Crystallography model building

Jones T A and S Thirup 1986. Using Known Substructures in Protein Model Building and Crystallography. EMBO journal 5 819-822. 

TH Jones, S Thinip. Using known substructures in protein model building and crystallography. EMBO J 5 819-822, 1986. 

In the protein structure database PDB ( http //www. rcsb.org/pdb), by X-ray crystallography and NMR spectroscopy, experimentally solved 3D-protein structures are available to the public. Homology model building for a query sequence uses protein portions of known 3D-stmctures as structural templates for proteins with high sequence similarity. 

For many proteins, it is possible to generate structures of protein-ligand complexes quite rapidly. It is therefore not uncommon for many hundreds of structures to be determined in support of a drug discovery and optimization project. The major challenge for this level of throughput is informatics support. It is also this type of crystallography that is most in need of semiautomated procedures for structure solution and model building (see Section 12.6). 

Voigt-Martin et al. [13] have used MICE to solve the stmcture of 4-(4 -(N,N-dimethyl)aminobenzylidene)-pyrazolidine-3,5-dione at 1.4A in projection using 42 reflections. The potential maps do not resolve atoms with these data and models have to be fitted to the map density in a way reminiscent of macromolecular crystallography. This can pose problems in structure validation which were overcome in this case by simulation calculations. There is an excellent agreement between the solution and independent model building and high resolution electron microscopy studies. 

Badger, J. (2003). An evaluation of automated model building procedures for protein crystallography. Acta Crystulhgr. D 59, 823-827. 

We will now describe some details of the interpretation of the electron-density map that has been calculated. There are two major methods used in protein crystallography to build a model of the molecule from the various features found in an electron-density map. These methods differ from those used for small-molecules because the number of atoms is so large, and because individual atoms are not resolved in most protein crystal structures. A scheme, which is a continuation of Figure 8.10 (Chapter 8), is given in Figure 9.13. 

Maths for Chemists Volume II Power Series, Complex Numbers and Linear Algebra builds on the foundations laid in Volume I, and goes on to develop more advanced material. The topics covered include power series, which are used to formulate alternative representations of functions and are important in model building in chemistry complex numbers and complex functions, which appear in quantum chemistry, spectroscopy and crystallography matrices and determinants used in the solution of sets of simultaneous linear equations and in the representation of geometrical transformations used to describe molecular symmetry characteristics and vectors which allow the description of directional properties of molecules. 

For peptides and nucleic acids, the system should provide rapid generation of a model from sequence data in any of the commonly observed conformations (e.g., a-helix, /J-sheet, /2-turn, B-DNA, Z-DNA). For peptides, it should be possible to make insertions or deletions in the sequence easily and to mutate side chains for homology model-building applications, where the sequence of the unknown structure is mapped onto the three-dimensional structure of a sequentially homologous protein whose structure has previously been determined by X-ray crystallography. 

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