BUSTER User Manual
Rigid-body refinement tutorial: SCAFet

BUSTER tutorial 4


Rigid-Body Refinement of a Molecular Replacement model

This tutorial illustrates the use of BUSTER for Maximum Likelihood rigid-body refinement of a model as obtained from a Molecular Replacement solution.

The structure of the SCAFet lectin from Scilla Campanulata bluebell bulbs was solved by Wright et al. in 1999 at 3.3 Å at room temperature [1,2,3]. There are 6 monomers in the a.u., arranged in a doughnut-shaped hexamer. The structure was deposited with PDB accession code 1DLP.

A molecular replacement search was run against a new low-temperature 2.9 Å dataset for the same protein; the search model was the NCS-averaged SCAFet monomer from the 3.3 Å structure, AAs 1-235. This search model is fairly complete but has main-chain and side-chain errors in it. The 6 correct solutions of this MR search are used for the rigid-body refinement of the SCAFet hexamer in this tutorial.

What does BUSTER do here

  1. cycles 1-10: BUSTER refines then the positions of the atoms, each of the six monomers being treated as a single rigid body; only the positions of the atoms are refined, individual B factors refinement is turned off. The weight for the non-bonded contacts has been set to zero to prevent clashes due to imperfections of the MR search model. No missing atoms are declared.

Input Preparation

A few parameters need some attention: see the simple quick input guide

Main items of the output

We list here a few of the key items you might want to check in the LIST.html output file:
  1. R-factors plots:

    The R values at cycle 0 are between 38 and 54%, across all resolutions. The R values at cycle 10 show a marginal improvement at medium-high resolution, mainly due to the scaling and a more adequate error model; the low resolution values are worse, due to the fact that the model after rigid-body refinement now clashes with the bulk solvent mask.

    Upon entering a new cycle of rigid-body refinement the initial masks for the bulk solvent will be more accurate, and the initial R factors improve the ones shown aside for cycle zero.

  2. Correlation coefficients plots: the correlation coefficients curves are a good tool to monitor the improvement in the structural model during refinement and MaxEnt completion; they are independent of the overall scale factor but do depend on the values of the relative scale factors between the partial and missing structures.

    The (Fobs,Ffrag) and (Fobs,Fcalc) curves only differ at low resolution because in Fcalc there is a contribution from the bulk solvent, while in Ffrag only the atoms in the PDB model for the partial structure are taken into account.

    Most importantly, the (Fcalc,Fexpct) curve depends on the imperfection parameters that parameterise the BUSTER internal error model. The larger the internal estimate for the error on the calculated F, the more this CC curve departs from unity. A comparison between the (Fcalc,Fexpct) and the (Fobs,Fcalc) correlation coefficients curves can inform as to the adequacy of the BUSTER internal error model: if the latter is correct, after the first cycle the two curves should be close to one another.

    The (Fobs,Fobs+d) curve is a measure of the noise on the data vs. resolution. This correlation coefficient is lower than unity when the noise on the data becomes large (typically at high resolution, where the I/s(I) is lowest).

Final Results

References

[1] L.M. Wright, E.J.M. Van Damme, A. Barre, A.K. Allens, F. Van Leuven, C.D. Reynolds, P. Rouge and W.J. Peumans. Biochem. J. 340, 299-308 (1999)
[3] L.M. Wright, C.D. Reynolds, P.J. Rizkallah, A.K. Allen, W.J. Peumans, E.J.M. Van Damme, and M. Donovan. Protein and Peptide Letters, 6(4), 253-258 (1999)
[2] L.M. Wright, C.D. Reynolds, P.J. Rizkallah, A.K. Allen, E.J.M. Van Damme, M. Donovan and W.J. Peumans. FEBS Letters 468, 19-22 (2000)
Eric Blanc, <blanc@GlobalPhasing.com>
Pietro Roversi, <pietro@GlobalPhasing.com>

Last modified: Fri Jan 9 11:24:09 GMT 2004