|Python-based Hierarchical ENvironment for Integrated Xtallography|
Main PHENIX Modules
Detection of twinning and other pathologies is facilitated via the program phenix.xtriage. This command line driven program analyses an experimental data set and provides diagnostics that aid in the detection of common idiosyncrasies such as the presence of pseudo translational symmetry, certain data processing problems and twinning. Other sanity checks, such as a Wilson plot sanity check and an algorithm that tries to detect the presence of ice rings from the merged data are performed as well. If twin laws are present for the given unit cell and space group, a Britton plot is computed, an H-test is performed and a likelihood based method is used to provide an estimate of the twin fraction. Twin laws are deduced from first principles for each data set, avoiding the danger of over-looking twin laws by incomplete lookup tables. If a model is available, more efficient twin detection tools are available. The RvsR statistic is particularly useful in the detection of twinning in combination with pseudo rotational symmetry. This statistic is computed by phenix.xtriage if calculated data is supplied together with the observed data. A more direct test for the presence of twinning is by refinement of the twin fraction given an atomic model (which can be performed in phenix.refine). The command line utility phenix.twin_map_utils provides a quick way to refine a twin fraction given an atomic model and an X-ray data set and also produces
Automated Structure Solution Using Experimental Phasing Techniques
Structure solution via SAD, MAD or SIR(AS) can be carried out with the AutoSol wizard. The AutoSol wizard performs heavy atom location, phasing, density modification and initial model building in an automated manner. The heavy atoms are located with substructure solution engine also used in phenix.hyss, a dual space method similar to SHELXD and Shake and Bake. Phasing is carried out with PHASER for SAD cases and with SOLVE for MAD and SIR(AS) cases. Subsequent density modification is carried out with RESOLVE. The hand of the substructure is determined automatically on the basis of the quality of the resulting electron density map. It is noteworthy that the whole process is not necessarily linear but that the wizard can decide to step back and (for instance) try another set of heavy atoms if appropriate. In the resulting electron density map, a model is built (currently limited to proteins). Further model completion can be carried out via the AutoBuild wizard. The AutoBuild wizard iterates model building and density modification with refinement of the model in a scheme similar to other iterative model building methods, for example ARP/wARP.
Automated Structure Solution Via Molecular Replacement
Structure solution via molecular replacement is facilitated via the AutoMR wizard. The AutoMR wizard guides the user through setting up all necessary parameters to run a molecular replacement job with PHASER. The molecular replacement carried out by PHASER uses likelihood based scoring function, improving the sensitivity of the procedure and the ability to obtain reasonable solutions with search models that have a relatively low sequence similarity to the crystal structure being determined. Besides the use of likelihood based scoring functions, structure solution is enhanced by detailed bookkeeping of all search possibilities when searching for more then a single copy in the asymmetric unit or when there the choice of space group is ambiguous. When a suitable molecular replacement solution is found, the AutoBuild wizard is invoked and rebuilds the molecular replacement model given the sequence of the model under investigation.
Automated Model Building
Automated model building given a starting model or a set of reasonable phases can be carried out by the AutoBuild wizard. A typical AutoBuild job combines density modification, model building, macromolecular refinement and solvent model updates ('water picking') in an iterative manner. Various modes of building a model are available. Depending on the availability of a molecular model, model building can be carried by locally rebuilding an existing model (rebuild in place) or by building in the density without any information of an available model. The rebuilding in place model building is a powerful building scheme that is used by default for molecular replacement models that have a high sequence similarity to the sequence of the structure that is to be built. A fundamental feature of the AutoBuild wizard is that it builds various models, all from slightly different starting points. The dependency of the outcome of the model building algorithm on initial starting conditions provides a straightforward mechanism to obtain a variety of plausible molecular models. It is not uncommon that certain sections of a map are built in one model, while not in another. Combining these models allows the AutoBuild wizard to converge faster to a more complete model than when using a single model building pass for a given set of phases. Dedicated loop fitting algorithms are used to close gaps between chain segments. This feature, together with the water picking and side chain placement, typically results in highly complete models of high quality that need minimal manual intervention before they are ready for deposition.
The refinement engine used in the AutoBuild and AutoSol wizards can also be run from the command line with the phenix.refine command. The phenix.refine program carries out likelihood based refinement and has the possibility to refine positional parameters, individual or grouped atomic displacement parameters, individual or grouped occupancies. The refinement of anisotropic displacement parameters (individual or via a TLS parameterization) is also available. Positional parameters can be optimized using either traditional gradient-only based optimization methods, or via simulated annealing protocols. The command line interface allows the user to specify which part of the model should be refined in what manner. It is in principle possible to refine half of the molecule as a rigid group with grouped B values, whereas the other half of the molecule has a TLS parameterization. The flexibility of specifying the level of parameterization of the model is especially important for the refinement of low resolution data or when starting with severely incomplete atomic models. Another advantage of this flexibility in refinement strategy is that a user can perform a complex refinement protocol that carries out simulated annealing, isotropic B refinement and water picking in 'one go'. Another main feature of phenix.refine is the way in which the relative weights for the geometric and ADP restraints with respect to the X-ray target are determined. Considerable effort has been put into devising a good set of defaults and weight determination schemes that results in a good choice of parameters for the data set under investigation. Defaults can of course be overwritten if the user chooses to. Besides being able to handle refinement against X-ray data, phenix.refine can refine against neutron data or against X-ray and neutron data simultaneously.
Automated ligand density analysis
Automated fitting of ligands into the electron density is facilitated via the LigandFit wizard. The ligand building is performed by finding an initial fit for the largest rigid domain of the ligand and extending the remaining part of the ligand from this initial 'seed'. Besides being able to fit a known ligand into a difference map, the LigandFit wizard is capable of identifying ligands on the basis of the difference density only. In the latter scheme, density characteristics for ligands occurring frequently in the PDB are used to provide the user with a range of plausible ligands.
Calculating ligand geometries and defining chemical restraints
Stereo chemical dictionaries of ligands whose chemical description is not available in the supplied monomer library for the use in restrained macromolecular refinement can be generated with the electronic ligand builder and optimization workbench (eLBOW). eLBOW generates a 3D geometry from a number of chemical input formats including MOL2 or PDB files and SMILES strings. SMILES is a compact, chemically dense description of a molecule that contains all element and bonding information and optionally other stereo information such as chirality. To generate a 3D geometry from an input format that contains no 3D geometry information, eLBOW uses a Z-Matrix formalism in conjunction with a table of bond lengths calculated using the Hartree-Fock method with a 6-31G(d,p) basis set to obtain a Cartesian coordinate set. The geometry is then optionally optimized using the semi-empirical quantum chemistry method AM1. The AM1 optimization provides chemically meaningful and accurate geometries for the class of molecule typically complexed with proteins. eLBOW outputs the optimized geometry and a standard CIF restraint file that can be read in by phenix.refine and can also be used for real space refinement during manual model building sessions in the program COOT. An interface is also available to use eLBOW within COOT.