肽的定量构效关系研究
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摘要
从分子结构表征和定量构效关系(quantitative structure-activity relationship, QSAR)建模方法与技术这两个关键内容出发,对48 个苦味二肽、58 个血管收缩素转化酶抑制剂、31 个血管舒缓激肽促进剂、21 个后叶催产素、152 个HLA-A*0201限制性CTL表位和34个抗菌肽进行了定量构效关系研究以及建模方法与技术的比较研究。
分子结构表征是定量构效关系研究的一个关键环节。结构描述子能否反映分子与生物活性相关的结构信息,决定了定量构效关系研究的成败。文中提出的两种氨基酸结构描述子矢量VSTV 和VHSE 均来源于主成分特征提取的思路。其中VSTV(principal component score vector of structural and topological variables)来源于20 种天然氨基酸的25 种拓扑结构信息,并通过主成分分析而产生。由于VSTV 是基于氨基酸的结构和拓扑性质,因此具有计算方法简便,不依赖实验数据以及拓展性能好等优点。VHSE(principal component score vector of hydrophilicity, steric, and electronic properties)则是来源于20 种天然氨基酸的50 种物理化学性质,通过对其中18 个疏水性质,17 个立体性质和15 个电性性质分别进行主成分分析而产生。其中VHSE1、VHSE2代表氨基酸的疏水性特征;VHSE3、VHSE4代表氨基酸的立体特征;VHSE5~VHSE8则代表氨基酸的电性特征。与z 标度以及其它氨基酸描述子相比,VHSE 具有物化意义明确、信息量大和结果更易解释等特点。从上述的6个肽体系的构效关系研究结果看,VSTV 和VHSE 能较好地表征肽分子与生物活性相关的结构信息,并取得了与已有文献结果相当或更优的结果。
建模方法与技术是定量构效关系研究的一个重要内容。在进行定量构效关系研究的同时,详细地比较分析了多元线性回归(multiple linear regression, MLR)、主成分回归(principal component regression, PCR)、偏最小二乘回归(partial least squares, PLS)、人工神经网络(artificial neural network, ANN)和支持向量机(support vector machine, SVM)在线性或非线性体系的应用,其间包含了变量筛选和模型验证方法的研究和讨论。研究结果显示:经典的MLR 在满足相关条件的前提下,通常可以取得较好的结果。PCR 和PLS 可以较好地解决变量数较多且存在多重共线性的情况,并且在大多数情况下PLS 结果要优于PCR。当结构描述子与生物活性存在非线性的关系时,BP-ANN 是一种较好的选择。文中由于采用了验证集对过拟合现象进行了适当控制,使得BP-ANN 的预测能力有了较大幅度的提高。SVM 作为一种新的机器学习方法,在本文的构效关系研究中亦取得了较优的结果,尤其是SVM能较好的解决小样本、非线性、高维数和局部最小等实际问题,并且从原理上解
Structural description and modeling techniques are two essential contents of the quantitative structure-activity relationship (QSAR) studies. Based on the intense researches on these two points, the QSAR studies related to 48 bitter tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, 31 bradykinin-potentiating pentapeptides, 21 oxytocin analogues, 152 HLA-A*0201 restrictive CTL epitopes, and 34 antimicrobial peptides were dwelled on in detail.
Structural description is a key step in the QSAR studies. Whether the structural descriptors can reflect the structural variations determines the success of QSAR studies. Two kinds of amino acid descriptors, i.e. VSTV and VHSE, were derived from the ideal of principal components extraction. VSTV was derived from principal component analysis (PCA) on 25 structural and topological variables of 20 coded amino acids. So, the VSTV descriptors are of easy computation, experiment independent and can be easily expanded to other non-coded amino acids. VHSE was derived from the principal component analysis on independent families of 18 hydrophobic properties, 17 steric properties, and 15 electronic properties, respectively, which were included in total 50 physicochemical properties of 20 coded amino acids. For amino acids, VHSE1 and VHSE2 are related to hydrophobic properties, VHSE3 and VHSE4 related to steric properties, and VHSE5~VHSE8 related to electronic properties. As a new set of amino acid descriptors, VHSE is of relatively definite physicochemical meaning, easy interpretation and more information contained in comparison with z-scales and other amino acid descriptors. When VSTV and VHSE were applied in the QSAR studies of 6 peptide datasets mentioned above, equivalent or better results were obtained in comparison with those obtained with z-scales and other 2-D or 3-D descriptors.
