title: Effects of neighboring sequence environment in predicting cleavage sites of signal peptides.
abstract: Signal peptide has a pivotal role in the translocation of secretory protein. Some models have been designed to predict its cleavage site. It is reported that the cleavage site has relationship with the neighboring sequence environment, i.e., hydrophobic core h-region, and the specific patterns in c-region. In some studies, this finding does facilitate the prediction of cleavage site. However, in these models, sequence environment information is merely taken account of as model inputs and no detailed investigation into its effect on the prediction of cleavage site has been made. In this work, we analyze the constraint on cleave site placed by the hydrophobic core of signal peptide and then use it to improve the performance of the signal peptide cleavage site prediction. Our model is designed as follows: firstly, a sliding window is used to scan sample and artificial neural network (ANN) is employed to give cleavage site/non-cleavage site scores. Then, based on an estimated hydrophobic h-region a correcting function is proposed to improve the prediction result, in which the sequence environment is taken into account. A trend of cleavage site is indicated by our analysis for each position, which is consistent with experimental findings. Through this correcting step, the improvement of prediction accuracy is over 7%. It therefore demonstrates the neighboring sequence environment is helpful for determination of cleavage site. Program written in Matlab can be downloaded from http://www.scucic.cn/combined model/source code.html.
authors.affiliation: College of Chemistry, Sichuan University, 610064 Chengdu, PR China.
authors.forename: Yizhou
authors.initials: Y
authors.lastname: Li
authors.forename: Zhining
authors.initials: Z
authors.lastname: Wen
authors.forename: Cuisong
authors.initials: C
authors.lastname: Zhou
authors.forename: Fuyuan
authors.initials: F
authors.lastname: Tan
authors.forename: Menglong
authors.initials: M
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D008954:Models, Biological; D016571:Neural Networks, Computer; D021382:Protein Sorting Signals / Q000502:physiology*
chemical_list: D021382:Protein Sorting Signals