Using Chemical Shift Perturbations to Study the Conformation of Protein-Ligand Complexes
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Using Chemical Shift Perturbations to Study the Conformation of Protein-Ligand Complexes The J-SURF/SDILICON Approach Guillermo Moyna Department of Chemistry & Biochemistry, University of the Sciences in Philadelphia, Philadelphia, PA 19104-4495 Pfizer Global Research and Development

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Using Chemical Shift Perturbations to Study the Conformation of Protein-Ligand Complexes The J-SURF/SDILICON Approach Guillermo Moyna

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Using Chemical Shift Perturbations to Study the Conformation of Protein-Ligand Complexes

The J-SURF/SDILICON Approach

Guillermo Moyna

Department of Chemistry & Biochemistry, University of the Sciences in Philadelphia, Philadelphia, PA 19104-4495

Pfizer Global Research and Development

November 20th 2003


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  • NMR in Drug Design of Protein-Ligand Complexes

  • NMR-based methods such as SAR-by-NMR, STD-NMR, and Structure-

  • Based NMR Screening (SbN) are successful at finding mM-mM hits when

  • none are available from High-Throughput Screening (HTS).

  • Structures of these hits bound to their  targets are needed to guide the

  • synthesis of higher affinity lead compounds.

  • Structures of complexes are difficult by NMR and/or X-ray, particularly for

  • poor binders. Chemists want to see the structure now…

  • New methods are needed to rapidly generate structures of weak hits

  • bound to their targets.

3D Structure

Determination

of mM-mM

Protein-Ligand

Complexes

CADD

Target

Structures

Leads


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  • Rapid NMR-based Structure Determination of Protein-Ligand Complexes

  • Chemical shift perturbations (Dds) can be used to determine residues

  • affected by ligand binding: Dd maps.

  • Advantages:

    • Very easy to generate and interpret.

    • Exquisitely sensitive to binding (mM).

  • Disadvantages:

    • Poor resolution.

    • Biased by large residues. Small or

    • buried groups are de-emphasized.

  • Can fast/accurate methods based solely on Dd be developed? Two new

  • tools will be discussed:

    • SDILICON: Dds replace/complement NOEs as intermolecular

    • constraints.

    • J-Surfaces: Dds are transformed into ligand spatial localization.


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  • Dd of Protein-Ligand Complexesin 3D Structure Refinement

  • By definition, chemical shifts are indicators of 3D structure. In proteins,

  • Dds (dobs - drcoil) are related to the protein’s 3D fold.

  • To employ Dd data in structure refinement

  • shielding equations are needed. The main

  • contributors are aromatic rings, peptide

  • groups, and charged moieties.

  • For example, effects from ring currents in

  • aromatic rings can be accounted for using

  • the Haigh-Mallion equation:

_

+

+

_

H

ri

i

rj

pH

j


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  • Dd of Protein-Ligand Complexesin Protein/Peptide 3D Structure Refinement

  • Relationships for other anisotropic groups in proteins have been

  • parametrized (Case/Williamson/Wishart). Final equations used in modeling:

  • We have used these to study small peptides (Fmoc-Pro-Pro-Xaa). Limited

  • NOE data, but large Dds (-0.7 to -1.2 ppm):

  • Moyna, G.; Williams, H. J.; Nachman, R. J.; Scott, A. I. J. Peptide Res. 1999, 53, 294.

NOE + Dd

NOE


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  • Dd of Protein-Ligand Complexesin Protein-Ligand 3D Structure Refinement

  • A similar approach can be used to study protein-ligand complexes if

  • certain assumptions are made:

    • Dd perturbations on protein nuclei are caused only by the ligands.

    • Limited conformational rearrangement of the protein upon binding.

    • Ligands have anisotropic groups (aromatic rings, carbonyls, etc.).

  • The first two are to some extent the case with weak (mM-mM) binders.

  • These are usually the hits missed by normal HTS approaches…

  • More than95% of all compounds in the MDL Drug Data Report (MDDR)

  • have aromatic rings.

  • We call the method Shift DIrected LIgand CONformation (SDILICON)

Claritin

Chlortrimeton


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  • Running SDILICON of Protein-Ligand Complexes

  • SDILICON uses protein Dds to optimize the orientation/conformation of the

  • ligand at the binding site. Sybyl mol2 or PDB files can be used.

  • A job control file (‘.sdl’ file) has

  • information on the ligand, perturbed

  • nuclei, ligand anisotropic groups

  • (rings, multiple bonds, charges),

  • ligand rotatable bonds, etc., etc.

Ligand atom IDs

(have to match the

mol2/PDB file)

Ligand rotatable

bond atom pairs

  • (Too) Many command-line options

  • control the optimization. i.e., ‘-rc’

  • controls the ring-current method,

  • ‘-ff’ what type of potential energy

  • function to use, etc., etc.

