IV Jornada del GRIB

Computer-assisted
Drug Discovery
Ferran Sanz
Unitat de Recerca en Informàtica Biomèdica (GRIB)
IMIM, Universitat Pompeu Fabra
Barcelona
www.imim.es/grib
What does it take to discover a new drug?
9 12 years average drug development timeline.
9 Total cost over 800 million $.
www.imim.es/grib
IT tools along the Drug Life Cycle
Target
identification
Computational
genomics
Target
validation
Virtual
screening
Systems biology
FEATURES
Identification
& obtention of
compounds
Structure-based
drug design
Preclinical
Clinical
assessment
trials
Postmarketing
monitoring
ADMET
prediction
Biosimulation
BENEFITS
Stardardisation and interoperability
Knowledge management and integration
www.imim.es/grib
www.imim.es/grib
Raw genomic data
www.imim.es/grib
Gene identification and annotation
www.imim.es/grib
Gene identification and annotation
By:
signal identification
sequence comparison
www.imim.es/grib
Comparative genomics
www.imim.es/grib
Protein sequences and protein structures
QIKDLLVSSSTDLDTTLVLVNAIYFK
GMWKTAFNAEDTREMPFHVTKQESKP
VQMMCMNNSFNVATLPAEKMKILELP
FASGDLSMLVLLPDEVSDLERIEKTI
NFEKLTEWTNPNTMEKRRVKVYLPQM
KIEEKYNLTSVLMALGMTDLFIPSAN
LTGISSAESLKISQAVHGAFMELSED
GIEMAGSTGVIEDIKHSPESEQFRAD
HPFLFLIKHNPTNTIVYFGRYWSP
www.imim.es/grib
Protein interaction networks (systems biology)
www.imim.es/grib
Prediction of protein structures
„
Relatively few 3D structures of proteins
experimentally known:
2001
Estimation for
2005
No. sequences
Known
600,000
millions
No. 3D-struct.
exp. Known
14,000
~40,000
Source: Lecture of A. Sali, 2001
⇒ Need of prediction of protein structures:
• “ab initio” prediction
• homology modelling
www.imim.es/grib
Homology modelling of cytochrome P450 1A2:
sequence alignment
www.imim.es/grib
Homology model of cytochrome P450 1A2
www.imim.es/grib
Computer-assisted drug design
Direct
CADD
Indirect
CADD
(similarity)
(complementarity)
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Structure-based drug design
www.imim.es/grib
Indirect CADD: CYP1A2 substrates
O
N
H3 C
N
H
O
CH3
O
7-ETHOXYRESORUFIN
PHENACETIN
O
N
H3C
N
NH2
N
CH3
MeIQ
CH3
CH3
O
O
O
CH3
N
N
HOOC
N
N
N
N
CH3
CAFFEINE
NH
O
F
ENOXACIN
www.imim.es/grib
Molecular Interaction Potentials (MIP)
probe
• Useful tool to study and
compare molecular interaction
features
• Definition: Interaction
energies of the studied
compounds with relevant
molecular probes placed
around them
• Probes: H+ (MEP); NH3+; OH;
CH3; etc.
• Usually computed in grids of
points around the compounds
Energy
Compound
studied
• Usually plotted by means of
isopotential surfaces
www.imim.es/grib
MEP distributions of CYP1A2 substrates
7-ETHOXYRESORUFIN
PHENACETIN
CAFFEINE
MeIQ
ENOXACIN
www.imim.es/grib
CYP1A2 MEP-based pharmacophore
MEP MINIMUM
OXIDATION
SITE
2.
23
.1
-7 .
4
.
6
Å
5Å
HETEROCYCLIC
SYSTEM
MEP MINIMUM
Same plane
www.imim.es/grib
www.imim.es/grib
Issues to be considered in molecular library sampling
„
Selection of the relevant molecular descriptors
„
Characteristics of the descriptors space
In general, the descriptors are not independent and,
consequently, there is a need of avoiding redundancy
„
Sampling methodology
www.imim.es/grib
Information redundancy
Descriptors
MW
Descriptors
DM
MW MVol DM
Compound 1
Compound 1
Compound 2
Compound 2
Compound 3
Compound 3
……
……
Compound n
Compound n
The same
information
(approx.)
is considered
twice
Reduced
relative
weight
Different relative distances between the compounds !
