In-silico screening without structural comparisons: Peptides to non-peptides in one step Maybridge...
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Transcript of In-silico screening without structural comparisons: Peptides to non-peptides in one step Maybridge...
In-silico screening without structural comparisons:
Peptides to non-peptidesin one step
Maybridge Workshop 23-24 Oct ‘03Bregenz Austria
Founded in November 2001Funding by The Wellcome Trust
Cresset Biomolecular Discovery
Virtual Screening
Virtual screening is the process of trying to find biologically-active molecules using a computer
Protein-based (X-ray, docking) Need a protein structure Problems with scoring functions
Ligand-based Structural similarity Not specific enough
The Science Problem
The Problem is that: There is no logical way to change Structural
Class and retain Biological Activity
Since we know that: Different structures can give the same
biological effect
Then the Answer is to: Define what it is that the target actually sees
if not structure
Fields, XEDs and FieldPrints
Fields A new method of describing molecular
properties
XEDs A new molecular modelling approach
FieldPrints A new virtual screening method
Fields
Chemically different, biologically similar molecules have a similar electron cloud.It is this that is seen by the target
Can we use a representation of that electron cloud to explore molecules’ biological properties?
Fields represent the key binding information contained in the electron cloud
COX-2 Inhibitor
N
SH2NO
O
N
F FF
Br
COX-2 Inhibitor
COX-2 Inhibitor
COX-2 Inhibitor
COX-2 Inhibitor
R. P. Apaya, B. Lucchese, S. L. Price and J. G. Vinter, (1995), J. Comp-Aid. Mol. Design, 9, 33-43.
The Field Template for a COX-2 Inhibitor
ACCs get Fields Wrong
Without a good description of atoms, the field points are incorrect!
R. P. Apaya, B. Lucchese, S. L. Price and J. G. Vinter, (1995), ’The matching of electrostatic extrema: A useful method in drug design? A study of phosphodiesterase III inhibitors’, J. Comp-Aid. Mol. Design, 9, 33-43.
Atom-centred charges
O
+0.16
-0.34
-0.05 -0.05
Fields from ACC’s
XEDs make Fields work
The Field Points from XED agree well with those obtained from Quantum Mechanics
Vinter & Trollope 1994 unpublished.
ACCs XEDs
C O
-0.5
-0.5
-0.5
-0.5
-0.5
-1.75
-1.75
+5
+1
eXtended Electron Distributions
H -0.1+0.1
H
-0.5
-0.5
+0.9+0.1
J. G. Vinter, (1994) ‘Extended electron distributions applied to the molecular mechanics of intermolecular interactions’, J Comp-Aid Mol Design, 8, 653-668.
The XED force field improves the description of electrostatics by extending electrons away from the nucleus
O
+0.16
-0.34
-0.05 -0.05
XEDsACCs
XEDs Model Life Better
X-ray structure of Benzene
Benzene docked onto Benzene using XEDs
Benzene docked onto Benzene using ACCs
Aromatic-Aromatic Interactions
GSK (SKF) “Azepanone-Based Inhibitors of Human and Rat Cathepsin K”, J. Med. Chem. 2001, Vol. 44, No. 9
Aromatic-Aromatic Interactions
XEDs - Summary
A much better treatment of electrostatics
o Simplified force field
o Hydrogen bonding
o Anomeric and gauche effects
o Aromatic-aromatic interactions
+
1rd7 + 1ra3Crystal Structures
=
=
+
Fields direct ligand binding mode
Dihydrofolate Reductase
Fields - Summary
Protein’s eye view
Represent “electron cloud” NOT structure
Distillate of important binding information
Peptide/Steroid/Organictreated identically
J. G. Vinter and K. I. Trollope, (1995). ‘Multi-conformational Composite Molecular Fields in the Analysis of Drug Design. Methodology and First Evaluation using 5HT and Histamine Action as examples’, J. Comp-Aid. Mol Design, 9, 297-307.
Virtual Screening with Fields
If field points are describing the ‘binding properties’ of molecules:
Can they be used for virtual screening?
Can we construct a fast & accurate way of searching a Field Database?
