抗流感病毒药物神经氨酸酶抑制剂定量构效关系研究及分子设计
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摘要
对流感药物的疏忽引发了全球性的流感药物短缺问题。目前市场上只有2种主要的流感药物。同时,对禽流感的担心,促使各国政府采取了相应的药物储备措施,结果导致流感药物的需求变得更加紧张。不过这也带来了一个积极的结果:流感药物研究领域开始复苏。自从1983年确定了流感病毒神经氨酸酶(NA)的晶体结构及其与天然底物唾液酸的共晶结构以来,流感病毒NA抑制剂的研究,尤其是其唾液酸类似物的研究取得了突破性进展。对晶体结构的了解允许人们进行分子模拟研究,进而设计开发高效、高选择性的抑制剂。如果能通过进一步结构优化,提高其活性将有望成为一类新的高效抗流感病毒药物,而构效关系研究是药物设计的一种重要方法,它对于设计和筛选生物活性显著的药物以及阐述药物的作用机理等具有指导作用。因而构建这些化合物的分子结构与生物活性之间的定量相关模型对于研究、设计和开发出高效抗流感药物具有重要意义。
本论文从神经氨酸酶抑制剂的作用机制出发,在总结前人的研究成果的基础上,运用2D-QSAR和3D-QSAR的技术,设计出全新的可能具有抗病毒活性的目标化合物,以供生物活性筛选。开展的工作主要有以下几个方面。
①运用分子距边矢量(MEDV)对123种神经氨酸酶抑制剂进行了模拟建摸研究,得到了3变量的定量构效关系模型:R=0.705,SD=3.136,R~2_(CV)=0.457,SDCV=1.308。
在MEDV描述子所建模型的计算结果中不论是估计能力还是预测能力均不是很理想。从上面的结果可以看到在进行交互检验时其R~2_(CV)值为0.457,这表明在建模的过程中出现了过拟和现象。其主要原因可能是结构的复杂性增加,其高级结构影响就变得明显。整个分子的性质就不仅仅取决于该分子内原子的电性大小以及彼此之间的距离了,还包括原子间得空间距离、相互之间的空间作用力等。MEDV已经不能准确描述该分子。从而说明MEDV描述子不尚太适用于神经氨酸酶抑制剂的结构表征。
②在前面用MEDV描述子对123种神经氨酸酶抑制剂的所建模型的计算结果中,估计能力还和预测能力均不是很理想,故改用了实验室的MEIV描述子进行模拟建摸研究,同样得到了3变量的定量构效关系模型,建摸结果为R=0.715,SD=3.136,R~2_(CV)=0.475,SDCV=4.823。
从上面的结果可以看到在进行交互检验时其R~2_(CV)值从0.457提高到了0.475, SDCV从1.308降到了1.286,普通检验R值从0.705提高到了0.715,SD从1.298降到了1.293,但结果总的来说都不是很理想。从而说明MEIV描述子同样不太适用于神经氨酸酶抑制剂的结构表征。即2D-QSAR技术对神经氨酸酶抑制剂的结构表征存在一定的缺陷和不足。
③从实验室两种2D-QSAR即MEDV,MEIV建模效果来看结果都不是十分理想,于是再利用基于与化合物的三种非键作用方式(静电、立体、疏水)的三维分子表征方法—三维全息原子场作用矢量(3D-HoVAIF)对神经氨酸酶抑制剂的123个化合物进行定量构效关系(QSAR)研究。运用逐步线性回归(SMR)筛选变量后。用多元线性回归(MLR)建模得到了9变量模型,相关系数为R=0.885,留一法检验的复相关系数为R~2_(CV)=0.736。显然,3D-HoVAIF能够较好地表征神经氨酸酶抑制剂的结构,所建模型具有很好的内部估计能力和外部预测能力。
为了深入分析3D-HoVAIF对神经氨酸酶抑制剂样本集的表达和建模性能,这里将123个抑制剂分为训练集和测试集,分别为100和23个样本。同样的用本实验室提出的三维全息原子场作用矢量(3D-HoVAIF)对100个神经氨酸酶抑制剂进行结构表征,然后采用逐步回归对变量进行筛选后,运用偏最小二乘建立3D-HoVAIF描述子与神经氨酸酶抑制剂活性之间的QSAR模型。结果:复相关系数(R),交互校验的复相关系数(Q2)和模型的标准偏差分别为R2 =0.805,SD=0.936,Q2=0.657,并对文献中23个药物和设计的10个系列化合物进行了预测,模型具有良好的稳定性和预测能力。表明三维全息原子场作用矢量能较好表征该类分子结构信息值得进一步推广应用。
④基于分子二维结构信息的MEDV,MEIV和分子三维结构信息的三维原子场全息作用矢量(3D-HoVAIF)的构效关系研究,再根据NA的活性点以及与NAI的缀合特性和参考已有研究成果,我们重点放在已上市的神经氨酸酶抑制剂(NAI)的结构改造,尤其是母环的改造上,设计出了10个系列的化合物,并对其活性进行了预测,其中部分化合物活性值较高,有望成为下一步合成药物,对新型神经氨酸酶抑制剂的开发可提供一定的理论指导,有一定的理论价值和实践价值。
The negligence of the drug, which used on influenza, caused a global shortage of it. There are only two kinds of major drugs of influenza on the market. At the same time, the fear of avian flu has led to the governments to take corresponding measures of drug storage with a result of the demand of influenza’s drug became more and more intense. However, it also has brought about a positive outcome with the research of influenza’s drug started to resuscitate. Since the 1983, when the crystal structures of influenza virus neuraminidase and it with its natural substrate of sialic acid have been determined, researches of neuraminidase inhibitors particular its sialic acid analogues for influenza virus has been obtained great progress. Understaning of the crystal structure allows people to do the research