Prediction of the disulphide bonding state of cysteines in proteins using Conditional Random Fields Online publication date: Sat, 24-Jan-2015
by Watshara Shoombuatong, Patrinee Traisathit, Sukon Prasitwattanaseree, Chatchai Tayapiwatana, Robert Cutler, Jeerayut Chaijaruwanich
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 5, No. 4, 2011
Abstract: The formation of disulphide bonds between cysteines plays a major role in protein folding, structure, function and evolution. Many computational approaches have been used to predict the disulphide bonding state of cysteines. In our work, we developed a novel method based on Conditional Random Fields (CRFs) to predict the disulphide bonding state from protein primary sequence, predicted secondary structures and predicted relative solvent accessibilities (all-state information). Our experiments obtain 84% accuracy, 88% precision and 94% recall, using all-state information. However, our results show essentially identical results when using protein sequence and predicted relative solvent accessibilities in the absence of secondary structure.
Online publication date: Sat, 24-Jan-2015
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