FieldPrint™ Search Method
SOO
N
NH2ClNHN
N
SHN
N
O
SO
O
NH
O NH2Cl
Cl O
N
NS NH2
Cl
NO
~125,000,000
101101100001111011001… 0010100100101…
The current database contains 2,500,000 commercially available compounds
50 conformations stored for each compound (125,000,000 conformations)
Results consist of similarity score for whole database
Hits can be filtered (e.g. supplier, MW, Lipinski etc.)
The Database
Refinement
The FieldPrint search ‘front-loads’ the database
We refine the FieldPrint results by performing true 3D field overlays
Overlays are usually performed on the top ~10-20% of the database (ranked by FieldPrint score)
Results are expressed as a field similarity
The 3D Field Overlay Principle
Fields – Examples
PPACK
D-Phe-Pro-Arg-CH2Cl
O
NN
O
NO
N
NN
NS
OO
Cl
O
N
N
PEPTIDE to NON-PEPTIDE
FieldPrint™ Performance
Thrombin (49 Spikes) PPACK (D-Phe-Pro-Arg-CH2Cl)
Retrieval of known inhibitors (spikes) from 600,000 compounds
0
20
40
60
80
100
0 20 40 60 80 100
% spikesfound
% ranked database screened
FieldPrint™ - Thrombin Spikes
NO N
HN
O
O
NS
HN
HN NH2NH2
N
H2N NH
3 From BMCL_8_3409
S
HO
X
RN
O
9 from JMC_43_649
NY
ONH
N
RN
SO O
9 From JCAMD_13_221
N
O
SNH
O O O
NHn
N
NH2
3 from BMCL_8_817
2 from BMCL_8_1697
O
N
NH2
Ph / H
H2NN
OO N
H
Cl
Cl
4 from JMC_41_1011
4 from JMC_40_830
N
OO N
H
NH2Ph / H
SN
O O
O
O
O
N
HN
NH2HN
NHHN
SOO
X
7 from JMC_42_4584
O
N
NHHN
SOO
X
N
S
7 From BMCL_10_1563
HIV NNRTI (52 Spikes)
FieldPrint™ Performance (2)
COX-2 Inhibitors (32 Spikes)
Retrieval of known inhibitors (spikes) from 600,000 compounds
0
20
40
60
80
100
0 20 40 60 80 100
0
20
40
60
80
100
0 20 40 60 80 100
Validation
James Black Foundation (JBF) funded by Johnson&Johnson
GPCR target Exhausted Medicinal Chemistry of current series.
Molecule in clinical development Back-up series required Two active diverse molecules available for template
3 Month deadline Commission mid-August 2002. Generate and search database. Supply list of
compounds by mid-October 2002. Results returned early December 2002
FieldPrint™ Validation
A GPCR (43 Spikes) Distilled to 1000 Compounds
Visual inspection to 100
88 Purchased and tested
27 had pKb > 5 (better than M)
4 had pKb > 6 (better than 1M)
No structural similarity to any known actives.
MW range 350-600
Collaboration with the James Black Foundation
0
20
40
60
80
100
0 20 40 60 80 100
Intelligent Lead Discovery
Change structural class [e.g. peptides to non-peptides, steroids to non-steroids]
As well as proteases, kinases (X-ray information)
we can;handle poorly defined targets [e.g. GPCRs,
Ion Channels]
because;no protein data is necessary
andminimal ligand 2D data is required
Where can Cresset be used?
Fast and flexible lead finding for new programs allowing multiple starting points for medicinal chemistry programs
Lead switching on existing programs
Patent busting
Moving away from ADMET problems
Finding back up series
Why should Cresset be used?
BO
NN
O
NO
N
NN
NS
OO
Cl
O
N
N
A
Diverse Structural Classes with Same Function
Peptide to non-peptide
Much more cost effective than HTSHTS 2,500,000 molecules @ £1 per molecule Cresset distils this to just a few hundred!
Significantly faster than conventional routesCresset could go from A to B in weeksMerck took 3 (?) years with 10 (?) Medicinal Chemists!
Cost in Time and Money
Acknowledgements Cresset
Dr J. G. Vinter Dr T. J. Cheeseright Dr M. D. Mackey Dr Sally Rose (consultant)
James Black Foundation (KCL, JnJ sponsored)
Prof. C. Hunter (Sheffield University)
The Wellcome Trust
Intelligent Lead Discovery