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8/14/2019 DeepViewManual h5n1

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Tackling Receptor Flexibility in

Computer-Aided Drug Design

Rommie Amaro . NBCR Mini-Symposium . August 4, 2008


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Computer-aided drug design

Van Drie, J., J. Comp. Aid. Mol. Des., 21: 591-601 (2007)

Challenges:

solvation effects, entropy,

rigorous thermodynamics

prediction of

lead/candidate

pharmocokinetic properties

networks /

polypharmacology

receptor flexibility


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Tackling receptor flexibility:

relaxed complex scheme

Amaro, Baron, and McCammon, J. Comp. Aid. Mol. Des..,in press (2008)


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Developing new antivirals against

avian influenza Biological introduction

Investigating the dynamics and flexibility of N1

(molecular dynamics)

Extracting meaningful information and reducing

redundancy (clustering analysis)

Finding new druggable hot spots (computational solvent

mapping)

Identifying new drugs for experimental testing (virtualscreening)

Summary & future work


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Influenza virus

~100 nm diameter

Neuraminidase

(9 subtypes)

Hemaggluttinin

(16 subtypes)

M2 ion channel

Host-derived lipid

envelope

8 RNA segments

No proof reading during

replication - highly variable


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Influenza

Epidemics are normal, seasonal influenza outbreaks

est. 300,000-500,000 people die each year due toepidemic influenza

deaths highest among > 65 yrs old, children < 2 yrs,immunocompromised

Pandemics are rare events that occur every 10-50 years.

In the last 400 years, at least 31 pandemics have beenrecorded

Circulate around the globe in successive waves

With global travel, est. a new pandemic would reach almost allcorners of the earth within 3-6 months, an estimated 2 billionof the worlds 6.5 billion people will be infected


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Origin of pandemic viruses

Clercq, Nat Rev. Drug Disc.,5: 1015-1025 (2006)

40 milliondeaths 1-1.5 million

deaths

0.75 - 1 million

deaths

antigenicdrift antigenic

shift


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It is especially virulent (~ 50% mortality rate) & being spread by migratory birds

Bird to mammal, bird to human transmission

Like other influenza viruses, it continues to evolve.

H5N1 influenza cases 2003-2008


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Points of intervention in the viral replication

cycle

Clercq, Nat Rev. Drug Disc.,5: 1015-1025 (2006)


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Group 1 and 2 neuraminidases

Russell et al, Nature, 443: 45-49 (2006).

9 neuraminidase (NA) strains:

GroupGroup 1 ?1 ?

2 phylogenetically distinct groups:


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Group 1 and 2 neuraminidases

Russell et al, Nature, 443: 45-49 (2006).

2 phylogenetically distinct groups:


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Goals

To develop a more effective, orally-available drug

against N1

Methodological goal: to develop optimized scheme forreceptor flexibility in inhibitor discovery process

Use the structural information from MD as a predictive

guide and to expand the receptor ensemble

Improve final ranking of compounds and account for

induced-fit effects, as part of improved drug discovery

scheme


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Molecular dynamics to probe structure

& dynamics

Van der Waals& electrostatics

t t. . .

U

!

R( ) = kbond r ! ro( )2

+ k" "!"o( )2

+ kdihed 1+ cos n#+ $( )%& '( + 4)ij*ij

rij

+

,-

.

/0

12

!*ij

rij

+

,-

.

/0

6%

&11

'

(22+

qiqj

)rijnonbondedpairs

3nonbonded

paris

3dihedrals

3angles

3bonds

3

Classical dynamics

at 300K : !

Fi= ma = m

i

d2!

ri

dt2= !

!

"U!

R( )


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Molecular dynamics simulations

2HTY (open loop, apo) 2HU0 (open loop, holo)

N1 tetramer, (ligands), ions

Explicit solvent, 150mM NaCl

112,457 atoms

NAMD2 on supercomputers

5 ns/day

40 ns for the tetramer

(eq. of 160 ns of monomer)

Amaro, R. E., Minh, D.D.L., Cheng, L.S., Lindstrom, Jr., W., Olson, A.J., Lin, J.-H., Li, W.W., and McCammon, J.A., JACS, 129: 7764 7765 (2007).


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Remarkable loop flexibility

Molecular dynamics allows

sampling of receptor side

chains and larger local

motions (e.g. loop sampling)

Can account for induced

effects of particular ligand

(e.g. Tamiflu)

Changes in ligand binding

site can be exploited anddesigned around


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150-loop dynamics

open, closed



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Implications for Antiviral Drug Design

430-loop and 150-loop

very flexible

Structural reorganization

reveals new pockettopography

Goal:

To use these new

structural insights fordrug discovery/design

efforts



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Clustering distills essential information

Extracted snapshots from 4 chains explicit 40 nssimulations (160 ns for both apo & holo)

Alignment based on Catoms

Then computed RMSD distance matrix usingsubset of 62 residues (sidechains included) liningthe binding pocket


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Computational solvent mapping

Assesses druggability of receptor surfaces using complementary physics-based approach

14 organic probes to flood receptor surface

Probes clustered and ranked by interaction energy with surface

Hot spots indicate areas of high functional group affinity


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Hot spots predict areas of affinity

Landon, M., Amaro, R.E., Baron, R., Ngan, C.H., Ozonoff, D., McCammon, J.A., and Vadja, S., Chemical Biology & Drug Design (2008).

