TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information

被引:16
作者
Alballa, Munira [1 ,2 ]
Aplop, Faizah [3 ]
Butler, Gregory [1 ,4 ]
机构
[1] Concordia Univ, Dept Comp Sci & Software Engn, Montreal, PQ, Canada
[2] King Saud Univ, Coll Comp & Informat Sci, Riyadh, Saudi Arabia
[3] Univ Malaysia Terengganu, Sch Informat & Appl Math, Terengganu, Malaysia
[4] Concordia Univ, Ctr Struct & Funct Genom, Montreal, PQ, Canada
关键词
MULTIPLE SEQUENCE ALIGNMENT; CLASSIFICATION DATABASE; MEMBRANE TRANSPORTERS; TOPOLOGY PREDICTION; WEB SERVER; GENOME; ANNOTATION; NETWORKS; TCDB;
D O I
10.1371/journal.pone.0227683
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
Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.
引用
收藏
页数:23
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共 58 条
[21]   Comparing two K-category assignments by a K-category correlation coefficient [J].
Gorodkin, J .
COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2004, 28 (5-6) :367-374
[22]   Bioinformatics approaches for functional annotation of membrane proteins [J].
Gromiha, M. Michael ;
Ou, Yu-Yen .
BRIEFINGS IN BIOINFORMATICS, 2014, 15 (02) :155-168
[23]   Functional discrimination of membrane proteins using machine learning techniques [J].
Gromiha, M. Michael ;
Yabuki, Yukimitsu .
BMC BIOINFORMATICS, 2008, 9 (1)
[24]   The Gene Ontology (GO) database and informatics resource [J].
Harris, MA ;
Clark, J ;
Ireland, A ;
Lomax, J ;
Ashburner, M ;
Foulger, R ;
Eilbeck, K ;
Lewis, S ;
Marshall, B ;
Mungall, C ;
Richter, J ;
Rubin, GM ;
Blake, JA ;
Bult, C ;
Dolan, M ;
Drabkin, H ;
Eppig, JT ;
Hill, DP ;
Ni, L ;
Ringwald, M ;
Balakrishnan, R ;
Cherry, JM ;
Christie, KR ;
Costanzo, MC ;
Dwight, SS ;
Engel, S ;
Fisk, DG ;
Hirschman, JE ;
Hong, EL ;
Nash, RS ;
Sethuraman, A ;
Theesfeld, CL ;
Botstein, D ;
Dolinski, K ;
Feierbach, B ;
Berardini, T ;
Mundodi, S ;
Rhee, SY ;
Apweiler, R ;
Barrell, D ;
Camon, E ;
Dimmer, E ;
Lee, V ;
Chisholm, R ;
Gaudet, P ;
Kibbe, W ;
Kishore, R ;
Schwarz, EM ;
Sternberg, P ;
Gwinn, M .
NUCLEIC ACIDS RESEARCH, 2004, 32 :D258-D261
[25]   PREDICTION OF PROTEIN ANTIGENIC DETERMINANTS FROM AMINO-ACID-SEQUENCES [J].
HOPP, TP ;
WOODS, KR .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES, 1981, 78 (06) :3824-3828
[26]   The MetaCyc database [J].
Karp, PD ;
Riley, M ;
Paley, SM ;
Pellegrini-Toole, A .
NUCLEIC ACIDS RESEARCH, 2002, 30 (01) :59-61
[27]   PDBTM: Protein Data Bank of transmembrane proteins after 8 years [J].
Kozma, Daniel ;
Simon, Istvan ;
Tusnady, Gabor E. .
NUCLEIC ACIDS RESEARCH, 2013, 41 (D1) :D524-D529
[28]  
Kwak SG, 2017, KOREAN J ANESTHESIOL, V70, P144, DOI 10.4097/kjae.2017.70.2.144
[29]   A SIMPLE METHOD FOR DISPLAYING THE HYDROPATHIC CHARACTER OF A PROTEIN [J].
KYTE, J ;
DOOLITTLE, RF .
JOURNAL OF MOLECULAR BIOLOGY, 1982, 157 (01) :105-132
[30]   Annotation-based inference of transporter function [J].
Lee, Thomas J. ;
Paulsen, Ian ;
Karp, Peter .
BIOINFORMATICS, 2008, 24 (13) :I259-I267