title: 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.
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
authors.forename: Jafar
authors.initials: J
authors.lastname: Razmara
authors.forename: Safaai B
authors.initials: SB
authors.lastname: Deris
authors.forename: Rosli Bin Md
authors.initials: RB
authors.lastname: Illias
authors.forename: Sepideh
authors.initials: S
authors.lastname: Parvizpour
keywords: Artificial signal peptide prediction; Hidden markov model; Protein secretion