www.imim.es/grib
Principal Components Analysis (PCA)
x2
Series of compounds originally
described by 3 variables (x1, x2 and x3),
perhaps including redundant information
PC2
PC1
x1
x3
PC2
PC1
Description of the compounds using a reduced
number of new variables (PC1 and PC2) that are
independent (not redundant at all) and retain the
major part of the variability between the compounds
www.imim.es/grib
Issues to be considered in molecular library sampling
„
Selection of the relevant molecular descriptors
„
Characteristics of the descriptors space
In general, the descriptors are not independent and,
consequently, there is a need of avoiding redundancy
„
Sampling methodology
www.imim.es/grib
Sampling methodologies
„
„
„
Randomly (“lottery”)
Systematic sampling on a molecular descriptors or PCA
space
More sophisticated methods (k-means, MDC)
Exemplified by the selection a maximum diversity sample of 50
amines from the 923 available in the Aldrich catalogue (to be
introduced as substituents on a scaffold)
www.imim.es/grib
Simple random sampling
aa
a
aa
a
aa
aaa
a
aaa
aaa
aa
a a
a
a
a
MolProp2
a
a
MolProp3
MolProp1
• Too low presence of "extreme"compounds
• Non-optimal drop in diversity
www.imim.es/grib
Factorial sampling
a
a
a
a
MolProp2
a
a
a
MolProp3
a
a
MolProp1
• Too high presence of "extreme" compounds
www.imim.es/grib
k-means clustering method
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k-means clustering method
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www.imim.es/grib
k-means clustering method
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k-means clustering method
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www.imim.es/grib
k-means clustering method
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www.imim.es/grib
k-means clustering method
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www.imim.es/grib
k-means clustering method
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www.imim.es/grib
k-means clustering method
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... until stability
www.imim.es/grib
k-means clustering method
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www.imim.es/grib
Library vs. sample score plots
www.imim.es/grib
k-means clustering method
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www.imim.es/grib
k-means clustering method
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www.imim.es/grib
Using different seeds for clustering
www.imim.es/grib
Descubriendo nuevas dianas: El caso de los GPCR
„
„
„
Proteínas de membrana relacionadas con los
procesos de transducción de señal (transferencia de
información de fuera a dentro de las células)
Más de la mitad de los medicamentos actúan en
GPCRs
Gran familia de proteínas (2001):
„
4170 secuencias de GPCR descritas (Swiss-Prot + TrEMBL)
„
597 humanas
„
448 completas
„
„
86 "huerfanas", sin función conocida
Véase: http://www.gpcr.org/7tm/
www.imim.es/grib
www.imim.es/grib
GPCR modelling (difficulties)
„
„
„
A single high-resolution (2.8 Å)
3D structure experimentally known:
bovine rhodopsin
High sequence variability
Diversity of ligand binding modes:
www.imim.es/grib
GPCR modelling (common features)
„
„
„
Seven transmembrane α-helices:
Interaction with few and homologous G-proteins in the
intracellular domain
Highly conserved residues in the transmembrane
domain
www.imim.es/grib
GPCR classification
Class A (rhodopsin-like)
Class B
GPCRs
Class C
Class D
Class E
Amine
Peptide
Hormone protein
(Rhod)opsin
Olfactory
Prostanoid
Nucleotide-like:
Purinergic receptors
Adenosine receptors
Cannabinoid
Platelet activating factor
Gonadotropin-releasing hormone
Thyrotropin-releasing hormone &
Secretagogue
Melatonin
Viral
Lysosphingolipid & LPA (EDG)
Leukotriene B4 receptor
Orphan/other
www.imim.es/grib
Adenosine receptors
•
Natural agonist:
•
4 subtipes: A1 ; A2A ; A2B ; A3
•
A1 receptor: Involved in cardiovascular modulation
(infarct) and renal control (diuresis)
www.imim.es/grib
A1AR
mutagenesis
data
www.imim.es/grib
hA1AR modelling
Sequence alignment
Building of each TMH
TMH prediction
Energy minimization
TMH bundle packing
Rhodopsin template
