On 19-2-2015 1:38, Smith Lee wrote:
Dear All, It often finds for the Ramachandran favored determined by Coot, MolProbity regards as Ramachandran outliers. There are earlier posts regards Coot and MolProbity has different database for the determination of the Ramachandran plots. Then will you please let me know the correct way to correct the Ramachandran outliers by Coot in order to meet the MolProbity Standards? I am looking forward to getting your reply. Smith
Dear Lee Smith,
The ideas behind protein structure validation by usage of the Ramachandran plot originally were to count outliers: J. Appl. Cryst. (1993). 26, 283-291 [ doi:10.1107/S0021889892009944 ] PROCHECK: a program to check the stereochemical quality of protein structures R. A. Laskowski, M. W. MacArthur, D. S. Moss and J. M. Thornton, or to quantify these outliers: Objectively judging the quality of a protein structure from a Ramachandran plot. Kleywegt and Jones (Structure Volume 4, Issue 12, 15 December 1996, Pages 1395–1400 Phi/Psi-chology: Ramachandran revisited) have a few years later looked at phi,psi combinations again, and tightened the Ramachandran plot contour-islands a bit. The Ramachandran plot obviously has different contour lines for different amino acid types. That is most obvious for proline and glycine, but in the course http://swift.cmbi.ru.nl/teach/B2/ I explain why the contour islands for Asp are systematically wider than for Glu, for example. This idea (R.W.W. Hooft, C.Sander and G.Vriend, CABIOS (1997), 13, 425-430) got implemented in WHAT_CHECK. MolProbity (MolProbity: structure validation and all-atom contact analysis for nucleic acids and their complexes Nucl. Acids Res. (2004) 32 (suppl 2): W615-W619. doi: 10.1093/nar/gkh398) went back one step and used the old ProCheck methodology, but they added the charm of colour bars. These approaches, though, all are meant for validation purposes. Many validation programs have been written, looking at many aspects of protein structure quality, but the Ramachandran plot remained one of the two most powerful validation methods mainly because it is hard to optimize against the Ramachandran plot, and because the Ramachandran plot is nicely reflecting very many aspects of protein structure quality. If a protein structure producess a poor Ramachandran plot, then you should not try to improve the Ramachandran plot, but you should try to improve the structure. You can look if all ouliers fall in one loop, and try to rebuild that loop, but in general, a poor Ramachandran plot means that the whole structure has problems. Ramachandran plot quality correlates nicely with resolution. So, if you have low resolution data, you have little hope. You can use WHAT_CHECK to see how your structure does relative to other structures that roughly have the same resolution. If you want me to take a look at your structure, than please feel free to send me the PDB file (and the mtz file), and we can take a look (secrecy of coordinates guaranteed, of course). Gert Het Radboudumc staat geregistreerd bij de Kamer van Koophandel in het handelsregister onder nummer 41055629. The Radboud university medical center is listed in the Commercial Register of the Chamber of Commerce under file number 41055629.
