XtalPred: a web server for prediction of protein crystallizability
By Lukasz Slabinski, Lukasz Jaroszewski, Leszek Rychlewski, Ian A. Wilson, S. Lesley, Adam Godzik. Bioinform., 2007
304 citations recorded by the source corpus.
UNLABELLED XtalPred is a web server for prediction of protein crystallizability. The prediction is made by comparing several features of the protein with distributions of these features in TargetDB and combining the results into an overall probability of crystallization. XtalPred provides: (1) a detailed comparison of the protein's features to the corresponding distribution from TargetDB; (2) a summary of protein features and predictions that indicate problems that are likely to be encountered during protein crystallization; (3) prediction of ligands; and (4) (optional) lists of close homologs from complete microbial genomes that are more likely to crystallize. AVAILABILITY The XtalPred web server is freely available for academic users on http://ffas.burnham.org/XtalPred
Subfields: Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/0478b649859390d87af4d7ae5fe302ff8fe8e50e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- Gaetano T. Montelione
- Yuanpeng Janet Huang
- Lukasz Kurgan
- R. Xiao
- Ke Chen
- P. Rossi
- Oliver F. Lange
- Nikolaos G. Sgourakis
In Molecular Biology
- Gaetano T. Montelione
- Yuanpeng Janet Huang
- Lukasz Kurgan
- R. Xiao
- Ke Chen
- P. Rossi
- Nikolaos G. Sgourakis
- Santrupti Nerli
Citations in the corpus
- High-throughput production of human proteins for crystallization: The SGC experience (2010, methodology): “We have addressed this using XtalPred (Slabinski et al., 2007), looking at all crystal structures solved by the SGC (Supplementary Fig.”
- Computational prediction of secondary and supersecondary structures. (2013, background): “These characteristics include multiple alignment (17), prediction of protein-ligand interactions (18-20), prediction of residue depth (21, 22), structural classes and folds (23-25), residue contacts (26, 27), disorder (28-30), folding rates and types (31-33), and target selection for structural geno”
- The High-Throughput Protein Sample Production Platform of the Northeast Structural Genomics Consortium (2010, background): “More specifically, in the area of protein production, both experimental and bioinformatics studies have been published describing efforts to identify parameters and procedures that correlate with success, such as high levels of protein solubility or clone to PDB deposition rates (Dyson et al., 2004;”