Glide Aug 2002 - Our Mission | ACS Division of Chemical...

GlideA New Paradigm for Rapid,

Accurate Docking and ScoringA New Paradigm for Rapid,

Accurate Docking and Scoring

Current Status and Future PlansThomas A. Halgren

Glide ~ Potential Utility

Lead Discovery! Dock sets of purchasable compounds to choose compounds to buy! Dock compound collection to choose compounds to assay! Especially, use in combination with HTS to:

" Reduce number of compounds that need to be assayed" Identify additional hits that HTS might miss

! Dock sets of CombiChem compounds to generate focused CombiChem libraries

Lead Optimization! Dock active compounds to confirm binding mode or to suggest

an alternative binding mode! Evaluate new ligand designs before synthesis

Criteria for High-Throughput Docking

! Screen databases rapidly on a timescale compatible with drug discovery needs

! Determine correct ligand binding mode! Predict binding affinity with high accuracy

! must be able to rank known ligands well and screen out ligands that won’t bind

! Provide a user-friendly setup and a highly automated docking protocol

! Facilitate analysis of docking results

Glide offers an attractive combination ofthese features

Glide Hierarchical Docking Strategy

Glide’s docking algorithmapproximates a completesystematic search overligand positions, orienta-tions, and conformationsin the receptor site.

Increasingly demandingtests are applied as thesearch space is reduced.

Glide "Funnel"Ligand conformations

1. Site-point search

2b. Subset test

2c. Greedy score

3. Grid minimization

4. Final scoring(GlideScore)

2a. Diameter test

Top hits

2d. Refinement

Conformation Generation ~ Definitions

O

N

O

O-

Hrotamer group

rotamer group

S

N

The four internal rotatable bonds are part of the core region

Glide generates anddocks many coreconformations, buttreats the rotamergroups sequentially,rather than combina-torially. This speedsup the calculation.

Ligand Placement ~ Definitions

Ligand Center, used insite-point search

Ligand Diameter

Atoms close to Ligand Diameter, used in diameter test

Line between two most widely separated atoms

Placed at the center of the ligand diameter

! Generate a 2-Å grid of site points in the active site! Pre-compute histograms of distances between

site point and receptor surface in grid setup! Compare site point – receptor surface histograms

with the ligand center – ligand surface histogram

! Reject mismatched site points

Stage 1 ~ Fast Site-Point Search

Site Point-Receptor Surface Ligand Center-Ligand Surface

Stage 2 ~ Rough Scoring

! Diameter test – check steric clashes of atoms near ligand diameter for ~300 pre-specified orientations of the ligand diameter

! Subset test – rotate about ligand diameter in 15��increments; score atoms capable of making H-bonds, ligand-metal interactions

! Greedy scoring – score all atom positions ±±±±1 Å in x,y,z directions; use best score

! Refinement – move whole ligand ±±±±1 Å in x,y,z directions and re-score; reduce ~5000 poses to ~400 for energy minimization

! Use pre-computed OPLS-AA vdW and electrostatic grids

! Anneal from soft to hard potential" Smoothing reduces large initial

energy/gradient terms from close contacts,permits freer movement

! Also optimize torsional angles when doing flexible docking

! Use Monte Carlo moves to explore nearby torsional minima for a small number of low-energy poses

Stage 3 ~ Energy Minimization

! Choose best pose(s) based on Emodel, a combination of:" Coulomb-vdW energy" GlideScore, an enhanced version of ChemScore" Internal strain energy for potential directing

conformational-search algorithm

! Final scoring based GlideScore:" employs all ChemScore terms" includes a contribution from the CvdW energy" adds terms that penalize non-physical interactions

Stage 4 ~ Final Scoring

Docking Accuracy ~ Test Set

Test-set includes 285 structures from the PDB! RMS < 1.0 Å for half of test-set! RMS < 1.5 Å for two-thirds of test-set

! Larger RMSDs! ligands with rotated phobic groups ! very large ligands (>20 rotatable bonds)! ligands highly exposed to solvent, few/no

H-bonds to receptor

Glide Speed and Accuracy ~ Summary

Number of Rot. Bonds

Number of Cases

Ave. RMS Top Ranked

PoseAve. CPU

Time (min)*

0-3

4-6

7-10

0-8

0-10

0-20

51

92

48

164

191

266

0.99

1.50

1.79

1.36

1.44

1.71

0.2

0.6

1.7

0.5

0.8

2.4

*1.3 GHz Linux Pentium III

Flexible docking of MMFFs-opt’d co-crystallized PDB ligands

Comparison to Gold for Gold Test Set

≤≤≤≤ 20 RB (86 cases) All Ligands (93 cases)

