Interactions of Aromatic Residues in Amyloids: A Survey of Protein Data Bank Crystallographic Data

2017 ◽  
Vol 17 (12) ◽  
pp. 6353-6362 ◽  
Author(s):  
Ivana M. Stanković ◽  
Dragana M. Božinovski ◽  
Edward N. Brothers ◽  
Milivoj R. Belić ◽  
Michael B. Hall ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Data Bank ◽  
Crystallographic Data ◽  
Aromatic Residues

Bond-valence analyses of the crystal structures of FeMo/V cofactors in FeMo/V proteins

2020 ◽  
Vol 76 (5) ◽  
pp. 428-437 ◽  
Author(s):  
Wan-Ting Jin ◽  
Min Yang ◽  
Shuang-Shuang Zhu ◽  
Zhao-Hui Zhou
Keyword(s):  
Error Analysis ◽  
Crystal Structures ◽  
Protein Data Bank ◽  
Data Bank ◽  
Bond Valence ◽  
Crystallographic Data ◽  
Data Sets ◽  
Electron Configuration ◽  
The Individual ◽  
Bond Valence Method

The bond-valence method has been used for valence calculations of FeMo/V cofactors in FeMo/V proteins using 51 crystallographic data sets of FeMo/V proteins from the Protein Data Bank. The calculations show molybdenum(III) to be present in MoFe7S9C(Cys)(HHis)[R-(H)homocit] (where H4homocit is homocitric acid, HCys is cysteine and HHis is histidine) in FeMo cofactors, while vanadium(III) with a more reduced iron complement is obtained for FeV cofactors. Using an error analysis of the calculated valences, it was found that in FeMo cofactors Fe1, Fe6 and Fe7 can be unambiguously assigned as iron(III), while Fe2, Fe3, Fe4 and Fe5 show different degrees of mixed valences for the individual Fe atoms. For the FeV cofactors in PDB entry 5n6y, Fe4, Fe5 and Fe6 correspond to iron(II), iron(II) and iron(III), respectively, while Fe1, Fe2, Fe3 and Fe7 exhibit strongly mixed valences. Special situations such as CO-bound and selenium-substituted FeMo cofactors and O(N)H-bridged FeV cofactors are also discussed and suggest rearrangement of the electron configuration on the substitution of the bridging S atoms.

scholarly journals A mosaic bulk-solvent model improves density maps and the fit between model and data

2021 ◽  
Author(s):  
Pavel V. Afonine ◽  
Paul D. Adams ◽  
Oleg V Sobolev ◽  
Alexandre Urzhumtsev
Keyword(s):  
Protein Data Bank ◽  
Model Building ◽  
Data Bank ◽  
Crystallographic Data ◽  
Software Packages ◽  
Map Interpretation ◽  
Density Maps ◽  
Solvent Model ◽  
Modeling Power ◽  
R Factors

Bulk solvent is a major component of bio-macromolecular crystals and therefore contributes significantly to diffraction intensities. Accurate modeling of the bulk-solvent region has been recognized as important for many crystallographic calculations, from computing of R-factors and density maps to model building and refinement. Owing to its simplicity and computational and modeling power, the flat (mask-based) bulk-solvent model introduced by Jiang & Brunger (1994) is used by most modern crystallographic software packages to account for disordered solvent. In this manuscript we describe further developments of the mask-based model that improves the fit between the model and the data and aids in map interpretation. The new algorithm, here referred to as mosaic bulk-solvent model, considers solvent variation across the unit cell. The mosaic model is implemented in the computational crystallography toolbox and can be used in Phenix in most contexts where accounting for bulk-solvent is required. It has been optimized and validated using a sufficiently large subset of the Protein Data Bank entries that have crystallographic data available.

scholarly journals The Protein Data Bank: data distribution and query functionality

2002 ◽  
Vol 58 (s1) ◽  
pp. c214-c214
Author(s):  
W. F. Bluhm ◽  
T. Battistuz ◽  
E. Clingman ◽  
N. Deshpande ◽  
W. Fleri ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Data Distribution ◽  
Data Bank

FLIPPER: predicting and characterizing linear interacting peptides in the Protein Data Bank

2021 ◽  
pp. 166900
Author(s):  
Alexander Miguel Monzon ◽  
Paolo Bonato ◽  
Marco Necci ◽  
Silvio C.E. Tosatto ◽  
Damiano Piovesan
Keyword(s):  
Protein Data Bank ◽  
Data Bank

scholarly journals Validation and correction of Zn–Cys x His y complexes

2016 ◽  
Vol 72 (10) ◽  
pp. 1110-1118 ◽  
Author(s):  
Wouter G. Touw ◽  
Bart van Beusekom ◽  
Jochem M. G. Evers ◽  
Gert Vriend ◽  
Robbie P. Joosten
Keyword(s):  
Crystal Structures ◽  
Protein Data Bank ◽  
Model Validation ◽  
Data Bank ◽  
Zinc Complexes ◽  
Zinc Ions ◽  
Tetrahedral Complex ◽  
Context Sensitive

Many crystal structures in the Protein Data Bank contain zinc ions in a geometrically distorted tetrahedral complex with four Cys and/or His ligands. A method is presented to automatically validate and correct these zinc complexes. Analysis of the corrected zinc complexes shows that the average Zn–Cys distances and Cys–Zn–Cys angles are a function of the number of cysteines and histidines involved. The observed trends can be used to develop more context-sensitive targets for model validation and refinement.

scholarly journals Protein Data Bank: the single global archive for 3D macromolecular structure data

2018 ◽  
Vol 47 (D1) ◽  
pp. D520-D528 ◽  
Author(s):  
◽  
Stephen K Burley ◽  
Helen M Berman ◽  
Charmi Bhikadiya ◽  
Chunxiao Bi ◽  
...  
Keyword(s):  
Protein Data Bank ◽  
Structure Data ◽  
Data Bank ◽  
Macromolecular Structure
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