一种表征蛋白质可分泌性的结构融合度特征

Characterization of protein secretion based on structural fusion degree

摘要:

选择适宜的信号肽是实现外源蛋白高效分泌表达的一个重要因素。本研究利用生物信息学方法分析信号肽与外源蛋白之间的相容程度，将其定义为结构融合度，并从数学角度分析拼接信号肽与目的蛋白邻近残基之间的相互作用，提出了信号肽拼接区域与目标蛋白之间的数学模型，利用该模型进行结构融合度特征提取，以此来表征外源蛋白质的可分泌性。模拟结果显示结构融合度特征能有效区分枯草芽孢杆菌宿主的可分泌和不可分泌蛋白。研究结果有助于信号肽的选择，对目的蛋白分泌表达的优化具有一定的指导意义。

关键词:信号肽，蛋白质分泌，结构融合度，特征提取

Abstract:

Selection of suitable signal peptides is an important factor for efficient secretion of heterologous proteins. We defined structural fusion degree (SFD) as the compatibility degree of target proteins and signal peptides by a bioinformatics approach. We mathematically analyzed the interaction of fused signal peptides and adjacent residues of proteins, and proposed a mathematical model of extended signal region and the protein. SFD Features was extracted from this model to characterize the secretability of heterologous proteins. Simulation tests showed that SFD features can effectively discriminate high secretory proteins from poor ones in the host Bacillus subtilis. Results from this research will be useful in signal peptide selection and have a better guiding significance for the optimization of heterologous protein secretion.

Key words:signal peptide, secretion of protein, structural fusion degree, feature extraction