Structures revealed by

MD have new high

affinity areas for ligands,

ligand-extensions to bind

These hot spots vary in

size, number, and moiety

Indicates which

residues in new areas

may be important tooptimize against


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Discovering new inhibitors:

virtual screen with molecular dynamics

Typical virtual screens use only one crystal structure

Virtual screen of 3 most dominant MD cluster

representative structures & crystal structures

Rapid docking with AutoDock

Against the NCI diversity set ~ 2000 compounds

Top candidates filtered for druglikeness & clustering

Identified 27 novel putative inhibitors, half of which would

not have been found based on crystal structures alone

Ordering of known sialic acid analog inhibitors is correct

(positive controls: Tamiflu, Relenza, DANA)

Cheng, L.S., Amaro, R.E., Xu, D., Li, W.W., Arzberger, P.A., and McCammon, J.A., Journal of Medicinal Chemistry, in press (2008).


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Ensemble-based virtual screening

Closed 150-loop Open 150-loop

Including full receptor flexibility opens new areas for ligand binding


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Potential cross-cavity binders

Several compounds are

predicted to bind 2 or

more cavities

May provide addtl

selectivity for N1

Many ligands predictedto dock to the CS-map

hot spots


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Rescoring can be important!

African trypanosomiasis

RNA editing ligase required for survival of parasite

Rescoring of top compounds provided important enrichment of

recommended set

Limited experimental resources, best inhibitors would not have been

tested without rescoring

Amaro, R., Schnaufer, A., Interthal, H., Hol, W., Stuart, K., and McCammon, J.A., submitted (2008)


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Future methodological work:

relaxed complex scheme

Amaro, Baron, and McCammon, J. Comp. Aid. Mol. Des..,in press (2008)

Developing a

workflow tool

using Vision

Needs to be

flexible so new

modules can be

easily added

Developingcyberinfrastructure

to launch jobs,

deal with &

manipulate data


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Avian Flu Grid: an international

collaborative effort

SDSC node

(in USA )

AIST cluster

(in Japan)

gfsd

GRAM

Job submission

(globusrun)

mpirun

File I/O

Gfarm filesystem

USM node

(in Malaysia)

Users

Users

Data, program

- Computational server

- Storage server

PRAGMA testbed

Collaboration

AIST , ASGC, CNIC, CUHK, GUCAS,

IOIT-HCM, LZU, MIMOS, NECTEC,

NGO, SDSC, ThaiGrid , UZH, VPAC

32 institutions in 16

countries across the

Pacific Rim and USA

N1 project science

driver for technology

development

Developing computational environment

(infrastructure) and scientific

applications

Portal for datasharing

http://www.pragma-grid.net/


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Training and Outreach

H5N1 projects serve as training projects for undergraduate

students through PRIME and high schoolers through the

Pinhead Institute

Thursday & Friday Track III sessions will teach YOU how to

set up an MD simulation, perform analysis, submit a virtual

screen and perform a relaxed complex scheme rescoring


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Molecular Graphics Lab

Professor Andy McCammon

The McCammon Group

Acknowledgements

The SAFI & Avian Flu Grid Teams


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H5N1: why so deadly?

H5N1 seems to inducehypercytokinemia, a.k.a. cytokine storm

Overreaction of the innate immune

system, which is highly complex in its

interactions with other signaling

molecules, is suspected to play a role in

the virulence

Preference for sialic acid receptors in

the lower respitory tract (as opposed to

upper) = delayed side effects (sneezing,

coughing, etc) = longer virus incubation

period, so when presents itself, higher

viral load, tougher on the body

Onset of symptoms to death: 9 days


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Biomolecular simulations & the future of

computer-aided drug design

Increased computing power, entering the petascale era

Simulations of hundreds of ns already possible, microseconds

soon to follow

Highly optimized parallel code allow building of complexity(bigger systems), without sacrificing speed

Enabling of grid-based technologies offer alternative computing

platforms for docking or other small-processor request jobs

As compute power grows, so will the scope and level of CADDmodeling

Good predictions cut time to positive experiment, assist in

understanding mechanism of action, drive discoveries!


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Generalized Born MD

- Projects with Xiaolin Cheng & Ivaylo Ivanov (McCammon group)

- GB: Represents the solvent implicitly as continuum with the dielectric

properties of water, and includes the charge screening effects of salt:

N1-apo, closed loop | N9-apo, closed loop

N1-tamiflu, closed loop | N9-tamiflu, closed loop

N1-tamiflu, open loop | N1-apo, open loopTetramer N1 system = HUGE! (20K+ atoms)...

