Homology-based loop modeling yields more complete crystallographic protein structures.

Inherent protein flexibility, poor or low-resolution diffraction data or poorly defined electron-density maps often inhibit the building of complete structural models during X-ray structure determination. However, recent advances in crystallographic refinement and model building often allow completion of previously missing parts. This paper presents algorithms that identify regions missing in a certain model but present in homologous structures in the Protein Data Bank (PDB), and 'graft' these regions of interest. These new regions are refined and validated in a fully automated procedure. Including these developments in the PDB-REDO pipeline has enabled the building of 24 962 missing loops in the PDB. The models and the automated procedures are publicly available through the PDB-REDO databank and webserver. More complete protein structure models enable a higher quality public archive but also a better understanding of protein function, better comparison between homologous structures and more complete data mining in structural bioinformatics projects.

PDB_REDO: constructive validation, more than just looking for errors.

Developments of the PDB_REDO procedure that combine re-refinement and rebuilding within a unique decision-making framework to improve structures in the PDB are presented. PDB_REDO uses a variety of existing and custom-built software modules to choose an optimal refinement protocol (e.g. anisotropic, isotropic or overall B-factor refinement, TLS model) and to optimize the geometry versus data-refinement weights. Next, it proceeds to rebuild side chains and peptide planes before a final optimization round. PDB_REDO works fully automatically without the need for intervention by a crystallographic expert. The pipeline was tested on 12 000 PDB entries and the great majority of the test cases improved both in terms of crystallographic criteria such as R(free) and in terms of widely accepted geometric validation criteria. It is concluded that PDB_REDO is useful to update the otherwise `static' structures in the PDB to modern crystallographic standards. The publically available PDB_REDO database provides better model statistics and contributes to better refinement and validation targets.

Automatic rebuilding and optimization of crystallographic structures in the Protein Data Bank.

MOTIVATION: Macromolecular crystal structures in the Protein Data Bank (PDB) are a key source of structural insight into biological processes. These structures, some >30 years old, were constructed with methods of their era. With PDB_REDO, we aim to automatically optimize these structures to better fit their corresponding experimental data, passing the benefits of new methods in crystallography on to a wide base of non-crystallographer structure users. RESULTS: We developed new algorithms to allow automatic rebuilding and remodeling of main chain peptide bonds and side chains in crystallographic electron density maps, and incorporated these and further enhancements in the PDB_REDO procedure. Applying the updated PDB_REDO to the oldest, but also to some of the newest models in the PDB, corrects existing modeling errors and brings these models to a higher quality, as judged by standard validation methods. AVAILABILITY AND IMPLEMENTATION: The PDB_REDO database and links to all software are available at http://www.cmbi.ru.nl/pdb_redo. CONTACT: r.joosten@nki.nl; a.perrakis@nki.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

"Conditional Restraints": Restraining the Free Atoms in ARP/wARP.

The automated building of a protein model into an electron density map remains a challenging problem. In the ARP/wARP approach, model building is facilitated by initially interpreting a density map with free atoms of unknown chemical identity; all structural information for such chemically unassigned atoms is discarded. Here, this is remedied by applying restraints between free atoms, and between free atoms and a partial protein model. These are based on geometric considerations of protein structure and tentative (conditional) assignments for the free atoms. Restraints are applied in the REFMAC5 refinement program and are generated on an ad hoc basis, allowing them to fluctuate from step to step. A large set of experimentally phased and molecular replacement structures showcases individual structures where automated building is improved drastically by the conditional restraints. The concept and implementation we present can also find application in restraining geometries, such as hydrogen bonds, in low-resolution refinement.

A knowledge-driven approach for crystallographic protein model completion.

One of the most cumbersome and time-demanding tasks in completing a protein model is building short missing regions or ;loops'. A method is presented that uses structural and electron-density information to build the most likely conformations of such loops. Using the distribution of angles and dihedral angles in pentapeptides as the driving parameters, a set of possible conformations for the C(alpha) backbone of loops was generated. The most likely candidate is then selected in a hierarchical manner: new and stronger restraints are added while the loop is built. The weight of the electron-density correlation relative to geometrical considerations is gradually increased until the most likely loop is selected on map correlation alone. To conclude, the loop is refined against the electron density in real space. This is started by using structural information to trace a set of models for the C(alpha) backbone of the loop. Only in later steps of the algorithm is the electron-density correlation used as a criterion to select the loop(s). Thus, this method is more robust in low-density regions than an approach using density as a primary criterion. The algorithm is implemented in a loop-building program, Loopy, which can be used either alone or as part of an automatic building cycle. Loopy can build loops of up to 14 residues in length within a couple of minutes. The average root-mean-square deviation of the C(alpha) atoms in the loops built during validation was less than 0.4 A. When implemented in the context of automated model building in ARP/wARP, Loopy can increase the completeness of the built models.