CN112816453B - 蛋白在预测药物性能上的应用 - Google Patents
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Abstract
本发明的蛋白在预测药物性能上的应用属于蛋白应用领域,是将蛋白用于预测药物性能上,所述的药物包括农药、人用药和兽药是通过下列步骤实现的:制备pH值为7.4的0.02 M磷酸盐缓冲液,用制得的缓冲液根据信号值对蛋白液进行溶解并稀释;将步制得的蛋白稀释液与待测定药物以1:1~300的摩尔比进行混合,并利用荧光光谱、或同步荧光、或三维荧光、或圆二色光谱、或UV‑Vis吸收光谱、或线状光谱、或带状光谱对其性能进行预测。其通过荧光光谱和毒性获得了五个指标(淬灭常数,结合位点数,结合常数,自由能,结合距离)之间的BP神经网络模型,来预测药物性能,对药物研发产业的发展具有重要的现实意义。
Description
技术领域
本发明属于蛋白应用领域,特别是涉及一种蛋白在预测药物性能上的应用。
背景技术
所有新药在进入市场之前都必须经过一系列的测试。而毒性是一项重要指标,是药物开发失败的主要原因,据报道它会导致大约30%的候选药物消耗。特别是,许多药物由于研发后期的高毒性被迫退出市场,这不仅浪费时间,而且浪费成本。几年前,建议将毒理学评估提早至小分子的早期药物发现过程。药物毒性的早期评估有助于实现药物的精确设计和合成,有效缩短药物开发周期,减少成本。提出了许多对药物毒性进行早期预测的策略和新方法,以筛选出最有希望的候选药物。其中,值得注意的是,SAR研究,细胞毒性测定和生物标志物检测意义重大。但是它们都有缺点,例如,“SAR”研究过于简单,高度敏感,但不具体,由于假阳性结果可能导致新的有希望的候选药物的消耗;细胞毒性试验通常是最早的毒性试验之一。,但这种方法需要较高的实验条件和较长的周期。此外,由于缺乏新陈代谢,细胞类型敏感性,培养条件等方面的差异,体外细胞毒性试验预测体内靶器官毒性的能力经常被质疑;生物标志物的检测是发现、发展和发展生物标志物的关键工具。但其发展与一些挑战有关,例如,某些生物标志物背后的科学原因无法始终得到验证,这给生物标志物的验证和鉴定带来了挑战。此外,生物标志物的开发可能与其他测试要求有关,这可能会增加开发成本。基于早期毒性评估的重要性,本文旨在寻找一种可以更轻松,快速,普遍地预测药物毒性的新方法。
农药在维持粮食安全和减少收成损失方面发挥着极其重要的作用。但是,大多数农药是有毒的,对环境、人类、动物和其他生物构成威胁。随着人类环境保护意识的增强,许多农药禁止使用具有良好活性但高毒性的产品。特别是氨基甲酸酯农药是一种广泛用于农业生产的农药,但是一旦进入人体,可能会引起急性或慢性中毒的危险。氨基甲酸酯农药对人体有毒的原因是它们可以抑制胆碱酯酶的活性,因此预测药物对人体的毒性是十分必要的。
发明内容
本发明旨在于克服现有技术的不足,提供了一种蛋白在预测药物性能上的应用,通过与载体蛋白结合信息与毒性关系研究了氨基甲酸酯类农药的毒性预测。
本发明的蛋白在预测药物性能上的应用,是将蛋白用于预测药物性能上,所述的药物包括农药、人用药和兽药。
作为本发明的进一步改进,所述的蛋白包括人血清白蛋白、牛血清白蛋白等载体蛋白;以及血红蛋白、球蛋白、血红蛋白、肌蛋白、胶原蛋白、酶蛋白、蜜蜂蛋白、鱼蛋白等蛋白。
作为本发明的进一步改进,蛋白在预测药物性能上的应用是通过下列步骤实现的:
(1)制备缓冲液:制备pH值为7.4的0.02 M磷酸盐缓冲液;
(2)制备蛋白溶液:用步骤(1)制得的缓冲液根据信号值对蛋白液进行溶解并稀释;
(3)配制检测液:将步骤(2)制得的蛋白稀释液与待测定药物以1:1~300的摩尔比进行混合,并利用荧光光谱、或同步荧光、或三维荧光、或圆二色光谱、或UV-Vis吸收光谱、或线状光谱、或带状光谱对其性能进行预测。
作为本发明的进一步改进,蛋白稀释液与待测定药物的摩尔比1:1~100。
作为本发明的进一步改进,利用荧光发射光谱预测药物的毒性,其具体操作方法为:
在283 K~232 K下对溶液体系预热3分钟,在300~500 nm之间测量了不同药物的情况下人血清白蛋白的发射光谱,激发波长设定为280 nm,激发和发射的狭缝宽度均为15nm; HSA浓度保持恒定在5×10-7 M,农药浓度为0~105×10-7 M;
根据以下公式校正荧光信号的内部滤波效果:
在该公式中,Fcor和Fobs分别是校正后的荧光信号和观察到的荧光信号,Aex和Aem分别代表溶液体系在激发和发射波长处的吸光度;
通过荧光发射光谱得到不同药物的淬灭常数、结合常数、结合位点数、结合距离和自由能后,并利用BP神经网络构建建模;预测时,将通过荧光光谱检测的待测农药的上述五数值输入模型中,即可得到农药的LD50值,根据世界卫生组织的农药危害分级标准,得到毒性等级,完成其毒性的预测。
作为本发明的进一步改进,利用同步荧光光谱的测定药物对人血清白蛋白质构象变化的影响,其具体操作方法为:
在283 K~232 K下对溶液体系预热2~5分钟,扫描速度设300 nm/min;在200至400 nm范围内同时扫描激发和发射光谱,得到不同药物时HSA的同步荧光光谱,激发波长和发射波长之间的间隔分别设置为15 nm和60 nm,当Δλ= 15 nm时,HSA的浓度保持在2×10-6M、农药在0~42×10-6 M;当Δλ=60 nm时,HSA的浓度保持在5×10-7 M、农药在0~105×10-7M。
作为本发明的进一步改进,利用三维荧光光谱测定药物对人血清白蛋白质构象变化的影响,其具体操作方法为:
在283 K~232 K下对溶液体系预热2~5分钟,扫描速度设300 nm/min;对8×10-8M HSA,和对8×10-8 M HSA与8×10-7 M农药混配的样品分别进行了三维荧光光谱分析,记录了发射波长为250~500 nm,初始激发波长设置为210 nm,激发间隔10 nm,激发和发射狭缝宽度均为15 nm。
作为本发明的进一步改进,利用圆二色性光谱测定人血清白蛋白质二级结构的变化,其具体操作方法为:
在283 K~232 K下对溶液体系预热2~5分钟,在200~500 nm波长范围内记录了HSA的CD光谱,HSA浓度保持在2×10-6 M,农药在0~20×10-6 M。
作为本发明的进一步改进,利用UV-Vis吸收光谱测定药物对HSA的影响,具体操作步骤如下:
在283 K~232 K下对溶液体系预2~5分钟,在190~500 nm范围内记录了添加了各种药物的HSA的UV-Vis光谱,波长间隔设置为0.5 nm,狭缝宽度设置为2nm;系统中的HSA固定为5×10-6M的浓度,并用0~175×10-6M农药滴定。
上述中的M为mol/L。
本发明的蛋白在预测药物性能上的应用,通过紫外-可见光谱、荧光、同步荧光、三维荧光、圆二色性(CD)光谱系统研究了药物与蛋白的相互作用信息。为了用最少的设备和最少的时间更容易地预测毒性,通过荧光光谱和毒性获得了五个指标(淬灭常数,结合位点数,结合常数,自由能,结合距离)之间的BP神经网络模型,来预测药物性能,对药物研发产业的发展具有重要的现实意义。
附图说明
图1在不同浓度的久效威砜存在下人血清白蛋白的荧光发射光谱;其中(a)5×10- 7M HSA; (b-i)在5、15、30、45、60、75、90和105×10-7 M久效威砜的存在下5×10-7 M HSA;(j)仅105×10-7 M久效威砜,pH = 7.4,T = 310 K。
图2是 Stern-Volmer,用于在不同温度下用久效威砜对人血清白蛋白荧光进行淬灭;其中[Q]-淬灭剂(久效威砜)的浓度; F0-在没有淬灭剂的情况下的荧光信号; F-在淬灭剂存在下,pH = 7.4的荧光信号。
图3是在不同温度下硫杂氧砜与人血清白蛋白之间相互作用的对数[(F0-F)/ F]与对数[Q]的关系图;其中[Q]-淬灭剂(久效威砜)的浓度; F0-在没有淬灭剂的情况下的荧光信号; F-在淬灭剂存在下,pH = 7.4的荧光信号。
图4是根据Van’t Hoff方程,LnK与1 / T的关系图;其中K-结合常数; T-绝对温度,pH = 7.4。
图5是人血清白蛋白的荧光光谱与久效威砜的吸收光谱重叠;其中(a)5×10-7 M人血清白蛋白;(b)5×10-7 M久效威砜,pH=7.4,T=310 K。
