Computational Medicinal Chemistry Approaches for GPCR ... · Computational Medicinal Chemistry...
Transcript of Computational Medicinal Chemistry Approaches for GPCR ... · Computational Medicinal Chemistry...
O
ClCl
O
N
NH
O
OH
O
N
Cl
Computational Medicinal Chemistry Approaches for GPCRStructure-Based Drug Discovery
© 2019 Heptares Therapeutics Limited
Disclaimer: Sosei Heptares is a trading name. Sosei and the logo are Trade Marks of Sosei Group Corporation, Heptares is a Trade Mark of Heptares Therapeutics Limited. StaR® is a Trade Mark of Heptares Therapeutics Limited
Juan Carlos Mobarec, Conor G. Scully, Lydia Siragusa*,Francesca Deflorian, Robert T. Smith, Alicia Higueruelo, Jonathan S. Mason, Miles Congreve, Chris De Graaf
Lemborexant
Suvorexant
EMPA
dual Ox1/OX2
dual Ox1/OX2
OX2 selective
Targeting lipophilic hotspots and unhappy water sites determine
ligand binding
Trapped unhappy water in Ox1
2.8 log units selectivity from trapped water, ligand would appear to dock fine if water energetics not considered
OX2/Ox1 selectivity:
Binding pocket Ox1 (A3x33) > Ox2 (T3x33)
Examples
Pharmacophore?
Unpublished in-house structures Ox1/Ox2, bound to different ligands and with different water networks
WaterFLAP: MIF based water
networks.
WaterMap: MD based hydration site thermodynamics.
C sp3 probe 1 kcal/mol
C sp2 probe -2.8 kcal/mol
Water probe -5 kcal/mol
GRID Probes:
Pseudo apo water G: Energetically very unhappy water in A2A
Ligand perturbated G: Energetically stabilised water in A2A
WaterFLAP water energetics A2A QSAR
Collaboration S. Cross, G. Cruciani
Bortolato, Mason et al. (2018) Methods Mol Biol
J. Christopher et al., Unpublished
Lipophilic hotspots & water networks in OX1,OX2 and A2a SBDD from hit-ID to clinical trials: Case A2a antagonist
Partnered with:
MonotherapyAZD4635
(A2a antagonist)
Combination with oleclumab(anti-CD73)
Combination with durvalumab(anti-PD-L1)
• SBDD used to re-engineer a virtual screening hit series to target a lipophilic hotspot deep in the pocket, leading high LE
and LLE drug candidates
• Atom by atom optimisation: ligand efficiency (LE)
• Design polar contacts: control lipophilicity (LLE)
• Multiple structures: receptor flexibility & selectivity
• Druglike properties: In vivo efficacy & safety
Langmead (2012) J Med Chem; Congreve (2012) J. Med. Chem
FEP+ based binding affinity prediction for ligand optimization
Ligand/protein tautomer/protonation state
In-house structures of GPCR target with different ligands
C
C
C
C
Linker changesc. x3 Oral Bioavailability
RHSLHS
Pose at 0 nsPose at 20 ns
H278Pose at 0 nsPose at 20 ns
N2566.55
H278
4a
4g
4e
GCMC
Pose at 0 nsPose at 20 ns
A2a - 4a
N256
H2787.43
4a
Pose at 0 nsPose at 20 ns
A2a - 4a
N2566.55
H2787.43
Pose at 0 nsPose at 20 ns
A2a – 4e
N2566.55
H278 Pose at 0 nsPose at 20 ns
A2a – 4e
N2566.55
H278
4e
4g
lacking water
network
4a
4g
4e
No GCMC
R2=0.95
In-house GPCR structures with representative ligand LHS and variable RHS
Ring conformation sampling
FEP+ guided GPCR LO example 1
A2a – 4g A2a – 4g
Alternative residue rotamers
FEP+ guided GPCR LO example 2
Binding site solvation F. Deflorian D. Branduardi
J. Vendome
Structural chemogenomics codification of GPCRome
B
H
G
C
major
D
I
E
J
L
K
minor
A
BioGPS: GRID based identification and comparison of GPCR binding sites across structural GPCRome.
