Ranking Gene Ontology terms for predicting non-classical secretory proteins in eukaryotes and prokaryotes

Abstract

Protein secretion is an important biological process for both eukaryotes and prokaryotes. Several sequence-based methods mainly rely on utilizing various types of complementary features to design accurate classifiers for predicting non-classical secretory proteins. Gene Ontology (GO) terms are increasing informative in predicting protein functions. However, the number of used GO terms is often very large. For example, there are 60,020 GO terms used in the prediction method Euk-mPLoc 2.0 for subcellular localization. This study proposes a novel approach to identify a small set of m top-ranked GO terms served as the only type of input features to design a support vector machine (SVM) based method Sec-GO to predict non-classical secretory proteins in both eukaryotes and prokaryotes. To evaluate the Sec-GO method, two existing methods and their used datasets are adopted for performance comparisons. The Sec-GO method using m=436 GO terms yields an independent test accuracy of 96.7% on mammalian proteins, much better than the existing method SPRED (82.2%) which uses frequencies of tri-peptides and short peptides, secondary structure, and physicochemical properties as input features of a random forest classifier. Furthermore, when applying to Gram-positive bacterial proteins, the Sec-GO with m=158 GO terms has a test accuracy of 94.5%, superior to NClassG+ (90.0%) which uses SVM with several feature types, comprising amino acid composition, di-peptides, physicochemical properties and the position specific weighting matrix. Analysis of the distribution of secretory proteins in a GO database indicates the percentage of the non-classical secretory proteins annotated by GO is larger than that of classical secretory proteins in both eukaryotes and prokaryotes. Of the m top-ranked GO features, the top-four GO terms are all annotated by such subcellular locations as GO:0005576 (Extracellular region). Additionally, the method Sec-GO is easily implemented and its web tool of prediction is available at iclab.life.nctu.edu.tw/secgo.

Designing novel construction for cell surface display of protein E on Escherichia coli using non-classical pathway based on Lpp-OmpA

“…The N-terminal signal peptide may play a fundamental role in these systems as the label signaling secretion (Dalbey and Kuhn 2012 ; Mergulhao et al 2005 ). Recently, in a system which called non-classical secretion pathway, some bacterial proteins have been found to be secreted without any apparent signal peptide (Muesch et al 1990 ; Huang 2012 ). The non-classical secretion pathway is a new phenomenon that has been identified in cells of many mammals and bacteria, but its functional mechanism has not been so far recognized (Hung et al 2010 ; Wang et al 2013 ).…”

Cloning and characterization of a putative gene encoding serine protease inhibitor (251Hbpi) with antifungal activity against Trichophyton rubrum from Hevea brasiliensis leaves

“…InterPro database analysis indicates the 251HbPI amino acid sequence lacks signal peptide, unlike other H. brasiliensis PIs. However, secretory proteins that lack signal peptide can be secreted without N-terminal signal peptides (Bendtsen et al, 2005), such as fibroblast growth factor-1, fibroblast growth factor-2, interleukins-1 beta, and galectins, and can be exported by a distinct non-classical secretion pathway (Huang, 2012). Many bacteria proteins are released via the secretion and twin-arginine translocation pathways, which are non-classical secretion pathways (Bendtsen et al, 2005).…”

Rule-Based Knowledge Acquisition Method for Promoter Prediction in Human andDrosophilaSpecies

“…Prom-Machine [39] uses top 128 of the 256 4-mer motifs to improve prediction sensitivity and specificity. Our earlier works estimated the scores of amino acids [45] and of GO terms [46] for predicting DNA-binding proteins and nonclassical secretory proteins, respectively. That motives this work, in which a score for each 4-mer motif is calculated and the 128 top-ranked 4-mer motifs based on those scores are identified.…”

“…To the best of our knowledge, these three descriptors of physicochemical properties are used herein for the first time in identifying promoter DNA sequences and their sequence-based representation differs from the structural profiles of McPromoter [29]. The 128 top-ranked frequency descriptors of 4-mer motifs are extracted from 256 4-mer combinations of nucleotides (4-base-long nucleotide sequences) according to the scores that equal the difference between the occurrence frequencies of the 4-mer motif in the positive and negative datasets [45, 46]. PromHD further utilizes an effective feature mining algorithm (called DNASDmining), which is based on an inheritable biobjective genetic algorithm [47, 48], to mine informative DNASDs.…”