Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method

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	Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method
	Cui, J ; Han, LY ; Cai, CZ ; Zheng, CJ ; Ji, ZL ; Chen, YZ ; Ji ZL(纪志梁)
刊名	http://dx.doi.org/10.1159/000088839
	2005
关键词	COMPLETE GENOME SEQUENCE SUPPORT VECTOR MACHINES RESTRICTION-MODIFICATION SYSTEM COMPLETE NUCLEOTIDE-SEQUENCE ESCHERICHIA-COLI K-12 AMINO-ACID-SEQUENCE CRYSTAL-STRUCTURE DNA-SEQUENCE PSEUDOMONAS-AERUGINOSA ANGSTROM RESOLUTION
英文摘要	A substantial percentage of the putative protein-encoding open reading frames (ORFs) in bacterial genomes have no homolog of known function, and their function cannot be confidently assigned on the basis of sequence similarity. Methods not based on sequence similarity are needed and being developed. One method, SVMProt (http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi), predicts protein functional family irrespective of sequence similarity ( Nucleic Acids Res. 2003; 31: 3692-3697). While it has been tested on a large number of proteins, its capability for non-homologous proteins has so far been evaluated for a relatively small number of proteins, and additional tests are needed to more fully assess SVMProt. In this work, 90 novel bacterial proteins (non-homologous to known proteins) are used to evaluate the capability of SVMProt. These proteins are such that none of their homologs are in the Swiss-Prot database, their functions not clearly described in the literature, and they themselves and their homologs are not included in the training sets of SVMProt. They represent proteins whose function cannot be confidently predicted by sequence similarity methods at present. The predicted functional class of 76.7% of each of these proteins shows various levels of consistency with the literature-described function, compared to the overall accuracy of 87% for the SVMProt functional class assignment of 34,582 proteins that have at least one homolog of known function. Our study suggests that SVMProt is capable of assigning functional class for novel bacterial proteins at a level not too much lower than that of sequence alignment methods for homologous proteins. Copyright (C) 2005 S. Karger AG, Basel.
语种	英语
出版者	J MOL MICROB BIOTECH
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/90549]
专题	生命科学－已发表论文
推荐引用方式 GB/T 7714	Cui, J,Han, LY,Cai, CZ,et al. Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method[J]. http://dx.doi.org/10.1159/000088839,2005.
APA	Cui, J.,Han, LY.,Cai, CZ.,Zheng, CJ.,Ji, ZL.,...&纪志梁.(2005).Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method.http://dx.doi.org/10.1159/000088839.
MLA	Cui, J,et al."Prediction of functional class of novel bacterial proteins without the use of sequence similarity by a statistical learning method".http://dx.doi.org/10.1159/000088839 (2005).