cadd and molecular modeling

8/9/2019 CADD and Molecular Modeling

1/54

CADD and Molecular

Modeling : Importance inPharmaceutical Development

Dr. Sanjeev Kumar SinghDepartment of BioinformaticsAlagappa Universitye-mail- [email protected]


2/54

Working at the Intersection

Structural Biology

Biochemistry

Medicinal Chemistry

Toicology

Pharmacology

Biophysical Chemistry

In!ormation Technology


3/54

Structural Biology

"astest gro#ing

area o! $iology

Protein and nucleic

acid structure and

!unction

%o# proteins

control livingprocesses


4/54

Medicinal Chemistry

&rganic Chemistry

Applied to disease

'ample: design

ne# en(ymeinhi$itor drugs

doxorubicin (anti-cancer)


5/54

Pharmacology

Biochemistry o! %uman Disease

Di!!erent !rom Pharmacy: distri$utiono! pharmaceuticals) drug deliverysystems


6/54

*e# Ideas "rom *ature

Natural ProductsChemistry

Chemical Ecology

During the next twodecades: the majoractivity in organismalbiology

Examles: enicillin!taxol (anti-cancer)


7/54

Bio+Chem,in!ormatics

The collection) representation and organisation o!

chemical data to create chemical in!ormation) to #hich

theories can $e applied to create chemical kno#ledge-

Aim Toeamine ho# computational techni.ues can $e used

to assist in the design o! novel $ioactive compounds-

To give an idea o! ho# computational techni.ues can

similarly $e applied to other emerging areas such as Bio,

in!ormatics) Chemin!ormatics / Pharmain!ormatics-


8/54

&vervie#

Drug discovery process

%o# do drugs #ork0

&vervie# o! Computer,Aided DrugDesign


9/54

Pharmaceutical+AgrochemicalIndustry

Identi!ication o! novel compounds #ith use!ul andcommercially valua$le $iological properties-

vastly comple) multi,disciplinary task

many stages over etended periods o! time

1isk most novel compounds do not result in a drug-

those that do may cause unepected) long,termside,e!!ects"


10/54

Why CADD20

Drug Discovery today are !acing a serious

challenge $ecause o! the increased cost and

enormous amount o! time taken to discover

a ne# drug) and also $ecause o! rigorouscompetition amongst di!!erent

pharmaceutical companies-


11/54

Drug Discovery / Development

Identify disease

Isolate protein

involved in

disease (2-5 years)

Find a drug effective

against disease protein

(2-5 years)

Preclinical testing

(1-3 years)

Formulation

Human clinical trials

(2-10 years)

cale-up

F!" approval

(2-3 years)

i

l

e

I

N

D

i

l

e

N

DA
http://images.google.com/imgres?imgurl=www.elements.nb.ca/theme/health/patty/sick.jpg&imgrefurl=http://www.elements.nb.ca/theme/health/theme.htm&h=128&w=75&prev=/images%3Fq%3Dsick%2Bclipart%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26safe%3Doff


12/54

Drug Development Process

On average it takes 12 -15years and costs ~$500 -800million to bring a drug tomarket

develop

assay

lead

optimisation

lead

identification

clinical

trials

to market

10,000scompounds

1 drug


13/54

Cont2


14/54

Technology is impacting this process

Identify disease

Isolate protein

Find drug

Preclinical testing

GENOMICS, PROTEOMICS & BIOPHARM.

HIGH THROUGHPUT SCREENING

MOLECULAR MODELING

VIRTUAL SCREENING

COMBINATORIAL CHEMISTRY

IN VITRO & IN SILICO ADME MODELS

Potentially producing many more targets

and #personali$ed% targets

creening up to 100&000 compounds a

day for activity against a target protein

'sing a computer to

predict activity

apidly producing vast numers

of compounds

*omputer grap+ics , models +elp improve activity

issue and computer models egin to replace animal testing
http://images.google.com/imgres?imgurl=www.elements.nb.ca/theme/health/patty/sick.jpg&imgrefurl=http://www.elements.nb.ca/theme/health/theme.htm&h=128&w=75&prev=/images%3Fq%3Dsick%2Bclipart%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26safe%3Doff


