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MOLECULAR MODELING & IN SILICODRUG DESIGN

Amirhossein sakhtemanAmirhossein sakhteman

MOLECULAR MODELING AND In silico DRUG DESIGN

1-Benefits? 2- The levels of computations 3- Energy Minimization 4- MD and Monte-Carlo simulation 5- Homology modeling 6- Virtual screening 7- QSAR

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R&D SPENDING UP, NEW DRUGS DOWN

Taken from http://www.newscientistjobs.com/biotech/ernstyoung/blues.jsp

DRUG DISCOVERY & DEVELOPMENT

Identify disease

Find a drug effectiveagainst disease protein(2-5 years)

Isolate proteininvolved in disease (2-5 years)

Preclinical testing(1-3 years)

Formulation &

Human clinical trials(2-10 years)

Scale-up

FDA approval(2-3 years)

The pharmaceutical industry is a high-riskindustry with very long development times and short product lifespans

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GENOMICS, PROTEOMICS & BIOPHARM.

HIGH THROUGHPUT SCREENING

Potentially producing many more targetsand “personalized” targets

Identify disease

Isolate protein

VIRTUAL SCREENING

COMBINATORIAL CHEMISTRY

Screening up to 100,000 compounds aday for activity against a target protein

Using a computer topredict activity

R idl d i t b Find drug

Preclinical testing

MOLECULAR MODELING

IN VITRO & IN SILICO ADME MODELS

Rapidly producing vast numbersof compounds

Computer graphics & models help improve activity

Tissue and computer models begin to replace animal testing

INSILICO METHODS IN DRUG DISCOVERY

Molecular docking Virtual High through put screening Virtual High through put screening.

QSAR (Quantitative structure-activity relationship)

Pharmacophore mapping Fragment based screening

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MOLECULAR MODELING AND In silico DRUG DESIGN

1-The benefits? 2- The levels of computations 3- Energy Minimization 4- MD and Monte-Carlo simulation 5- Homology modeling 6- Virtual screening 7- QSAR

COMPUTATIONAL CHEMISTRY

∆G = ∆H - T∆S Molecular Behaviors; conformational Molecular Behaviors; conformational

analyssis; energy optimizaitions

Drug receptor Interactions;MD simulations; Drug receptor Interactions;MD simulations; Docking studies, pharmacophore search, etc.

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FOUR LEVELS OF THEORY IN COMPUTATIONS

Ab initio DFT Quantum Mechanics DFT Semiempirical Empirical

Quantum Mechanics

Molecular Mechanics

QM: SCHRODINGER’S EQUATION

ˆ - Hamiltonian operator

H Eˆ H

ˆ H ˆ T ˆ V

Gravity?

2

2mi

2

i

N

Ceie j

ri rji j

N

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ELECTRONIC SCHRODINGER EQUATION

Solutions:(r ) cm m (r )

F

, the basis set, are of a known form Need to determine coefficients (cm)

Wavefunctions gives probability of finding electrons in space (e. g. s,p,d and f orbitals)

( ) m m ( )

m

m(r )

Molecular orbitals are formed by linear combinations of electronic orbitals (LCAO)

HYDROGEN MOLECULE

HOMO HOMO

LUMO

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AB INITIO

Hartree Fock (HF) Hartree Fock (HF) No experimental datra Calculation of hamiltonian for each electron For small molecules Gaussian Gamess

DENSITY FUNCTION

Energy depends to density of the electron Energy depends to density of the electron Kohn-Sham method For energy calculations in semiconductors, insulators Carbon nanotubes

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SEMIEMPIRICAL (E-E INTERACTIONS)

CNDO (Complete Neglect of Differential Overlap) CNDO (Complete Neglect of Differential Overlap) MNDO (Modified Neglect of Differential Overlap) PCILO AM1 PM3

MOLECULAR MECHANICS

Bonding Non-bonding

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LEONAED-JONES

MM FORCE FIELDS

AMBER(Assisted Model Building and Energy Refinement)

CHARMM CHARMM (Chemistry at HARvard Macromolecular Mechanics)

Gromos OPLS CVFF MM

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MOLECULAR MODELING AND In silico DRUG DESIGN

1-The benefits? 2- The levels of computations 3- Energy Minimization 4- MD and Monte-Carlo simulation 5- Homology modeling 6- Virtual screening 7- QSAR

ENERGY MINIMIZATIONENERGY MINIMIZATION

Local minimum vs global minimumg Many local minima; only ONE global minimum Methods: Newton-Raphson (block diagonal), steepest

descent, conjugate gradient, others.

global minimumglobal minimum

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POTENTIAL ENERGY SURFACEPOTENTIAL ENERGY SURFACE

maximasaddle point

minimum

MOLECULAR MODELING AND In silico DRUG DESIGN

1-The benefits? 2- The levels of computations 3- Energy Minimization 4- MD and Monte-Carlo simulation 5- Homology modeling 6- Virtual screening 7- QSAR

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MOLECULAR DYNAMICS SIMULATION

F = ma/ 2 / 2 F = ma = −dE/dr = m.d2r/dt2

r(t+∆t)= r(t) + V∆t + a ∆t2/2

To find the stable conformation of a system To study residue distances

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MONTE-CARLO SIMULATION

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PARALLEL COMPUTING: UNIX BASED OS

