Deepviewmanual H5n1

Tackling Receptor Flexibility in Computer-Aided Drug Design

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

Computer-aided drug design

Challenges: • solvation effects, entropy, rigorous thermodynamics • prediction of lead/candidate pharmocokinetic properties • networks / polypharmacology • receptor flexibility

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

Tackling receptor flexibility: relaxed complex scheme

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

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 (virtual screening) • Summary & future work

Influenza virus Hemaggluttinin (16 subtypes) Neuraminidase (9 subtypes) Host-derived lipid envelope M2 ion channel

8 RNA segments

~100 nm diameter

No proof reading during replication - highly variable

Influenza • Epidemics are normal, seasonal influenza outbreaks • est. 300,000-500,000 people die each year due to epidemic 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 been recorded • Circulate around the globe in successive waves • With global travel, est. a new pandemic would reach almost all corners of the earth within 3-6 months, an estimated 2 billion of the world’s 6.5 billion people will be infected

Origin of pandemic viruses

antigenic drift

40 million deaths

antigenic shift

1-1.5 million deaths

0.75 - 1 million deaths Clercq, Nat Rev. Drug Disc.,5: 1015-1025 (2006)

H5N1 influenza cases 2003-2008

• • •

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.

Points of intervention in the viral replication cycle

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

Neuraminidase as drug target HAs preferentially recognize sialic acid-gal linkage:

Human-type

avian-type

- Developed against N9, not N1 - Effective for N1, but not as good sialic acid

DANA

zanamivir (Relenza)

Oseltamivir (Tamiflu)

Clercq, Nat Rev. Drug Disc.,5: 1015-1025 (2006) Moscana, New Eng. J. Med., 353: 1363-1373 (2006)

- Resistance is a problem

Group 1 and 2 neuraminidases 9 neuraminidase (NA) strains:

2 phylogenetically distinct groups:

Group 1 ?

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

Group 1 and 2 neuraminidases 2 phylogenetically distinct groups:

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

Goals • To develop a more effective, orally-available drug against N1 • Methodological goal: to develop optimized scheme for receptor 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

Molecular dynamics to probe structure & dynamics ! U R =

( )

12 6 % ' + . + . * * qi q j 2 2 ij ij 1 2 k r ! r + k " ! " + k 1 + cos n # + $ + 4 ) ! + ( ) % ' ( ) ( ) 3 bond 3 " 3 dihed & 3 ij 1-, r 0/ -, r 0/ 2 3 ) r o o ( bonds angles dihedrals nonbonded ij & ij ( nonbonded ij paris

pairs

Van der Waals & electrostatics Classical dynamics at 300K :

Δt

2! ! ! ! d ri Fi = ma = mi 2 = !"U R dt

( )

Δt

...

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).

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 and designed around

150-loop dynamics open, closed

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).

Implications for Antiviral Drug Design

430-loop and 150-loop very flexible Structural reorganization reveals new pocket topography

Goal: To use these new structural insights for drug discovery/design efforts

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).

Clustering distills essential information • • •

Extracted snapshots from 4 chains explicit 40 ns simulations (160 ns for both apo & holo) Alignment based on Cα atoms Then computed RMSD distance matrix using subset of 62 residues (sidechains included) lining the binding pocket

Computational solvent mapping

• Assesses druggability of receptor surfaces using complementary physicsbased 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

“Hot spots” predict areas of affinity • 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 to optimize against

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

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).