图6训练集的BP模型的LD50预测值与实际值的关系图。
图7基于与载体蛋白结合的信息预测药物毒性的平台。
图8是Δλ= 15 nm的同步荧光光谱测得的酪氨酸残基的荧光,其中, a-h具有不同浓度的久效威砜的人血清白蛋白。当Δλ=15 nm时,HSA浓度分别为2×10-6,久效威砜与人血清白蛋白的比例为0:1、3:1、6:1、9:1、12:1、15:1、18:1和21:1,pH = 7.4,T = 310 K,i代表血清白蛋白。
图9是Δλ= 60 nm的同步荧光光谱测得的色氨酸残基的荧光其中,a-h具有不同浓度的久效威砜的人血清白蛋白。当Δλ=60 nm时,HSA浓度分别为5×10-7。久效威砜与人血清白蛋白的比例为0:1、3:1、6:1、9:1、12:1、15:1、18:1和21:1,pH = 7.4,T = 310 K,i代表血清白蛋白。
图10是利用三维荧光光谱测得的人血清白蛋白质构象图。
图11是利用三维荧光光谱测和久效威砜对人血清白蛋白质构象变化图。
图12利用圆二色性光谱测定久效威砜对人血清白蛋白质二级结构的变化图;其中a为2×10-6M人血清白蛋白; (b-c)2×10-6M人血清白蛋白,久效威砜与人血清白蛋白之比为5:1、10:1,pH = 7.4,T = 296 K。
图13是利用UV-Vis吸收光谱测定久效威砜对人血清白蛋白质影响的光谱图,其中, (a) 5 × 10-6 M HSA only; (b - i) 5 × 10-6 M HSA in the presence of 5,25, 50, 75, 100, 125, 150 and 175 × 10-6 M pesticide; (j) 5 × 10-6 Mpesticide only, pH = 7.4, T = 310 K。
图14预测集的BP模型的LD50预测值与实际值的关系图。
具体实施方式
本发明的蛋白在预测药物性能上的应用,所述的药物包括农药、人用药和兽药。
所述的农药包括氨基甲酸酯类农药,具体有涕灭威、久效威砜、杀线威,久效威砜、克百威、久效威、灭多威、涕灭威砜、灭害威、猛杀威、残杀威、混灭威、乙硫苯威、灭除威、速灭威、仲丁威、异丙威、丁酮砜威;
所述的人用药包括青霉素类、头孢菌素类、氨基糖苷类、大环内酯类、四环素类、氯霉素类和林可酰胺类、氨基酸类药物、多肽类药物、蛋白类药物、酶类药物、核酸类药物、多糖类药物、脂类药物、生物胺等;
所述的兽药包括青霉素钠、氨苄西林、阿莫西林、头孢噻呋、红霉素、泰乐菌素、替米考星、四环素、强力霉素、土霉素、大观霉素、庆大霉素、卡那霉素、甲砜霉素、氟苯尼考、恩诺沙星、环丙沙星、达氟沙星、磺胺嘧啶、痢菌净、盐酸沙拉沙星、阿维菌素、甲硝唑、盐霉等。
所述的蛋白包括人血清白蛋白、牛血清白蛋白等载体蛋白;以及血红蛋白、球蛋白、血红蛋白、肌蛋白、胶原蛋白、酶蛋白、蜜蜂蛋白、鱼蛋白等蛋白。
本发明的蛋白在预测药物性能上的应用,其特征在于是通过下列步骤实现的:
(1)制备缓冲液:制备pH值为7.4的0.02 M磷酸盐缓冲液;
(2)制备蛋白溶液:用步骤(1)制得的缓冲液根据信号值(仪器最大信号值为1000,如果信号值过高,则稀释溶液。如果信号值偏低,则升高浓度。原则上在1000以下,浓度越高越好)对蛋白液进行溶解并稀释;
(3)配制检测液:将步骤(2)制得的蛋白稀释液与待测定药物以1:1~300的摩尔比进行混合,并利用荧光光谱、或同步荧光、或三维荧光、或圆二色光谱、或线状光谱、或带状光谱对其性能进行预测。
下面以氨基甲酸酯类农药、人血清白蛋白(HSA)为例,来说明氨基甲酸酯类农药对人血清白蛋白(HSA)的影响:
(1)选取成品农药
选取15种氨基甲酸酯类农药,具体有久效威砜、杀线威,久效威砜、克百威、久效威、灭多威、灭害威、猛杀威、残杀威、混灭威、乙硫苯威、灭除威、速灭威、异丙威、丁酮砜威;
(2)配置PBS溶液
a. 0.2 M PBS
分别称NaCl (10.5471 g),KCl (0.2637 g),Na2HPO4 (1.8985 g),KH2PO4 (0.3164g),加二次蒸馏水800 ml,用NaOH调PH至7.4,定容至1000 ml;
