载脂蛋白调节脂蛋白代谢及免疫炎症反应，参与人类多种疾病如动脉粥样硬化、糖尿病、心肌梗死和肿瘤等的发生发展。但面对海量数据和新型载脂蛋白的不断涌现，传统的生化实验研究已显得力不从心。借助计算的方法系统研究载脂蛋白势在必行。我们前期研究发现，载脂蛋白与非载脂蛋白序列之间存在特征性差异，转录因子NF-κB的活性改变可调节载脂蛋白M的表达。本项目拟在此基础上，建立一套载脂蛋白的理论识别和调控模型。通过搜集和构建全面的载脂蛋白数据库，采用多尺度的特征描述方法寻找载脂蛋白家族特有的序列、关联特征和规律性信息，进而利用特征筛选和整合机器学习方法，构建合理、高准确率的载脂蛋白预测模型；对预测得到的可能载脂蛋白，采用脂质-蛋白质共沉降技术进行实验验证；并利用动力学方法研究NF-κB对新型载脂蛋白M的调控机制。此项系统性的研究对深入理解和阐明载脂蛋白的生理学功能、合理选择药物靶点有着重要的指导意义和现实价值。

Apolipoprotein is in charge of maintaining the basic life process by regulating the metabolism of lipoprotein and immuno-inflammatory responses. The change in expression and function of apolipoprotein plays an important role in various diseases such as atherosclerosis, diabetes, myocardial infarction and tumor. In the presence of mass data and springing up of new apolipoproteins, it is difficult to study apolipoproteins by use of tranditional biochemistry method. It is imperative to systematically research apolipoproteins by computational approaches. Our previous studies showed that the apolipoproteins are different from non-apolipoproteins on sequence composition. The transcription factor, NF-κB can influence the expression of apolipoprotein M. Thus, the project is to construct a set of theoretical models for the identification of apolipoproteins. By searching and building high-quality apolipoprotein database, the multi-scale feature will be used to figure out the specific sequence and correlation information of the apolipoproteins. Based on feature selection algorithms and deep learning technique, we will construct a reliable and accurate predictive model for apolipoprotein prediction.The lipid-protein cosedimentation technique will be performed to verify the candidate apolipoproteins predicted by the model. Moreover, we will use kinetic methods to investigate the regulatory mechanism of apolipoprotein M by NF-κB. These systematic researches in the project are of great significance for understanding and clarifying the physiological function of apolipoprotein and rationally selecting drug targets.