Energy minimization
Molecular Dynamics
Conformation selection
Binding site identification
Ligand docking explorations
www.imim.es/grib
hA1AR modelling: sequence analysis
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Helices building and optimisation
• Standard angles (φ = -59º; ψ = -44º; ω = 180º) for all the residues
except Pro (φ = -71º)
• Molecular mechanics optimisation using AMBER 6 (PARM94 ff)
• Dielectric constant ε = 4r
• 10 Å cutoff
• Constraints in Cα progressively reduced: 50, 25, 10, 5, 0 kcal/mol2
• Minimisation algorithm:
• 500 steepest descent cycles + 500 conjugated gradients cycles for the 50
kcal/mol2 constraint
• 500 conjugated gradients cycles for the rest of constraints
• Until RMS < 0.001 kcal/mol when no constraint is applied
www.imim.es/grib
TMH bundle packing & optimisation
Packing of the 7 TMH bundle
• Superposition on rhodopsin considering Cα trace of
segments defined by highly conserved residues
• Checking of:
• highly conserved H-bond clusters (Asn in TMH1 +
Asp in TMH2 + crystallisation H2O + Asn in TMH7)
• hydrophobicity profiles
• geometry of residues involved in ligand recognition
• side chains bumps
TMH bundle optimisation
• Same as for TMH alone, but keeping force constant = 5 kcal/molÅ2
www.imim.es/grib
bRho crystallographic water molecule
• bRho: crystallographic water molecule between highly conserved residues
• Incorporation of a crystallographic water molecule on the hA1AR model:
www.imim.es/grib
Molecular dynamics of the TMH bundle
•
Software: AMBER 6 (PARM94 ff)
•
Dielectric constant ε = 4r
MD analysis:
•
10 Å cutoff
• AMBER 6 (CARNAL)
•
Time: 1000 ps (1 ns)
• Visual inspection (VMD)
•
Temp.: 310 K
• H-bonds / aromatic clusters
•
Time step: 2 fs
• Protonation of TM histidines (δ or ε)
•
SHAKE algorithm
• Clustering of conformers
•
Constrain in Cα: 5 kcal/molÅ2
www.imim.es/grib
Molecular dynamics
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MD analysis: protonation of histidines
H
N
δ
Nε
O
N
δ
O
NH 3 +
Nε
H
O
O
NH 3 +
1st trial: Both histidines protonated in the Nε position: Interaction
between His7.43 and Glu1.39 not detected. Consequently, Nε
protonation in His7.43 not feasible.
2on trial: Both histidines protonated in the Nδ position: Interaction
between His7.43 and Glu1.39 observed, but Nδ protonation in His6.52
forces this residue to be exposed to the lipidic bilayer.
3rd trial: Protonation of His6.52 on Nε and His7.43 on Nδ: Agreement
with experimental data, since His7.43 interacts with Glu1.39, and
His6.52 is oriented toward the inner part of the bundle.
www.imim.es/grib
MD analysis: H-bonds
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PCA/MDC analysis on the MD results
•
PCA using the RMSs of all sidechains
(last 700 ps)
•
Cluster analysis on the PCA space
•
Selection of the conformer for docking
analysis
RMS res. 1
RMS res. 2
RMS res. n
Conf. 1
Conf. 2
Conf. 700
www.imim.es/grib
hA1AR: Location of the binding site
NH2
•
Adenosine = adenine
N
N
•
CH2OH
O
N
N
+ ribose
HO
OH
Ribose docking exploration (GROUP module of GRID):
polar
acidic
basic
www.imim.es/grib
Validation of the ribose binding site
•
Comparison with experimental data (ribose-contraining complexes from PDB)
•
Description of each binding site using GRID Independent Descriptors (GRIND/ALMOND)
•
Comparison of the GRIND correlograms using Hodgkin similarity index
www.imim.es/grib
Adenosine docking
NH2
Done using AUTODOCK 3.0:
7
N
6
N
5
8
•
Receptor rigid, ligand flexible
•
Exploration: Lamarckian genetic algorithm
•
Scoring function: AMBER-like force-field
•
100 independent experiments: clustering on RMS, energetic evaluation
A
O
HOCH2
N9
4
N
3
1
2
5'
OH OH
3'
2'
B
37% population
37% population
∆Gbinding = -10.6 kcal/mol
∆Gbinding = -10.4 kcal/mol
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Adenosina en A1: Simulación de la dinámica molecular
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hA1AR agonists
Bulky:
Alkylamine
(bulky)
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hA1AR agonists
Formula
Compound
A1AR
Ki (nM)
∆G
(kcal/mol)
73a
-9.8
NH2
N
HO
O
HO
N
N
N
ADO
(adenosine)
OH
R6
HN
H
R5’ =
O
N
NECA
(5’-N-ethyl
