A series of PDB-related databanks for everyday needs
By Wouter G. Touw, Coos A. B. Baakman, Jon Black, Tim A. H. te Beek, Elmar Krieger, Robbie P. Joosten, G. Vriend. Nucleic Acids Res., 2014
828 citations recorded by the source corpus.
We present a series of databanks (http://swift.cmbi.ru.nl/gv/facilities/) that hold information that is computationally derived from Protein Data Bank (PDB) entries and that might augment macromolecular structure studies. These derived databanks run parallel to the PDB, i.e. they have one entry per PDB entry. Several of the well-established databanks such as HSSP, PDBREPORT and PDB_REDO have been updated and/or improved. The software that creates the DSSP databank, for example, has been rewritten to better cope with π-helices. A large number of databanks have been added to aid computational structural biology; some examples are lists of residues that make crystal contacts, lists of contacting residues using a series of contact definitions or lists of residue accessibilities. PDB files are not the optimal presentation of the underlying data for many studies. We therefore made a series of databanks that hold PDB files in an easier to use or more consistent representation. The BDB databank holds X-ray PDB files with consistently represented B-factors. We also added several visualization tools to aid the users of our databanks.
Subfields: Materials Chemistry, Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/cc01245cf80a4e59ca77ab60cb90734cc2c77261
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In Materials Chemistry
- George M. Church
- Surojit Biswas
- Douglas E. V. Pires
- David B. Ascher
- Nazim Bouatta
- Peter K. Sorger
- Charlotte Rochereau
- Ratul Chowdhury
In Molecular Biology
- Douglas E. V. Pires
- Surojit Biswas
- David B. Ascher
- George M. Church
- Thanh-Binh Nguyen
- Qisheng Pan
- Peter K. Sorger
- Nazim Bouatta
In Spectroscopy
Citations in the corpus
- Single-sequence protein structure prediction using language models and deep learning (2022, methodology): “To investigate the structural basis for these differences in performance, we applied the DSSP algorithm 43 to determine the fraction of each secondary structure element (helical-alpha, 5 and 3/10; beta-strand; bridge; and unstructured loops, bends and hydrogen-bonded turns) in PDB structures for…”
- ProViz—a web-based visualization tool to investigate the functional and evolutionary features of protein sequences (2016, neutral): “The query protein sequence is also annotated with protein feature data from numerous resources (Figure 2A and Supplementary Table S3) (3,9,10,12– 15, 18 , 19 , 22 , 35 , 36 ).”
- RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network (2019, methodology): “PyRosetta was used [3] since it was easier to integrated the code with the neural network’s python script and combine the Rosetta engine with Biopython [4] and DSSP [12] [18].”
- Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, methodology): “Secondary structure type (SST), obtained from the DSSP algorithm [90,91].”