X-PLOR Version 3.1: A System for X-ray Crystallography and NMR
By A. Brunger. 1992
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Jane S. Richardson
- T. Croll
- R. Tejero
- Peter Güntert
- David C Richardson
- Aneerban Bhattacharya
- Randy J. Read
- Pavel V. Afonine
Citations in the corpus
- Features and development of Coot (2010, neutral): “Kleywegt & Jones (1996) included the Ramachandran plot in the restraints during refinement using X-PLOR (Bru¨nger, 1992) and reported that the number of Ramachandran outliers was reduced by about a third using moderate force constants.”
- Automated NMR structure calculation with CYANA. (2004, background): “On the other hand, in practice the algorithm might be expected to tolerate a provided that most missing assignments are of “unimportant” atoms that are involved in only few NOEs.”
- De novo protein structure determination using sparse NMR data (2000, methodology): “Protein global folds were generated for each NOE constraint set using the program X-PLOR and standard NMR substructure embedding and distance geometry/simulated annealing protocols (dg_sub_em bed.inp / dgsa.inp / refine.inp) (Brünger, 1992).”
- Automated NMR protein structure calculation (2003, methodology): “The widely used automated NOESY assignment procedure ARIA [49–52] has been interfaced initially with the structure calculation program XPLOR [53] and later with the program CNS [54].”
- Atomic resolution structures of trypsin provide insight into structural radiation damage. (2001, methodology): “The initial re®nement included simulated annealing (3000 K) and conjugate-gradient minimization in X-PLOR (BruÈ nger, 1992), accompanied by conjugate-gradient leastsquares minimization with SHELXL (Sheldrick & Schneider, 1997).”
- New ways to boost molecular dynamics simulations (2015, background): “We, therefore, believe that MD simulations are best approached with a capable “home base” on the CPU, which can handle the countless complications in real-life applications (like knowledge-based force fields,[6] X-ray,[7] and nuclear magnetic resonance (NMR) refinement[8]) and offloads tasks to the ”
- De novo protein design: towards fully automated sequence selection. (1997, methodology): “An ensemble of 32 structures (Figure 4c) was obtained using X-PLOR (BruÈ nger, 1992) with standard protocols for hybrid distance geometry-simulated annealing.”
- An integrated platform for automated analysis of protein NMR structures. (2005, methodology): “AutoStructure generates input constraint lists suitable for either XPLOR/CNS or DYANA for protein structure generation calculations.”
- A software tool for the prediction of Xaa-Pro peptide bond conformations in proteins based on 13C chemical shift statistics (2002, methodology): “Information about the presence of a cis peptide bond thus has to be provided as input for structure determination programs like, X-PLOR (Brünger, 1992; Badger et al., 1999), DYANA (Güntert et al.”
- Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS (2002, methodology): “Future plans are to combine ATNOS and CANDID into one autonomous software package for use in conjunction with a selection of the commonly used structure calculation algorithms, such as XPLOR, CNS and DYANA (Brünger, 1992; Brünger et al., 1998; Güntert et al., 1997).”
- Evaluating protein structures determined by structural genomics consortia (2006, methodology): “in global quality scores for structures determined with manually and automatically analyzed NOESY data, nor any significant difference when automated NOESY assignments were determined using the programs AutoStructure,(20) Candid,(19) or ARIA.(17,31) While detailed features of NMR structures do somet”