carboxamidoadenosine )
13.6b
-10.8
N
R5’
O
CPA
R6 =
(N 6cyclopentyladenosine)
R6 =
CCPA
R2 = Cl
(2-chloro-N 6cyclopentyladenosine)
2.25b
-11.9
0.83b
-12.5
a EC Inhibition of adenylate cyclase in rat A AR (Daly et al. Biochem
50
1
b Klotz et al. Naunyn Schmiedebergs Arch Pharmacol (1998); 357:1
HO
N
N
N
R2
OH
Pharmacol (1992); 43:1089)
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Compound
Position A
Position B
∆G
∆G
Rank
Rank
ADO
NECA
CPA
CCPA
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Position A
Position B
∆G
Rank
∆G
ADO
-10.4
1
NECA
-
-
CPA
-10.8
6
CCPA
-10.7
6
Compound
Rank
www.imim.es/grib
Position A
Position B
∆G
Rank
∆G
Rank
ADO
-10.4
1*
-10.6
3*
NECA
-
-
-11.1
1
CPA
-10.8
6
-13.5
1
CCPA
-10.7
6
-12.7
1
Compound
* quasi-degenerated solutions
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www.imim.es/grib
Docking of A1AR agonists
Explanation for structure-affinity relationships:
9 Hydrophobic pocket around R6
9 Steric limitation for R2 and R5’
9 Highly polar binding pocket for the ribose moiety
Leu3.33
Ile7.39
Bulky:
Leu6.51
Thr7.42
His7.43
Alkylamine
R5’
(bulky)
O
HN
R6
Thr3.36
N
N
N
N
R2
Trp6.48
Ser7.46
Ser1.46
HO
OH
Asp2.50
www.imim.es/grib
5-HT2A and 5-HT2C receptors
Involved in psychological disorders, including
depression, mania, anxiety, dipolar disorder and
schizophrenia
www.imim.es/grib
Incorporation of loops
Database searching for similar loops:
•
Same number of residues forming
the loop (±2)
•
Distance between extremes equal
to rhodopsin (±5 Å)
www.imim.es/grib
Docking of serotonin
F339
F340
Mutagenesis experiments on 5-HT2A
D155N
S159A
F339L
F340L
Ki: ↓ 37 fold
Ki: ↓ 18 fold
Ki: ↑ 2.4 fold
Ki: ↓ 27 fold
www.imim.es/grib
Docking of ketanserin
O
Mutagenesis experiments on 5-HT2A
H
D120N
D155N
F339A
F340Y
W76A
W336A
Y370A
Ki: ↓ 10
Ki: ↓ 75
Ki: ↓ 12
Ki: ↓ 73
Ki: ↓ 10
Ki: ↓ 100
Ki: ↓ 20
O
N
+
N
N
O
www.imim.es/grib
F
Docking of QF610B in 5-HT2A
I
Possible docking
hypotheses for:
N O
O
N
F
R
S
(R = H)
IIa
IIb
www.imim.es/grib
Docking of QF610B in 5-HT2A
I
Docking results for:
N O
O
N
F
R
S
(R = H)
IIa
IIb
www.imim.es/grib
Docking of QF610B in 5-HT2A
N O
O
N
F
R
S
Compound
R
pK i 5HT 2A
QF610B
H
8.56
QF620B
n-pentyl
7.68
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Docking of QF620B in 5-HT2A
I
Docking results for:
N O
O
N
F
R
S
(R = n-pentyl)
IIa
IIb
www.imim.es/grib
Docking of QF620B in 5-HT2A
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Current challenges on GPCR modelling
•
Consideration of the structural water molecules
•
Modelling of the receptor loops
•
Modelling of the heterogeneous environment
•
Modelling of the receptor activation processes
•
Influence of ligands in
receptor conformations
•
GPCR dimerisation
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www.imim.es/grib
An
tip
atí sicót
pic ico
os s
GRIND
MIP distributions are automatically simplified, obtaining the most favorable
regions for the interaction with the considered probe
NH
GRIND
Distances between the most favorable interaction regions (and the
corresponding energy values) are summarized in spectra-like plots called
"correlograms”
Correlograms allow the alignment-free comparison of compounds
NH
distance
GRIND
Usually, several correlograms are obtained for each compound, using
several probes or pairs of probes
Correlograms are
stored in a
data matrix
NH-O=
compounds
NH-NH
Análisis 3D-QSAR
Biological
activity
Compound 1
Compound 2
Compound 3
……
Compound n
Autocorrelogram values
using probe A
Autocorrelogram values
using probe B
Cross-correlogram values
(probe A vs. probe B)
Y
X1 X2 …
Xm
Z1 Z2
Zm
Y1
Y2
Y3
…
Yn
X11 X21 …
Xm1
Z11 Z21 …
Zm1
X1n X2n …
Xmn
Z1n Z2n …
Zmn
Análisis
estadístico
PLS
Actividad = f (características estructurales)
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Modelo 3D-QSAR para antagonistas 5HT2A
(n=52; LV=2; r2=0.85; q2=0.74)
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Consistencia entre modelado directo e indirecto
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Pharmacogenomics vs. Pharmacogenetics
Pharmacogenomics is the application of genomic approaches and
technologies to the identification of drug targets.
