Rob W. W. Hooft

The FAIR Guiding Principles for scientific data management and stewardship

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

Determination of absolute structure using Bayesian statistics on Bijvoet differences

A description is given of a maximum-likelihood approach to absolute structure determinations of biologically active molecules.

A series of PDB related databases for everyday needs

The Protein Data Bank (PDB) is the world-wide repository of macromolecular structure information. We present a series of databases that run parallel to the PDB. Each database holds one entry, if possible, for each PDB entry. DSSP holds the secondary structure of the proteins. PDBREPORT holds reports on the structure quality and lists errors. HSSP holds a multiple sequence alignment for all proteins. The PDBFINDER holds easy to parse summaries of the PDB file content, augmented with essentials from the other systems. PDB_REDO holds re-refined, and often improved, copies of all structures solved by X-ray. WHY_NOT summarizes why certain files could not be produced. All these systems are updated weekly. The data sets can be used for the analysis of properties of protein structures in areas ranging from structural genomics, to cancer biology and protein design.

PRODRG, a program for generating molecular topologies and unique molecular descriptors from coordinates of small molecules

SummaryA software package is described that operates on small molecules observed in the PDB collection of protein structures. Molecular topology files for many molecular modeling programs can be generated automatically. The three-dimensional coordinates of small molecules can be converted to molecular descriptor strings that encode them uniquely in order to enable small-molecule recognition, despite high variability in atom and molecule nomenclature. From this descriptor a plausible 3D structure can be regenerated using energy minimisation. Alternatively, an ensemble of structures can be generated using a distance-geometry-based algorithm.

Positioning hydrogen atoms by optimizing hydrogen-bond networks in protein structures

A method is presented that positions polar hydrogen atoms in protein structures by optimizing the total hydrogen bond energy. For this goal, an empirical hydrogen bond force field was derived from small molecule crystal structures. Bifurcated hydrogen bonds are taken into account. The procedure also predicts ionization states of His, Asp, and Glu residues. During optimization, sidechain conformations of His, Gln, and Asn residues are allowed to change their last χ angle by 180° to compensate for crystallographic misassignments. Crystal structure symmetry is taken into account where appropriate. The results can have significant implications for molecular dynamics simulations, protein engineering, and docking studies. The largest impact, however, is in protein structure verification: over 85% of protein structures tested can be improved by using our procedure. Proteins 26:363–376

The value of data

Data citation and the derivation of semantic constructs directly from datasets have now both found their place in scientific communication. The social challenge facing us is to maintain the value of traditional narrative publications and their relationship to the datasets they report upon while at the same time developing appropriate metrics for citation of data and data constructs.

An adaptive umbrella sampling procedure in conformational analysis using molecular dynamics and its application to glycol

A novel way has been developed for using the umbrella potential method of calculating the potential of mean force using molecular dynamics. During a simulation, the umbrella potential is improved periodically, taking into account all results obtained previously. The method was tested by the determination of the potential of mean force for the central dihedral angle in glycol (as a molecule in vacuo and in solutions in water and in CCl4). Advantages over other methods are that a relatively small amount of manual intervention is necessary and that an intermediate result after a small simulation time is already a complete, although crude estimate of the potential of mean force. A robust method to estimate the error in obtained energy differences between conformations and in the parameters of the potential of mean force (PMF) is also supplied.

The PDBFINDER database: a summary of PDB, DSSP and HSSP information with added value

MOTIVATION The Protein Data Bank currently contains more than 4700 protein coordinate sets. It is often desirable to make a selection from these files based on a criterion like R-factor, experimental method, length of the amino acid sequence, or the number of homologous sequences in SWISSPROT. Doing this using the distributed form of the Protein Data Bank can be a tedious task, because (1) this requires reading one file for every single entry, and (2) not all of the information is present in a consistent computer readable way in all of the entries. RESULTS The PDBFINDER database provides an easy to interpret file containing summary information about all Protein Data Bank files. Summary information from the DSSP (Definition of Secondary Structure of Proteins) and HSSP (Homology derived Secondary Structure of Proteins) databases is also included. Furthermore, where essential data were missing from the Protein Data Bank file, this information has been retrieved from the original literature. AVAILABILITY The latest version of the PDBFINDER database can be downloaded by anonymous ftp from swift.embl-heidelberg.de, directory:/pdbfinder. CONTACT E-mail address hooft@embl-heidelberg.de.

Verification of protein structures : Side-chain planarity

Nine of the 20 natural amino acids contain a planar group in their side chains. For these groups, normal deviations from planarity were derived by the study of similar fragments in accurately determined small molecule structures. Comparison of these deviations with values found from a representative set of high-quality protein structures revealed that the planarity of the aromatic residues and arginine in protein structures is comparable to similar fragments in small molecules. For Asn, Gin, Asp and Glu, however, the deviations are up to twice as large as in comparable small-molecule structures, suggesting that adding an extra planarity restraint for these residue types could improve refinement procedures.

Using the t-distribution to improve the absolute structure assignment with likelihood calculations

The previously described method for absolute structure determination [Hooft, Straver & Spek (2008). J. Appl. Cryst. 41, 96–103] assumes a Gaussian error distribution. The method is now extended to make it robust against poor data with large systematic errors with the introduction of the Student t-distribution. It is shown that this modification makes very little difference for good data but dramatically improves results for data with a non-Gaussian error distribution.