Avg. RMSD Max. RMSD Avg. RMSD Max. RMSD

Glide 1.48 5.8 Glide 1.48 5.8 1.57 1.57 6.86.8

Gold 2.89 14.0 3.03 14.0

RMSD (Å) comparison of structures from the PDB for the Gold test set

*

*Rotatable Bonds

http://www.ccdc.cam.ac.uk/prods/gold/rms_tab.html

Comparison to FlexX for FlexX Test Set

≤≤≤≤ 20 RB (177 cases) All Ligands (191 cases)

Avg. RMSD Max. RMSD Avg. RMSD Max. RMSD

Glide 1.46 8.3 Glide 1.46 8.3 1.66 13.51.66 13.5

FlexX 3.43 13.4 3.67 15.5

RMSD (Å) comparison of structures from the PDB for the FlexX test set

*

*Rotatable Bonds

http://cartan.gmd.de/flexx/html/flexx-eval.html

Scoring Accuracy ~ Database Screens Used

! Ten receptors featuring a wide variety of types of binding sites used to evaluate database enrichment

! Thymidine Kinase, Estrogen Receptor, CDK-2 Kinase, Sugar-Binding protein, HIV Protease, Thrombin, Thermolysin, P38 MAP Kinase, Cox-2, HIV-RT

! Roughly 1100 “decoy” ligands from CMC or PDB! 877 CMC ligands; 229 PDB ligands

! Up to 20 rotatable bonds; MMFF94s optimized

! Known binders from literature, from PDB test set, or provided by pharmaceutical colleagues

GlideScore 1.8 ~ Evolved from ChemScore

��Gbind = C0 + Clipo �� f(rlr) + Chbond �� g(��r) h(��a)

+ Cmetal �� f(rlm) + Crotb Hrotb + Cclash Vclash

! ChemScore (Eldridge, JCAMD 1997, 11, 425-445) does:

! reward favorable lipophilic, hydrogen bonding, and metal ligation contacts

! penalize freezing out of rotatable bonds

! ChemScore doesn’t:

! penalize steric clashes (though a later version does)

! penalize lipophilic or hydrophilic mismatches

But GlideScore does

Enrichment Factors ~ Definitions

! Enrichment Factor (EF) usually defined as:EF = {Ntotal / Nsampled} #### {Hitssampled / Hitstotal}

! Better definition (EF’):

EF’ = {50% / Avg%ranksampled} #### {Hitssampled / Hitstotal}

! EF’ counts all sampled hits equally, not just last hit found

! Report enrichment factors EF or EF’ based on finding

70% or 80% of known binders

! Might be better to report EF or EF’ based on sampling

2%, 1% or less of a larger ranked database! Relevant measure is how many compounds can be assayed

Database Screening ~ GlideComp 1.8 Scoring

! GlideComp 1.8 is a combination of GlideScore 1.8 and the Coulomb-van der Waals energy:

GlideComp = 0.6*GlideScore + 0.08*E_CvdW

! The CvdW energy is the OPLS-AA nonbonded interaction energy as computed on the grid" ionic charges are reduced by ~50% to place charge-

charge, charge-dipole, and dipole-dipole interactions on a common energy scale

" exception: anionic-ligand/metal-cation interations! Glide 1.8 (and 2.0) also allow user to require that a

ligand achieve a specified hydrogen-bonding (hb) or metal-ligation (ml) score to be reported and counted

Enrichment Factors EF’(70%) for Glide 1.8

Database Screen

GlideScore w/o hb/ml

GlideComp w/o hb/ml

GlideComp w hb/ml

Thymidine Kinase 3 8 16

CDK-2 Kinase 6 7 7 P38 MAP Kinase 4 7 8

Estrogen Recep. 98 91 94 Thrombin 22 20 27

HIV Protease 46 47 54

Sugar-Bind. Prot. 82 105 105 Thermolysin 4* 6* 6*

* Emodel gives EF’ = 22 (w/o hb/ml filters) and EF’ = 23 (w hb/ml)

GlideCompusually doesbetter than GlideScore; hb or ml thresholdfilters some-times help

Main problems for Glide 1.8 scoring:! Enrichment factors for CDK-2 kinase and

P38 MAP kinase are too low (~7)

" Some methods don’t do this well!

