A series of PDB related databases for everyday needs

By Robbie P. Joosten, Tim A. H. te Beek, Elmar Krieger, Maarten L. Hekkelman, Rob W. W. Hooft, Reinhard Schneider, Christopher Sander, G. Vriend. 2010

1,027 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Protein Sequence Analysis Using the MPI Bioinformatics Toolkit (2020, methodology): “You could turn off secondary structure scoring if you suspect that the match between the query and template was scored highly because of a chance similarity of their PSIPRED-predicted or DSSP-determined secondary structures.”
Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2 (2013, methodology): “Phi-psi dihedral angles DSSP database (Joosten et al., 2011) Only used for sites that map to a 3-”
Protein sequence design with a learned potential (2022, methodology): “Additionally, we restrict the model from designing glycines at non-loop positions, based on DSSP assignment 53,54 .”
ProteinNet: a standardized data set for machine learning of protein structure (2019, background): “One MSA repository does exist [11], but it appears out of date and is unsuitable for applications requiring deep homology searches [12].”
Enhancing structure prediction and design of soluble and membrane proteins with explicit solvent-protein interactions (2017, methodology): “Only buried interface amino-acids, as defined by Rosetta’s InterfaceAnalyzer (Leaver-Fay et al., 2011) and the relative solvent-accessible calculations from DSSP (Joosten et al., 2011), were allowed to be repacked and hydrated as an anchor site.”
Protein sequence design with a learned potential (2020, methodology): “The model is restricted from designing glycines at non-loop positions, based on DSSP assignment [97, 81].”
Automatically fixing errors in glycoprotein structures with Rosetta (2018, neutral): “This functionality should prove beneficial for large-scale model validation efforts such as ‘‘PDB redo’’ (Joosten et al., 2011; Terwilliger et al., 2012).”
RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network (2019, methodology): “Some orange can be seen where the DSSP algorithm classified the amino acids as sheets (though there were none).”
PDB_REDO: constructive validation, more than just looking for errors (2012, methodology): “(ii) The program pepflip (Joosten, Joosten et al., 2011) is used to flip the orientation of peptide planes in the model if this improves the fit of the peptide with, in order of importance, the mFo DFc map at the position of the oxygen, the 2mFo DFc map of the whole peptide and a combination of the…”
Mega-scale experimental analysis of protein folding stability in biology and design (2023, methodology): “Using the DSSP algorithm 74,75 , we obtained secondary structure information based on AlphaFold models (Fig.”