Metal-to-ligand charge transfer chirality-based sensing of ...
Structure-based maximal affinity model predicts small-molecule … · 2008-01-29 ·...
Transcript of Structure-based maximal affinity model predicts small-molecule … · 2008-01-29 ·...
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Structure-based maximal affinity model predicts
small-molecule druggability
Alan [email protected]
IMA Workshop (Jan 17, 2008)
Druggability prediction• Introduction• Affinity model• Some results
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Why estimate ‘druggability’?
60% of programs fail in HTS and Hit-to-lead
Brown & Superti-Furga Drug Discovery Today (2003)
Traditional way: Sequence homology
Certain gene families tend to be druggable• e.g., Kinases and GPCRs• Used to estimate “druggable genome”
Hopkins & Groom Nature Rev. Drug Disc. (2002)
Unprecedented targets and gene families
Not all members of a gene family are equally druggable
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HTS way: Screening a diverse library
• NMR screening hit-rate* • Diverse compound collection screening hit-rate
• Reagent, screening investment
* Hajduk et al. J Med Chem. 2005
Biophysically-inspired way: Structure-based
“Druggable” “Undruggable”
Qualitative, intuitive: Can we make this quantitative?
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• Maximal affinity of ligands ~1.5 kcal/mol/atom Kuntz et al. PNAS (1999)
• Extend to binding sites?
• Restrict to “drug-like” ligands
Concept of maximal affinity
Oral drugs tend to have drug-like properties
0%
5%
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0 250 500 750 1000
weightMolecular Weight (Da)
0%5%
10%15%20%25%30%
0 50 100 150 200 250
tpsaPolar surface area (tPSA, A2)
Similar to Lipinski et al. 2001, Palm et al. 1999Marketed oral tablets in MDDR v.2001
>90% of oral drugs fall within physiochemical ranges
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Translating to the protein binding pocket
550 MW ~ 300A2
Maximal affinity predicted, ΔGMAP-POD
ΔGMAP-POD ~ – γ(r)
Non-polar surface areafor binding site
300A total
300A2 surface area ~ 550 MW
A NP
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-40 -30 -20 -10 0 10 20 30 40
γ(r) (kcal/mol)
Curvature r (Å)
= 45 kcal/mol/A2
p = 1.4ASharp et al. Science (1991)Dill et al. J Phys Chem B (2003)De Young & Dill, J Phys Chem (1990)
Curvature-dependent HPO desolvation term
One fitted parameter
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Implementation
Algorithms
Generate tetrahedrarepresentation
Define pockettetrahedra
Calculate protein core
Curvature:New sphere fitting approach using geometric inversion
Precisely defining pocket for surface area calculation
Liang, et al. (1998) Protein Sci.
Koehl, POCKET
Brannan , Esplen, Gray (1999) Geometry
Coleman, Burr, Souvaine, Cheng (2005) Proteins
Appolonius (200BC)
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All models are wrong, some are useful.– George Box
Validation on 27 targets
Druggable Undruggable
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Scholarship finds outliers are prodrugs
Druggable Undruggable
Prediction of druggable and difficult targets
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Prediction and validation with two novel targets
Predicted Druggability
Raw hits20 uM, 60% cutoff
Confirmed hits IC50<5uMIC50<1uM
• Unprecedented targets/ unprecedented gene families• Predictions made before targets entered portfolio • Screened 11k “chemical space” diverse compound set
H-PGDS30
200 raw hits
33 confirmed hits11 confirmed hits
Fungal HSD240
16 raw hits
2 confirmed hits 0 confirmed hits
Cheng et al. (2007) Nature Biotechnology
Do experimental maximal affinities correlate?
• Experimental affinities for orally bioavailablecompounds.
• Literature mining; values are approximate (combination of Kd’s, Ki’s, IC50’s)
Correlation is very encouraging
Cheng et al. (2007) Nature Biotechnology
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Druggability in practice: Caveats
• Large conformational changes(especially loops)
• Unspecified binding sites
• Metal chelation• Covalent adducts• Active transport• Prodrug strategy• Alternate delivery/approaches
Predictions are for oral, passively absorbed, non-covalent drugs
Take a measured risk for compelling biology• These are predictive risk assessment tools• Significant conformational change
Binding site structures are treated explicitly
Druggability prediction• Model based on nonpolar desolvation• Correlation with HTS and Phase II
outcomes
Target space
Druggable Disease modifying
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Expanding druggable space• Structure-based drug design• Allosteric sites
Shape• VDW, hydrophobic• Can be optimized “by eye” with reasonable
success.
ΔGelectrostatic = ΔGinteraction + ΔGdesolvation + ΔGdesolvationligand proteinprot-ligand
Charge• Hydrogen bonds, Ionic pairs• More difficult to optimize b/c affinity is not as
intuitive (not just interaction, also desolvation)
Structure-based drug design
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Tidor Lab charge optimization
Ligand charge
Free
Ener
gy
Ligand desolvation (Q2)
P-L interaction—Coulombic (Q)
Tidor et al. Protein Sci. (1998)
Protein desolvation
Net Electrostatic Energy
Charge optimization in lead progression
• Applied to available series of six co-crystal structures for neuraminidase (antiviral target)
• Goal, retrospectively study utility in lead progression
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Neuraminidase case study
• Focus on “R-groups”
• Increasingly potent compounds--generally R-groups closer to optimal charge distribution
• Lead optimization results in charge optimization
Armstrong, Tidor, Cheng. J Med Chem (2006)
Crystallographic water for Oseltamivir binding
Optimal charge distribution provides an explanation for crystallographic water
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Towards Identifying Druggable Allosteric Sites
Protein surface
Druggable Functionallyrelevant
Computational bioinformatics approach
1. Large sequence alignment
2. Identify coupled residues3. Map to structure
Lockless & Ranganathan, Science (1999)
Statistical coupling analysis
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“Local” version of druggability equation
Potential allosteric sites in p38a/kinases
• Top site identical to small molecule allosteric inhibitor site recently identified in cAbl (Nature Chem. Biol. 2006)
• Other predicted site: Inhibitor recently found for Jnk1 (Abbott Pharmaceuticals, Oct 2007, Manuscript in preparation)
Coleman, Salzberg, Cheng, J Chem Inf Model (2006)
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Summary and References
Druggability
Expanding druggablespace Finding allosteric sites by
combining functional residue prediction and druggability predictions.
Charge optimization is helpful for SBDD in polar binding sites
Nonpolar desolvationdrives maximal drug-like affinity. This is quantitatively useful.
J Chem Inf Model (2006) 46, 2631–2637
Proteins (2005) 61, 1068–1074
Nature Biotechnology(2007) 25, 71–75
J Med Chem (2006) 49, 2470–2477
Acknowledgements
Computational geometryRyan Coleman (Pfizer, Tufts Univ.)
Diane Souvaine (Tufts Univ.)
Structure-based druggabilityKate Smyth and Patricia Soulard (Pfizer Biology)
Qing Cao, Daniel Caffrey, Anna Salzberg, Enoch Huang, RTC MI colleagues
Advice from Eric Fauman, Ken Dill (UCSF),Pfizer Cambridge and Pfizer Global R&D colleagues
Charge optimizationKathryn Armstrong (MIT, Pfizer)
Bruce Tidor (MIT)
Allosteric sitesAnna Salzberg (Brandeis, Pfizer)