! A different scoring function (Emodel) needs tobe used for thermolysin to give decent enrichment

! Having to choose and apply specific h-bond (hb)and metal-ligation (ml) filters is awkward for the user" Without a h-bond filter, the enrichment factor is

also too low for thymidine kinase

Glide 1.8 Problems

! Try descriptors based on FlexX, PLP (ScreenScore;Roche Basel) and ChemScore (done)

! Add additional Schrödinger descriptors! Add many more database screens: HIV-RT, cox-2,

neuraminidase, gyrase B, gelatinase A, squalene synthase, aldose reductase, acetylcholine-esterase, thymidylate synthase ... (in progress)

! Fit scoring models by optimizing parameters to maximize enrichment factors (done)

! Test robustness by excluding data and refitting(initial tests suggest overfitting is not a serious problem)

Improving Glide Scoring ~ Approach

7.06.03.010%

12.08.02.05%

20.015.05.02%

GS 2.0GC 1.8GS 1.8EF

Thymidine Kinase, w/o and w/ –1.8 kcal hb filter

8.09.09.010%

16.014.06.05%

25.015.05.02%


0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.00

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0

10%

5%

2%

Database Enrichment for Glide 2.0 vs. Glide 1.8

50% of theactives are found in first 2% of the rankeddatabase forGlideScore 2.0; 90% arefound in thefirst 10%

GlideScore2.0 is clearlybetter

8.06.05.010%

14.010.08.05%

25.010.015.02%


9.08.07.010%

12.08.08.05%

15.010.020.02%


cdk-2 Kinase ~ 1dm2 and 1aq1 receptor sites

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.00

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0


GlideScore 2.0 is clearly better for the 1dm2 site; is better at 5% and 10% of database for the 1aq1 site

7.17.15.010%

12.98.68.65%

10.73.53.52%


5.84.54.510%

10.96.76.05%

19.713.610.62%


p38 MAP kinase (1a9u) and cox-2 (1cx2)

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.00

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0


GlideScore 2.0 is better for both p38 and cox-2

9.09.09.010%

18.016.018.05%

40.040.045.02%


10.09.09.010%

20.018.018.05%

50.040.045.02%


Estrogen receptor (3ert and 1err)

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0


All three scoring methods perform very well for both receptor sites

10.010.010.010%

13.820.016.25%

18.828.128.12%


10.010.010.010%

17.316.016.05%

36.633.330.02%


Thrombin (1dwc) and HIV protease (1hpx)

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.00

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0


GlideScore 2.0 is a bit worse for thrombin, but a bit better for HIV protease

10.010.010.010%

20.020.020.05%

50.033.350.02%


Sugar-Binding Protein (1abe)/Thermolysin (1tmn)

10.010.06.04.010%

18.016.08.04.05%

35.025.010.010.02%

GS 2.0EmodelGC 1.8GS 1.8EF

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 Emodel GS 2.0


GlideScore 2.0 results are excellent - even for thermolysin, where GlideScore 1.8 does poorly

5.53.92.710%

3.66.73.65%

3.06.03.02%


4.54.22.410%

2.45.43.05%

6.19.10.02%


HIV reverse transcriptase (1vrt and 1rt1 sites)

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0

0

10

20

30

40

50

60

70

80

90

100

GS 1.8 GC 1.8 GS 2.0


HIV-RT is a tough case, but GlideScore 2.0 is slightly better than GlideScore 1.8

0

10

20

30

40

50

60

70

80

90

100

Th

Kin

ase

(1ki

m)

Est

. Rec

ep. (

3ert

)

CD

K-2

(1d

m2)

Suga

r-B

ind.

Pro

t.

HIV

Pro

teas

e

Thr

ombi

n

The

rmol

ysin

p38

MA

PK

inas

e

Cox

-2

Cox

-2 (

site

1 lig

s)

HIV

RT

(1v

rt)

HIV

RT

(1r

tl)

CD

K-2

(1a

q1)

Th

Kin

ase*

(1ki

m)

Est

. Rec

ep. (

1err

)

* -1.8 H-bond filter

Glide 2.0 Enrichment Factors ~ EF’(70%)

Improving Glide Scoring ~ Current Results

Database Screen GlideComp

1.8 GlideScore

2.0 Thymidine Kinase 8 12 Estrogen Recep. 91 83 CDK2 Kinase 7 23 P38 MAP Kinase 7 12 Sugar-Bind. Prot. 105 82 HIV Protease 47 57 Thrombin 20 17 Thermolysin 6 46 Cox-2 4 6 Cox-2 (site 1 ligs) 15 41 HIV-RT 3 5

EF’ values shown,computed for 70% recovery of active binders

None of the enrichment factors shown here useeither hydrogen-bonding or metal-ligation filters

Improving Glide Scoring ~ Cox-2 Case

Cox-2 screen had 33 active ligands from literature plus 1106 CMC or PDB database ligands

! Only 19 actives could dock with negative Coulomb-vdW energies anywhere in the primary binding pocket

! However, 13 of these “site 1” ligands show up in the first 20 positions in the ranked database!

