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acACS: Improving the Prediction Accuracy of Protein Subcellular Locations and Protein Classification by Incorporating the Average Chemical Shifts Composition
oleh: Guo-Liang Fan, Yan-Ling Liu, Yong-Chun Zuo, Han-Xue Mei, Yi Rang, Bao-Yan Hou, Yan Zhao
Format: | Article |
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Diterbitkan: | Wiley 2014-01-01 |
Deskripsi
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.