Page 182 - Macromolecular Crystallography

MODEL BUILDING, REFINEMENT, AND VALIDATION 171

fromelectron-densitymapsusingpatternmatching. Acta Morris, R. J., Perrakis, A. and Lamzin, V. S. (2002).
Crystallogr. D 56, 722–734. ARP/wARP’s model-building algorithms. I. The main
Ioerger, T. R. and Sacchettini, J. C. (2002). Automatic mod- chain. Acta Crystallogr. D 58, 968–975.
eling of protein backbones in electron-density maps via Morris, R. J., Perrakis, A. and Lamzin, V. S. (2003).
prediction of Calpha coordinates. Acta Crystallogr. D 58, ARP/wARP and automatic interpretation of protein
2043–2054. electron density maps. Method Enzymol. 374, 229–244.
Ioerger, T. R. and Sacchettini, J. C. (2003). TEXTAL Morris, R. J., Zwart, P. H., Cohen, S., Fernandez, F. J.,
system: artificial intelligence techniques for auto- Kakaris, M., Kirillova, O., Vonrhein, C., Perrakis, A.
mated protein model building. Methods Enzymol. 374, and Lamzin, V. S. (2004). Breaking good resolutions with
244–270. ARP/wARP. J. Synchr. Rad. 11, 56–59.
Ioerger, T. R., Holton, T., Christopher, J. A. and Sacchettini, Murshudov, G. N., Vagin, A. A. and Dodson, E. J.
J. C. (1999). TEXTAL: a pattern recognition system for (1997). Refinement of macromolecular structures by the
interpreting electron density maps. Proc. Int. Conf. Intell. maximum-likelihood method. Acta Crystallogr. D 53,
Syst. Mol. Biol. 130–137. 240–255.
Isaacs, N. W. and Agarwal, R. C. (1985). Free atom inser- Ness, S. R., de Graaff, R. A., Abrahams, J. P. and Pannu,
tion and refinement as a means of extending and refining N. S. (2004). CRANK: new methods for automated
phases. Methods Enzymol. 115, 112–117. macromolecular crystal structure solution. Structure 12,
Jones, T. A. and Thirup, S. (1986). Using known sub- 1753–1761.
structuresinproteinmodelbuildingandcrystallography Ogata, C. M. (1998). MAD phasing grows up. Nat. Struct.
EMBO J. 5, 819–822. Biol. 5, 638–640.
Jones, T. A., Zou, J.-Y., Cowan, S. W. and Kjeldgaard, M. Panjikar, S., Parthasarathy, V., Lamzin, V. S., Weiss, M. S.
(1991). Improved methods for building protein models and Tucker, P. A. (2005). Auto-Rickshaw: an automated
in electron density maps and the location of errors in crystal structure determination platform as an efficient
these models. Acta Crystallogr. A 47, 110–119. tool for the validation of an X-ray diffraction experiment.
Kleywegt, G. J. and Jones, T. A. (1997). Template convo- Acta Crystallogr. D 61, 449–457.
lution to enhance or detect structural features in macro- Perrakis, A., Harkiolaki, M., Wilson, K. S. and Lamzin,
molecular electron-density maps. Acta Crystallogr. D 53, V. S. (2001). ARP/wARP and molecular replacement.
179–185. Acta Crystallorg. D 57, 1445–1450.
Lamzin, V. S. and Wilson, K. S. (1993). Automated Perrakis, A., Morris, R. and Lamzin, V. S. (1999). Auto-
refinement of protein models. Acta Crystallogr. D 49, mated protein model building combined with iterative
129–147. structure refinement. Nat. Struct. Biol. 6, 458–63.
Lamzin, V. S., Perrakis, A. and Wilson, K. S. (2001). Perrakis, A., Sixma, T. K., Wilson, K. S. and Lamzin,
The ARP/wARP suite for automated construction and V. S. (1997). wARP: improvement and extension of crys-
refinement of protein models. In: International Tables tallographic phases by weighted averaging of multiple
for Crystallography. Volume F: Crystallography of biologi- refined dummy atomic models. Acta Crystallogr. D 53,
cal macromolecules, Rossmann, M. G. and Arnold, E. eds., 448–455.
pp. 720–722. Dordrecht, Kluwer Academic Publishers, Pichler, A., Knipscheer, P., Oberhofer, E., van Dijk, W.J.,
The Netherlands. Korner, R., Olsen, J.V., Jentsch, S., Melchior, F. and
Lapedes, A. and Farber, R. (1988). How neural nets work. Sixma, T.K. (2005) SUMO modification of the ubiquitin-
In: Neural Information Processing Systems, Anderson, conjugating enzyme E2-25K. Nat. Struct. Mol. Biol. 12,
D. Z., ed. American Institute of Physics, New York. 264–269.
Levitt, D. G. (2001). A new software routine that auto- Press, W. H., Flannery, B. P., Teukolsky, S.A. and Vetterling,
mates the fitting of protein X-ray crystallographic W. T. (2002). Numerical Recipes in C: The Art of Sci-
electron-density maps. Acta Crystallogr. D 57, 1013– entific Computing. Cambridge University Press, New
1019. York, USA.
Lippmann, R. P., Kukolich, L. C. and Singer, E. Ramakrishnan, C. and Ramachandran, G. N. (1965). Stere-
(1993). LNKnet: neural network, machine learning, ochemical criteria for polypeptide and protein chain
and statistical software for pattern classification. Lincoln conformations. Biophys. J. 5, 909–903.
Laboratory J. 6, 249–268. Rice, L.M. and Brunger, A.T. (1994) Torsion angle
Morris, R. J. (2004). Statistical pattern recognition for dynamics: reduced variable conformational sampling
macromolecular crystallographers. Acta Crystallogr. D enhances crystallographic structure refinement. Proteins
60, 2133–2143. 19, 277–290.