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Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria.


ABSTRACT: A hidden Markov model (HMM) has been utilized to predict and generate artificial secretory signal peptide sequences. The strength of signal peptides of proteins from different subcellular locations via Lactococcus lactis bacteria correlated with their HMM bit scores in the model. The results show that the HMM bit score +12 are determined as the threshold for discriminating secreteory signal sequences from the others. The model is used to generate artificial signal peptides with different bit scores for secretory proteins. The signal peptide with the maximum bit score strongly directs proteins secretion.

SUBMITTER: Razmara J 

PROVIDER: S-EPMC3669786 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria.

Razmara Jafar J   Deris Safaai B SB   Illias Rosli Bin Md RB   Parvizpour Sepideh S  

Bioinformation 20130413 7


A hidden Markov model (HMM) has been utilized to predict and generate artificial secretory signal peptide sequences. The strength of signal peptides of proteins from different subcellular locations via Lactococcus lactis bacteria correlated with their HMM bit scores in the model. The results show that the HMM bit score +12 are determined as the threshold for discriminating secreteory signal sequences from the others. The model is used to generate artificial signal peptides with different bit sco  ...[more]

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