Composition Profiler: a tool for discovery and visualization of amino acid composition differences
By Vladimir Vacic, Vladimir N Uversky, A. Keith Dunker, Stefano Lonardi. BMC Bioinformatics, 2007
402 citations recorded by the source corpus.
BackgroundComposition Profiler is a web-based tool for semi-automatic discovery of enrichment or depletion of amino acids, either individually or grouped by their physico-chemical or structural properties.ResultsThe program takes two samples of amino acids as input: a query sample and a reference sample. The latter provides a suitable background amino acid distribution, and should be chosen according to the nature of the query sample, for example, a standard protein database (e.g. SwissProt, PDB), a representative sample of proteins from the organism under study, or a group of proteins with a contrasting functional annotation. The results of the analysis of amino acid composition differences are summarized in textual and graphical form.ConclusionAs an exploratory data mining tool, our software can be used to guide feature selection for protein function or structure predictors. For classes of proteins with significant differences in frequencies of amino acids having particular physico-chemical (e.g. hydrophobicity or charge) or structural (e.g. α helix propensity) properties, Composition Profiler can be used as a rough, light-weight visual classifier.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/be8499e7dc462e67c0914471274e0ee157170810
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In Molecular Biology
- Vladimir N Uversky
- Bin Xue
- Dan Ofer
- Michal Linial
- Julien Jorda
- A. Kajava
- Lukasz Kurgan
- Zsuzsanna Dosztányi
Citations in the corpus
- Bilingual language model for protein sequence and structure (2024, neutral): “For the final evaluation on the test set, we chose a configuration providing a trade-off between similarity (to the native) in terms of structure and unsurprising sequence (proxied by Kullback-Leibler divergence between the amino acid distribution in UniProt and the sequences generated [53]).”
- A decade and a half of protein intrinsic disorder: Biology still waits for physics (2013, background): “Identification of IDPs as unique entities belonging to a new protein tribe is directly related to the recognition that their amino acid sequences are dramatically different from those of ordered proteins.10,12,13,30–32 For example, it has been pointed out that the low content of hydrophobic residues”
- ProFET: Feature engineering captures high-level protein functions (2015, methodology): “An additional subdivision of disorder propensity was adapted from Composition Profiler (Vacic et al. , 2007): 1:‘ARSQEGKP‘, 2:‘ILNCFYVW’ and 3:‘DHMT’.”
- Protein tandem repeats: the more perfect the less structured (2010, methodology): “These biases in the amino acid compositions of IDPs and IDRs can be visualized using a normalization procedure known as compositional profiling [32, 33, 37].”