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The absence of solvent molecules in high-resolution protein crystal structure models deposited in the Protein Data Bank (PDB) contradicts the fact that, for proteins crystallized from aqueous media, water molecules are always expected to bind to the protein surface, as well as to some sites in the protein interior. An analysis of the contents of the PDB indicated that the expected ratio of the number of water molecules to the number of amino-acid residues exceeds 1.5 in atomic resolution structures, decreasing to 0.25 at around 2.5 Å resolution. Nevertheless, almost 800 protein crystal structures determined at a resolution of 2.5 Å or higher are found in the current release of the PDB without any water molecules, whereas some other depositions have unusually low or high occupancies of modeled solvent. Detailed analysis of these depositions revealed that the lack of solvent molecules might be an indication of problems with either the diffraction data, the refinement protocol, the deposition process or a combination of these factors. It is postulated that problems with solvent structure should be flagged by the PDB and addressed by the depositors.

The absence of solvent molecules in high-resolution protein crystal structure models deposited in the Protein Data Bank (PDB) contradicts the fact that, for proteins crystallized from aqueous media, water molecules are always expected to bind to the protein surface, as well as to some sites in the protein interior. An analysis of the contents of the PDB indicated that the expected ratio of the number of water molecules to the number of amino-acid residues exceeds 1.5 in atomic resolution structures, decreasing to 0.25 at around 2.5 Å resolution. Nevertheless, almost 800 protein crystal structures determined at a resolution of 2.5 Å or higher are found in the current release of the PDB without any water molecules, whereas some other depositions have unusually low or high occupancies of modeled solvent. Detailed analysis of these depositions revealed that the lack of solvent molecules might be an indication of problems with either the diffraction data, the refinement protocol, the deposition process or a combination of these factors. It is postulated that problems with solvent structure should be flagged by the PDB and addressed by the depositors., (open access.)

The quality of macromolecular structure models crucially depends on refinement and validation targets, which optimally describe the expected chemistry. Commonly used software for these two procedures has been designed and developed in a protein‐centric manner, resulting in relatively few established features for the refinement and validation of nucleic acid‐containing structure models. Here, new nucleic acid‐specific approaches implemented in PDB‐REDO are described, including a new restraint model using noncovalent geometries (base‐pair hydrogen bonding and base‐pair stacking) as refinement targets. New validation routines are also presented, including a metric for Watson–Crick base‐pair geometry normality (ZbpG). Applying the PDB‐REDO pipeline with the new restraint model to the whole Protein Data Bank (PDB) demonstrates an overall positive effect on the quality of nucleic acid‐containing structure models. Finally, we discuss examples of improvements in the geometry of specific nucleic acid structures in the PDB. The new PDB‐REDO models and pipeline are available at https://pdb‐redo.eu/. [ABSTRACT FROM AUTHOR]

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.

Taking advantage of the known planarity of the N‐acetyl group of N‐acetylglucosamine, an analysis of the quality of carbohydrate structures found in the protein databank was performed. Few obvious defects of the local geometry of the carbonyl group were observed. However, the N‐acetyl group was often found in the less favorable cis conformation (12% of the cases). It was also found severely twisted in numerous instances, especially in structures with a resolution poorer than 1.9 Å determined between 2000 and 2015. Though the automated PDB‐REDO procedure has proved able to improve nearly 85% of the structural models deposited to the PDB, and does prove able to cure most severely twisted conformations of the N‐acetyl group, it fails to correct its high rate of cis conformations. More generally, for structures with a resolution poorer than 1.6 Å, it produces N‐acetylglucosamine models in slightly poorer agreement with experimental data, as measured using real‐space correlation coefficients. Significant improvements are thus still needed, at least as far as this carbohydrate structure is concerned. [ABSTRACT FROM AUTHOR]

N‐Glycosylation is one of the most common post‐translational modifications and is implicated in, for example, protein folding and interaction with ligands and receptors. N‐Glycosylation trees are complex structures of linked carbohydrate residues attached to asparagine residues. While carbohydrates are typically modeled in protein structures, they are often incomplete or have the wrong chemistry. Here, new tools are presented to automatically rebuild existing glycosylation trees, to extend them where possible, and to add new glycosylation trees if they are missing from the model. The method has been incorporated in the PDB‐REDO pipeline and has been applied to build or rebuild 16 452 carbohydrate residues in 11 651 glycosylation trees in 4498 structure models, and is also available from the PDB‐REDO web server. With better modeling of N‐glycosylation, the biological function of this important modification can be better and more easily understood. [ABSTRACT FROM AUTHOR]