Drug Design and Discovery for Developing Countries, 3-5th July, 2008

User User

Cluster D

Receive input files fr

Haw

k and run dockijobs on each assign

nodes

Output files generated

uploaded to Haw

k

Hawk server –Malaysia

(hawk.usm.my)

Process ligand input file, create parameter files & shell script files

Cluster C

Submit input files and distribute jobs to several clusters on Grid environment

Receive input files from Hawk and run jobs on each assigned nodes

rom

ng ed

Output files generated and uploaded to Hawk

d and k

Cluster B Cluster A

Grid environment

Input files

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Linus TorvaldsBill Gates

PARALLEL COMPUTING: OS

Bill Gates

Steve Jobs

MOLECULAR MODELING AND In silico DRUG DESIGN

1-The benefits? 2- The levels of computations 3- Energy Minimization 4- MD and Monte-Carlo simulation 5- Homology modeling 6- Virtual screening 7- QSAR

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10.10.2011

- The form of restraints was obtained from a statistical analysis relied on a database of 105 family alignments that included 416 proteins with known 3D structure [Šali & Overington, 1994]

Alignment file: Can be prepare by ModellerDSPrimeClustalwT-Cofee

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HOMOLOGY MODELING LACTATE DEHYDROGENASE ISOZYMES

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HOMOLOGY MODELIING OF GPCRS

MOLECULAR MODELING AND In silico DRUG DESIGN

1-The benefits? 2- The levels of computations 3- Energy Minimization 4- MD and Monte-Carlo simulation 5- Homology modeling 6- Virtual screening 7- QSAR

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HIGH THROUGHPUT SCREENING

Test 10,000-100,000’s of compounds Robotics

Individually tested Pfizer: > 250,000 compound library

Combinatorial Chemistry Parallel testing Parallel testing Cleverly prepared mixtures Recover most active compounds

AN EXAMPLE: HIGH-THROUGHPUT SCREENING

Screening perhaps millions of compounds in a corporate collection to see if any show activity against a certain disease protein

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Molecular Docking

• Docking is the computational determination of binding affinity

RL

between molecules (protein structure and ligand).

• Given a protein and a ligand find out the binding free energy of the complex formed by docking them.

LRL

R

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“DOCKING” COMPOUNDS INTO PROTEINS COMPUTATIONALLY

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FINDING ACTIVE SITES: GRID

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Drug Design and Discovery for Developing Countries, 3-5th July, 2008

Best hypothesis generated from the training set

.

Blue = NIMagenta = HBDGreen = HBA

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Modeling and informatics in drug design

Ligand based strategySearch for similar compounds

database known actives structures found

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MOLECULAR MODELING AND In silico DRUG DESIGN

1-The benefits? 2- The levels of computations 3- Energy Minimization 4- MD and Monte-Carlo simulation 5- Homology modeling 6- Virtual screening 7- QSAR

QSAR

QSAR is statistical approach that attempts to relate physical and chemical properties of molecules to their biological p p gactivities.

Various descriptors like molecular weight, number of rotatable bonds LogP etc. are commonly used.

Many QSAR approaches are in practice based on the data dimensions.

It ranges from 1D QSAR to 6D QSAR It ranges from 1D QSAR to 6D QSAR.

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COMFAASSUMPTIONS

Activity is directly related to structural properties of systemproperties of system

Structural properties determined by non-bonding forces

COMFA

Cramer and Milne (1979) Comparison of molecules by alignment and field generation Comparison of molecules by alignment and field generation

Wold (1986) Proposes using PLS instead of PCA for overrepresented

(1000’s of field non-orthogonal “variables”) problem (correlate field values with activities)

Cramer, Patterson and Bunce (1988) Introduced CoMFA Introduced CoMFA

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OUTLINE OF COMFA

Hypothesize mechanism for binding Structure of binding site Structure of binding site Most important/difficult

Find equilibrium geometry Construct lattice or grid of points Compute interaction of probe with molecule at each

point Apply PLS Predict

COMFALATTICE CONSTRUCTION

Construct lattice or grid of points for field analysis

Steroid (1 representative conformer shown)14 x 11 x 7 = 1078 points

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COMFAFIELD DATA GENERATION

Compute interaction of probe with molecule at each pointpoint Interaction is typically non-covalent (e.g. non-bonding forces)

Steric, electrostatic and hydrophobic

Probe depends on interaction Kim et. al.

H+ (electrostatic) CH3 (steric)

H O (h d h bi ) H2O (hydrophobic)

COMFAFIELD DATA GENERATION

Compute interaction of probe with molecule at each pointpo t Ncalc=Ngrid * Ncmpds* Nprobes

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QSAR/QSPR-REGRESSION TYPES

Partial Least SquaresC lid ti d t i b f Cross-validation determines number of descriptors/components to use

Derive equation Use bootstrapping and t-test to test

coefficients in QSAR regression

APPLICATIONVALIDATION

Cross Validation Leave-One-Out Q2 1

yi yi,pred 2i1

2

100

Leave One Out

External Predictions Test Set 21 compounds

yi y 2

i1

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COMFAASSUMPTIONS

Activity is directly related to structural properties of systemproperties of system Dynamical corrections?

Structural properties determined by non-bonding forces Covalent Covalent Hydrophobic