Euk-mPLoc: A fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites

被引:320
作者
Chou, Kuo-Chen
Shen, Hong-Bin
机构
[1] Gordon Life Sci Inst, San Diego, CA 92130 USA
[2] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai 200030, Peoples R China
[3] So Yangtze Univ, Sch Informat Engn, Wuxi, Peoples R China
关键词
large-scale prediction; eukaryotic protein; multiple locations; ensemble classifier; fusion; optimal threshold; Euk-mPLoc;
D O I
10.1021/pr060635i
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
One of the critical challenges in predicting protein subcellular localization is how to deal with the case of multiple location sites. Unfortunately, so far, no efforts have been made in this regard except for the one focused on the proteins in budding yeast only. For most existing predictors, the multiple-site proteins are either excluded from consideration or assumed even not existing. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. For instance, according to the Swiss-Prot database (version 50.7, released 19-Sept-2006), among the 33 925 eukaryotic protein entries that have experimentally observed subcellular location annotations, 2715 have multiple location sites, meaning about 8% bearing the multiplex feature. Proteins with multiple locations or dynamic feature of this kind are particularly interesting because they may have some very special biological functions intriguing to investigators in both basic research and drug discovery. Meanwhile, according to the same Swiss-Prot database, the number of total eukaryotic protein entries (except those annotated with '' fragment '' or those with less than 50 amino acids) is 90 909, meaning a gap of (90 909-33 925) = 56 984 entries for which no knowledge is available about their subcellular locations. Although one can use the computational approach to predict the desired information for the blank, so far, all the existing methods for predicting eukaryotic protein subcellular localization are limited in the case of single location site only. To overcome such a barrier, a new ensemble classifier, named Euk-mPLoc, was developed that can be used to deal with the case of multiple location sites as well. Euk-mPLoc is freely accessible to the public as a Web server at http://202.120.37.186/bioinf/euk-multi. Meanwhile, to support the people working in the relevant areas, Euk-mPLoc has been used to identify all eukaryotic protein entries in the Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The large-scale results thus obtained have been deposited at the same Web site via a downloadable file prepared with Microsoft Excel and named "Tab_Euk-mPLoc.xls". Furthermore, to include new entries of eukaryotic proteins and reflect the continuous development of Euk-mPLoc in both the coverage scope and prediction accuracy, we will timely update the downloadable file as well as the predictor, and keep users informed by publishing a short note in the Journal and making an announcement in the Web Page. Keywords: Large-scale prediction center dot Eukaryotic protein center dot Multiple locations center dot Ensemble classifier center dot Fusion center dot Optimal threshold center dot Euk-mPLoc
引用
收藏
页码:1728 / 1734
页数:7
相关论文
共 37 条
[1]   Gene Ontology: tool for the unification of biology [J].
Ashburner, M ;
Ball, CA ;
Blake, JA ;
Botstein, D ;
Butler, H ;
Cherry, JM ;
Davis, AP ;
Dolinski, K ;
Dwight, SS ;
Eppig, JT ;
Harris, MA ;
Hill, DP ;
Issel-Tarver, L ;
Kasarskis, A ;
Lewis, S ;
Matese, JC ;
Richardson, JE ;
Ringwald, M ;
Rubin, GM ;
Sherlock, G .
NATURE GENETICS, 2000, 25 (01) :25-29
[2]   Prediction of protein structural class with Rough Sets [J].
Cao, YF ;
Liu, S ;
Zhang, LD ;
Qin, J ;
Wang, J ;
Tang, KX .
BMC BIOINFORMATICS, 2006, 7 (1)
[3]   Relation between amino acid composition and cellular location of proteins [J].
Cedano, J ;
Aloy, P ;
PerezPons, JA ;
Querol, E .
JOURNAL OF MOLECULAR BIOLOGY, 1997, 266 (03) :594-600
[4]   Using pseudo-amino acid composition and support vector machine to predict protein structural class [J].
Chen, Chao ;
Tian, Yuan-Xin ;
Zou, Xiao-Yong ;
Cai, Pei-Xiang ;
Mo, Jin-Yuan .
JOURNAL OF THEORETICAL BIOLOGY, 2006, 243 (03) :444-448
[5]   Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network [J].
Chen, Chao ;
Zhou, Xibin ;
Tian, Yuanxin ;
Zou, Xiaoyong ;
Cai, Peixiang .
ANALYTICAL BIOCHEMISTRY, 2006, 357 (01) :116-121
[6]   Predicting protein localization in budding yeast [J].
Chou, KC ;
Cai, YD .
BIOINFORMATICS, 2005, 21 (07) :944-950
[7]   Structural bioinformatics and its impact to biomedical science [J].
Chou, KC .
CURRENT MEDICINAL CHEMISTRY, 2004, 11 (16) :2105-2134
[8]   Protein subcellular location prediction [J].
Chou, KC ;
Elrod, DW .
PROTEIN ENGINEERING, 1999, 12 (02) :107-118
[9]   PREDICTION OF PROTEIN STRUCTURAL CLASSES [J].
CHOU, KC ;
ZHANG, CT .
CRITICAL REVIEWS IN BIOCHEMISTRY AND MOLECULAR BIOLOGY, 1995, 30 (04) :275-349
[10]   A NOVEL-APPROACH TO PREDICTING PROTEIN STRUCTURAL CLASSES IN A (20-1)-D AMINO-ACID-COMPOSITION SPACE [J].
CHOU, KC .
PROTEINS-STRUCTURE FUNCTION AND GENETICS, 1995, 21 (04) :319-344