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PI3-‐Kinases: An Example of Holo-‐WaterMap Modelling
Daniel D. Robinson
INTRODUCTION – PERPLEXING SAR AND STRUCTURAL DATA
Perplexing SAR: Stereochemistry Controls AcKvity and SelecKvity
Compound ID R1 R2 PI3K-‐β (nM) PI3K-‐δ (nM) SelecKvity
(Fold)
A H H 4 28 7
B(U) H Me 23 468 20
B(D) Me H 6 6 1
C Me Me 102 580 6
D(U) H Et 135 1233 10
D(D) Et H 10 11 1
E(U) H cPr 381 1610 4
E(D) cPr H 2 2 1
F(U) H iPr 615 1231 2
F(D) iPr H 4 1 0.25
G(U) H Ph 470 1984 4
G(D) Ph H 4 5 1
The SAR shows two clear trends: • The ‘up’ (U) stereochemistry
is consistently weaker on both β and δ isoforms
• The (U) stereochemistry shows moderate selecTvity for PI3K-‐β, at least for the smaller R-‐groups
Structural Data• Crystal-‐structures of B(U) bound to PI3K-‐β/δ were available – These structures showed that the criTcal subsTtuents made few, if any, direct interacTons with their respecTve host proteins
• If a direct interacTon could not explain the SAR, maybe a solvent effect could?...
Prepared PI3K-‐β/δ crystal structures
ENTRIES: PREPARED CRYSTAL STRUCTURES VIEW: DEFAULT VIEW
HOLO-‐WATERMAP ANALYSIS
Why Holo-‐WaterMap Analysis? • Classical apo-‐WaterMaps concern themselves with the ligand-‐induced desolvaTon of the host pocket – As such it considers the water-‐molecules that are no longer present in the pocket
• Holo-‐WaterMap analysis does the opposite – It focuses on the effect of the ligand on the residual solvaTon – Depending on the ligand and the binding-‐site, the ligand may stabilise or destabilise the residual water-‐molecules • Forming a bridging interacTon may well stabilise a water-‐molecule • Trapping a water-‐molecule between the ligand and the protein is likely to lead to significant destabilisaTon
Aside: GeneraKng Holo-‐WaterMaps – From the GUI
• Holo-‐WaterMaps can be generated immediately from the WaterMap GUI panels – Place the structure of interest into the Workspace
– Bring up the: WaterMap-‐>Perform Calculations Panel
– ‘Pick’ the ligand to instruct WaterMap where to concentrate its efforts
– Select the ‘Retain ligand’ opTon – Deselect the ‘Truncate protein’ opTon
Aside: GeneraKng Holo-‐WaterMaps – From the Command Line
• The calculaTon of holo-‐WaterMaps is beder performed from the command line. – The uTlity $SCHRODINGER/utilities/create_wm_job makes this quite easy
• Firstly, this uTlity allows us to script the creaTon of many holo-‐WaterMap jobs – Useful when there are a number of ligands
• Secondly this uTlity gives us access to the –extended_gcmc opTon – This opTon more exhausTvely explores the solvaTon surrounding the ligand • This is parTcularly useful for holo-‐WaterMaps as the presence of the ligand frequently generates regions where it is difficult to equilibrate bulk water
Holo-‐WaterMap: PI3K-‐β A • The holo-‐WaterMap of PI3K-‐β complexed to ligand A reveals a very interesTng looking water-‐molecule – This water is ‘trapped’ between the ligand and the ‘base’ of the acTve-‐site
• The posiTon of the water-‐molecule is such that it would be influenced by the R1/R2 subsTtuents of the ligands
Holo-‐WaterMap: PI3K-‐β B(U) + D(U)
Holo-‐WaterMap PI3K-‐β B(U)
The same water-‐molecule is found in the complexes of the other ligands.
Holo-‐WaterMap PI3K-‐β D(U)
The general trend is that the water-‐molecule’s energy increases with the R-‐group’s size.
Holo-‐WaterMap: PI3K-‐δ A • The same water-‐molecule is found in the complex of PI3K-‐δ with ligand A
Holo-‐WaterMap: PI3K-‐δ B(U) + D(U)
Holo-‐WaterMap PI3K-‐δ B(U)
We see the same trend of increasing water-‐energy with R-‐group size.