The modeling methods and related techniques are also important for the success of QSAR studies. The modeling methods such as multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS), back-propagation artificial neural network (BP-ANN), and support vector machine (SVM) were systematically studied in this paper. In addition, the techniques related to variables screening and model validations were also discussed. The results showed that MLR, as a classic modeling method, behaved as well as other modeling methods if the application conditions were met. When the ratio of samples to variables was less than 3
or when multiple collinear among variables was encountered, PCR and PLS were better alternative to MLR. In the most situations, PLS performed better than PCR. When the structural descriptors had nonlinear relationship with response variable, BP-ANN was then a better choice. In BP-ANN modeling, validation dataset was used to control over-fitting, find the optimal topological network structure and train network weights. The predictive power of BP-ANN was efficiently enhanced with the proper use of validation dataset. As a new modeling method, SVM is based on the structural risk minimization principle, which incorporates capacity control to prevent over-fitting. So SVM is of better generalization performance than PLS and ANN, and thus is especially suitable for QSAR modeling on small dataset. In this paper, SVM achieved good performance in QSAR modeling. However, there are many issues, i.e. selection of kernel functions and corresponding parameters, leaving to be studied in detailed. For a QSAR dataset, not all variables are relevant to biological activity. So those redundant variables should be deleted from model in order to promote predictive capability especially when the number of variables is very large. In this paper, stepwise multiple regression (SMR) and GA-PLS were used to find an optimal variable subset. When the number of variables was less than 50, SMR, the classic variable selection method, was recommended. When the number of variables was more than 50, GA-PLS was then an alternative choice. However, the over-fitting should be avoided by proper validation methods especially in GA-PLS modeling. Model validation is an absolutely necessary step in QSAR modeling. In this paper, all samples were firstly divided into training dataset and predictive dataset according to D-optimal design technique. The training dataset was used to establish QSAR models and perform internal validation such as leave one out (LOO), leave 1/n out (LNO), leave many out (LMO) and Y random permutations test. On the base of internal validation, external validation was also performed using the predictive dataset. Several evaluation functions were used to evaluate predictive power of the resulting QSAR models.
分子结构表征是定量构效关系研究的一个关键环节。结构描述子能否反映分子与生物活性相关的结构信息,决定了定量构效关系研究的成败。文中提出的两种氨基酸结构描述子矢量VSTV 和VHSE 均来源于主成分特征提取的思路。其中VSTV(principal component score vector of structural and topological variables)来源于20 种天然氨基酸的25 种拓扑结构信息,并通过主成分分析而产生。由于VSTV 是基于氨基酸的结构和拓扑性质,因此具有计算方法简便,不依赖实验数据以及拓展性能好等优点。VHSE(principal component score vector of hydrophilicity, steric, and electronic properties)则是来源于20 种天然氨基酸的50 种物理化学性质,通过对其中18 个疏水性质,17 个立体性质和15 个电性性质分别进行主成分分析而产生。其中VHSE1、VHSE2代表氨基酸的疏水性特征;VHSE3、VHSE4代表氨基酸的立体特征;VHSE5~VHSE8则代表氨基酸的电性特征。与z 标度以及其它氨基酸描述子相比,VHSE 具有物化意义明确、信息量大和结果更易解释等特点。从上述的6个肽体系的构效关系研究结果看,VSTV 和VHSE 能较好地表征肽分子与生物活性相关的结构信息,并取得了与已有文献结果相当或更优的结果。
建模方法与技术是定量构效关系研究的一个重要内容。在进行定量构效关系研究的同时,详细地比较分析了多元线性回归(multiple linear regression, MLR)、主成分回归(principal component regression, PCR)、偏最小二乘回归(partial least squares, PLS)、人工神经网络(artificial neural network, ANN)和支持向量机(support vector machine, SVM)在线性或非线性体系的应用,其间包含了变量筛选和模型验证方法的研究和讨论。研究结果显示:经典的MLR 在满足相关条件的前提下,通常可以取得较好的结果。PCR 和PLS 可以较好地解决变量数较多且存在多重共线性的情况,并且在大多数情况下PLS 结果要优于PCR。当结构描述子与生物活性存在非线性的关系时,BP-ANN 是一种较好的选择。文中由于采用了验证集对过拟合现象进行了适当控制,使得BP-ANN 的预测能力有了较大幅度的提高。SVM 作为一种新的机器学习方法,在本文的构效关系研究中亦取得了较优的结果,尤其是SVM能较好的解决小样本、非线性、高维数和局部最小等实际问题,并且从原理上解
Structural description and modeling techniques are two essential contents of the quantitative structure-activity relationship (QSAR) studies. Based on the intense researches on these two points, the QSAR studies related to 48 bitter tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, 31 bradykinin-potentiating pentapeptides, 21 oxytocin analogues, 152 HLA-A*0201 restrictive CTL epitopes, and 34 antimicrobial peptides were dwelled on in detail.
