The Protein Data Bank.

By Helen M. Berman, Tammy Battistuz, Talapady N. Bhat, Wolfgang Bluhm, Philip E. Bourne, Philip E. Bourne, K. Burkhardt, Zukang Feng, Gary Gilliland, L. Iype and 10 more. 2002

Subfields: Computational Biology & Bioinformatics

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Citations in the corpus

HH-suite3 for fast remote homology detection and deep protein annotation (2019, methodology): “These queries were searched against the PDB70 database containing 35000 profile HMMs of average length 234.”
I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation (2017, methodology): “There were four targets (PDB ID: 1NNX, 2O1Q, 3B79, 3DOJ) in the test cases for which the LOMETS-based MR succeeded but I-TASSER-MR failed.”
The SWISS-MODEL Repository—new features and functionality (2016, methodology): “The SWISSMODEL Template Library (SMTL) provides sets of atomic coordinates derived from experimental structures in the PDB (3).”
Towards automated crystallographic structure refinement with phenix.refine (2012, methodology): “The PDB format (Bernstein et al., 1977; Berman et al., 2000) is the most commonly used format for exchanging macromolecular model data and is therefore available as the input format for refinement in PHENIX.”
Toward the estimation of the absolute quality of individual protein structure models (2010, methodology): “QMEAN is a scoring function consisting of a linear combination of six structural descriptors as described elsewhere in more detail (Benkert et al., 2008, 2009).”
DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, background): “For example, the SCOP 1.75 curated in 2009 has 1195 unique folds, whereas SCOP 2.06 only has 26 more folds identified from the recent PDB (Chandonia et al., 2016).”
Learning generative models for protein fold families (2011, methodology): “(B) The edges of the model used in the discriminative task, overlaid on the structure of the WW domain of a ubiquitin protein ligase(PDB id: 1I5H)[5]”
Graph Kernels (2008, positive): “, 1997), ID 1a6x in the Protein Data Bank (Berman et al., 2000).”
The Rosetta all-atom energy function for macromolecular modeling and design (2017, background): “32 Later, the authors added terms for packing of van der Waals spheres, hydrogen bonding, secondary-structure, and van der Waals interactions to improve the performance of ab initio structure prediction.”
End-to-end differentiable learning of protein structure (2018, methodology): “We also adopted the CASP division of test structures into free modeling (FM) targets that assess prediction of novel folds, and template‐based (TBM and TBM‐hard) targets that assess prediction of folds with known homologs in the Protein Data Bank (PDB) (Bernstein et al., 1977).”
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins (2014, background): “For many years, accurate contact prediction was hampered by the effects of the relatedness of homologous sequences and the inability to extract correct residue pairings from vastly intertwined networks of contacts (de Juan et al., 2013).”
Data growth and its impact on the SCOP database: new developments (2007, background): “The SCOP domains correspond to 34 495 entries in the Protein Data Bank (PDB) (2).”