Holo-‐WaterMap PI3K-‐δ D(U)
However, PI3K-‐δ appears to be a bit more sensiTve than PI3K-‐β, with greater instability
for a given R-‐group.
Intermediate Conclusions • The greater acTvity of the X(D) compounds vs. the X(U) compounds appears to be explained by a single water-‐molecule – The X(D) compounds all displace this water-‐molecule – The X(U) compounds acTvely trap the molecule between themselves and the ‘base’ of the acTve-‐site
• Increasing the size of the subsTtuent of the X(U) compounds traps the water-‐molecule even more – This increases the energy of the water-‐molecule, broadly accounTng for the loss in potency
– The trapping effect appears to be more significant in PI3K-‐δ than β, this may account for the observed selecTvity
SEMI-‐QUANTITATIVE HOLO-‐WATERMAP ANALYSIS
Aside: The Origins of Holo-‐WaterMap Analysis The first study to use holo-‐WaterMaps for ligand scoring was reported by Snyder et al.* They studied a series of ligands bound to CA-‐II and correlated the
WaterMap-‐results with ITC
CA-‐II is very rigid and the hydraTon-‐paderns around the ligand were highly-‐conserved. In this case it
was easy to idenTfy corresponding hydraTon-‐sites in pairs of holo-‐WaterMaps and sum the changes
in water-‐energies
*Snyder et al. PNAS, 2011, 108 (44), 17889–17894
ConKnuous-‐WaterMaps and Holo-‐WaterMap • In general, finding corresponding hydraTon-‐
sites between two WaterMaps is not easy – Frequently the changes between WaterMaps are
too great – Manually pairing up hydraTon-‐sites between
WaterMaps is highly tedious and error prone – There are also occasional ‘edge effects’
• A subtle movement of hydraTon-‐sites can mean that the N-‐closest sites to one ligand are not the N-‐closest sites to another
• ConTnuous-‐WaterMaps have none of these problems – A point (x,y,z) in one conTnuous-‐WaterMap is
comparable to the same point in a second conTnuous-‐WaterMap • All that is required is a good alignment
– Defining the region of interest is much easier • A simple ‘distance from the ligand’ can be used, rather
than having to make judgement calls on what hydraTon-‐sites should and shouldn’t be included
Aside – The GeneraKon of ConKnuous WaterMaps • The generaTon of conTnuous WaterMaps follows basically the same procedure, however the clustering stage is removed and replaced with a set of calculaTons on a high-‐resoluTon grid (0.5Å) covering the region of interest: – The thermodynamic properTes are calculated for each lamce point
• The enthalpies are averaged over water-‐molecules found inside each cell • The entropies are calculated for all water molecules within a 1Å sphere, centered on each lamce point – 30o bins are used for rotaTonal entropy calculaTon
CreaKng a Custom Analysis Script for Holo-‐WaterMap Analysis
• The conTnuous-‐WaterMap data is saved as a 3D-‐array in a .CNS format file (stored within the output .zip archive of a WaterMap calculaTon) – The Schrödinger Python API has a specific
module dedicated to loading/creaTng, manipulaTng and saving such data • This module is known as schrodinger.analysis. visanalysis
• In addiTon to being useful for analysing conTnuous-‐WaterMap data, the module can also be used to process data such as Jaguar ESP/electron-‐density informaTon and SiteMap grids – The API’s documentaTon gives several
examples of its use
schrodinger.analysis.visanalysis
volumedata
Implements the VolumeData class, which represents 3D-‐volume information e.g. SiteMap grids, electrostatic potentials, ‘densities’
volumedataio
Allows VolumeData objects to be created from various file formats. Permits VolumeData objects
to be saved to new .vis and .ccp4 files.
volumedatautils Various utility functions. Includes, bounding-‐box calculations, interpolation and fast grid-‐
point location facilities.
