Prediction of the disulphide bonding state of cysteines in proteins using Conditional Random Fields
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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