Docking & Dynamics (10)

8/13/2019 Docking & Dynamics (10)

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DOCKING AND DYNAMICS

SIMULATION

Usman Sumo Friend Tambunan

Arli Aditya Parikesit

Syarifuddin Idrus


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- Overview of Docking

- Task


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Overview of Docking

Docking is a method which predicts the

preferred orientation of one molecule to a

second when bound to each other to form astable complex.


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Overview of Docking


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Macromolecular docking

Molecular Docking

Protein-protein Docking

Protein Nucleic acid Docking

the computational modelling of the quarternary structureof complexes formed by two or more interacting

biological macromolecules, i.e.

Protein

ligand docking is a molecular modelling technique

The goal of protein-ligand docking is to predict the

position and orientation of a ligand (a small molecule)

when it is bound to a protein receptor or enzyme


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Protein-Protein Interaction

Protein-Protein interaction:

Surface contact, shape complementarity

Intermolecular forces:

Van der Waals, hydrogen bonding, electrostatic force


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Protein-DNA Interaction

Protein-DNA

interaction:

DNA recognition by

proteins is primarily

mediated by certain

classes of DNA

binding domains andmotifs


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Protein-RNA Interaction

Protein-RNA

interaction:

RNA recognition by

proteins is primarily

mediated by certain

classes of RNA

binding domains and

motifs


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Protein-Ligand Interaction

Ligand Binding:

A small molecule

ligand normally binds

to a cavity of a protein.

Effect of Binding:

Activate, inhibit,

being metabolized or

transported bythe protein


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Protein-ligand docking

Predicts... The pose of the molecule

in the binding site

The binding affinity or ascore representing thestrength of binding

A Structure-Based Drug Design (SBDD) method

structure means using protein structure Computational method that mimics the binding of a ligand to

a protein

Given...


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Pose vs. Binding site

Binding site (or active site)

the part of the protein where the ligand

binds

generally a cavity on the protein surface

can be identified by looking at the crystal

structure of the protein bound with a knowninhibitor

Pose (or binding mode)

The geometryof the ligand in the binding

site

Geometry = location, orientation andconformation

Protein-ligand docking is no t about

identifying the binding site


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The main uses of protein-liganddocking are for

Virtual screening, to identifypotential lead compoundsfrom a large dataset

Pose prediction, best affinityfor drug design, catalyticsite/core


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Virtual screening is the computational or in silico analogue of

biological screening

The aim is to score, rank or filtera set of chemical structuresusing one or more computational procedures

Docking is just one way to do this

It can be used

to help decide which compounds to screen (experimentally)

which libraries to synthesise

which compounds to purchase from an external company

to analyse the results of an experiment

Virtual screening


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Pose prediction

If we know exactly where and how a

known ligand binds...

We can see which parts areimportant for binding

We can suggest changes to

improve affinity

Avoid changes that will clash with

the protein


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Components of docking software

Typically, protein-ligand docking software consist of

two main components which work together:

1. Search algorithm Generates a large number of poses of a molecule in the

binding site

2. Scoring function Calculates a score or binding affinity for a particular pose

To give:

The pose of the molecule inthe binding site

The binding affinity or ascore representing thestrength of binding


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Search Algorithms

We can classify the various search algorithms

according to the degrees of freedom that theyconsider

Rigid docking or flexible docking

With respect to the ligand structure

Rigid docking

The ligand is treated as a rigid structure during the

docking

Only the translational and rotational degrees of freedom are

considered To deal with the problem of ligand conformations, a large

number of conformations of each ligand are generated in

advance and each is docked separately

Examples: FRED (Fast Rigid Exhaustive Docking) from

OpenEye, and one of the earliest docking programs, DOCK


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AR Leach, VJ Gillet, An Introduction to Cheminformatics

Ligand atoms are then matched to

the sphere centres so that the

distances between the atoms

equal the distances between the

corresponding sphere centres,

within some tolerance.

The ligand conformation is then

oriented into the binding site. Afterchecking to ensure that there are

no unacceptable steric

interactions, it is then scored.

New orientations are produced by

generating new sets of matchingligand atoms and sphere centres.

The procedure continues until all

possible matches have been

considered.


