Simple jQuery Dropdowns
Please use this identifier to cite or link to this item:
Title: OsFP: A web server for predicting the oligomeric states of fluorescent proteins
Authors: Saw Simeon
Watshara Shoombuatong
Nuttapat Anuwongcharoen
Likit Preeyanon
Virapong Prachayasittikul
Jarl E.S. Wikberg
Chanin Nantasenamat
Mahidol University
Uppsala Universitet
Keywords: Chemistry;Computer Science
Issue Date: 20-Dec-2016
Citation: Journal of Cheminformatics. Vol.8, No.1 (2016)
Abstract: © 2016 The Author(s). Background: Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence. Results: After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment. Conclusion: osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at while the source code and data set is provided on GitHub at Graphical Abstract.
ISSN: 17582946
Appears in Collections:Scopus 2016-2017

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.