Aside: The Underlying Code Load .CNS file containing the conTnuous holo-‐WaterMap. This returns a VolumeData instance: holoWM = volumedataio.LoadCNSFile(filenameWM)
Load the molecule that defines the integraTon volume. molecule is an instance of the Schrödinger structure class: reader = structure.StructureReader(filenameStr) molecule = reader.next()
Locate the points in the holo-‐WaterMap that are within X(Å) of the nearest ligand atom. The DataPointLocator class allows for fast distance-‐based lookup: dpl = volumedatautils.DataPointLocator(holoWM) for at in molecule.atom: dpl.SearchForDataPointsWithin(
[at.x, at.y, at.z], integrationRadius) dpl.UniquifyResults()
Perform the integraTon (summaTon): acc = 0.0 for x, y, z in dpl.Results: acc += holoWM.getData()[x][y][z] print “Accumulated WM = %f” % acc That’s it!
Aside: Actually Running the Custom Code • Running custom code is very easy: – Firstly we ensure that the custom code can be found
• Saving it in the working-‐directory is a good idea • AlternaTvely, add it to the PYTHONPATH
– Extract the conTnuous-‐WaterMap .CNS file from the main WaterMap .zip archive
– Invoke the custom code using the standard $SCHRODINGER/run command: $SCHRODINGER/run INTEGRATE_WM.py <continuousWM.cns> <ligand.mae> <integration radius>
– The results are simply printed on stdout
Results
PI3KβR² = 0.7447
PI3KδR² = 0.8047
-13
-12
-11
-10
-9
-8
-7 12 14 16 18 20 22 24 26 28 30 32
ΔG B
ind (kcal/m
ol)
∫EnergyH2O (arbitrary units) Results
The results from the conTnuous-‐WaterMaps show a very nice
(semi-‐)quanTtaTve relaTonship with the observed acTviTes.
That we can explain 75% and 80% of the variance of binding-‐energy on PI3K-‐β and δ respecTvely is quite
remarkable given that this approach uderly neglects any effects of
protein-‐ligand interacTons and/or ligand strain terms. It does however
underline the importance of solvaTon-‐effects on fully understanding SAR.
A Possible Structural Basis
PI3K-‐β and PI3K-‐δ Water-‐Networks
Holo-‐WaterMap PI3K-‐β B(U)
We can readily trace a water-‐network from 856Asp to the main high-‐energy water-‐molecule. The
orientaTons of the waters and their separaTons make for a convincing network.
Holo-‐WaterMap PI3K-‐δ B(U)
A similar network exists in PI3K-‐δ between the high-‐energy water and 836Asn. The network is visually convincing, but
doesn’t appear as Tghtly bound. Something that is borne out by the enthalpy/entropy values calculated by WaterMap.
Examining the Water-‐Network with Pure Molecular-‐Dynamics
• The water-‐networks derived from the WaterMap results are visually quite convincing
• We can further invesTgate these water-‐networks via a normal MD-‐simulaTon* – An unrestrained simulaTon removes the necessary, but arTficial, restraints created by WaterMap and allows us to esTmate the water-‐structure in the presence of general thermal-‐moTons
• The water-‐density can visualised by a simple script – The reliability of this ‘mean water-‐density’ is enTrely dependent on the alignment of the various snapshots • Bad alignments and/or excessive flexibility makes a simple spaTal-‐averaging somewhat ill-‐defined
– In this case things aren’t too bad
*20ns, 1atm, 300K, NPT simulaTon
MD-‐Derived Water-‐Density
MD-‐Derived Water-‐Density PI3K-‐β B(U)
The MD-‐derived water-‐network is in good agreement with the PI3K-‐β results from WaterMap. A slight difference in the
MD-‐results is that 852Glu has moved to assist 856Asp in forming the network to the trapped water-‐molecule.
MD-‐Derived Water-‐Density PI3K-‐δ B(U)
The MD and WaterMap results also broadly agree in the case of PI3K-‐δ. However, MD suggests a diminished role for 836Asn in supporTng the water-‐network. Leaving 832Asp to act as its anchor. The network is clearly more sparse than in PI3K-‐β.
60%
70%
80%
%Occ
WRAPPING UP
Acknowledgments Many thanks to: The team at Sanofi: Thomas Bertrand, Jean-‐Christophe Carry, Frank Halley, Andreas Karlsson, Magali Mathieu, Hervé Minoux, Marc-‐Antoine Perrin Benoit Robert and Laurent Schio
Schrodinger: Woody Sherman