Ensemble-based virtual screening Closed 150-loop

Open 150-loop

Including full receptor flexibility opens new areas for ligand binding

Potential cross-cavity binders

Several compounds are predicted to bind 2 or more cavities May provide addt’l selectivity for N1 Many ligands predicted to dock to the CS-map hot spots

Relaxed complex scheme rescoring Rank

NSC

Mean Predicted Energy Ki ( M)

1

109836

-10.63

Chemical Structure

Apo Crystal Rank

Holo Crystal Rank

SA-cavity

15

1

SA-cavity

212

10

SA-cavity 150-cavity 430-cavity

6

18

SA-cavity

238

5

SA-cavity

230

12

H N

HN

0.016

Binding Site

HN

N

O-

O N+

NH

O

N C

2

211332

-10.34

HN

0.026 H2 N

3

45583

-10.09

N H

NH 2

N

0.040

N OH O

O S

S

HO

OH O

O

O

-

Oseltamivir

-9.82

HN

0.063

O

NH2

(0.3 – 1.0)

O

O

NH2

-

Zanamivir

-9.38

O

0.133

N

NH 2

HN OH

(0.5 – 2.5)

HO

OH O OH

O

O

106920

-9.20

0.180

71

65

• Binding spectrum reorders compounds

O N+

OH

4

• Redock top compounds into MD snapshots (receptor flexibility)

O

O-

N OH

430-cavity

99

O

O HO

5

17245

-9.18

0.187

O

HO

O

150-cavity

10

O

NH2

6

350191

-9.14

0.200

NH2 O S P HO OH

SA-cavity

336

113

• Final set of 27 recommended compounds being experimentally tested

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)

Future methodological work: relaxed complex scheme Developing a workflow tool using Vision

Needs to be flexible so new modules can be easily added

Developing cyberinfrastructure to launch jobs, deal with & manipulate data

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

Avian Flu Grid: an international collaborative effort mpirun

GRAM

Job submission (globusrun )

SDSC node (in US A)

File I/O

AIST cluster (in Japan)

Users

Collaboration

gfsd

USM node (in Malaysia)

Users

Data, program Gfarm filesystem PRAGMA testbed - Computational server - Storage server AIST , ASGC, CNIC, CUHK, GUCAS, IOIT-HCM, LZU, MIMOS, NECTEC, NGO, SDSC, ThaiGrid , UZH, VPAC

• Developing computational environment (infrastructure) and scientific applications • Portal for datasharing http://www.pragma-grid.net/

• 32 institutions in 16 countries across the Pacific Rim and USA • N1 project “science driver” for technology development MGrid-CHARMM MD Simulation Job Preparing

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

Acknowledgements Professor Andy McCammon The McCammon Group

The SAFI & Avian Flu Grid Teams

Molecular Graphics Lab

H5N1: why so deadly? • H5N1 seems to induce hypercytokinemia, 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

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 CADD modeling • Good predictions cut time to positive experiment, assist in understanding mechanism of action, drive discoveries!

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 loop Tetramer N1 system = HUGE! (20K+ atoms)... - 16 ns for each system, Amber igb version 5, monomer only, with Amber’s 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

GB-MD Preliminary Results open, closed N1-apo-closed

N1-tami-closed

N1-apo-open

N9-apo-closed

N1-tami-open

N9-tami-closed

GB-MD Preliminary Results

N1-closed

N1-open

N9-closed

Grid Maps • Fast energy evaluation is achieved by precalculating atomic affinity 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• Also in themakes macromolecule electrostatic maps • Define receptor atom types, ligand atom types npts 60 60 60 spacing

0.375

gridcenter 1.602 18.973 4.55

AutoDock User’s Guide, v3.0.5, Morris et al.

AutoDock4 force field

(

L"L bound

!G = V

L"L unbound

"V

) + (V

0

P"P bound

P"P unbound

"V

) + (V

Intramolecular energies

!Sconf = Wconf N tors

P"L bound

P"L unbound

"V

0 + !Sconf

Intermolecular energies

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).

