Peptides



【Peptides】介绍
英文名称:Peptides
中文名称:Peptides,
期刊主页:http://www.elsevier.com/locate/peptides
出版地:

Peptides is a monthly, peer reviewed, scientific journal published by Elsevier. It was established in 1980 and is edited by Abba Kastin (Pennington Biomedical Research Center). Publishing formats of this journal are original research articles, short communications (2 to 6 pages), and review articles. According to the Journal Citation Reports, the 2009 impact factor for this journal is 2.705

Scope

This journal's focus is original contributions involving the chemistry, biochemistry, neurochemistry, endocrinology, gastroenterology, physiology, and pharmacology of peptides. The neurological, psychological, and behavioral effects of peptides are also studied, as part of the focus.

All topics regarding peptides are published in this journal. Coverage encompasses peptides research in plants, insects, lower vertebrates, animals and clinical studies in humans.


实验室在此刊物上发表的文章
  1. Effects of neighboring sequence environment in predicting cleavage sites of signal peptides

  2. Peptides,2008 Sep;29(9):1498-504. Epub 2008 Apr 26

    Yizhou Li  , Zhining Wen  , Cuisong Zhou  , Fuyuan Tan  , Menglong Li

    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.