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Flexible docking Flexible docking is the most common form of docking today

Conformations of each molecule are generated on-the-fly by the

search algorithm during the docking process

The algorithm can avoid considering conformations that do not fit

Exhaustive (systematic) searching computationally too expensive as

the search space is very large

One common approach is to use stochastic search methods

These dont guarantee optimum solution, but good solution within

reasonable length of time

Stochastic means that they incorporate a degree of randomness

Such algorithms include genetic algorithms (GOLD), simulated

annealing (AutoDock)


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The perfect scoring function will

Accurately calculate the binding affinity Will allow actives to be identified in a virtual screen

Be able to rank actives in terms of affinity

Score the poses of an active higher than poses of an

inactive Will rank actives higher than inactives in a virtual screen

Score the correct pose of the active higher than anincorrect pose of the active

Will allow the correct pose of the active to be identified

actives = molecules with biological activity


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Classes of scoring function

Broadly speaking, scoring functions can be

divided into the following classes:

Forcefield-based

Based on terms from molecular mechanics forcefields

GoldScore, DOCK, AutoDock, MOE Empirical

Parameterised against experimental binding affinities

ChemScore, PLP, Glide SP/XP

Knowledge-based potentials Based on statistical analysis of observed pairwise

distributions

PMF, DrugScore, ASP


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MOE : Molecular Operating Environment


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Preparing the protein structure

PDB structures often contain water molecules

In general, all water molecules are removed except where it isknown that they play an important role in coordinating to the ligand

PDB structures are missing all hydrogen atoms Many docking programs require the protein to have explicit

hydrogens. In general these can be added unambiguously, except

in the case of acidic/basic side chains

An incorrect assignment of protonationstates in the active site will give poorresults

Glutamate, Aspartate have COO- orCOOH

OH is hydrogen bond donor, O- is not

Histidine is a base and its neutral formhas two tautomers

HNH N

R

+

NH N

R

R

NHN


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For particular protein side chains, the PDB structure can

be incorrect Crystallography gives electron density, not molecular

structure

In poorly resolved crystal structures of proteins, isoelectronic

groups can give make it difficult to deduce the correct structure

Affects asparagine, glutamine, histidine Important? Affects hydrogen bonding pattern

May need to flip amide or imidazole

How to decide? Look at hydrogen bonding pattern in crystal

structures containing ligands

R

O

NH2

R

NH2

O

RN

N

N

NR


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Ligand Preparation

A reasonable 3D structure is required as starting point

During docking, the bond lengths and angles in ligands are held

fixed; only the torsion angles are changed

The protonation state and tautomeric form of a particular

ligand could influence its hydrogen bonding ability

Either protonate as expected for physiological pH and use asingle tautomer

Or generate and dock all possible protonation states and

tautomers, and retain the one with the highest score0

OH OH

+

Enol Ketone


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Basics of molecular dynamics


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Equations of motion for MD simulations

The classical MD simulations boil down tonumerically integrating Newtons equations ofmotion for the particles

Nix

V

xdt

xdm

iji i

iji

0

2

2


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Lennard-Jones potential

One of the most famous pair potentials for

van der Waals systems is the Lennard-Jones

potential

612

4)(ijij

ij

rr

r


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Lennard-Jones Potential


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Dimensionless Units

Advantages of using dimensionless units:

the possibility to work with numerical values of the order of

unity, instead of the typically very small values associated with

the atomic scale

the simplification of the equations of motion, due to the

absorption of the parameters defining the model into the

units

the possibility of scaling the results for a whole class of

systems described by the same model.


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Dimensionless units

When using Lennard-Jones

potentials in simulations, the most

appropriate system of units adopts

, m and as units of length, mass

and energy,respectively, and

implies making the replacements:


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Integration of the Newtonian Equation

Classes of MD integrators:

low-order methods leapfrog, Verlet, velocity

Verlet easy implementation, stability

predictor-corrector methods high accuracy

for large time-steps


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Boundary Condition

Periodic boundary

condition

Specular boundary

condition


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Initial state Atoms are placed in a BCC, FCC or Diamond lattice structure Velocities are randomly assigned to the atoms. To achieve

faster equilibration atoms can be assigned velocities with theexpected equilibrium velocity, i.e. Maxwell distribution.

Temperature adjustment: Bringing the system to requiredaverage temperature requires velocity rescaling. Gradualenergy drift depends on different factors- integration method,potential function, value of time step and ambienttemperature.