)

AutoDock force field Semi-empirical: combines traditional MM force fields with empirical weights and an empirical approach for entropic contributions " Aij Bij % " Cij Dij % qi q j ! rij2 ( V = Wvdw * $ 12 ! 6 ' + Whbond * E(t) $ 12 ! 10 ' + Welec * + Wsol * SiVj + S jVi e r r r r ( (r )r # ij i, j # ij i, j i, j i, j ij & ij & ij ij

(

W’s are the weighting factors optimized to calibrate the empirical free energy based on 188 experimentally characterized complexes

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

)

2) 2

)

AutoDock force field Semi-empirical: combines traditional MM force fields with empirical weights and an empirical approach for entropic contributions " Aij Bij % " Cij Dij % qi q j ! rij2 ( V = Wvdw * $ 12 ! 6 ' + Whbond * E(t) $ 12 ! 10 ' + Welec * + Wsol * SiVj + S jVi e r r r r ( (r )r # ij i, j # ij i, j i, j i, j ij & ij & ij ij

(

Normal Lennard-Jones potential describing dispersion/repulsion interactions Parameters A and B taken from the Amber force field.

)

2) 2

)

AutoDock force field Semi-empirical: combines traditional MM force fields with empirical weights and an empirical approach for entropic contributions " Aij Bij % " Cij Dij % qi q j ! rij2 ( V = Wvdw * $ 12 ! 6 ' + Whbond * E(t) $ 12 ! 10 ' + Welec * + Wsol * SiVj + S jVi e r r r r ( (r )r # ij i, j # ij i, j i, j i, j ij & ij & ij ij

(

)

2) 2

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 Å for O—H and N—H, and a depth of 1 kcal/mol at 2.5 Å for S—H. Directionality of the hydrogen bond interaction E(t) is dependent on the angle t away from ideal bonding geometry. Note that the directionality is only with respect to the receptor:

)

AutoDock force field Semi-empirical: combines traditional MM force fields with empirical weights and an empirical approach for entropic contributions " Aij Bij % " Cij Dij % qi q j ! rij2 ( V = Wvdw * $ 12 ! 6 ' + Whbond * E(t) $ 12 ! 10 ' + Welec * + Wsol * SiVj + S jVi e r r r r ( (r )r # ij i, j # ij i, j i, j i, j ij & ij & ij ij

(

)

2) 2

Electrostatics described by a screened coulombic potential

)

AutoDock force field Semi-empirical: combines traditional MM force fields with empirical weights and an empirical approach for entropic contributions " Aij Bij % " Cij Dij % qi q j ! rij2 ( V = Wvdw * $ 12 ! 6 ' + Whbond * E(t) $ 12 ! 10 ' + Welec * + Wsol * SiVj + S jVi e r r r r ( (r )r # ij i, j # ij i, j i, j i, j ij & ij & ij ij

(

)

Final term is a desolvation potential S: atomic solvation parameter for each atom type (estimate of E needed to transfer the atom between a fully hydrated state and fully buried state) V: estimate of the amount of desolvation when the ligand is docked, calculated with a volume-summing method similar to Stouten et al. AD4 has solvation constants for 22 atom types Stouten et al., Molecular Simulation, 10: 97-120 (1993). *on the wiki!

2) 2

)

AutoDock4 •

Fast energy evaluation is achieved by precalculating atomic affinity potentials – Affinity grids: each point stores the potential energy of a probe atom due to all atoms in the macromolecule – Each atom type in ligand gets a map

•

Full ligand flexibility around all torsions – Lamarckian genetic algorithm – Very efficient global search

•

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 atom types2

•

Calibrated against 188 diverse protein ligand complexes

AutoDock User’s Guide, v3.0.5, Morris et al.

2Stouten

et al., Molecular Simulation, 10: 97-120 (1993).

AutoDock4 force field

(

0

0 + !Sconf

) (

) (

L"L L"L P"P P"P P"L P"L !G = Vbound " Vunbound + Vbound " Vunbound + Vbound " Vunbound

Intramolecular energies

!Sconf = Wconf N tors

)

Intermolecular energies

Loss of torsional entropy upon binding

" Aij Bij % " Cij Dij % qi q j ! rij2 ( V = Wvdw * $ 12 ! 6 ' + Whbond * E(t) $ 12 ! 10 ' + Welec * + Wsol * SiVj + S jVi e r r r r ( (r )r # ij i, j # ij i, j i, j i, j ij & ij & ij ij

(

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

)

2) 2

)