Structure-Based Drug Design · 1 Structure-Based Drug Design Thomas Funkhouser Princeton University...
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1 Structure-Based Drug Design Thomas Funkhouser Princeton University CS597A, Fall 2005 Introduction Drugs • Molecules that can be introduced to change biological activity Slide courtesy of Bill Welsh Introduction Drug targets • Enzyme - inhibitors • Receptor - agonists or antagonists • Ion channels - blockers • Transporter - update inhibitors • DNA - blockers Slide courtesy of Bill Welsh Structure-Based Drug Design Goal: • Given a protein structure, and/or its binding site, and/or its active ligand (possibly bound to protein), find a new molecule that changes the protein’s activity Structure-Based Drug Design Receptor-based drug design: • Given a protein structure, and/or its binding site, and/or its active ligand (possibly bound to protein), find a new molecule that changes the protein’s activity HIV Protease Inhibitor Example courtesy of Bill Welsh Structure-Based Drug Design Ligand-based drug design: • Given an protein structure, and/or its binding site, and/or its active ligand (possibly bound to protein), find a new molecule that changes the protein’s activity Example courtesy of Joe Corkery
Transcript of Structure-Based Drug Design · 1 Structure-Based Drug Design Thomas Funkhouser Princeton University...
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Structure-BasedDrug Design
Thomas Funkhouser
Princeton University
CS597A, Fall 2005
Introduction
Drugs• Molecules that can be introduced
to change biological activity
Slide courtesy of Bill Welsh
Introduction
Drug targets• Enzyme - inhibitors• Receptor - agonists or antagonists• Ion channels - blockers• Transporter - update inhibitors• DNA - blockers
Slide courtesy of Bill Welsh
Structure-Based Drug Design
Goal:• Given a protein structure,
and/or its binding site,and/or its active ligand (possibly bound to protein),find a new molecule that changes the protein’s activity
Structure-Based Drug Design
Receptor-based drug design:• Given a protein structure,
and/or its binding site,and/or its active ligand (possibly bound to protein),find a new molecule that changes the protein’s activity
HIV Protease Inhibitor
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Example courtesy of Bill Welsh
Structure-Based Drug Design
Ligand-based drug design:• Given an protein structure,
and/or its binding site,and/or its active ligand (possibly bound to protein),find a new molecule that changes the protein’s activity
Example courtesy of Joe Corkery
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Challenges
Scoring of chemical models • Activity• Toxicity• “Druggability”• etc.
Search of chemical space• Add/remove/replace chemical groups• Conformations• etc.
Outline
Virtual drug screening• Ligand-based
§ 2D structure matching§ 3D structure matching§ QSAR
De novo drug design• Models
§ Simulation§ Knowledge-based
• Construction algorithms§ Incremental construction§ Fragment-based§ Stochastic optimization
Ligand-Based Drug Screening
HO
OH
17b-Es tradiol Andros tenediol
HO
OH
O
CH3O
Proges terone
HO
OHH3CO
Moxes trol Coumestrol
O OHO
OHO
HO
OH
Diethyls ti lbes trol Genis tein
OHO
OHOOH
Methoxychlor
H3CO
Cl Cl Cl
OCH3 HO OH
Bisphenol A
a- Zearalanol(Zeranol)
O
OOH
HOOH
4-Hydroxytamox ifen
ON
HOO
N
H3CO
Nafox idine
Tamox ifen
ON
Clomifene
Cl
ON
HO
OH
(CH2)10CO N(CH3)C 4H9
ICI 164,384
Ligands for Estrogen Receptor Slide courtesy of Bill Welsh
Ligand-Based Drug Screening
Strategies:• 2D matching• 3D matching• Pharmacore matching• Quantitative Structure Activity Relationships (QSAR)
2D Structure Matching
OHN
N
OH
NH
N NH2
O
N NH
N
NH2
NH
N
NH2
NH
N
N NH
N
NH
OH
Query
Slide courtesy of Bill Welsh
2D Substructure Matching
Query
N
O
O
N NO
O
O
O
N NN
N
NNN
N O
N
N
O
O
NO
O
N
OO
Slide courtesy of Bill Welsh
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2D Substructure Matching
N-NO-C(-N)-CCH3-Ar-CH3C-N-NN-Ar-Ar-ON-C-ON-Ar-O OH > 1CH3 > 1N > 1NH...
