Improve signal peptide prediction by using functional domain information

2016 ◽  
Author(s):  
Yi-Ze Zhang ◽  
Hong-Bin Shen
Keyword(s):  
Signal Peptide ◽  
Functional Domain ◽  
Signal Peptide Prediction ◽  
Peptide Prediction ◽  
Domain Information

A Novel Fuzzy Fisher Classifier for Signal Peptide Prediction

2011 ◽  
Vol 18 (8) ◽  
pp. 831-838 ◽  
Author(s):  
Cui-Fang Gao ◽  
Zi-Xue Qiu ◽  
Xiao-Jun Wu ◽  
Feng-Wei Tian ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Signal Peptide ◽  
Signal Peptide Prediction ◽  
Peptide Prediction

scholarly journals Comparison of Current Methods for Signal Peptide Prediction in Phytoplasmas

2021 ◽  
Vol 12 ◽  
Author(s):  
Christophe Garcion ◽  
Laure Béven ◽  
Xavier Foissac
Keyword(s):  
Signal Peptide ◽  
Transmembrane Domain ◽  
Signal Peptides ◽  
Prediction Ability ◽  
Signal Peptide Prediction ◽  
General Evaluation ◽  
Increased Sensitivity ◽  
Transmembrane Regions ◽  
Peptide Prediction

Although phytoplasma studies are still hampered by the lack of axenic cultivation methods, the availability of genome sequences allowed dramatic advances in the characterization of the virulence mechanisms deployed by phytoplasmas, and highlighted the detection of signal peptides as a crucial step to identify effectors secreted by phytoplasmas. However, various signal peptide prediction methods have been used to mine phytoplasma genomes, and no general evaluation of these methods is available so far for phytoplasma sequences. In this work, we compared the prediction performance of SignalP versions 3.0, 4.0, 4.1, 5.0 and Phobius on several sequence datasets originating from all deposited phytoplasma sequences. SignalP 4.1 with specific parameters showed the most exhaustive and consistent prediction ability. However, the configuration of SignalP 4.1 for increased sensitivity induced a much higher rate of false positives on transmembrane domains located at N-terminus. Moreover, sensitive signal peptide predictions could similarly be achieved by the transmembrane domain prediction ability of TMHMM and Phobius, due to the relatedness between signal peptides and transmembrane regions. Beyond the results presented herein, the datasets assembled in this study form a valuable benchmark to compare and evaluate signal peptide predictors in a field where experimental evidence of secretion is scarce. Additionally, this study illustrates the utility of comparative genomics to strengthen confidence in bioinformatic predictions.

Using signal peptide prediction with caution, a case study in Aspergillus niger xylanase

2012 ◽  
Vol 47 (12) ◽  
pp. 2527-2530 ◽  
Author(s):  
Liangwei Liu ◽  
Long Chen ◽  
Hui Tian ◽  
Haiyu Yang ◽  
Li Zhao
Keyword(s):  
Aspergillus Niger ◽  
Signal Peptide ◽  
Signal Peptide Prediction ◽  
Peptide Prediction

Evaluating signal peptide prediction methods for Gram-positive bacteria

Biologia ◽  
2009 ◽  
Vol 64 (4) ◽  
Author(s):  
Xiaohui Zhang ◽  
Yudang Li ◽  
Yudong Li
Keyword(s):  
Signal Peptide ◽  
Cleavage Site ◽  
Surface Display ◽  
Extracellular Proteins ◽  
Consensus Method ◽  
Signal Peptides ◽  
Gram Positive ◽  
Gram Positive Bacteria ◽  
Signal Peptide Prediction ◽  
Peptide Prediction

AbstractGram-positive bacteria have been widely investigated for their huge capability to secrete proteins, such as those involved in gene expression, bacterial surface display and bacterial pathogenesis. The N-terminal signal peptide of a secretory protein is responsible for the translocation of polypeptide through the cytoplasmic membrane. Recently, the signal peptide prediction has become a major task in bioinformatics, and many programs with different algorithms were developed to predict signal peptides. In this paper, five prediction programs (SignalP 3.0, PrediSi, Phobius, SOSUIsignal and SIG-Pred) were selected to evaluate their prediction accuracy for signal peptides and cleavage site using 509 unbiased and experimentally verified Gram-positive protein sequences. The results showed that SignalP was the most accurate program in signal peptide (96% accuracy) and cleavage site (83%) prediction. Prediction performance could further be improved by combining multiple methods into consensus prediction, which would increase the accuracy to 98%, and decrease the false positive to zero. When the consensus method was used to predict Bacillus’s extracellular proteins identified by proteomics, more new signal peptides were successfully identified. It could be concluded that the consensus method would be useful to make prediction of signal peptides more reliable.

scholarly journals Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

2008 ◽  
Vol 4 (11) ◽  
pp. e1000213 ◽  
Author(s):  
Sheila M. Reynolds ◽  
Lukas Käll ◽  
Michael E. Riffle ◽  
Jeff A. Bilmes ◽  
William Stafford Noble
Keyword(s):  
Bayesian Networks ◽  
Signal Peptide ◽  
Dynamic Bayesian Networks ◽  
Transmembrane Topology ◽  
Signal Peptide Prediction ◽  
Peptide Prediction
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