1. ECCB 2020 Gent Introduction to protein structure validation (and improvement) Gert Vriend Protein structure validation

2. The plan for today: Gert Vriend: Introduction to validation Robbie Joosten: X-ray structure validation and improvement Jurgen Doreleijers:NMR structure validation (and improvement ) Bas Vroling (and you):YASARA All (and you):General validation practicals Split-up in groups:General validation issues X-ray specific issues NMR specific issues Continuation of validation practicals At the end:Overview of validation and related facilities And in-between we have coffee, lunch, tea, and whatever else they throw at us at any moment that anybody feels like it. Protein structure validation

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

4. What is real?

5. 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

6. X-ray

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

9. Multiple minima X-ray refinement / multiple minima

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

11. X-ray resolution

12. NMR data collection

13. NMR data NMR data consists of short inter-atomic distances between atoms. We call these NOEs. 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. NOEs are known with low precision. E.g. NOEs are binned 2.5-4.0, 4.0-5.5, and 5.5-7.0. NMR can also measure some angles, and relative orientations. The latter, called RDCs are powerful.

14. NMR Q-factor Error = Σ NOE/RDC-violations + Energy term 2

15. 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.

16. NMR versus X-ray NMRX-ray ‘Error’ 1-2 Å0.1-0.5 Å Mobilityyesnot really Crystal artefactsnoyes Material needed20 mg1 mg Cost of hardware4 M Euronear infinite (share) Drug designnoalmost Better combine and use the best of both worlds.

17. Why validation ? Why does a sane (?) human being spend twenty years to search for millions of errors in the PDB?

18. 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 red thread through this series, if a template is wrong the model will be wrong.

19. 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.

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

21. Why? Simple, proteins are very complex.

22. X-ray specific

23. Little things hurt big

24. How bad is bad? X-ray

25. Check with force fields A force field is a set of parameters together with a set of rules to use those parameters to see how normal something is. Most force fields are designed to score events, or to predict the future. In structure validation we often look at structures and count ‘things’ For example, we count that the number of buried hydrogen bond donors that do not make a hydrogen bond is 4.6+/-1.2 per 100 amino acids in well-solved proteins. So we call that normal, and now, using ΔG=-RTln(K), we can calculate the energy penalty for proteins with more than 4.6 unsatisfied buried unsatisfied hydrogen bonds.

26. Contact Probability

28. Contact probability box

29. One slide about homology modelling

30. His, Asn, Gln ‘flips’

31. Hydrogen bond network

32. Your best check:

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

34. How difficult can it be?

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

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