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Computational prediction of eukaryotic phosphorylation sites
- Bioinformatics
, 2011
"... Motivation: Kinase-mediated phosphorylation is the central mecha-nism of post-translational modication to regulate cellular responses and phenotypes. Signalling defects associated with protein phospho-rylation are linked to many diseases, particularly cancer. Characte-rizing protein kinases and thei ..."
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Cited by 13 (5 self)
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Motivation: Kinase-mediated phosphorylation is the central mecha-nism of post-translational modication to regulate cellular responses and phenotypes. Signalling defects associated with protein phospho-rylation are linked to many diseases, particularly cancer. Characte-rizing protein kinases and their substrates enhances our ability to understand and treat such diseases and broadens our knowledge of signaling networks in general. While most or all protein kinases have been identied in well-studied eukaryotes, the sites that they phosphorylate have been only partially elucidated. Experimental methods for identifying phospho-rylation sites are resource-intensive, so the ability to computationally predict potential sites has considerable value. Results: Many computational techniques for phosphorylation site prediction have been proposed, most of which are available on the web. These techniques differ in several ways, including the machine learning technique used; the amount of sequence information used; whether or not structural information is used in addition to sequence information; whether predictions are made for specic kinases or for kinases in general; and sources of training and testing data. This review summarizes, categorizes, and compares the available methods for phosphorylation site prediction, and provides an over-view of the challenges that are faced when designing predictors and how they have been addressed. It should therefore be useful both for those wishing to choose a phosphorylation site predictor for their particular biological application, and for those attempting to improve upon established techniques in the future. Contact:
using random forests and
"... Computational phosphorylation site prediction in plants ..."
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unknown title
"... Case study: using sequence homology to identify putative phosphorylation sites in an evolutionarily distant species (honeybee) ..."
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Case study: using sequence homology to identify putative phosphorylation sites in an evolutionarily distant species (honeybee)
unknown title
, 2012
"... Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights ..."
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Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights
2013, pages 1–9
, 2012
"... Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights ..."
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Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights
Saskatoon By
"... In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in w ..."
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In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis.
International Journal of Nanomedicine Dovepress submit your manuscript | www.dovepress.com
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www.mdpi.com/journal/ijms Article Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles
, 2012
"... Abstract: Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for h ..."
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Abstract: Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins ’ functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71 % for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server
