活性肽定量构效关系建模过程中1种肽段长度不等的表征方法的建立  

Development of a Peptide Length Differential Characterization Method for Peptide QSAR

作　　者：田淇 李耀旺 李博[1,2] Tian Qi;Li Yaowang;Li Bo(College of Food Science and Nutritional Engineering,China Agricultural University,Beijing 100083;Key Laboratory of Functional Dairy,Ministry of Education,Beijing 100083)

机构地区：[1]中国农业大学食品科学与营养工程学院,北京100083 [2]教育部功能乳品重点实验室,北京100083

出　　处：《中国食品学报》2021年第4期28-38,共11页Journal of Chinese Institute Of Food Science and Technology

基　　金：国家重点研发计划项目(2018YFD0901102)。

摘　　要：当肽段的氨基酸序列长度不等时,建立活性肽定量构效关系(QSAR)模型具有一定困难。自交互协方差(ACCs)技术虽然能够建立肽段不等的模型,但是预测效果并不理想。本研究提出1种两端排序法(TTPN)。首先从数据库中查找序列长度最短的肽,以氨基酸数量为基准,分别对每条肽的C端和N端取基准数量的氨基酸残基,组成新的,用于表征其结构的序列。之后建立描述变量矩阵X,活性数据矩阵Y进行定量构效关系(QSAR)研究。为验证TTPN法的有效性及适用范围,收集并建立3个数据库:苦味肽(BT)数据库、降血压肽(ACE)数据库和ORAC法测定的抗氧化肽数据库。QSAR结果表明,在3个数据库中TTPN均比ACCs法能更好地描述肽序列(如ACE数据库中TTPN和ACCs的R~2、Q~2分别为0.724,0.599和0.329,0.038)。此外,TTPN还能够描述信息被部分或完全描述的序列(2-14和7-14数据库的R~2、Q~2分别为0.717,0.693和0.922,0.753)。综上所述,TTPN法不仅为序列不等长肽QSAR建模提供了1种有效的处理方法,而且与ACCs法相比,应用TTPN技术所建立的QSAR模型具有较好的分析和预测能力,应用范围广泛,且根据模型得出的活性肽结构特征更易于解释。It is difficult for the bioactive peptides whose sequence lengths are different to establish QSAR(Quantitative structure-activity relationship)model.Although it could be solved by ACCs(Auto and cross auto covariances),its estimation ability is not satisfactory.Thus,a new method named TTPN(Two-terminal position numbering)was proposed.The principle of TTPN method is to find the shortest length peptide and standardize its number of amino acids,the information of same number amino acid residues of the C-terminus and N-terminus was extracted from database.Then the descriptive variable matrix X and the active data matrix Y were established for QSAR research.In order to verify the effectiveness and application scope of TTPN method,many peptides involve the BT(Bitter peptides),ACE(Angiotensin I-converting enzyme inhibitor)and antioxidant peptides measured by ORAC(Oxygen radical absorption capacity)were collected.All the results of QSAR showed that TTPN method was better than ACCs to describe the sequence of peptide(R2 and Q2 for TTPN and ACCs are 0.724,0.329;0.599,0.038 in ACE databases respectively).Furthermore,TTPN could also describe sequences in which information was partially or fully described(R2 and Q2 for 2-14 and 7-14 databases are 0.717,0.693 and 0.922,0.753 respectively).In conclusion,TTPN method not only provides an effective method for the establishment of QSAR model of peptide with distinct length,but also has a better analysis and prediction ability as well as a wider range of applications compared with ACCs method.The explanation of active peptide structure characteristics obtained from the model compared with ACCs method are more precious.

关 键 词：两端排序法 偏最小二乘法 抗氧化肽 定量构效关系 自由基 

分 类 号：TS201.2[轻工技术与工程—食品科学]