15/54

utomating t!e "## rocess

Gene sequence data

X-ray or

Homology

Screening

Library synthesis

%ed "!em&"ombic!em

'ibmaker(%

Designed libraries

Ligand binding data

PharmacophoreModel

Skelgen

Designed Templates
http://var/www/apps/conversion/tmp/scratch_1/C:%5CRASMOL%5CRASWIN32.exe%20-script%20H3design.rashttp://var/www/apps/conversion/tmp/scratch_1/.%5C%5Crw32b2a.exe%20-script%20H3design.rashttp://var/www/apps/conversion/tmp/scratch_1/.%5C%5Crw32b2a.exe%20-script%20H3design.rashttp://var/www/apps/conversion/tmp/scratch_1/.%5C%5Crw32b2a.exe%20-script%20H3design.rashttp://var/www/apps/conversion/tmp/scratch_1/C:%5CDocuments%5CPresentations%5CCurrent%5Censemble.rashttp://var/www/apps/conversion/tmp/scratch_1/.%5C%5Crw32b2a.exe%20-script%20protein.ras


16/54

Target

Identification

Target

Validation

Lead

IdentificationLead

Optimization

Target discovery Lead discovery

!ases o) "##

SAVING12 15 years, Costs: 500 - 800 million

US $

VHTSVHTS

Similarity

analysis

Similarity

analysis

Database

filtering

Database

filtering

Computer ided

Drug Design!CDD"

de novo

design

de novo

design

diversity

selection

diversity

selection

#iop$ores#iop$ores

lignmentlignment

Combinatorial

libraries

Combinatorial

libraries

D%&TD%&T

'S('S(


17/54

%o# Drugs Work

Substrate&nzyme

+

&nzyme)substrate

comple*

Lock)and)key model


18/54

Methodologies and strategies o!CADD:

Structure $ased drug design 3SBDD4 5DI1'CTD'SI6*7

"ollo#ed #hen the spatial structure o! thetarget is kno#n-

8igand $ased drug design 38BDD4 5I*DI1'CTD'SI6*7

"ollo#ed #hen the structure o! the target isunkno#n-


19/54

Computer,Aided Drug Design

9,D target structure unkno#n 38BDD4 1andom screening i! no actives are kno#n

Similarity searching

Pharmacophore mapping

SA1 3;D / 9D4 etc-

Com$inatorial li$rary design etc-

Structure,$ased drug design 3SBDD4 Molecular Docking

De novo design


20/54

#n Pharmacohore$

Pharmacoporic Studies on AC'inhi$itors

Pharmacological Studies on %I


21/54

What is Pharmacophore20

Pharmacohore model

%et o& oints in sace de&ining the binding o& ligandswith target"

'ey &actors in develoing such a model are the

determination o& &unctional grous essential &orbinding! their corresondence &rom one ligand toanother! and the common satial arrangement o& thesegrous when bound to the recetor

The pharmacophore model o! %I< protease-


22/54

Pharmacophore2--0 a molecular &ramewor that carries (horos) the

essential &eatures resonsible &or a drug*s(harmacon) biological activity+ Paul 'rlich) early=??@

a set o& structural &eatures in a molecule that isrecogni,ed at a recetor site and is resonsible &orthat molecule*s activity+ Peter 6und) =?


23/54

Basic "eatures

A set o! !eatures common to a series o! activemolecules

What are the !eatures20 %BD

%BA ve /,ve charged groups and %ydropho$ic regions

"unctional groups or molecules #ith similarphysical and chemical properties

Bioisosteres , su$stituents or groups thathave chemical or physical similarities and#hich produce $roadly similar $iologicalproperties


24/54

Pharmacophore model

%et o& oints in sace de&ining the binding o& ligandswith target"

'ey &actors in develoing such a model are thedetermination o& &unctional grous essential &orbinding! their corresondence &rom one ligand toanother! and the common satial arrangement o& thesegrous when bound to the recetor"


25/54

AC'

Angiotensionconverting en(yme

Converts

angiotensinI toangiotension II

Inhi$its $radykinin3vasodilator4


26/54

AC',inhi$itor

&rally availa$le/ potent drug


27/54

AC' distance map

oints de&ined

.ive distances

de&ined


28/54

Donor Hydrophobic core

Charged negative

Acceptor


29/54

deoy nucleoside

triphosphate !d"#$%

&'()' dideoy nucleoside)'-a*ido thymidine !A+#%

&'()'- didehydro dideoy nucleoside)'-nitro nucleoside

,S$ contours for nucleosidic drugs. ed coloured contours indicate a value of -./0 forelectrostatic potential and yello1 contours indicate a value of -/./2

Pharmacophoric Features ofNucleosidic HIV-1RT Inhibitors


30/54

Concluding remarks on Nucleosidicinhibitors

Different su3stituents at the )

position sho1 similar sugar ring puckeringand only slight differences in nucleosidic 3ase disposition and interactionsprotein.

