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 =

Slides:



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

Homework 2 (due We, Feb. 5): Reading: Van Holde, Chapter 1 Van Holde Chapter 3.1 to 3.3 Van Holde Chapter 2 (we’ll go through Chapters 1 and 3 first. 1.Van.
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.
Chemistry 6440 / 7440 Molecular Mechanics. Resources Grant and Richards, Chapter 3 Leach, Chapter 3 Jensen, Chapter 2 Cramer, Chapter 2 Burkert and Allinger,
Solvation Models. Many reactions take place in solution Short-range effects Typically concentrated in the first solvation sphere Examples: H-bonds,
Force Field of Biological System 中国科学院理论物理研究所 张小虎 研究生院《分子建模与模拟导论》课堂 2009 年 10 月 21 日.
Questions 1) Are the values of r0/theta0 approximately what is listed in the book (in table 3.1 and 3.2)? -> for those atom pairs/triplets yes; 2) In the.
Molecular Modeling: Molecular Mechanics C372 Introduction to Cheminformatics II Kelsey Forsythe.
SMA5233 Particle Methods and Molecular Dynamics Lecture 3: Force Fields A/P Chen Yu Zong Tel:
Theoretical Models “The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely.
Molecular Mechanics Force Fields Basic Premise If we want to study a protein, piece of DNA, biological membranes, polysaccharide, crystal lattice, nanomaterials,
Ion Solvation Thermodynamics from Simulation with a Polarizable Force Field Gaurav Chopra 07 February 2005 CS 379 A Alan GrossfeildPengyu Ren Jay W. Ponder.
Computational Solid State Physics 計算物性学特論 第2回 2.Interaction between atoms and the lattice properties of crystals.
Ideal Gases and the Compressibility Factor 1 mol, 300K for various gases A high pressure, molecules are more influenced by repulsive forces. V real.
Molecular Dynamics, Monte Carlo and Docking Lecture 21 Introduction to Bioinformatics MNW2.
Molecular Dynamics Simulation (a brief introduction)
Case Studies Class 5. Computational Chemistry Structure of molecules and their reactivities Two major areas –molecular mechanics –electronic structure.
Molecular Modeling of Crystal Structures molecules surfaces crystals.
Lecture 3 – 4. October 2010 Molecular force field 1.
Vibrational Spectroscopy I
1 Molecular Simulation 黃鎮剛 交通大學 生物科技系及生物資訊所. 2 Empirical Force Field
Protein Tertiary Structure Prediction. Protein Structure Prediction & Alignment Protein structure Secondary structure Tertiary structure Structure prediction.
Molecular Modeling Part I Molecular Mechanics and Conformational Analysis ORG I Lab William Kelly.
Protein Folding & Biospectroscopy Lecture 5 F14PFB David Robinson.
Introduction. What is Computational Chemistry?  Use of computer to help solving chemical problems Chemical Problems Computer Programs Physical.
Molecular Modeling Fundamentals: Modus in Silico C372 Introduction to Cheminformatics II Kelsey Forsythe.
Molecular Modeling Part I. A Brief Introduction to Molecular Mechanics.
Algorithms and Software for Large-Scale Simulation of Reactive Systems _______________________________ Ananth Grama Coordinated Systems Lab Purdue University.
02/03/10 CSCE 769 Dihedral Angles Homayoun Valafar Department of Computer Science and Engineering, USC.
Molecular Dynamics Collection of [charged] atoms, with bonds – Newtonian mechanics – Relatively small #of atoms (100K – 10M) At each time-step – Calculate.
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.
1.Solvation Models and 2. Combined QM / MM Methods See review article on Solvation by Cramer and Truhlar: Chem. Rev. 99, (1999)
Chapter 22, Macromolecules and aggregates Ideality and reality Simplicity of small systems and complexity of real systems Entropy and order Dealing with.
Molecular Mechanics Part 2
Computational Chemistry Molecular Mechanics/Dynamics F = Ma Quantum Chemistry Schr Ö dinger Equation H  = E 
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 simulation methods Ab-initio methods (Few approximations but slow) DFT CPMD Electron and nuclei treated explicitly. Classical atomistic methods.
Molecular Mechanics Studies involving covalent interactions (enzyme reaction): quantum mechanics; extremely slow Studies involving noncovalent interactions.
Computational Chemistry Molecular Mechanics/Dynamics F = Ma Quantum Chemistry Schr Ö dinger Equation H  = E 
20 B Week II Chapters 9 -10) Macroscopic Pressure Microscopic pressure( the kinetic theory of gases: no potential energy) Real Gases: van der Waals Equation.
Algorithms and Software for Large-Scale Simulation of Reactive Systems _______________________________ Metin Aktulga, Sagar Pandit, Alejandro Strachan,
MODELING MATTER AT NANOSCALES 3. Empirical classical PES and typical procedures of optimization Classical potentials.
Homework 2 (due We, Feb. 1): Reading: Van Holde, Chapter 1 Van Holde Chapter 3.1 to 3.3 Van Holde Chapter 2 (we’ll go through Chapters 1 and 3 first. 1.Van.
Lecture 16 – Molecular interactions
Ch 23 Pages Lecture 17 – Intramolecular interactions.
1 Statistical Mechanics and Multi- Scale Simulation Methods ChBE Prof. C. Heath Turner Lecture 18 Some materials adapted from Prof. Keith E. Gubbins:
1 CE 530 Molecular Simulation Lecture 14 Molecular Models David A. Kofke Department of Chemical Engineering SUNY Buffalo
LSM3241: Bioinformatics and Biocomputing Lecture 6: Fundamentals of Molecular Modeling Prof. Chen Yu Zong Tel:
Hydro-pathy/phobicity/philicity One of the most commonly used properties is the suitability of an amino acid for an aqueous environment Hydropathy & Hydrophobicity.
1 MODELING MATTER AT NANOSCALES 2. Energy of intermolecular processes.
Second virial coefficient
Developing a Force Field Molecular Mechanics. Experimental One Dimensional PES Quantum mechanics tells us that vibrational energy levels are quantized,
--Experimental determinations of radial distribution functions --Potential of Mean Force 1.
Intermolecular Forces “Review” of electrostatics -> today Force, field, potentials, and energy Dipoles and multipoles Discussion of types of classical.
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.
Van der Waals dispersion in density functional theory
Chapter 2 Molecular Mechanics
Introduction to Molecular Simulation
Algorithms and Software for Large-Scale Simulation of Reactive Systems
Computational Analysis
Lecture 12 Optical Properties Md Arafat Hossain Outlines.
CZ5225 Methods in Computational Biology Lecture 7: Protein Structure and Structural Modeling Prof. Chen Yu Zong Tel:
Molecular simulation methods
Algorithms and Software for Large-Scale Simulation of Reactive Systems
Introduction to Molecular Simulation
Physical Chemistry Chapter VI Interaction between Molecules 2019/5/16
Presentation transcript:

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 = dE/dr System’s energy Kr 2 Bond stretching + A/r 12 – B/r 6 VDW interaction + Q 1 Q 2 /r Electrostatic forces Bond spring CHALLNGE: thousands or even millions of atoms to take care of

PRICIPLE: Given positions of each atom x(t) at time t, its position at next time-step t +  t is given by: x(t +  t)  x(t) + v(t)  t + ½*F / m * (  t) 2 Key parameter: integration time step  t. Controls accuracy and speed. At every step we need to re-compute all forces acting on all the atoms in the system. Molecular Dynamics: Smaller  t – higher accuracy, but need more steps. How many? force

A Classical Description Displacement from Equilibrium Energy Bond Bend Bond Stretch Torsion about a Bond Electrostatic Interaction Van der Waals Interaction Hook’s Law Balls and Springs

Chemical Intuition behind Functional Groups: structual units behave similarly in different molecules. Atom Types: An atom‘s behavior depends not only on the atomic number, but also on the types of atomic bonding that it is involved in. Example: Selected atom types in the MM2 force field TypeSymbol Description 1Csp 3 carbon, alkane 2Csp 2 carbon, alkene 3Csp 2 carbon, carbonyl, imine 4Csp carbon 22Ccyclopropane 29Cradical

Energy Decomposition E total = E stretch + E bend + E torsion + E vdW + E electrostatic Bonded energies Non-bonded energies

Streching Energy: Morse Potential A Morse potential requires three parameters and is not efficient to calculate  (R) = D e {1  exp [  (R  R 0 )]} 2 where D e = dissociation energy,  =  (  /2D e ) 1/2,  is the reduced mass,  is related to the bond stretching frequency by  (k s /  ) 1/2, k s is the force constant, and R 0 is the equilibrium bondlength. A Morse potential describes the energy profile of a bond stretch quite accurately over a wide range of bondlength. R