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�
�
�
�
�
�
�
Slide courtesy of Bill Welsh
2D Substructure Matching
Which keys are present?
Slide courtesy of Bill Welsh
2D Substructure Matching
Tanimoto coefficient:
struct A: 00010100010101000101010011110100 13 bitsstruct B: 00000000100101001001000011100000 8 bits
A & B: 00000000000101000001000011100000 6 bits A ∩BA or B: 00010100110101001101010011110100 15 bits A ∪B
BAB)(A
T ∪∩=
)(
T = 6 / 15 = 0.4
Slide courtesy of Bill Welsh
2D Substructure Matching*Activity data from Uehling et al, J. Med.Chem. 1995
0
0.5
1
1.5
2
2.5
1.00 0.95 0.90 0.85
Act
ivity
diff
eren
ce (
log
units
)
Tanimoto similarity
Slide courtesy of Bill Welsh
Outline
Virtual drug screening• Ligand-based
§ 2D structure matchingØ 3D structure matching§ QSAR
De novo drug design• Models
§ Simulation§ Knowledge-based
• Construction algorithms§ Incremental construction§ Fragment-based§ Stochastic optimization
3D Structure Matching
Search database of molecules for ones with similar 3D shape and chemistry
A B
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3D Structure Matching
Search database of molecules for ones with similar 3D shape and chemistry
Ai Bi
��∈∈
−+−=BB
iAA
i
ii
BABABAd22),(
3D Substructure MatchingO
O N
a
b
c
a = 8.62 0.58 Angstroms
b = 7.08 0.56 Angstroms
c = 3.35 0.65 Angstroms +-
+-
+-
OO
O
O
OO
N
OO
O
N
N
N O
O
O
O
O
O
N
N
N
N
S
OO
O O P O
O
O P O
O PO
O
O
ON
N
N
N
N
OO
O O
O
N
N
N
O
N
O
OO
Slide courtesy of Bill Welsh
3D Substructure Matching
Slide courtesy of Bill Welsh
5.24.2-4.7
6.7
4.85.1-7.1
3D Substructure Matching
Slide courtesy of Bill Welsh
C(u)O(s1)
O(s1)
A
A
[O,S]
O
3.6 - 4.6 Å
3.3 - 4.3 Å
6.8 - 7.8 Å
OCl H
O
Cl
H
3.2Å
4.3Å
Rigid
Flexible
3D Substructure Matching
Slide courtesy of Bill Welsh
DISTANCE CONSTRAINTS(Qualitative Affinity prediction mostly)
DISTANCE CONSTRAINTS(Qualitative Affinity prediction mostly)
BIOPHASE
LIGAND
RECEPTORRECEPTOR
A
B
C
A'
B'
C '
Pharmacore Matching
HO
OH
17b-Es tradiol Andros tenediol
HO
OH
O
CH3O
Proges terone
HO
OHH3CO
Moxes trol Coumestrol
O OHO
OHO
HO
OH
Diethyls ti lbes trol Genis tein
OHO
OHOOH
Methoxychlor
H3CO
Cl Cl Cl
OCH3 HO OH
Bisphenol A
a- Zearalanol(Zeranol)
O
OOH
HOOH
4-Hydroxytamox ifen
ON
HOO
N
H3CO
Nafox idine
Tamox ifen
ON
Clomifene
Cl
ON
HO
OH
(CH2)10CO N(CH3)C 4H9
ICI 164,384
Ligands for Estrogen Receptor Slide courtesy of Bill Welsh
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Pharmacore Matching
HO
OH
17b-Es tradiol Andros tenediol
HO
OH
O
CH3O
Proges terone
HO
OHH3CO
Moxes trol Coumestrol
O OHO
OHO
HO
OH
Diethyls ti lbes trol Genis tein
OHO
OHOOH
Methoxychlor
H3CO
Cl Cl Cl
OCH3 HO OH
Bisphenol A
a- Zearalanol(Zeranol)
O
OOH
HOOH
4-Hydroxytamox ifen
ON
HOO
N
H3CO
Nafox idine
Tamox ifen
ON
Clomifene
Cl
ON
HO
OH
(CH2)10CO N(CH3)C 4H9
ICI 164,384
Ligands for Estrogen Receptor Slide courtesy of Bill Welsh
HO
OH12 A
Outline
Virtual drug screening• Ligand-based
§ 2D structure matching§ 3D structure matchingØ QSAR
De novo drug design• Models
§ Simulation§ Knowledge-based