Structural description is a key step in the QSAR studies. Whether the structural descriptors can reflect the structural variations determines the success of QSAR studies. Two kinds of amino acid descriptors, i.e. VSTV and VHSE, were derived from the ideal of principal components extraction. VSTV was derived from principal component analysis (PCA) on 25 structural and topological variables of 20 coded amino acids. So, the VSTV descriptors are of easy computation, experiment independent and can be easily expanded to other non-coded amino acids. VHSE was derived from the principal component analysis on independent families of 18 hydrophobic properties, 17 steric properties, and 15 electronic properties, respectively, which were included in total 50 physicochemical properties of 20 coded amino acids. For amino acids, VHSE1 and VHSE2 are related to hydrophobic properties, VHSE3 and VHSE4 related to steric properties, and VHSE5~VHSE8 related to electronic properties. As a new set of amino acid descriptors, VHSE is of relatively definite physicochemical meaning, easy interpretation and more information contained in comparison with z-scales and other amino acid descriptors. When VSTV and VHSE were applied in the QSAR studies of 6 peptide datasets mentioned above, equivalent or better results were obtained in comparison with those obtained with z-scales and other 2-D or 3-D descriptors.
The modeling methods and related techniques are also important for the success of QSAR studies. The modeling methods such as multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS), back-propagation artificial neural network (BP-ANN), and support vector machine (SVM) were systematically studied in this paper. In addition, the techniques related to variables screening and model validations were also discussed. The results showed that MLR, as a classic modeling method, behaved as well as other modeling methods if the application conditions were met. When the ratio of samples to variables was less than 3
or when multiple collinear among variables was encountered, PCR and PLS were better alternative to MLR. In the most situations, PLS performed better than PCR. When the structural descriptors had nonlinear relationship with response variable, BP-ANN was then a better choice. In BP-ANN modeling, validation dataset was used to control over-fitting, find the optimal topological network structure and train network weights. The predictive power of BP-ANN was efficiently enhanced with the proper use of validation dataset. As a new modeling method, SVM is based on the structural risk minimization principle, which incorporates capacity control to prevent over-fitting. So SVM is of better generalization performance than PLS and ANN, and thus is especially suitable for QSAR modeling on small dataset. In this paper, SVM achieved good performance in QSAR modeling. However, there are many issues, i.e. selection of kernel functions and corresponding parameters, leaving to be studied in detailed. For a QSAR dataset, not all variables are relevant to biological activity. So those redundant variables should be deleted from model in order to promote predictive capability especially when the number of variables is very large. In this paper, stepwise multiple regression (SMR) and GA-PLS were used to find an optimal variable subset. When the number of variables was less than 50, SMR, the classic variable selection method, was recommended. When the number of variables was more than 50, GA-PLS was then an alternative choice. However, the over-fitting should be avoided by proper validation methods especially in GA-PLS modeling. Model validation is an absolutely necessary step in QSAR modeling. In this paper, all samples were firstly divided into training dataset and predictive dataset according to D-optimal design technique. The training dataset was used to establish QSAR models and perform internal validation such as leave one out (LOO), leave 1/n out (LNO), leave many out (LMO) and Y random permutations test. On the base of internal validation, external validation was also performed using the predictive dataset. Several evaluation functions were used to evaluate predictive power of the resulting QSAR models.
引文
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