Glycosylation is one of the most common forms of protein post‐translational modification, but is also the most complex. Dealing with glycoproteins in structure model building, refinement, validation and PDB deposition is more error‐prone than dealing with nonglycosylated proteins owing to limitations of the experimental data and available software tools. Also, experimentalists are typically less experienced in dealing with carbohydrate residues than with amino‐acid residues. The results of the reannotation and re‐refinement by PDB‐REDO of 8114 glycoprotein structure models from the Protein Data Bank are analyzed. The positive aspects of 3620 reannotations and subsequent refinement, as well as the remaining challenges to obtaining consistently high‐quality carbohydrate models, are discussed. [ABSTRACT FROM AUTHOR]

Abstract: The Protein Data Bank (PDB) is the global archive for structural information on macromolecules, and a popular resource for researchers, teachers, and students, amassing more than one million unique users each year. Crystallographic structure models in the PDB (more than 100,000 entries) are optimized against the crystal diffraction data and geometrical restraints. This process of crystallographic refinement typically ignored hydrogen bond (H‐bond) distances as a source of information. However, H‐bond restraints can improve structures at low resolution where diffraction data are limited. To improve low‐resolution structure refinement, we present methods for deriving H‐bond information either globally from well‐refined high‐resolution structures from the PDB‐REDO databank, or specifically from on‐the‐fly constructed sets of homologous high‐resolution structures. Refinement incorporating HOmology DErived Restraints (HODER), improves geometrical quality and the fit to the diffraction data for many low‐resolution structures. To make these improvements readily available to the general public, we applied our new algorithms to all crystallographic structures in the PDB: using massively parallel computing, we constructed a new instance of the PDB‐REDO databank ( https://pdb-redo.eu). This resource is useful for researchers to gain insight on individual structures, on specific protein families (as we demonstrate with examples), and on general features of protein structure using data mining approaches on a uniformly treated dataset. [ABSTRACT FROM AUTHOR]

Ramachandran plots report the distribution of the (ϕ, ψ) torsion angles of the protein backbone and are one of the best quality metrics of experimental structure models. Typically, validation software reports the number of residues belonging to "outlier," "allowed," and "favored" regions. While "zero unexplained outliers" can be considered the current "gold standard," this can be misleading if deviations from expected distributions are not considered. We revisited the Ramachandran Z score (Rama-Z), a quality metric introduced more than two decades ago but underutilized. We describe a reimplementation of the Rama-Z score in the Computational Crystallography Toolbox along with an algorithm to estimate its uncertainty for individual models; final implementations are available in Phenix and PDB-REDO. We discuss the interpretation of the Rama-Z score and advocate including it in the validation reports provided by the Protein Data Bank. We also advocate reporting it alongside the outlier/allowed/favored counts in structural publications. • Current standard of "zero unexplained Ramachandran outliers" can be misleading • We revisited Ramachandran Z score (Rama-Z) as a validation tool • We enhanced Rama-Z and developed an algorithm to estimate its uncertainty • We advocate reporting Rama-Z in structural publications and validation reports Counting the number of Ramachandran outliers is not sufficient for protein backbone validation. Sobolev et al. revisited the underutilized Ramachandran Z score. The authors describe its reimplementation in Phenix and PDB-REDO and showcase its utility. They advocate including it in the validation reports provided by the Protein Data Bank. [ABSTRACT FROM AUTHOR]

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.

The Protein Data Bank (PDB) is the global archive for structural information on macromolecules, and a popular resource for researchers, teachers, and students, amassing more than one million unique users each year. Crystallographic structure models in the PDB (more than 100,000 entries) are optimized against the crystal diffraction data and geometrical restraints. This process of crystallographic refinement typically ignored hydrogen bond (H-bond) distances as a source of information. However, H-bond restraints can improve structures at low resolution where diffraction data are limited. To improve low-resolution structure refinement, we present methods for deriving H-bond information either globally from well-refined high-resolution structures from the PDB-REDO databank, or specifically from on-the-fly constructed sets of homologous high-resolution structures. Refinement incorporating HOmology DErived Restraints (HODER), improves geometrical quality and the fit to the diffraction data for many low-resolution structures. To make these improvements readily available to the general public, we applied our new algorithms to all crystallographic structures in the PDB: using massively parallel computing, we constructed a new instance of the PDB-REDO databank (https://pdb-redo.eu). This resource is useful for researchers to gain insight on individual structures, on specific protein families (as we demonstrate with examples), and on general features of protein structure using data mining approaches on a uniformly treated dataset., (© 2017 The Protein Society.)

The dramatic increase in the number of protein sequences and structures deposited in biological databases has led to the development of many bioinformatics tools and programs to manage, validate, compare, and interpret this large volume of data. In addition, powerful tools are being developed to use this sequence and structural data to facilitate protein classification and infer biological function of newly identified proteins. This chapter covers freely available bioinformatics resources on the World Wide Web that are commonly used for protein structure analysis.