• Construction algorithms§ Incremental construction§ Fragment-based§ Stochastic optimization
QSAR
Learn model for activity as function of descriptors (properties) computed from molecules
Compound Activity (pK i) "Y"
Descriptors (Xi) Mol. Vol. (Å3) LogP Dipole Mom (µµµµ)
1 2.34 420 2.8 0.97 2 1.89 332 4.6 2.23 3 0.23 198 -0.3 3.36 4 3.67 467 3.7 0.45 5 2.55 359 -1.5 1.77
etc. etc. etc. etc. etc.
Slide courtesy of Bill Welsh
QSAR
Use model to predict activities for new leads from their descriptors
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��� �������
����� ����� ������������������
���������� ��� �� !"#�$%�&�'� !#(�$��)��&�* !+,�$µ�&
V logP µµµµHO
OH
Slide courtesy of Bill Welsh
Outline
Virtual drug screening• Ligand-based
§ 2D structure matching§ 3D structure matching§ QSAR
De novo drug design• Models
§ Simulation§ Knowledge-based
• Construction algorithms§ Incremental construction§ Fragment-based§ Stochastic optimization
De Novo Drug Design
General Strategy:Ø Given a protein structure• Build model of binding site• Construct molecule that fits model
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De Novo Drug Design
General Strategy:• Given a protein structureØ Build model of binding site• Construct molecule that fits model
De Novo Drug Design
General Strategy:• Given a protein structure• Build model of binding siteØ Construct molecule that fits model
Outline
Virtual drug screening• Ligand-based
§ 2D structure matching§ 3D structure matching§ QSAR
De novo drug designØ Models
§ Simulation§ Knowledge-based
• Construction algorithms§ Incremental construction§ Fragment-based§ Stochastic optimization
Binding Site Models
Example courtesy of Joe Corkery
Binding Site Models
Simulation• e.g., GRID
Knowledge-based• e.g., X-SITE
C3Dry
N1 OPredicted
Binding SiteModel for
1kp8-1-H-ATP-1-_using GRID
[Goodford85]
Outline
Virtual drug screening• Ligand-based
§ 2D structure matching§ 3D structure matching§ QSAR
De novo drug design• Models
§ Simulation§ Knowledge-based
Ø Construction algorithms§ Incremental construction§ Fragment-based§ Stochastic optimization
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Incremental Construction
2. Add reactants (substituents)
4. Minimize & Score
O
NNH2
O
1. Docking scaffolds
O
NNH2
O
NH2
O
NNH2
O
O
NNH2
O
3. Find conformation
Slide courtesy of Bill Welsh
Fragment-Based Methods
Slide courtesy of Bill Welsh
Linking
Building
Protein
Fragments
Stochastic Optimization
Monte Carlo search of chemical space:• Start from initial drug• Make random state changes
(add/delete/move chemical group)• Accept up-hill moves with probability
dictated by “temperature”• Reduce temperature after each move• Stop after temperature gets very small
Summary
Virtual drug screening• Ligand-based
§ 2D structure matching§ 3D structure matching§ QSAR
De novo drug design• Models
§ Simulation§ Knowledge-based
• Construction algorithms§ Incremental construction§ Fragment-based§ Stochastic optimization
Discussion
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