acACS: Improving the Prediction Accuracy of Protein Subcellular Locations and Protein Classification by Incorporating the Average Chemical Shifts Composition

作者：Fan, Guo-Liang^*; Liu, Yan-Ling; Zuo, Yong-Chun; Mei, Han-Xue; Rang, Yi; Hou, Bao-Yan; Zhao, Yan

来源：The Scientific World Journal, 2014, 2014: 864135.

DOI：10.1155/2014/864135

• AMINO-ACID-COMPOSITION
• REMOTE HOMOLOGY DETECTION
• SECONDARY STRUCTURE
• GENERAL-FORM
• EVOLUTIONARY INFORMATION
• EMPIRICAL CORRELATION
• STRUCTURAL CLASS
• LOCALIZATION
• SEQUENCE
• IDENTIFICATION

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.

• 出版日期2014
• 单位内蒙古大学