AcACS: Improving the Prediction Accuracy of Protein Subcellular Locations and Protein Classification by Incorporating the Average Chemical Shifts Composition. (2nd July 2014)

• Record Type: Journal Article
• Authors: Fan, Guo-Liang; Liu, Yan-Ling; Zuo, Yong-Chun; Mei, Han-Xue; Rang, Yi; Hou, Bao-Yan; Zhao, Yan
• Other Names: Lin Hao Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2014(2014)
• Publication Date: 2014-07-02
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2014/864135
• Languages: English
• ISSNs: 2356-6140

Abstract

Abstract : The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.