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Seminar series 2

Protein structure validation. Seminar series 2. Structure validation. Everything that can go wrong, will go wrong, especially with things as complicated as protein structures. What is real?. What is real?. ATOM 1 N LEU 1 -15.159 11.595 27.068 1.00 18.46

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Seminar series 2

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  1. Protein structure validation Seminar series 2

  2. Structure validation Everything that can go wrong, will go wrong, especially with things as complicated as protein structures.

  3. What is real?

  4. What is real? ATOM 1 N LEU 1 -15.159 11.595 27.068 1.00 18.46 ATOM 2 CA LEU 1 -14.294 10.672 26.323 1.00 9.92 ATOM 3 C LEU 1 -14.694 9.210 26.499 1.00 12.20 ATOM 4 O LEU 1 -14.350 8.577 27.502 1.00 13.43 ATOM 5 CB LEU 1 -12.829 10.836 26.772 1.00 13.48 ATOM 6 CG LEU 1 -11.745 10.348 25.834 1.00 15.93 ATOM 7 CD1 LEU 1 -11.895 11.027 24.495 1.00 13.12 ATOM 8 CD2 LEU 1 -10.378 10.636 26.402 1.00 15.12

  5. X-ray

  6. X-ray

  7. X-ray And now move the atoms around till the calculated reflections best match the observed ones. ‘FFT-inv’ FFT-inv

  8. X-ray refinement / multiple minima Multiple minima

  9. X-ray R-factor 2 Error = Σ w.(obs-calc) R-factor = Σ w.|obs-calc|

  10. X-ray resolution

  11. NMR data collection

  12. From NMR spectra to structure B A If proton A and proton B are close in space, so-called ‘cross peaks appear’ in a spectrum due to the Nuclear Overhauser Effect (NOE). The NOE depends on the distance between proton A and B

  13. H1 H2 Distance(Å) A B 3 A C 4 B D 2 C D 1 .. .. .. From NMR spectra to structure B A The NMR data thus contains distance information! Most NOEs are between close neighbours in the sequence. Those hold little information. The ‘good’ NOEs are between atoms far away in the sequence. There are few of those, normally.

  14. From NMR spectra to structure The list of distances can be used in a computer simulation that is reminiscent of protein folding.

  15. From NMR spectra to structure .. until the protein is ‘folded’.

  16. From NMR spectra to structure NMR ‘ensemble’

  17. NMR refinement Multiple minima

  18. NMR refinement Green lines: Distance OK Red lines: Protons too far away from each other

  19. NMR Q-factor Error = Σ NOE-violations + Energy term2

  20. NMR versus X-ray With X-ray you measure reflections. Each reflection holds information about each atom. With NMR you measure pair-wise distances, angles, and orientations. These all hold local information. X-ray requires crystals, and crystals cause/are artefacts. NMR is in solution, but provides much less precision.

  21. NMR versus X-ray NMR X-ray ‘Error’ 1-2 Å 0.1-0.5 Å Mobility yes not really Crystal artefacts no yes Material needed 20 mg 1 mg Cost of hardware 4 M Euro near infinite (share) Drug design no almost Better combine and use the best of both worlds.

  22. More about (protein) crystallography and NMR in: Kristalstructuur Magnetische Resonantie Fourier Analyse and Structuur, Functie, Bioinformatica, of course

  23. Why validation ? Why have we spend twenty years to search for millions of errors in the PDB?

  24. Validation because: Everything we know about proteins comes from PDB files. Errors become less dangerous when you know about them. And, going back to the connecting thread through this series, if a template is wrong the model will be wrong.

  25. What kind of errors can the software find? Administrative errors. Crystal-specific errors. NMR-specific errors. Really wrong things. Improbable things. Things worth looking at. Ad hoc things.

  26. Smile or cry? A 5RXN 1.2 B 7GPB 2.9 C 1DLP 3.3 D 1BIW 2.5

  27. Little things hurt big

  28. X-ray specific

  29. His, Asn, Gln ‘flips’

  30. Hydrogen bond network

  31. Your best check:

  32. Contact Probability

  33. Contact Probability

  34. Contact probability box A positive nitrogen around a Phe

  35. X-ray How bad is bad? In a normal distribution, half of the points are above and half of the points are below average In a normal distribution, 68% of the points are within 1 standard deviation of the mean ΔG = -RT ln K 95% are within 2 sd Less than 1 in 10000 points is more than 4 sd away from the mean

  36. One slide about homology modelling

  37. How difficult can it be? 1CBQ 2.2 A

  38. How difficult can it be?

  39. How difficult can it be?

  40. How difficult can it be?

  41. How difficult can it be? 1CBQ 2.2 A 1.00 0.33 0.33 0.33 0.33

  42. How difficult can it be? This is what standard viewers show: 1CBQ 2.2 A Even if the oxygen labels would have been reversed, the so-called ‘asymmetric unit’ could also have been chosen in a much better way…

  43. Errors or discoveries? Buried histidine. Warning for buried histidine triggered biochemical follow -up and new mechanism for KH-module of Vigilin. (A. Pastore, 1VIG).

  44. Acknowledgements: Rob Hooft Elmar Krieger Sander Nabuurs Chris Spronk Maarten Hekkelman Robbie Joosten

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