  • Making the control files by hand is

  • not bad, but its tedious and can lead

  • to many errors.

Perturbed protein

atoms (1H/13C/15N)

Ligand anisotropic

groups


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  • Running SDILICON of Protein-Ligand Complexes

  • SDILICON uses protein Dds to optimize the orientation/conformation of the

  • ligand at the binding site. Sybyl mol2 or PDB files can be used.

  • A job control file (‘.sdl’ file) has

  • information on the ligand, perturbed

  • nuclei, ligand anisotropic groups

  • (rings, multiple bonds, charges),

  • ligand rotatable bonds, etc., etc.

  • (Too) Many command-line options

  • control the optimization. i.e., ‘-rc’

  • controls the ring-current method,

  • ‘-ff’ what type of potential energy

  • function to use, etc., etc.

  • Making the control files by hand is

  • not bad, but its tedious and can lead

  • to many errors.

  • Solved with a Sybyl ‘custom’ GUI…


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  • SDILICON Pros/Cons of Protein-Ligand Complexes

  • Pros…

    • SDILICON is a C/C++ command-line standalone. Runs on anything

    • with a decent C/C++ compiler (LINUX, IRIX, Mac OS X, etc.).

    • Simple code that is, for those willing, simple to modify and improve.

    • SDILICON is fast. Multiple ligands can be oriented in their binding site

    • in a matter of minutes. This include racemic mixtures…

    • A variety of optimization methods are available, including Line-

    • Minimization, RIPS, and Genetic Algorithms.

  • Cons…

    • Current version is ‘developmental’. A nicer interface would help…

    • No ligand flexibility. Good binding modes may be missed due to a

    • ligand fragment bumping against the protein.

    • Were do we put the ligands to begin with?

  • We have developed other tools that also use Dds to solve this last, perhaps

  • most important, problem.


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  • Locating the Ligand of Protein-Ligand Complexes

  • A Dd for a proton puts a geometric constraint on the location of the

  • perturbing group (i.e., the ligand).

  • Largest perturbations are due to aromatic rings. A magnetic point dipole

  • (Pople) can be used as a probe to locate the ligand. Depending on Dd:

  • If only one proton is perturbed, the ligand

  • can be anywhere in a sphere of radius rmax.


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  • Locating the Ligand of Protein-Ligand Complexes

  • A Dd for a proton puts a geometric constraint on the location of the

  • perturbing group (i.e., the ligand).

  • Largest perturbations are due to aromatic rings. A magnetic point dipole

  • (Pople) can be used as a probe to locate the ligand. Depending on Dd:

  • If only one proton is perturbed, the ligand

  • can be anywhere in a sphere of radius rmax.

  • If more than one proton is perturbed, the

  • probability of locating the ligand will be

  • higher in the intersection of the spheres.

  • McCoy, M. A.; Wyss, D. F. J. Am. Chem. Soc. 2002, 124, 11758.


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  • Locating the Ligand - J-Surfaces of Protein-Ligand Complexes

  • Since these surfaces describe the most likely location of the ligand’s

  • electron density we call them J-surfaces.

  • Intersection of spheres with equal densities would make small shifts

  • dominate the J-surface.Solved by using uniform density for all spheres

  • and considering the intersection point density.

  • The density r3 dependency balances the Dd 1/r3 dependency, providing

  • self-consistency (i.e., effects from protons with large Dds far from binding

  • site are de-emphasized…).

  • Apart from giving a clear spatial location for the ligand, J-surfaces give

  • excellent starting points for the SDILICON optimization algorithm.

1H Dd data from

1H-15N HSQC

(HCV NS3 Protease)

Dd Map

J-Surface + vdW


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  • Case Study I - Calmodulin/W7 Complex of Protein-Ligand Complexes

  • Ca2+-bound calmodulin (CaM) binds to two molecules of inhibitor W7 with

  • similar affinity (~10 mM). The structure of the complex was determined by

  • Ikura and co-workers using intermolecular NOE constraints.

  • There are a total of 31 non-NH protons with |Dd| larger than 0.1 ppm, 19 on

  • the N-termini and 12 on the C-termini. Mapped skyblue (-) and red (+).

  • Osawa, M. et al. J. Mol. Biol. 1998, 276, 165.

W7

C-Termini

N-Termini


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  • CaM/W7 Complex of Protein-Ligand Complexes

  • Using the reported Dd perturbations a J-surface for the complex was

  • computed.

