Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria |
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| Authors: | Jafar Razmara Safaai B Deris Rosli Bin Md Illias Sepideh Parvizpour |
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| Affiliation: | Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia |
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| Abstract: | A hidden Markov model (HMM) has been utilized to predict and generate artificial secretory signal peptide sequences. Thestrength of signal peptides of proteins from different subcellular locations via Lactococcus lactis bacteria correlated with theirHMM bit scores in the model. The results show that the HMM bit score +12 are determined as the threshold for discriminatingsecreteory signal sequences from the others. The model is used to generate artificial signal peptides with different bit scores forsecretory proteins. The signal peptide with the maximum bit score strongly directs proteins secretion. |
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| Keywords: | Artificial signal peptide prediction Protein secretion Hidden markov model |
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