- 16 ns for each system, Amber igb version 5, monomer only, with

Ambers fast pmemd MD engine (~5500 atoms: big for GB)

- On new NCSA Abe machine, scales to 256 - 512 procs, ~ 8 ns/day

-Comparative dynamics analysis between N1 vs. N9, tamiflu bound and

apo systems, open & closed loops possibly sample more open/closed

loop transitions

Manuscript in preparation may use snapshots for CADD work


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GB-MD Preliminary Results

open, closed

N1-apo-closed

N1-tami-closed

N1-apo-openN1-tami-open

N9-apo-closed N9-tami-closed


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GB-MD Preliminary Results

N9-closedN1-closed N1-open


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Grid Maps

Fast energy evaluation is achieved by precalculating atomicaffinity potentials (grid maps), one for each atom type in the

ligand

Calculated by autogrid & a .gpf file

Affinity grid: each point stores the potential energy of a

probe atom due to all atoms in the macromolecule Also makes electrostatic maps

Define receptor atom types,

ligand atom types

npts 60 60 60

spacing 0.375

gridcenter 1.602 18.973 4.55

AutoDock Users Guide, v3.0.5, Morris et al.


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AutoDock4 force field

!G = VboundL"L

"VunboundL"L( )+ VboundP

"P"Vunbound

P"P( )+ VboundP"L"Vunbound

P"L+ !Sconf( )

Intramolecular energies Intermolecular energies

!Sconf =WconfNtors Loss of torsional entropy upon binding

Huey, Morris, Olson & Goodsell, J. Comp. Chem, A Semi-empirical Free Energy

Force Field with Charge-based Desolvation, preprint (2006).

0 0


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V=Wvdw Aij

rij12

! Bijrij6

"#$ %

&'+Whbond E(t) C

ij

rij12

! Dijrij10

"#$ %

&'+Welec q

iqj

((rij)rij+Wsol SiVj + SjVi( )e !

rij2 2)2( )

i, j*

i, j*

i, j*

i, j*

Normal Lennard-Jones potential describing

dispersion/repulsion interactionsParameters A and B taken from the Amber forcefield.

Semi-empirical: combines traditional MM force fieldswith empirical weights and an empirical approach for

entropic contributions

AutoDock force field


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Semi-empirical: combines traditional MM force fields withempirical weights and an empirical approach for entropic

contributions

V=Wvdw Aij

rij12

! Bijrij6

"#$ %

&'+Whbond E(t) C

ij

rij12

! Dijrij10

"#$ %

&'+Welec q

iqj


rij2 2)2( )i, j*

i, j*

i, j*

i, j*

Directional H-bond term based on a 10/12 potential.

C and D give a maximal well depth of 5 kcal/mol at 1.9 for OH andNH, and a depth of 1 kcal/mol at 2.5 for SH.

Directionality of the hydrogen bond interaction E(t) is dependent on the anglet away from ideal bonding geometry. Note that the directionality is only withrespect to the receptor:



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Semi-empirical: combines traditional MM force fields withempirical weights and an empirical approach for entropic

contributions

V=Wvdw Aij

rij12 ! B

ij

rij6"

#$ %

&'+Whbond E(t) C

ij

rij12 ! D

ij

rij10"

#$ %

&'+Welec q

iqj


rij2 2)2( )i, j*

i, j*

i, j*

i, j*

Electrostatics described by a screened coulombic potential



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AutoDock4

Fast energy evaluation is achieved byprecalculating atomic affinity potentials

Affinity grids: each point stores the potentialenergy of a probe atom due to all atoms inthe macromolecule

Each atom type in ligand gets a map

Full ligand flexibility around all torsions Lamarckian genetic algorithm

Very efficient global search

AutoDock Users Guide, v3.0.5, Morris et al.

Based on comprehensive thermodynamic model that allows incorporation of

intramolecular energies into the predicted free energy of binding

Charge-based method for evaluation of desolvation for typical set of atomtypes2

Calibrated against 188 diverse protein ligand complexes2Stouten et al., Molecular Simulation, 10: 97-120 (1993).


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AutoDock4 force field

!Sconf =WconfNtors Loss of torsional entropy upon binding

Huey, Morris, Olson & Goodsell, J. Comp. Chem, A Semi-empirical Free Energy Force Field with Charge-based Desolvation, preprint (2006).

V=WvdwAij

rij12

!Bij

rij6

"

#$

%

&'+Whbond E(t)

Cij

rij12

!Dij

rij10

"

#$

%

&'+Welec

qiqj

((rij)rij+Wsol SiVj + SjVi( )e

!rij2

2)2( )

i, j

*i, j

*i, j

*i , j

*

!G = VboundL"L

"VunboundL"L( ) + Vbound

P"P"Vunbound

P"P( )+ VboundP"L

"VunboundP"L

+ !Sconf( )

Intramolecular energies Intermolecular energies

0 0

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