,S$ plots have clearly indicated that the charge environment of the

drugs is complementary to the receptor charge environment. $ositivepotential areas have 3een o3served in the active site of 456-0# 1hereD"A 3inding occurs.

P+armacop+oric Features of .ucleosidic HI/-1 In+iitors. Arpita 7adav8 and Sanjeev Kumar Singhioorg , ed *+em 11& 2003& 101


31/54

-09.0) kcal:mol &')' dideoy thymidine-0).)) kcal:mol A+# -0;.-"itro nucleoside-&0.)/ kcal:mol

).2?

Threshold interaction energ of NRTI!s"nucleosidic inhibitors for Re#erse

transcriptase$ to undergo competiti#e

inhibition

0 2 * + 8 10 12

-22

-20

-18

-1+

-1*

-12

Inter

action&nergy

!+cal,mol"

,"50

%.

orrelation of interaction energy 1ith potency


32/54

orrelation graph indicates the reuirement of a threshold 3indingenergy C0& kcal:mol for the drug to 3e a3le to undergo competitiveinhi3ition efficiently. ess than this 3inding energy: interaction energy 1ill

make the drug ineffective or very high concentrations 1ill 3e reuired forinhi3ition of en*yme. Ehich may lead to cytotoicity.

+res+old interaction energy of .I4s (nucleosidic in+iitors for everse

transcriptase) to undergo competitive in+iition

Arpita 7adav8 and Sanjeev Kumar Singh ioorg , ed *+em letts 1& 200&2677-260

ncluding remarks on interaction energ studi


33/54

2.785

2.021

2.514

$yrrolyl hetro aryl sulfone 1ith lysine

2.514

4.0

2.785

2.514

Pyrrolyl hetro aryl sulfone with HEPTlys101

Pyrrolyl hetro aryl sulfone with trovirdinelys101

Common binding mode for structuralland chemicall di#erse non- nucleosidic

HIV-1RT inhibitors


34/54

Concluding remarks of Non nucleosidi

onformational study of non-nucleosidic drugs indicated that eachdrug has a F6>- shaped conformation.

ach drug has a -"4 group in a position that it can make 4- 3ond

1ith the car3onyl group of lysine 0/0 in conformity 1ith earlier studieson pyrrolyl hetero aryl sulfone. #his indicates the importance of lysine0/0 in 3inding ""#5>s.

*ommon inding mode for structurally and c+emically diverse non- nucleosidic

HI/-1 in+iitors8

"rpita 9adav: and an;eev *H=& 723& 2005& 205-20?


35/54

DISC&: DIStance C&mparisons

6enerate some num$er o! lo#,energy con!ormations

!or each active compound

The resulting con!ormations are represented $y the

positions o! potential pharmacophore points-

%ydrogen,$ond donors and acceptors charged

atoms ring centroids and centres o! hydropho$ic

regions-


36/54

uantitative Structure,Activity1elationships 3SA14

A SA1 relates a numerical description o! molecular structure

or properties to kno#n $iological activity

Activity f 3molecular descriptors4

Success o! SA1: right descriptors right method 3!orm o!

f 4

A SA1 should $e

eplanatory 3!or structures #ith activity data4

predictive 3!or structures #ithout activity data4

A SA1 can $e used to eplain or optimise: localised properties o! molecules such as $inding

properties

#hole molecule properties such as uptake and distri$ution


37/54

9D SA1

CoM"A and CoMSIA

Molecules are descri$ed $y the values o!molecular !ields calculated at points in a 9Dgrid

The molecular !ields are usually steric andelectrostatic

Partial least s.uares 3P8S4 analysis used tocorrelate the !ield values #ith $iologicalactivity

A common pharmacophore is re.uired-


38/54

Esing the Model

The P8S results arepresented as contourplots

Steric Bulk:

6reen Steric"avoura$le

Fello# StericEn!avoura$le

'lectrostatics:

1ed 'lectronegative"avoura$le

Blue 'lectronegativeEn!avoura$le


39/54


40/54

- reen contours stand )or points /!ere sterically bulkier groups areanticipated to increase t!e biological activity3- (!e yello/ contours are used to underscore t!e points /!ere bulkiergroups could lo/er t!e biological property3- (!e electrostatic red plots s!o/ /!ere t!e presence o) a negativec!arge is e6pected to en!ance t!e activity3- (!e blue contours indicate /!ere introducing or keeping positivec!arges are e6pected to better t!e observed activity3