of molecular simulations and then to design inhibitors with high efficiency and selectivity. If through further structural optimization increased. Activity is expected to as a new class of highly effective anti-influenza virus drugs. And quantitative structure activity relationship study is an important method of designing drug, so, construction of these compounds between the molecular structure and biological activity quantitative correlation has important significance to design and development of high efficiency anti-influenza drug.
In this paper, based on the neuraminidase inhibitors of the mechanism, sum up the results of research on their predecessors, both 2-D QSAR and 3-D-QSAR technology are used to design a new compounds which may have anti-viral activity for biological activity screening. The work carried out mainly in the following aspects.
①The molecular electronegativity-distance vector are used to 123 kinds of neuraminidase inhibitors, and the quantitative relationship models have been constructed by using multiple linear regression technique. The correlation coefficient R and standard deviation SD between the estimated properties and observed experimentally properties have been used to evaluate the estimation abilities for internal samples. The correlation coefficient RCV and standard deviation SDCV between the properties observed experimentally and the properties predicted by leave-one-out crossvalidation technique have been used to evaluate the predictive abilities of models.
The model with the regression coefficient (R) of 0.705 and the standard deviation (SD) of 3.136 could be achieved. Then the model was evaluated by performing the cross validation with the leave-one-out (LOO) procedure and the results with correlation coefficient(R~2_(CV)) of 0.457and standard deviation (SDCV) of 1.308 could be obtained.
The results show that MEDV can not be used to well express the structures of these organic compounds. Because the complexity of the structure which senior become apparent. On the nature of the molecule does not depend on the elements but also within the power of the atomic size and the distance between each other, including atomic seen from space, the space between each other, and so on. So MEDV is no longer able to accurately describe the elements. The results are not very satisfactory. The MEDV is not applicable to characterizing of the structure of neuraminidase inhibitors.
②In the model of MEDV discussed above, the results obtained in both the predictive ability and evaluate the predictive abilities are not very satisfactory. And anther molecular descriptor called the MEIV was used to describe the structures of 123 kinds of neuraminidase inhibitors, and the anther QSAR model of three variables could be obtained.Modeling results are R = 0.715, SD = 1.308, R~2_(CV) = 0.475, SDCV = 1.286.
