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利用亚位点偶联预测信号肽。

Using subsite coupling to predict signal peptides.

作者信息

Chou K C

机构信息

Computer-Aided Drug Discovery, Pharmacia and Upjohn, Kalamazoo, MI 49007-4940, USA.

出版信息

Protein Eng. 2001 Feb;14(2):75-9. doi: 10.1093/protein/14.2.75.

Abstract

Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is a first important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy. Based on a model that takes into account the coupling effect among some key subsites, the so-called [-3, -1, +1] coupling model, a new prediction algorithm is developed. The overall rate of correct prediction for 1939 secretory proteins and 1440 non-secretary proteins was over 92%. It has not escaped our attention that the new method may also serve as a useful tool for helping investigate further many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells.

摘要

给定一个新生蛋白质序列,如何预测其信号肽或“邮政编码”序列?对于科学家而言,将信号肽用作寻找新药或对细胞进行基因治疗重编程的载体,这是首要的重要问题。基于一个考虑了某些关键子位点间耦合效应的模型,即所谓的[-3, -1, +1]耦合模型,开发了一种新的预测算法。对1939个分泌蛋白和1440个非分泌蛋白的总体正确预测率超过了92%。我们也注意到,该新方法可能还会成为一个有用的工具,有助于进一步探究细胞中邮政编码蛋白分选系统分子机制的许多不明细节。

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