C5aR4156
GPR405706
ECFP4 > 0.4MACCS > 0.8
64 Similar Bioactive Ligands
• Shared pharmacophore features to target GPCR-membrane interface.• Design ideas/rules for conserved GPCR PAM/NAM pockets.
ligands
Lipophilic HB donor HB acceptorLigands
L. Sygura, G. CrucianiComputational chemistry
ON
ClCl NH
O
OH
O
CH3 CH3
OCH3
SBDD in allosteric binding sites: Cases GLP-1R and PAR2
C
Hit 1
GLP-1R pKi 5.0
HTL26005
GLP1 pKi 8.3
MW 527, clogP 5.1
LE 0.31, LLE 3.2
MK-0893
GLP-1R pKi 7.3
GCGR pKi 8.8
HTL26119
GLP1 pKi 7.8
MW 574, clogP 6.4
LE 0.3 LLE 1.4
Clinically studied
GCGR antagonist
Simple novel starting
point for SBDD efforts
GCGR pKi 6.9
Less potent, non-selective
in functional assay
Selective vs GCGR in binding
Less potent as a functional
antagonist
Virtual
Screen
In silico
design
SBDD
NH
O
OH
O
NN
OCl
ClO
NH
O
OH
O
Jazayeri (2016) Nature
GCGR
• Collaboration with AZ, fragment and HTS screening
• Small molecule antagonists inhibit peptide and proteaseactivation of the receptor
• Difficult to optimise in the absence of structuralunderstanding
• Binding site identified in PAR2 X-ray structure
• AZ8838 buried in small binding pocket (TM1-3/7, ECL2)
• X-Chem DNA encoded library technology
• Binding hits confirmed as functional PAR2 antagonists
• AZ3451 binds in novel extra-helical site
• Interaction with PAR2 predominately hydrophobic
• Mechanism of action may be to restrict theconformational inter-helical rearrangement required forPAR2 activation
• PeptiDream DELT focuses on peptide display
• Successful hit generation approach for wide array of targets
• Utilising the PAR2 StaR in collaboration with Heptares Peptidream have identified several series of potent cyclic peptide antagonists of PAR2
• Current efforts to improve potency and stability of these very encouraging peptide lead cpds using SBDD
SLIGKV(model)
AZ3451 (PDB: 5NDZ)AZ8838 (PDB: 5NDD)
cyclic peptide antagonist(unreleased crystal structure)
A. O’ Brien and co-workers 2019PAR2
Cheng (2017) Nature
Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG United Kingdom
*Molecular Discovery Ltd. Middlesex , United Kingdom
A) Major and minor(ancestral/classical TM) binding sites
95% GPCR ligands in PDB target this pocket
B) G protein intracellular binding site (B)
CCR2 (5T1A)CCR9 (5LWE)β2R (5X7D)
C) Sodium pocketBLT1 (5X33)mGlu5 (4OO9; 5CGC; 5CGD; 6FFH; 6FFI)
D) TM3/4/5/EL2
PAF-lipid/OLC*
(5ZKP; 5ZKQ)
E) TM3/4/5/membrane
C5AR (5O9H; 6C1Q; 6C1R)FFAR1 (5KW2, 5TZY)
GCGR (4L6R*; 5EE7; 5XEZ; 5XF1)GLP-1R (5VEX; 5VEW)
F) TM5/6membrane
G) TM1/2/3/IL1/Membrane
H) TM3/4/IL/membrane
FFAR1 (5KW2, 5TZY) EP4 (5YWY; 5YFI; 5YHL)TA2 (6IIU)CCR2 (5T1A; 6GPS, 6GPX)CB1 (5TGZ, 5U09)
I) TM1/7/membrane J) TM1/7/H8/Membrane K) TM1/2/3/MembraneP2Y1 (4XNV)
L) TM3/4/5/membranePAR2 (5NDZ)
An increasing number of cryo-EM and X-ray crystal structures of GPCR-ligand complexes continue to reveal previously unknown ligand binding sites. Furthermore, emerging sets of
GPCR crystal structures of multiple diverse ligands bound to closely related receptors enable a protein-structure based view of how different ligands bind this major drug target class.
From the analysis of GPCR structures we gather several important learnings and repercussions for computational medicinal chemistry design that should be transferable and relevant
for many targets, including: A) The important roles of lipophilic hot spots and water networks as drivers of GPCR druggability, ligand binding, and selectivity. B) Diverse binding modes
of similar ligands across the structural GPCRome. C) Caveats when using pharmacophore-based similarity principles for modeling receptor-ligand complexes with different ligand
chemotypes.
Multiple ligand binding sites on GPCR structures
• GPCR binding sites can be identified with BioGPS, which utilizes GRID probes to locate and characterize protein pockets.• Pockets can be encoded into bitstrings which can be used to compare different pockets and measure similarity (e.g. Tanimoto).• Phylogenetic relationships can be build to compare pockets in the structural GPCRome.
F
Multiple in-house Ox1/Ox2 crystal structures for SBDD:- Multiple ligand binding modes – pharmacophore models- Critical to consider water networks in docking and FEP+- Design opportunities for smaller ligands with improved
properties
GCMC for exhaustive
water sampling in
protein binding pocket
during FEP+ production
Water mediated receptor-ligand interactions in crystal structuresNo direct polar (H-bond) interactions