The method used in this 123 kinds of neuraminidase inhibitors MEIV description of the model calculation results, The results show that MEIV is also unsatisfactory.And the ability to estimate and forecast capacity are not very satisfactory. From the above we can see the results of cross-examination (R~2_(CV)) has increased from 0.457 to 0.475, SDCV fell to 1.286 from 1.308, R has increased from 0.705 to 0.715, SD fell to 1.293 from 1.298, but to the overall results that is not very satisfactory. MEIV descriptors so that the same as MEDV is not applicable to characterizaing of the structures of the neuraminidase inhibitors. So, there are some shortcomings with 2D-QSAR technique.
③Because the results of modles of MEDV and MEIV were not very good, so a nother method based on three non-bonded (electrostatic, van der waals and hydrophobic) factors, directly related to bio-activities, called three dimensional holographic vector of atomic interaction field (3D-HoVAIF) was proposed to study the QSAR model of 123 compounds of neuraminidase inhibitors too. The descriptors were selected by stepwise multiple regression (SMR). The model have been constructed by using multiple linear regression technique.
The model with the regression coefficient (R) of 0.885 and the standard deviation (SD) of 0.848 could be achieved. Then the model was evaluated by performing the cross validation with the leave-one-out (LOO) procedure and the results with correlation coefficient(R~2_(CV)) of 0.736 and standard deviation (SDCV) of 0.935 could be obtained Obviously, the statistical results indicated that 3D-HoVAIF descriptors were proved to be potent in characterizing neuraminidase inhibitor activity. And the model has a good ability to estimate the internal and external predictive ability.
The applicable fields of 3-D HoVAIF are extended. 3-D HoVAIF has been related to neuraminidase inhibitors which divided into two parts, namely, training set and test set for the 100 and 23 samples. And the quantitative relationship models have been constructed by using multiple linear regression and partial least squares technique build. Results:The correlation coefficient (R), cross-validation of the correlation coefficient (Q2) and the standard deviation model for R~2 = 0.805, Q~2 = 0.657, SD = 0.936, respectively. The model has a good stability and predictability, The 23 drugs in the literature and design 10 series compounds were predicted. That three-dimensional holographic atomic field vector can better characterizing of the molecular structure , It should be further popularized.
④Based on the study of quantitive structure-activity relationship of 2D( MEDV and MEIV) and 3D (3D-HoVAIF) on neuraminidase inhibitors, and then according to the active spot of NA, linking activity of NAI and the reference of reported result, we focus on transformation of neuraminidase inhibitors (NAI) particularly their mother central have been listed of the structures. We designed 10 series of new compounds, and their activity was predicted. From the results, we found some of the compounds with high value of activity. These compounds may become the synthetic object of the next step. It may provide useful refrence for new development of neuraminidase inhibitors.
本论文从神经氨酸酶抑制剂的作用机制出发,在总结前人的研究成果的基础上,运用2D-QSAR和3D-QSAR的技术,设计出全新的可能具有抗病毒活性的目标化合物,以供生物活性筛选。开展的工作主要有以下几个方面。
①运用分子距边矢量(MEDV)对123种神经氨酸酶抑制剂进行了模拟建摸研究,得到了3变量的定量构效关系模型:R=0.705,SD=3.136,R~2_(CV)=0.457,SDCV=1.308。
在MEDV描述子所建模型的计算结果中不论是估计能力还是预测能力均不是很理想。从上面的结果可以看到在进行交互检验时其R~2_(CV)值为0.457,这表明在建模的过程中出现了过拟和现象。其主要原因可能是结构的复杂性增加,其高级结构影响就变得明显。整个分子的性质就不仅仅取决于该分子内原子的电性大小以及彼此之间的距离了,还包括原子间得空间距离、相互之间的空间作用力等。MEDV已经不能准确描述该分子。从而说明MEDV描述子不尚太适用于神经氨酸酶抑制剂的结构表征。