Pharmacogenetics is a subset of pharmacogenomics which uses
genomic/bioinformatic methods to identify genomic correlates, for
example SNPs (Single Nucleotide Polymorphisms), characteristic of
particular patient response profiles and use those markers to inform the
development and administration of therapies. (bioinformatics.org)
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Estrategias farmacogenómicas y farmacogenéticas
„
Durante el desarrollo del fármaco:
„
„
Descubrimiento de nuevas dianas.
Consideración de los polimorfismos genéticos:
„
„
™
„
Diseñando fármacos apropiados para grupos de individuos con
ciertas características genéticas ("druglets").
Seleccionando dianas y estructuras de fármacos no (o poco)
afectados por los polimorfismos genéticos ("best-in-class
drugs").
Hay que caracterizar genéticamente a los sujetos que
participan en ensayos clínicos.
Después de la comercialización:
„
„
Seleccionando el medicamento y la dosis más apropiados
para el perfil genético del paciente.
Relacionando efectos indeseables con características
genéticas.
www.imim.es/grib
Pharmacogenetic approaches
„
Pre-market approaches:
„
„
Target discovery.
Consideration of genetic polymorphisms in drug
design:
„
„
„
Designing relevant drugs for genetically defined
groups of people ("druglets").
Selecting targets and drug structures not (or
less) affected by genetic polymorphisms ("bestin-class drugs").
Post-market approaches:
„
„
Selecting drug and dosage most relevant for
the genetic profile of the patient.
Reporting side-effects with genetic information.
www.imim.es/grib
Pharmacogenetic approaches: "Druglets"
„
„
„
„
„
Theoretically attractive but …. Is it realistic?
If the development of a new drug implies >10
years and >500€ (a cost that could increase
because of the genetic studies during the clinical
trials).
If it is extremely expensive to develop a drug for
the whole population (without taking into account
the inter-individual variability)….
Is it realistic/economically affordable/payable to
develop a different drug for every genetically
distinct subpopulation?
More realistic approach: Reconsider as potential
"druglets" drugs previously discarded by their sideeffects in genetically characterized subpopulations.
www.imim.es/grib
Molecular modeling in pharmagenetics
Comparative modeling of:
„
proteins
„
protein-protein complexes
„
ligand-protein complexes
„
particularly, substrate-enzyme
complexes
considering the genetic variability
www.imim.es/grib
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Pointing to the most
effective drug target
sites taking into
account the genetic
variability
(Maggio et al.
Tibtech 2001;19: 266-272)
www.imim.es/grib
www.imim.es/grib
Single Nucleotide Polymorphisms (SNPs) database
snp.cshl.org
www.imim.es/grib
www.pharmgkb.org
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How are drugs discovered today?
•
•
•
•
Direct costs
Opportunity costs
Time
etc.
9 Multidisciplinary teams
9 Different organisations involved
9 Geographically scattered
9 Using different platforms
Slow,
inefficient
process
9 Strict security requirements
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Telecolaboración en I+D de medicamentos: Link3D
www.imim.es/grib
Telecolaboración en I+D de medicamentos: Link3D
www.imim.es/grib
Telecolaboración en I+D de medicamentos: Link3D
Características principales:
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Proyecto financiado por la Unión Europea.
Basado en una investigación exhaustiva de las necesidades de
usuario y pruebas en entornos reales.
Software diseñado para remplazar y suplementar reuniones físicas.
Incorpora audio y moderación avanzada. Permite de 2 a 10
participantes.
Cumple los estrictos requerimientos de seguridad y confidencialidad
de la industria farmacéutica. Autentificación basada en passwords y
certificados. Encriptado de los datos transmitidos.
Acepta la mayoría de los formatos de objetos gráficos (secuencias de
biopolímeros, estructuras 3D de biomoleculas, fórmulas moleculares,
imágenes biológicas, etc.).
Funciones avanzadas para la manipulación de los objetos
compartidos (cambio de formato de visualización, marcas, etc.).
Multi-platforma (MS-Windows, Linux, SGI-IRIX).
Bajos requerimientos de ancho de banda.
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Efectos esperados de la telecolaboración con Link3D
Classical working practice
GOAL
time
GOAL
time
Proposed working practice
Classical meeting
Scheduled virtual meeting
Informal virtual meeting
www.imim.es/grib
Agradecimientos
9
Dr. Manuel Pastor
9
Dr. Hugo Gutiérrez de Terán
9
Cristina Dezi
9
Fabien Fontaine
Universidad de Santiago de
Compostela
AlmirallProdesfarma
www.imim.es/grib