Stretching Energy: Harmonic Potential In molecular mechanics simulations, displacement of bond length from equilibrium is usually so small that a harmonic oscillator (Hooke’s Law) can be used to model the bond stretching energy:  (R) = 0.5  k s (R  R 0 ) 2 R E R

Refinement by High-order Terms R E Higher-order terms such as cubic terms can be added for a better fit with however an increased computation cost:  (R) = 0.5  k s (R  R 0 ) 2  [1  k(R  R 0 )  k  (R  R 0 ) 2 + …] R

Bending Energy Usually described by harmonic potentials:  (  ) = 0.5  k b (    0 ) 2 Refinement by adding higher-order terms can be done. 

Torsional Energy Associated with rotating about a bond An important contribution to the torsional barrier (other interactions, e.g., van der Waals interactions also contribute to the torsional barrier).  Potential energy profile is periodic in the torsional angle 

Torsional Energy: Fourier Series  E(  ) = 0.5  V 1 (1 + cos  )  V 2 (1 + cos 2  )  V 3 (1 + cos 3  )

B Out-of-plane Bending: Improper Torsional Energy To keep 4 atoms in a plane, e.g., the three C atoms and one H atoms as indicated in C 6 H 6 C C C H B'B' A C D  imp (  ) = 0.5  k imp (    0 ) 2 When ABCD in a plane, dihedral  ABCD is 0 or 180°. When B is out of the plane, dihedral  ABCD deviated from the ideal value, and the enegy penalty tries to bring it back to the plane.

Van der Waals Energy Interactions between atoms that are not directly bonded Very repulsive at short distances, slightly attractive at intermediate distances, and approaching zero at long distances R Repulsion is due to overlap of electron clouds of two atoms. Attraction is due to the dispersion or induced multipole interactions (dominated by induced dipole-dipole interactions).

VDW: Lennard-Jones Potential E LJ (R)=  [(R 0 /R) 12 – 2(R 0 /R) 6 ] R 0 AB = R 0 A + R 0 B  AB = (  AB  AB ) 1/2 The use of R  12 for repulsive is due to computational convenience. There are other functions in use, but not popular because of higher computational costs. Calculated pairwise but parameterized against experiemental data — implicitly include many-body effects.

Charge Distribution Model the charge distribution by distributed multipoles: point charges, dipoles,... Atomic Partial charges: assigning partial charges at atomic centers to model a polar bond Bond dipoles: a polar bond can also be modeled by placing a dipole at its mid-point (getting rare now). O:  e H: e C1:  e C2:  e H: e Partial Charges for H 2 O and n-butane (OPLS-AA)

Electrostatic Energy E el (R) = (Q A Q B )/(  R AB )  is dielectric constant that models the screening effect due to the surroundings. Calculated pairwise but parameterized against experimental or ab initio data, the many-body effects are implicitly accounted for Explicit polarization is more difficult, and is now one of the hot topics in molecular mechanics development.

Special Terms E cross brings correction to energy due to the couplings between stretches, bends, and torsions. E H-bond is an additional term to improve the accuracy of H-bonding energy that is mainly due to electrostatic contributions. Non-bonded interactions are not calculated for atom pair that are connected directly (1,2-interaction), and are often scaled down by a factor if the atoms are connected via two bonds (1,3-interaction) or three bonds (1,4-interaction).

Use of Cut-offs Non-bonded interactions are expensive to calculated — there are so many atom pairs! Non-bonded interactions decrease as distance increases, and beyond a certain distance, they can be so small that we want to neglect them. RmRm R Use of Cut-offs (typically 10 to 15 Å) helps us out! However, be careful when use them: Are the neglected interactions really negligible? Have we properly handled the discontinuity at the cut-off boundary?

Understand Force Fields The zero of energy is arbitrary. The absolute enegy is meaningless! Comparisons should be made for systems having the same number and types of structural units, or for a system at different conformations. The parameters are JUST parameters. Parameters such as ideal bondlengths are not experimental equilibrium bondlengths, although they are close to each other.

Understand Force Fields (2) The preditction power is limited. The accuray is usually not very good, and depends heavily on parameterization. It can only be used for systems having the functional groups that were included in parameterization. A good strategy to claim how reliable the results could be. Before apply your force field parameters to big molecules, validate them on small compunds where reliable experimental data are available for compasion. (You never prove it, but you can show that how much it is likely to be.)