Conformational energy from distribution of only the nuclei  Not without problems ? ?

 Energy as function of the relative positions of the atoms => conformational energy  Additive energy contributions  Spectroscopy of small molecules suggest that energy contributions from individual internal coordinates are independent, to a good approximation  Energy function as sum of independent contributions  Relative energies instead of absolutes  Easier to define energy penalty than absolute energy  Constant contributions can be ignored  Divided in “bonding” and “non-bonding” contributions

Describing the physics and chemistry of the atom interactions r or θ E eq E φ bond stretch angle bend bond rotation => dihedral

Describing the physics and chemistry of the atom interactions ++ ÷÷ ÷ ÷ + + Van der Waals interactions Electrostatic interactions

 Constants in the energy expression should be determined ex.  Based on experimental observations and QM computations.  Hard and tedious work to construct a good and general force field. ?

E pot ||F|| Gravitymghmg Spring½k(Δx)2½k(Δx)2 k Δxk Δx GenerallyEpEp ∂Ep/∂x∂Ep/∂x x equilibrium ∆x > 0 ∆x < 0 F = 0 F < 0 F > 0 F = - ∂E p /∂x ? ? ? ? ? ? ? ?

 The form of the potential energy function defines a force field  Function describing the potential energy of the molecule as a function of atom positions - conformational energy  +Parameterization of this energy function  Examples: MMFF, CHARMM, OPLS, GROMOS… Force Field

 Complex energy surface  Molecule specific  Only two out of 3N-6 variables shown here. Potential energy surface Energy Coord 1 Coord 2  Minima correspond to equilibrium structures

 Q1 Bond Stretch: Which of the three lines represent the stretching of the double C=C bond in propene and why?  Q2 Bond Rotation: Which line represents the single bond, which represents the double bond and why? How many interactions contribute in fact to the rotation around the single and the double bonds?  Q3 vdW Interactions: Which line represents the H-H interaction, which represents the C-H interaction and why?  Q4: What constitutes a force field, and why does it make sense to call it a ”force field”? Number 2. The equilibrium is found for a shorter distance (than for the solid line), and the graph is steeper, meaning the force constant is higher, meaning the bond is stronger. Number 1 = single bond, number 2 = double bond. Number 1 has three minima (characteristic of an sp3 bond) and a low rotation barrier. Number 2 has two minima (characteristic of an sp2 bond) and a high rotation barrier. The double bond rotation has four contributions ( , , , ) The single bond rotation has six contributions (6-2-3-{7,8,9} and {7,8,9}) Number 1 = H-H interaction, number 2 = C-H interaction. Hydrogen is a smaller atom than carbon, and therefore the minimum vdW distance is smaller for H-H than for H-C. A force field consists of a potential energy function and the parameters for the function. It is called a force field since the first derivative of the potential energy wrt the position of an atom gives the force acting on this atom from the rest of the atoms in the system.

A Virtual Experiment

 Both potential and kinetic energy  Given a start structure and a force field an MD simulation output the development of the system over time (nanosecond time scale)

,000 atoms 100 ps ,000 atoms 10 ns ,000,000 atoms 14 ns Satellite tobacco mosaic virus, complete with protein, RNA, ions ER DNA- binding domain LacI-DNA complex

ri(t)vi(t)ai(t)ri(t)vi(t)ai(t) Time line time step ∆t, δt typical ∆t ≈ 1· s = 1 fs r i (t+ δt) v i (t+ δt) a i (t+δt) atom positions atom velocities atom accelerations r i (0) v i (0) a i (0) ? ?

 Find initial coordinates r(t=0) for all atoms in the system  For proteins an X-ray or NMR structure is used or modified  Water and lipid can be found pre-equilibrated from the modeling software or on the web  Smaller molecules can be sketched naively and pre-optimized within the modeling software

 Avoid boundary effects  Every atom ’sees’ at most one picture of the other atoms.  Cutoff less than half the shortest box side  At least 10Å cutoff.

E pot ||F|| Spring½k(Δx)2½k(Δx)2 k Δxk Δx GenerallyEpEp ∂Ep/∂x∂Ep/∂x x equilibrium ∆x > 0 ∆x < 0 F = 0 F < 0 F > 0 F = - ∂E p /∂x = -G F = m a r(t=0) => F(r(t=0)) => a(t=0) ?

 Maxwell-Boltzmann distribution for kinetic energy ε k = ½mv 2 => v(t=0)  Initial distribution of speed reproducing the requested temperature  random directions of the velocities

Time line time step ∆t, δt typical ∆t ≈ 1· s = 1 fs ri(t)ai(t)vi(t)ri(t)ai(t)vi(t)

Good ? Bad Total simulation time e.g. 10 ns = conformations  Collisions should occur smoothly!  Time step ~ 1/10 T fast motion period  T C-H vib ~ 10 fs => Time step = 1 fs Time line time step ∆t, δt typical ∆t ≈ 1· s = 1 fs ?

 Build the system  Clean pdb-structure for unwanted atoms  Add missing atoms  Add the environment  Make a structure file describing connections  Minimization of the system  Some 2000 steps, gradient < 5 or so  To remove clashes  Equilibration of the system  Maybe constraining some atoms to their initial position too keep overall structure  Maybe starting from low temperature, and slowly increasing it to the wanted  Maybe letting the volume adjust properly to the size of the system  Energy and RMSD should level out  Production run  Constant temp, vol, pressure?

 Experimenting with different setups to see what happens – is the system stable?  Mutations, temperature, pressure, environment....  Test out hypotheses based on experiment  Detailed information at the atomic level  Free energy differences – site-directed mutagenesis  Other thermodynamics stuff  Poke it / steer it

 X-ray, NMR and various biophysical studies and mutation studies and more?  Model the hypothesis, does the modelled response fit the experiment? If so, both the experiment and simulation conclusion is strengthen and a higher level of understanding is gained  Shortcomings of MD:  Timescale - ns is very short – no conformational changes  System size – the dimensions of the model are less than nm  No electrons – polarization cannot be described

 6 simulation setups. 10 ns simulations of SERCA in a membrane consisting of either short, POPC, long, DMPC, or DOPC lipids, and SERCA in a membrane of 2:1 C12E8:POPC atoms.  X-ray low resolution scattering from bilayer leaflets. The bilayers in the crystals consist of 16:7 detergent:lipid (detergent C12E8, lipids from native membrane).  Try to come up with relevant and interesting things to study from the MD simulations.

Long POPC POPC+detergent

Membrane type Avg. Hydrophobic thickness (8-10 ns) (Å) Avg. Overall tilt (8-10 ns) (°) < 7 Å from protein> 7 Å from protein POPC:C12E8 (1:2)* Short DMPC DOPC POPC Long purePOPC31.3- purePOPC:C12E8 (1:2)*24.0- α

From Theoretical and Computational Biophysics Group, University of Illinois at Urbana-Champaign

 K + permeation  Voltage bias  Conduction via knock-on mechanism  Selective filter

 transmembrane pore of alpha- hemolysin  Electrophoretically-driven  58-nucleotide DNA strand

 Full structure of satellite tobacco mosaic virus, complete with protein, RNA, ions, and a small water box