! This gives an EF’(70) enrichment factor of 41 based on finding 13 or 19 site 1 ligands

! This is a better result than was obtained with Glide 1.8, and shows that Glide 2.0 is very effective at identifying known Cox-2 actives; it just can’t find all of them.! Probably shows limitations of docking to a rigid receptor

! GlideScore 2.0:" improves EF’s for most less well-treated screens " preserves high EF’s for well-treated screens " has less need for system-specific hydrogen-bonding or

metal-ligation filters" does not require different scoring function for

metalloproteins" therefore is easier to use

! The new scoring function does not use any ScreenScore or PLP terms, but does use the C-vdW interaction energy

! Key new terms are lipo-nonlipo terms (as in Fresno) and terms that reflect hydrophobic and hydrophilic complementa-

rity, evaluated using Merck-style “Active Site Mapping” grids

Improving Glide Scoring ~ Summary

Comparison to Other Methods ~ EF(80%)

Thymidine Kinase

Estrogen Receptor

Bissantz et al., J. Med. Chem. 2000, 43, 4759

01020304050607080

Glide-GlideScore Dock-DockE Dock-PMF

FlexX-FlexX FlexX-PMF FlexX-DockE

Gold-Gold Gold-DockE

Only Glide/GlideScore 2.0 gives a good enrich-ment factor for both thymidinekinase andthe estrogenreceptor.

Glide 2.5 ~ Improving Database Enrichment via Extra-Precision Docking and Scoring

! Iteratively re-dock one or a set of top-ranked poses generated by Glide in its normal mode of operation! Generate set of perturbed core conformations for each

pose! Constrain new docking to local region of receptor cleft

used by each such pose ! Can be ~ten times more expensive, but can apply to just

top 10-15% of docked ligands to keep costs in bounds.

! Extra precision scoring uses new technology that attempts to take better account of solvation/ desolvation phenomena and to more accuratelydiscriminate between good and bad interactions

Further Improving Database Enrichment; Tentative Results for Glide 2.5 and 2.5XP

Database Screen

Glide 1.8

Glide 2.0

Glide 2.5*

Glide 2.5XP

Thymidine Kinase 8 12 20 29 Estrogen Recep. 91 83 74 96 CDK2 Kinase 7 23 38 38 p38 MAP Kinase 7 12 12 44 Cox-2 (site 1 ligs) 15 41 39 39 Thrombin 20 17 22 40 Thermolysin 6 46 37 43 HIV Protease 47 57 55 46 HIV-RT 3 5 6 9

Enrichment factors EF’ for recovering 70% of active binders

* Not in � release of Glide 2.5, but may be in final release

GlideScore2.5XP (extraprecision mode) and even normal GlideScore 2.5 improve several cases significantly

0

10

20

30

40

50

60

70

80

90

100

Est

. Rec

ep. (

3ert

)

CD

K-2

(1d

m2)

Suga

r-B

ind.

Pro

t.

HIV

Pro

teas

e

Thr

ombi

n

The

rmol

ysin

p38

MA

PK

inas

e

Cox

-2 (

site

1 lig

s)

HIV

RT

(1v

rt)

HIV

RT

(1r

tl)

CD

K-2

(1a

q1)

Th

Kin

ase

(1ki

m)

Est

. Rec

ep. (

1err

)

Glide 2.5XP Enrichment Factors ~ EF’(70%)

Comparisonto 2.0 EF’sshows markedimprovement

! Initial implementation will allow user to require:! hydrogen bonds to designated receptor atoms! metal ligations to designated metal ions (e.g., Zn2+)

! Any ligand atom of proper type can satisfyconstraint

! Types initially recognized will be: ! Hbond donor! neutral Hbond acceptor! anionic Hbond acceptor

! A later implementation may:! allow hydrophobic constraints to be defined! allow specific functional groups to be specified

Constraints - Under Development for Glide 2.5

to allow discrimination for metalloproteins}

Current and Planned Improvements for Glide

! Improve the scoring function (Glide 2.0)

! Reduce memory requirements (Glide 2.0)

! Improve docking accuracy (Glide 2.5 – Fall 2002)

! Implement constraints (Glide 2.5 – Fall 2002)

! Further scoring accuracy (Glide 2.5 – Fall 2002)

! Improve efficiency ! Allow receptor flexibility

! Treat discrete sidechain positions/ionization states! Treat protein as an ensemble of configurations

Assessing Docking Hits ~ Active Site Mapping

! Maestro facility: provides visual feedback to user by displaying molecular surfaces and volumes

! Helps to qualitatively assess Glide docking hits:! Hydrophobic volumes – enclose regions in active-site

space appropriate for hydrophobic portions of ligand! Hydrophilic volumes – enclose hydrophilic regions! Visual comparison quickly highlights:

! mismatches in complementarity! “targets of opportunity” – e.g., hydrophobic regions

with room for a larger hydrophobic group

! Molecular and extra-radius surfaces can alsobe visualized

Glide Aug 2002 - Our Mission | ACS Division of Chemical...

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