Neuramindase (NA) and replication of virions

HA

Neuramindase (NA) and replication of virions

A enzyme, cleaves host receptorshelp release of new virions

NA

Modeling HTS against Inf-A NA on Grid

Ying-Ta Wu*Academia Sinica, Genomics Research Center

[email protected]

Neuraminidase Inhibitors

Zanamivir R=guanidine

Oseltamivir R=H R’=amine

R’

R

Peramivir R=H

: Predicted mutation site by structure overlay and sequence alignment: Reported mutation site

Mutation N1 N2

R292Koseltamivir Z

anamiviroseltamivir Z

anamivir

H274Y(F) oseltamivir oseltamivir

N294S oseltamivir? oseltamivir

E119V oseltamivir? oseltamivir

E119(G;A;D) oseltamivir? Zanamivir

Drug discovery at initial step

Target selected

Assay developed

HTS

HTS hits confirmed

Chemistry begins

Target structure obtained

Development candidate is taken forward

Database clustering

Similarity analysis/Virtual screening

Homology modeling

QSAR

Pharmacophores

Structure-based design/lead optimizing

2-4 years

libraryselecting

Target selected

Assay developed

HTS

HTS hits confirmed

Chemistry begins



Database clustering


Homology modeling

QSAR

Pharmacophores


2-4 years

libraryselecting

Screening is the first measure to take for the biological activity of each compound in a large compound collection against an disease target.

HTS: 10HTS: 1044 – 10 – 1055 cpd/day cpd/day

uHTS: >10uHTS: >1055 cpd/day cpd/day

“A needle in a haystack”

How to reduced pre-screening cost$ ?

Modified from DDT vol. 3, 4, 160-178(1998)

Modeling as a complement to HTS in drug discovery

Target selected

Assay developed

HTS

HTS hits confirmed

Chemistry begins



Database clustering


Homology modeling

QSAR

Pharmacophores


2-4 years

libraryselecting

Target selected

Assay developed

HTS

HTS hits confirmed

Chemistry begins



Database clustering


Homology modeling

QSAR

Pharmacophores


2-4 years

libraryselecting

focused library

screening focused library hit rate * cost

To improve hit rate$

Can large-scale “screening” be deployed on a Grid

platform?

Modeling Interacting Complexes

Virtual screening based on molecular docking is the most time consuming part in structure-based drug design workflow

•Problem size: Number of docking tasks = N x M– 8 predicted possible variants of Influenza A neuraminidase N1 as

targets– 300 K compound structures 2.4M docking jobs

•Computing challenge: CPU-bound application– Each Autodock docking requires ~ 30 mins CPU time– Required computing power in total ~ 137 CPU years(a rough measurement based on Xeon 2.8 GHz)

•Storage requirement: huge amount of output– Each docking produces results with the size of 130 KByte– Required storage space in total ~ 600 GByte (with 1 back-up)

Challenges of large scale in-silico screening

Application Characteristics

Evaluate potential targets and model their 3D structures

Prepare the large-scale docking using Autodock3.

Development of the grid environment for a large-scale deployment.

The deployment

H5N1

EGEE Grid Resources

Web Interface

DIANEMaster Process

Resource Broker

Grid Job Submission

Docking task pullingDocking complex returning

Virtual Cluster (DIANE workers)

Interactive scoringVisualization

translation / step=2.0 Å

quaternion / step =20 degree

torsion / step= 20 degree

number of energy evaluation

=1.5 X 106

max. number of generation

=2.7 X 104

run number =50

translation / step=2.0 Å

quaternion / step =20 degree

torsion / step= 20 degree

number of energy evaluation

=1.5 X 106

max. number of generation

=2.7 X 104

run number =50

2D compound library

3D structure

“drug-like”

Lipinski’s RO5

ionizationtautermization

3D structure library

structure generationenergy minimization

308,585

8 structures

Modeling Complex

Targets Compound

selection

Wisdom< 6 weeks

Enrichment of primary in silico HTS

GNA 2.4%

15% cut off

GNA=zanamivir

Original Type: T06

DAN 35%

4AM 13%

pKd=5.3

pKd=7.3pKd=7.5

Ki=4uM

Ki=150nM

Ki=1nM

Dna

4AM

GNA

Global effectiveness: (Hitssampled/Nsampled)/(Hitstotal/Ntotal)

Pearlman & Charifson, JMC, 2001

Pre-sceening (AUTODOCK) over collection and sample first 15%EF1

= (5/6)/15% = 5.5

Re-ranking (SDDB) first 15% and sample first 5% EF2 = (5/6)/(5%*15%) = 111

01 H00046 02 03 04 05 06 07 08 09 10 11 12

100±

14.8

A101.

3 92.9 81.9 118.

1 84.5 55.4 83.7 102.

6 116.

2 106.

8 83.0

B 92.3 80.4 75.4 74.8 50.6 78.4 51.3 83.4 102.

0 70.4 96.6

C 81.2 64.7 74.4 29.3 159.

3 80.8 76.9 73.1 86.8 92.0 81.6

D 57.7 54.2 73.0 47.1 75.1 65.0 83.4 52.7 75.8 85.5 88.1

0±

0.1

E 64.8 66.0 109.

9 51.0 37.9 61.8 84.2 63.5 71.4 83.9 90.4

F 65.3 63.9 83.5 63.5 77.1 56.5 79.0 61.7 51.3 78.7 92.0

G 68.4 43.4 67.9 69.1 38.9 47.6 80.2 81.4 58.0 63.5 82.7

H 74.3 78.5 85.6 72.5 78.0 72.2 92.5 92.6 85.2 73.8 92.9

H00047 02 03 04 05 06

A 137.3 114.

