ArticleProceedings of the National Academy of Sciences of the United States of America2007
Growth of novel protein structural data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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65 citing papers in PubMed.
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- How Many Protein Sequences Fold to a Given Structure? A Coevolutionary Analysis.Biophysical journal · 2017Article
- Augmenting the anisotropic network model with torsional potentials improves PATH performance, enabling detailed comparison with experimental rate data.Structural dynamics (Melville, N.Y.) · 2017Article
- NSiteMatch: Prediction of Binding Sites of Nucleotides by Identifying the Structure Similarity of Local Surface Patches.Computational and mathematical methods in medicine · 2017Article
- Atomic-level analysis of membrane-protein structure.Nature structural & molecular biology · 2016Review
- New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities.Structure (London, England : 1993) · 2016Article
- Navigating Membrane Protein Structure, Dynamics, and Energy Landscapes Using Spin Labeling and EPR Spectroscopy.Methods in enzymology · 2015Article
- Covering complete proteomes with X-ray structures: a current snapshot.Acta crystallographica. Section D, Biological crystallography · 2014Article
- Trends in structural coverage of the protein universe and the impact of the Protein Structure Initiative.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- FireDB: a compendium of biological and pharmacologically relevant ligands.Nucleic acids research · 2014Article
- Protein crystallography for aspiring crystallographers or how to avoid pitfalls and traps in macromolecular structure determination.The FEBS journal · 2013Review
- An estimated 5% of new protein structures solved today represent a new Pfam family.Acta crystallographica. Section D, Biological crystallography · 2013Article
- Defining and predicting structurally conserved regions in protein superfamilies.Bioinformatics (Oxford, England) · 2013Article
- Assessing the accuracy of template-based structure prediction metaservers by comparison with structural genomics structures.Journal of structural and functional genomics · 2012Article
- Structural genomics plucks high-hanging membrane proteins.Current opinion in structural biology · 2012Review
5 more citing papers are in PubMed but not listed here.
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1 author.
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Abstract
Contrary to popular assumption, the rate of growth of structural data has slowed, and the Protein Data Bank (PDB) has not been growing exponentially since 1995. Reaching such a dramatic conclusion requires careful measurement of growth of novel structures, which can be achieved by clustering entry sequences, or by using a novel index to down-weight entries with a higher number of sequence neighbors. These measures agree, and growth rates are very similar for entire PDB files, clusters, and weighted chains. The overall sizes of Structural Classification of Proteins (SCOP) categories (number of families, superfamilies, and folds) appear to be directly proportional to the number of deposited PDB files. Using our weighted chain count, which is most correlated to the change in the size of each SCOP category in any time period, shows that the rate of increase of SCOP categories is actually slowing down. This enables the final size of each of these SCOP categories to be predicted without examining or comparing protein structures. In the last 3 years, structures solved by structural genomics (SG) initiatives, especially the United States National Institutes of Health Protein Structure Initiative, have begun to redress the slowing growth of the PDB. Structures solved by SG are 3.8 times less sequence-redundant than typical PDB structures. Since mid-2004, SG programs have contributed half the novel structures measured by weighted chain counts. Our analysis does not rely on visual inspection of coordinate sets: it is done automatically, providing an accurate, up-to-date measure of the growth of novel protein structural data.
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