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

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 Gu

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  1. 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

  2. NMR in Drug Design • 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

  3. Rapid NMR-based Structure Determination • 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.

  4. Ddin 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

  5. Ddin 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

  6. Ddin 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

  7. Running SDILICON • 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

  8. Running SDILICON • 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…

  9. SDILICON Pros/Cons • 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.

  10. Locating the Ligand • 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.

  11. Locating the Ligand • 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.

  12. Locating the Ligand - J-Surfaces • 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

  13. Case Study I - Calmodulin/W7 Complex • 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

  14. CaM/W7 Complex • 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

  15. CaM/W7 Complex • 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…

  16. CaM/W7 Complex • 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

  17. CaM/W7 Complex • 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

  18. CaM/W7 Complex • 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)

  19. Case Study II - Neocarzinostatin (NCS) • 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

  20. apoNCS 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

  21. apoNCS 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

  22. apoNCS 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.

  23. apoNCS 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

  24. apoNCS 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

  25. apoNCS 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)

  26. Some Real Data • 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

  27. Some Real Data • 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…

  28. Some Real Data • 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

  29. Some Real Data • 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

  30. Some Real Data • Since only NH data was used, only back-calculated NH Dds were used to • compute Jcalc. Observed (Jobs) Calculated (Jcalc)

  31. Some Real Data • 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

  32. Something for SMASH 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

  33. Shift-Minimized Perylene dimer • 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

  34. Conclusions and Future Work • 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.

  35. Acknowledgments 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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