4 87.8 156.

0 150.2

B 79.2 78.5 67.4 108.

9 68.8

C 47.8 93.8 71.1 93.3 135.8

D 86.6 94.4 77.2 134.

6 -14.8

E 95.0 86.9 94.4 84.5 100±0.9F 72.8 89.1 84.3 82.1

G 69.3 96.7 74.6 74.5

0±0.1H 81.7 67.2 75.6

113.1

[sub]=100uM

Assay results of first 5% ranked

NA+

NA-

T06

n=123

Can point mutation to inhibitory effectiveness be predicted ?

cpd

E119A E119D H275F R293K E119A_o Y344_oOrig.

cpd

E119A E119D H275F R293K E119A_o Y344_oOrig.

T01E119A

T01:E119A T05:R293K

Effects of point mutation

pote

ntial

hits

Any additional information for medchem in hits optimization?

NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN

NNNNNNNNNNNNNNNNN

32

NNNNNNNNNNNNNNNNN

SSSSSSSSSSSSSSSSS

41


NNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNN

150


NNNNNNNNNNNNNNNNN

NNNNNNNNNNNNNNNNN

151

NNNNNNNNNNNNNNNNN

38

OOOOOOOOOOOOOOOOO

80

NNNNNNNNNNNNNNNNN

NNNNNNNNNNNNNNNNN

44

NNNNNNNNNNNNNNNNN


43

93


SSSSSSSSSSSSSSSSS

44

OOOOOOOOOOOOOOOOO

OOOOOOOOOOOOOOOOO

46

NNNNNNNNNNNNNNNNN

NNNNNNNNNNNNNNNNN

38

NNNNNNNNNNNNNNNNN

82

OOOOOOOOOOOOOOOOO

53

Popular rings and groups within hits

-NO2

-CO2

-PO3

-SO2

beta-lactams

ExamplesArg_371

Tyr_347

Ser_246

Arg_118

Arg_156

Arg_152

Glu_119

Russell et al, NATURE, 443, 45-49, 2006

NA- H00045NA NA+

0 ± 2.3

101.8 89.9 61.2 79.4 70.6 75.2 66.8 77.2 76.9 90.7

100 ±

7.2

71.7 70.0 66.9 76.2 66.6 62.2 69.1 77.8 60.2 69.3

62.4 61.4 59.6 72.5 67.5 63.3 71.6 57.3 66.9 74.4

66.5 67.1 62.9 61.2 62.5 66.6 68.5 66.4 69.7 70.9

75.3 59.3 71.5 67.2 55.3 69.8 74.1 70.4 60.1 68.1

69.8 65.6 62.4 59.9 64.0 63.6 70.4 67.4 61.5 70.1

80.0 57.8 60.9 51.2 53.0 74.1 74.9 73.5 45.8 63.4

67.7 62.2 57.1 55.6 71.0 57.8 71.3 69.7 65.0 63.0

Z’=0.72

Assay results of beta-lactam based compounds

A fluorometric assay was used to determine the NA activity with the fluorogenic substrate 2’-(4-methylumbelliferyl)-a-D-N-acetylneuraminic acid (MUNANA; Sigma). The fluorescence of the released 4-methylumbelliferone was measured.

N

NH

N+

SO

O

O–

O

O

O

O

O

O

HO

H3C

CH3

– We demonstrated that huge compound collection can be effectively enriched by executing docking tasks on Grid.

A estimated 105 year molecular docking process was shorten to 6 weeks by using WISDOM and DIANE frameworks

– A set of “potential hits” ( interacting complexes with higher affinities and proper docked poses) was selected in first 5% re-ranked, which covered 2250 compound out of initial 308585 compounds (enrichment = 111). Experimental assay confirms 7 actives out of 123 purchased “potential hits”, which proved the usefulness of our work.

– Mutation effects to compound activity may be predicted with similar method. Among the modeled 8 targets, the variants, T01(E119A) and T05(R293K) had greater impacts on the activities of “potential hits” and known drug, such zanamivir. The unique residue, Tyr344 also had effects on the compound binding and should be included in future drug design.

– A workflow that mimic real HTS procedures with integration of chemical information and tools for automating post-analysis is expected.

Summary

Academia Sinica: Target and docking preparation, grid deployment, output analysisGenomics Research Center Ying-Ta Wu Grid Computing Team Hurng-Chun Lee Li-Yung Ho Hsin-Yen Chen Simon C. Lin Eric Yen

LPC (CNRS/IN2P3): Grid application development and deploymentPCSV : Plate-forme de Calcul pour les Siences de la Vie Vincent Breton Nicolas Jacq Jean Salzemann Yannick Legre IT SERVICE Matthieu Reichstadt Emmanuel Medernach

Institute for Biomedical Technologies (CNR): docking preparation, grid deploymentLuciano Milanesi Ermanna Rovida Pasqualina D'Ursi Ivan Merelli

ARDA: DIANE support

TWGrid: infrastructure support of Taiwan

EMBRACE european network of excellence: project support

BioinfoGRID european project: project support

AUVERGRID : Infrastructure support

Massimo LamannaJakub Moscicki

Acknowledgments

a world-wide infrastructure providing over than 5,000 CPUs

Documents

Neuramindase (NA) and replication of virions