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

被引：13

作者：

Fan, Guo-Liang ^{[1
]}

Liu, Yan-Ling ^{[1
]}

Zuo, Yong-Chun ^{[2
]}

Mei, Han-Xue ^{[1
]}

Rang, Yi ^{[1
]}

Hou, Bao-Yan ^{[1
]}

Zhao, Yan ^{[1
]}

机构：

[1] Inner Mongolia Univ, Sch Phys Sci & Technol, Dept Phys, Hohhot 010021, Peoples R China

[2] Inner Mongolia Univ, Coll Life Sci, Key Lab Mammalian Reprod Biol & Biotechnol, Minist Educ, Hohhot 010021, Peoples R China

来源：

SCIENTIFIC WORLD JOURNAL | 2014年

基金：

中国国家自然科学基金;

关键词：

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

D O I：

10.1155/2014/864135

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：9

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