"o% 7teric "ontours "o% lectrostatic "ontours

- 3D-S!" #oM$! Study on !minothia%ole Deri&ati&es as #yclin Dependent 'inase (

)nhibitors* +igus Dessale, San.ee& 'umar Singh/ and P*0* 1haratam S!" #omb* Sci*

(245 (667 89-:4*


41/54

SA1 W&1G2

The developed model sho#ed a strong correlative and

predictive capa$ility having a cross validated correlation

co,e!!icient o! @-H !or CDGH and @- !or CDG;

inhi$itions-/ 3D-QSAR CoMFA studies on Indenopyrazole as CDK2 Inhibitors

San!ee" Ku#ar Sin$h%& 'i$us Dessale(& and ) * +harata# ,ur of Med Che#& ./& 2001& /3/0-/3/

The conventional and predictive correlation coe!!icients

#ere !ound to $e respectively @-?H9 and @-@J !or CDG=

and @-? and @-J !or CDG;-/ 3D-QSAR CoMFA Study on 4indole Deri"ati"es as Cy5lin

Dependent Kinase / 6CDK/7 and Cy5lin Dependent Kinase 26CDK27 Inhibitors San!ee" Ku#ar Sin$h%& 'i$us Dessale(& and )* +harata#& Med Che# 36/7& 2008& 89-:.


42/54

Structure Based Drug Design

eter!ine Protein "tru#ture

$dentify $ntera#tion "ites

e %ovo esi&n ' ata(ase

Evaluate "tru#ture

"ynthesi)e *andidate

Test *andidate

ead *o!,ound

Discovery or design ofmolecules that interact1ith 3iochemical targetsof kno1n )D structure


43/54

Structure $ased drug design

Molecular data$ase mining

Compounds #ith $est complementarity to$inding site are selected-

D&CG) Autodock) "le K etc-

De no"odrug designing


44/54

Structural Targets

9D structure o! target receptors determined$y

K,ray crystallography

*M1 %omology modeling

Protein Data Bank

Archive o! eperimentally determined 9Dstructures o! $iological macromolecules


45/54

K,ray crystallography


46/54

*M1


47/54

Molecular docking


48/54

Docking algorithms

Molecular !lei$ility

$oth ligand and protein rigid

!lei$le ligand and rigid protein

$oth ligand and protein !lei$le

search algorithm

use to eplore optimal positions o! the ligand#ithin the active site

scoring !unction

value should correspond to pre!erred $indingmode

e!!iciency very important !or data$ase searching


49/54

Scoring !unction

8igand,receptor $inding is driven $y

'lectrostatics 3including h,$onding4

Dispersion o! vd#Ls !orces

%ydropho$ic interaction Desolvation o! ligand and receptor

Molecular mechanics

Attempt to calculate interaction energy

directly


50/54
http://images.google.com/imgres?imgurl=www.molsoft.com/images/ligand.gif&imgrefurl=http://www.molsoft.com/icmpages/icmlite.htm&h=656&w=700&prev=/images%3Fq%3Dligand%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26oe%3DUTF-8


51/54

igand data3ase #arget $rotein

,olecular docking

igand docked into protein>s active site
http://images.google.com/imgres?imgurl=www.molsoft.com/images/ligand.gif&imgrefurl=http://www.molsoft.com/icmpages/icmlite.htm&h=656&w=700&prev=/images%3Fq%3Dligand%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26oe%3DUTF-8http://images.google.com/imgres?imgurl=www.molsoft.com/images/ligand.gif&imgrefurl=http://www.molsoft.com/icmpages/icmlite.htm&h=656&w=700&prev=/images%3Fq%3Dligand%26svnum%3D10%26hl%3Den%26lr%3D%26ie%3DUTF-8%26oe%3DUTF-8


52/54

%o# do my ligands dock into theprotein0


53/54

Collaboration with$

Pro&" %handhar 0hamad! National #nstitute o&1iomedical #nnovation! 2aan

Dr" Nigus Desselaw 0ddis 0baba 3niversity!

Ethioia Pro&" 2" 'astner! 3niversity o& %tuttgart! 4ermany

Pro&" '" Dharmalingam! 5adurai 'amaraj 3ni"!5adurai

Dr" 0rita 6adav! C%25 3niversity 'anur


54/54

T%A*G F&E