②在前面用MEDV描述子对123种神经氨酸酶抑制剂的所建模型的计算结果中,估计能力还和预测能力均不是很理想,故改用了实验室的MEIV描述子进行模拟建摸研究,同样得到了3变量的定量构效关系模型,建摸结果为R=0.715,SD=3.136,R~2_(CV)=0.475,SDCV=4.823。
从上面的结果可以看到在进行交互检验时其R~2_(CV)值从0.457提高到了0.475, SDCV从1.308降到了1.286,普通检验R值从0.705提高到了0.715,SD从1.298降到了1.293,但结果总的来说都不是很理想。从而说明MEIV描述子同样不太适用于神经氨酸酶抑制剂的结构表征。即2D-QSAR技术对神经氨酸酶抑制剂的结构表征存在一定的缺陷和不足。
③从实验室两种2D-QSAR即MEDV,MEIV建模效果来看结果都不是十分理想,于是再利用基于与化合物的三种非键作用方式(静电、立体、疏水)的三维分子表征方法—三维全息原子场作用矢量(3D-HoVAIF)对神经氨酸酶抑制剂的123个化合物进行定量构效关系(QSAR)研究。运用逐步线性回归(SMR)筛选变量后。用多元线性回归(MLR)建模得到了9变量模型,相关系数为R=0.885,留一法检验的复相关系数为R~2_(CV)=0.736。显然,3D-HoVAIF能够较好地表征神经氨酸酶抑制剂的结构,所建模型具有很好的内部估计能力和外部预测能力。
为了深入分析3D-HoVAIF对神经氨酸酶抑制剂样本集的表达和建模性能,这里将123个抑制剂分为训练集和测试集,分别为100和23个样本。同样的用本实验室提出的三维全息原子场作用矢量(3D-HoVAIF)对100个神经氨酸酶抑制剂进行结构表征,然后采用逐步回归对变量进行筛选后,运用偏最小二乘建立3D-HoVAIF描述子与神经氨酸酶抑制剂活性之间的QSAR模型。结果:复相关系数(R),交互校验的复相关系数(Q2)和模型的标准偏差分别为R2 =0.805,SD=0.936,Q2=0.657,并对文献中23个药物和设计的10个系列化合物进行了预测,模型具有良好的稳定性和预测能力。表明三维全息原子场作用矢量能较好表征该类分子结构信息值得进一步推广应用。
④基于分子二维结构信息的MEDV,MEIV和分子三维结构信息的三维原子场全息作用矢量(3D-HoVAIF)的构效关系研究,再根据NA的活性点以及与NAI的缀合特性和参考已有研究成果,我们重点放在已上市的神经氨酸酶抑制剂(NAI)的结构改造,尤其是母环的改造上,设计出了10个系列的化合物,并对其活性进行了预测,其中部分化合物活性值较高,有望成为下一步合成药物,对新型神经氨酸酶抑制剂的开发可提供一定的理论指导,有一定的理论价值和实践价值。
The negligence of the drug, which used on influenza, caused a global shortage of it. There are only two kinds of major drugs of influenza on the market. At the same time, the fear of avian flu has led to the governments to take corresponding measures of drug storage with a result of the demand of influenza’s drug became more and more intense. However, it also has brought about a positive outcome with the research of influenza’s drug started to resuscitate. Since the 1983, when the crystal structures of influenza virus neuraminidase and it with its natural substrate of sialic acid have been determined, researches of neuraminidase inhibitors particular its sialic acid analogues for influenza virus has been obtained great progress. Understaning of the crystal structure allows people to do the research of molecular simulations and then to design inhibitors with high efficiency and selectivity. If through further structural optimization increased. Activity is expected to as a new class of highly effective anti-influenza virus drugs. And quantitative structure activity relationship study is an important method of designing drug, so, construction of these compounds between the molecular structure and biological activity quantitative correlation has important significance to design and development of high efficiency anti-influenza drug.
In this paper, based on the neuraminidase inhibitors of the mechanism, sum up the results of research on their predecessors, both 2-D QSAR and 3-D-QSAR technology are used to design a new compounds which may have anti-viral activity for biological activity screening. The work carried out mainly in the following aspects.
①The molecular electronegativity-distance vector are used to 123 kinds of neuraminidase inhibitors, and the quantitative relationship models have been constructed by using multiple linear regression technique. The correlation coefficient R and standard deviation SD between the estimated properties and observed experimentally properties have been used to evaluate the estimation abilities for internal samples. The correlation coefficient RCV and standard deviation SDCV between the properties observed experimentally and the properties predicted by leave-one-out crossvalidation technique have been used to evaluate the predictive abilities of models.