  • The ligands determined from NOE data intersect the highest density

  • J-surfaces. Computation time after entering shifts is less than 1 second…

C-Termini

N-Termini


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  • CaM/W7 Complex of Protein-Ligand Complexes

  • Is the J-surface more informative than the regular Dd map regarding the

  • spatial location of the ligands? Looking at the C-termini binding site:

  • Clearly yes…


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  • CaM/W7 Complex of Protein-Ligand Complexes

  • Once the spatial locations of the two W7 ligands in the protein were

  • determined, SDILICON was used to optimize their binding site orientation.

  • Both ligands optimized simultaneously. Only 3 minutes of computation…

  • There is good agreement between SDILICON and NOE structures.

  • Initially, differences in the N-terminal binding mode assumed to be due to

  • conformational rearrangement upon W7 binding.

C-Termini

N-Termini


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  • CaM/W7 Complex of Protein-Ligand Complexes

  • The optimization was repeated after ligand rotatable bonds were

  • implemented into SDILICON. GAs were used to obtain a ‘global minimum’,

  • and the resulting structure line-minimized.

  • Better agreement with NOE structure. The rigid ligand side-chain was

  • bumping against the N-termini binding site. Rotatable bonds are needed,

  • even if the ligand’s anisotropic groups are part of a rigid framework…

C-Termini

N-Termini


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  • CaM/W7 Complex of Protein-Ligand Complexes

  • How consistent with the observed Dd data are the structures obtained from

  • SDILICON calculations?

  • We can back-calculate shift perturbations for all/some protein protons from

  • the SDILICON structure, use them to compute a theoretical J-surface (Jcalc),

  • and compare it to the observed J-surface (Jobs).

  • There is > 30% overlap between Jobs and Jcalc, indicating that the

  • SDILICON 3D model is consistent with the observed shift perturbations.

Observed (Jobs)

Calculated (Jcalc)

Intersection (JobsJcalc)


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  • Case Study II - Neocarzinostatin (NCS) of Protein-Ligand Complexes

  • CaM was an ideal case. Sulfur-aromatic interactions between methionines

  • and naphtalenes create large Dds that guide optimization. No NHs used.

  • apoNCS-CH9 complex studied by Caddick and co-workers. 38 NH and CH

  • protons with |Dd| larger than 0.1 ppm, only one |Dd| larger than 0.7 ppm.

  • Urbaniak, M. D. et al. Biochemistry2002, 41, 11731.

CH9


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  • apoNCS Complexes of Protein-Ligand Complexes

  • Once again, the J-surface clearly points to the spatial location of the

  • ligand in the protein binding site.

CH9

CH9

vdW-Accessible J-Surface


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  • apoNCS Complexes of Protein-Ligand Complexes

  • Once again, the J-surface clearly points to the spatial location of the

  • ligand in the protein binding site.

  • At lower densities, conformational rearrangement is detected.

Phe78 - Act as flaps over binding site

CH9

Thr85

Raw J-Surface - Lower density


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  • apoNCS Complexes of Protein-Ligand Complexes

  • The SDILICON calculations were done filtering out Dd perturbations not

  • contributing to the high density J-surface. Low-energy structures of CH9

  • obtained from a GA search used as starting points for line-minimization.

  • Clearly, the structure that puts the ligand further away from the binding site

  • is ‘wrong’. However, we need a non-biased method to confirm this.


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  • apoNCS Complexes of Protein-Ligand Complexes

  • Again, this can be done by comparing the Jobs surface to Jcalc surfaces

  • derived from both models.

  • Jcalc surfaces that deviate substantially from the Jobs surface indicate

  • structures inconsistent with the observed Dd data. These models can be

  • eliminated from the structure pool.

20% intersection

0% intersection


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  • apoNCS Complexes of Protein-Ligand Complexes

  • The previous examples use full Dd assignments for the J-surface and

  • SDILICON calculations. These are great, but take a long time to gather.

  • The minDd method is an alternative for quick/tentative assignments. NH

  • minDds for CH9 and three additional apoNCS binders were available.

  • minDds cannot be used with SDILICON (signs are lost, miss-assignments,

  • etc.). However, they show perturbations ideal for J-surface calculations…

  • Williamson, R. A. et al. Biochemistry1997, 36, 13882.

CH3

CH5

CH7


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  • apoNCS Complexes of Protein-Ligand Complexes

  • J-surfaces derived from minDds for all four ligands…

  • Accurate, even when iffy perturbations are used. Ideal for ‘automation’…

CH9 (Dd)

CH3 (minDd)

CH9 (minDd)

CH7 (minDd)

CH5 (minDd)


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  • Some Real Data of Protein-Ligand Complexes

  • Previous examples used polished data from academia. What about some

  • real-life stuff? Data from compounds deemed non-leads in SPRI HCV

  • Protease program (80 mM - 1 mM binders).

  • Only NH Dds available. The number of Dds varies from 3 (SCH17865) to

  • 22 (SCH10386), and their ranges are as small as -0.15 to -0.07 ppm

  • (SCH17865), to -0.59 to 0.63 ppm (SCH92).

