|
Title |
Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria
|
|
Authors |
Jafar Razmara*, Safaai B Deris, Rosli Bin Md Illias & Sepideh Parvizpour
|
|
Affiliation |
Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia |
|
|
jafar@utm.my; *Corresponding author |
|
Article Type |
Hypothesis |
|
Date |
Received March 03, 2013; Accepted April 05, 2013; Published April 13, 2013
|
|
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. |
|
Keywords |
Artificial signal peptide prediction, Protein secretion, Hidden markov model.
|
|
Citation |
Razmara et al.
Bioinformation 9(7): 345-348 (2013) |
|
Edited by |
P Kangueane
|
|
ISSN |
0973-2063
|
|
Publisher |
|
|
License |
This is an Open Access article which permits unrestricted use,
distribution, and reproduction in any medium, provided the original
work is properly credited. This is distributed under the terms of
the
Creative Commons Attribution License. |