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Conservation Laws

Momentum and energy are to be conserved throughout the

simulation period

Momentum conservation is intrinsic to the algorithm and

boundary condition

Energy conservation is sensitive to the choice of integration

method and size of the time step

Angular momentum conservation is not taken into account


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Equilibration

For small systems whose property fluctuate considerably,

characterizing equilibrium becomes difficult

Averaging over a series of timesteps reduces the fluctuation, butdifferent quantities relax to their equilibrium averages at different

rates

A simple measure of equilibration is the rate at which the velocity

distribution converges to the expected Maxwell distribution

)2/exp()( 21 Tvvvf d


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Velocity distribution as a function of time


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Interaction computations

All pair method

Cell subdivision

Neighbor lists


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Thermodynamic properties at equilibrium


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Thermophysical and Dynamic Properties at

equilibrium


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Example: Calculation of thermal conductivity at

eqilibrium

Following are the equations required to

calculate thermal conductivity:

i j

jijji

i

iiEt rvFvQ )(

N

jB

p ttjVTk

k1

2 )()0(

1QQ

iN

j

tjitjiN

ti1

))(()(1

)()0( QQQQ

Where Q is the heat flux

Where k is the thermal

conductivity


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Nonequilibrium dynamics

Homogeneous system: no presence of physical wall, all atoms

perceive a similar environment

Nonhomogeneous system: presence of wall, perturbations to

the structure and dynamics inevitable

Nonequlibrium more close to the real life experiments where

to measure dynamic properties systems are in non

equilibrium states like temperature, pressure or concentration

gradient


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Example: Calculation of thermal equilibrium at

nonequilibrium ( direct measurement)

To measure thermal conductivity of Silicon rod (1-D) heatenergy is added at L/4 and heat energy is taken away at3L/4

After a steady state of heat current was reached, the heatcurrent is given by

Using the Fouriers law we can calculate the thermalconductivity as follows

Stillinger-Weber potential for Si has been used which

takes care of two body and three body potential

tAJz

2

zT

Jk z

/


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Example: continued..

200

220

240

260

280

300

320

0 20 40 60 80 100 120 140 160 180 200

Series1

y = 0.2686x+ 233.34

R2= 0.9384

220

230

240

250

260

270

280

40 50 60 70 80 90 100 110 120 130

Molecular Dynamics Simulation of Thermal


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Molecular Dynamics Simulation of Thermal

Transport at Nanometer Size Point Contact on a

Planar Si Substrate

Calculated temperature profile in the Si

substrate for a 0.5 nm diameter contact

radius.

Schematic diagram of the simulation box.

Initial temperature is 300 K. Energy is added

at the center of the top wall and removed

from the bottom and side walls.


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Thermal conductivity of nanofluids at

Equilibrium

Schematic diagram

Nanoparticles


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Results


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Results continued


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TUTORIALS ON MOE

www.colby.edu/chemistry/CompChem/MOEt

utor.pdf

http://www.macresearch.org/review_moe_m

olecular_operating_environment

http://www.learningace.com/doc/1871621/8f

3cd975a09e395e187d19fe643e6a94/moetuto

r09
http://www.colby.edu/chemistry/CompChem/MOEtutor.pdfhttp://www.colby.edu/chemistry/CompChem/MOEtutor.pdfhttp://www.colby.edu/chemistry/CompChem/MOEtutor.pdfhttp://www.macresearch.org/review_moe_molecular_operating_environmenthttp://www.macresearch.org/review_moe_molecular_operating_environmenthttp://www.macresearch.org/review_moe_molecular_operating_environmenthttp://www.macresearch.org/review_moe_molecular_operating_environmenthttp://www.learningace.com/doc/1871621/8f3cd975a09e395e187d19fe643e6a94/moetutor09http://www.learningace.com/doc/1871621/8f3cd975a09e395e187d19fe643e6a94/moetutor09http://www.learningace.com/doc/1871621/8f3cd975a09e395e187d19fe643e6a94/moetutor09http://www.learningace.com/doc/1871621/8f3cd975a09e395e187d19fe643e6a94/moetutor09http://www.learningace.com/doc/1871621/8f3cd975a09e395e187d19fe643e6a94/moetutor09http://www.learningace.com/doc/1871621/8f3cd975a09e395e187d19fe643e6a94/moetutor09http://www.macresearch.org/review_moe_molecular_operating_environmenthttp://www.colby.edu/chemistry/CompChem/MOEtutor.pdf


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References

Rapaport D. C., The Art of Molecular Dynamics Simulation,2nd Edition, Cambridge University Press, 2004

W. J. Minkowycz and E. M. Sparrow (Eds), Advances inNumerical Heat Transfer,vol. 2, Chap. 6, pp. 189-226, Taylor& Francis, New York, 2000.

Koplik, J., Banavar, J. R. &Willemsen, J. F., Moleculardynamics of Poiseuille flow and moving contact lines, Phys.Rev. Lett. 60, 12821285 (1988); Molecular dynamics of fluidflow at solid surfaces, Phys.Fluids A 1, 781794 (1989).

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Docking & Dynamics (10)