SCH10363

SCH17865

SCH92

SCH9301

SCH415425


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  • Some Real Data of Protein-Ligand Complexes

  • We start seeing problems. When we have a limited number of small Dds,

  • it’s hard to pin down a binding site using J-surfaces. For example,

  • SCH17865, with only three Dds in the -0.15 to -0.07 range:

  • We basically have only three very large spheres (1/r3 dependency), which

  • results in a very large intersection volume (> 202 Å3).

  • SDILICON will not give us a unique family of conformers, but several

  • located in a large region of space…


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  • Some Real Data of Protein-Ligand Complexes

  • However, compounds SCH9301 and SCH92 give well defined J-surfaces

  • (< 50 and 30 Å3) in the same region of space (50 % intersection):

  • Similar results for SCH415425. All share a common binding site.

SCH9301

SCH92


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  • Some Real Data of Protein-Ligand Complexes

  • The orientation/conformation of SCH9301 was then computed with

  • SDILICON. A single ‘global-minimum’ was obtained with GAs and further

  • optimized by line-minimization.

  • In order to validate the resulting structure, we again looked at the Jobs

  • surface versus the Jcalc surface derived from the optimized structure…

H57

S138

K136

L135

R155

Y134

A156

A157


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  • Some Real Data of Protein-Ligand Complexes

  • Since only NH data was used, only back-calculated NH Dds were used to

  • compute Jcalc.

Observed (Jobs)

Calculated (Jcalc)


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  • Some Real Data of Protein-Ligand Complexes

  • Since only NH data was used, only back-calculated NH Dds were used to

  • compute Jcalc.

  • Although it won’t, this 3D model could be used to design new lead

  • compounds. Since no X-Ray data are available for these complexes, this

  • example shows the potential of the J-SURF/SDILICON approach in SbN.

Observed (Jobs)

Intersection (JobsJcalc) - 75% overlap


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  • Something for of Protein-Ligand ComplexesSMASH People…

  • The J-SURF/SDILICON approach is not limited to ligand- and

  • protein-protein complexes.We applied it successfully to the study of

  • perylene oligomerization.

  • In these compounds there is a

  • concentration-dependent upfield

  • shift of the Ha and Hb protons.

  • In the ‘dimer’, DdHa = -0.31 ppm

  • and DdHb = -0.51 ppm.

  • We are obviously dealing with ring-

  • currents and, to a lesser extent,

  • amide group anisotropy effects.

  • Ideal for J-SURF/SDILICON…

  • Wang, W.; Li, L.-S.; Helms, G.; Zhou, H.-H.; Li, A. D. Q.; J. Am. Chem. Soc. 2003,125, 1120.

Ha

Hb


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  • Shift-Minimized Perylene dimer of Protein-Ligand Complexes

  • These are the J-surface obtained for one of the monomers and the shift-

  • minimized dimer structure. The back-calculated Dd values for Ha and Hb

  • protons are -0.32 and -0.48 ppm respectively.

  • As expected, the highest J-density is right on top (bottom) of the rings. The

  • distance between rings obtained using Dd constraints is 3.51 Å, almost

  • identical to the distance obtained from ab initio calculations (3.55 Å).

3.51 Å


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  • Conclusions and Future Work of Protein-Ligand Complexes

  • J-surfaces are a simple and rapid way to spatially locate ligands from Dds.

  • The method also identifies protein regions which undergo rearrangement

  • upon ligand binding or mutation. A web-based ‘J-server’ coming soon…

  • SDILICON rapidly docks ligands based solely on Dd perturbations and

  • intermolecular non-bonded interactions. Structures obtained are similar in

  • quality to those determined from intermolecular NOEs.

  • Combined they provide a quick approach to locate and dock ligands in the

  • protein binding site. Ideal for high-throughput structure determination.

  • Current version (11/03) allows for rotation around ligand single bonds, and

  • for exhaustive conformational search with GAs.

  • http://tonga.usip.edu/gmoyna/sdilicon/

  • Parameters for anisotropic groups other than aromatic rings and amides,

  • such as sulfones, carboxylates, multiple bonds, 15N, etc., are required.


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Acknowledgments of Protein-Ligand Complexes

People

Dr. Mark McCoy (SPRI)

Prof. Stephen Caddick (U. of Sussex)

Prof. Alexander DeQuan Li (WSU)

Zhijian Li (USP - SDILICON GA)

Edward P. O’Brien (USP - J-SURF - Currently UMD)

Adam Wenocur (USP - J-SURF)

Prof. Randy J. Zauhar (USP - My Own C/C++ Guru…)

Funding

Schering-Plough Research Institute

Office of the VP of Academic Affairs, USP


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