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

• Main Authors: Watshara Shoombuatong, Patrinee Traisathit, Sukon Prasitwattanaseree, Chatchai Tayapiwatana, Robert Cutler, Jeerayut Chaijaruwanich
• Format: Journal
• Published: 2018
• Subjects: Biochemistry, Genetics and Molecular Biology; Computer Science; Social Sciences
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79960970337&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/49705
• Institution: Chiang Mai University

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. © 2011 Inderscience Enterprises Ltd.