The model with the regression coefficient (R) of 0.705 and the standard deviation (SD) of 3.136 could be achieved. Then the model was evaluated by performing the cross validation with the leave-one-out (LOO) procedure and the results with correlation coefficient(R~2_(CV)) of 0.457and standard deviation (SDCV) of 1.308 could be obtained.
The results show that MEDV can not be used to well express the structures of these organic compounds. Because the complexity of the structure which senior become apparent. On the nature of the molecule does not depend on the elements but also within the power of the atomic size and the distance between each other, including atomic seen from space, the space between each other, and so on. So MEDV is no longer able to accurately describe the elements. The results are not very satisfactory. The MEDV is not applicable to characterizing of the structure of neuraminidase inhibitors.
②In the model of MEDV discussed above, the results obtained in both the predictive ability and evaluate the predictive abilities are not very satisfactory. And anther molecular descriptor called the MEIV was used to describe the structures of 123 kinds of neuraminidase inhibitors, and the anther QSAR model of three variables could be obtained.Modeling results are R = 0.715, SD = 1.308, R~2_(CV) = 0.475, SDCV = 1.286.
The method used in this 123 kinds of neuraminidase inhibitors MEIV description of the model calculation results, The results show that MEIV is also unsatisfactory.And the ability to estimate and forecast capacity are not very satisfactory. From the above we can see the results of cross-examination (R~2_(CV)) has increased from 0.457 to 0.475, SDCV fell to 1.286 from 1.308, R has increased from 0.705 to 0.715, SD fell to 1.293 from 1.298, but to the overall results that is not very satisfactory. MEIV descriptors so that the same as MEDV is not applicable to characterizaing of the structures of the neuraminidase inhibitors. So, there are some shortcomings with 2D-QSAR technique.
③Because the results of modles of MEDV and MEIV were not very good, so a nother method based on three non-bonded (electrostatic, van der waals and hydrophobic) factors, directly related to bio-activities, called three dimensional holographic vector of atomic interaction field (3D-HoVAIF) was proposed to study the QSAR model of 123 compounds of neuraminidase inhibitors too. The descriptors were selected by stepwise multiple regression (SMR). The model have been constructed by using multiple linear regression technique.
The model with the regression coefficient (R) of 0.885 and the standard deviation (SD) of 0.848 could be achieved. Then the model was evaluated by performing the cross validation with the leave-one-out (LOO) procedure and the results with correlation coefficient(R~2_(CV)) of 0.736 and standard deviation (SDCV) of 0.935 could be obtained Obviously, the statistical results indicated that 3D-HoVAIF descriptors were proved to be potent in characterizing neuraminidase inhibitor activity. And the model has a good ability to estimate the internal and external predictive ability.
The applicable fields of 3-D HoVAIF are extended. 3-D HoVAIF has been related to neuraminidase inhibitors which divided into two parts, namely, training set and test set for the 100 and 23 samples. And the quantitative relationship models have been constructed by using multiple linear regression and partial least squares technique build. Results:The correlation coefficient (R), cross-validation of the correlation coefficient (Q2) and the standard deviation model for R~2 = 0.805, Q~2 = 0.657, SD = 0.936, respectively. The model has a good stability and predictability, The 23 drugs in the literature and design 10 series compounds were predicted. That three-dimensional holographic atomic field vector can better characterizing of the molecular structure , It should be further popularized.
④Based on the study of quantitive structure-activity relationship of 2D( MEDV and MEIV) and 3D (3D-HoVAIF) on neuraminidase inhibitors, and then according to the active spot of NA, linking activity of NAI and the reference of reported result, we focus on transformation of neuraminidase inhibitors (NAI) particularly their mother central have been listed of the structures. We designed 10 series of new compounds, and their activity was predicted. From the results, we found some of the compounds with high value of activity. These compounds may become the synthetic object of the next step. It may provide useful refrence for new development of neuraminidase inhibitors.
引文
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