注:NaOH为1 M,称1.2 g固体 + 30 ml水;
b. 0.02 M PBS(稀释蛋白用)
取10ml,0.2 M PBS,加水定容至100 ml;
取15ml,0.2 M PBS,加水定容至150 ml ;
过滤,置于4℃冰箱备用;
c.本步骤所需仪器及用品
500 ml锥形瓶、1000 ml/500 ml烧杯、玻璃棒、50 ml烧杯、PH计、PH计校准溶液、洗瓶、废液缸、滤纸、1 ml移液枪,枪头、针管、滤头、分析天平、称量纸、150 ml/200 ml烧杯*2、一次性滴管;
(3)配置HSA溶液
a. 10-4 M HSA母液
称0.0067 g + 1 ml,0.2 M PBS;
称0.0033g + 0.5 ml,0.2 M PBS;
b. 实验用HSA溶液
将HSA溶液逐级稀释,根据信号值确定最终HSA溶液浓度;
荧光用4×10-7 M HSA,紫外用5×10-6 M HSA可以参考;
c.本步骤所需仪器及用品
1.5 ml/1 ml离心管、分析天平、称量纸、HSA纯品、(2.5ul,10 ul,20 ul,30ul,50ul,1 ml)移液枪、枪头;
(4)配置农药溶液
a. 10-2 M农药母液
称农药标准品,用乙醇稀释;
b. 实验用农药溶液
根据HSA浓度以及相互作用比例逐级稀释农药溶液;
c.本步骤所需仪器及用品
1 ml/1.5 ml/5 ml离心管、乙醇、移液枪、枪头。
利用上述配制好的农药(以久效威砜为例进行说明)和HSA溶液,分别用荧光光谱、同步荧光、三维荧光、圆二色光谱和UV-Vis吸收光谱,对其进行检测,具体如下:
1、利用荧光发射光谱预测氨基甲酸酯类农药的毒性,其具体操作方法为:
在283 K~232 K下对溶液体系预热3分钟,在300~500 nm之间测量了氨基甲酸酯类农药的情况下人血清白蛋白的发射光谱,激发波长设定为280 nm,激发和发射的狭缝宽度均为15 nm; HSA浓度保持恒定在5×10-7 M,农药浓度为0~105×10-7 M;
根据以下公式校正荧光信号的内部滤波效果:
在该公式中,Fcor和Fobs分别是校正后的荧光信号和观察到的荧光信号,Aex andAem分别代表溶液体系在激发和发射波长处的吸光度;通过荧光发射光谱得到不同药物的淬灭常数、结合常数、结合位点数、结合距离和自由能后,并利用BP神经网络构建建模,其具体通过下列方式得到的:
(1)氨基甲酸酯类农药对HSA的荧光淬灭的影响
芳香族氨基酸(色氨酸残基、酪氨酸残基和苯丙氨酸残基)是HSA中唯一能发出荧光的氨基酸残基。HSA的固有荧光主要是由于214位的色氨酸引起的。以久效威砜为例,在310 K下,没有和存在久效威砜时的荧光发射光谱如图1所示。可见HSA的荧光信号随农药的滴定而减弱,并伴有蓝移现象,说明HSA的荧光被久效威砜淬灭。同时,依此方法对其它14种氨基甲酸酯类农药(杀线威,久效威砜、克百威、久效威、灭多威、灭害威、猛杀威、残杀威、混灭威、乙硫苯威、灭除威、速灭威、异丙威、丁酮砜威)进检测,HSA也被其他14种氨基甲酸酯类农药淬灭。
(2)氨基甲酸酯类农药对人血清白蛋白的淬灭机理
通常,淬灭分为动态淬灭和静态淬灭,它们的区别在于它们对温度和粘度的依赖性。动态淬灭主要取决于扩散,因此荧光配合物的淬灭常数随温度的升高而增加,反之,温度升高可能导致配合物的稳定性降低,因此静态淬灭常数降低;
F0 / F与[Q]之间存在线性关系,以区分是静态还是动态淬火。使用Stern-Volmer方程分析荧光淬灭数据,以阐明淬灭机理:
式中,F0和F分别是在不存在淬灭剂和存在淬灭剂的情况下的荧光信号。 Ksv是Stern-Volmer淬灭常数; Kq是生物分子的猝灭速率常数; τ0是没有淬灭剂的生物分子的平均寿命(约10-8 s); [Q]是淬灭剂的浓度。
生物高分子上所有类型淬灭剂的最大动态淬灭速率常数为2.0×1010 L·mol-1·s-1。可以根据该值判断静态淬火和动态淬火。如果Kq大于2.0×1010 L·mol-1·s-1,则属于静态淬火;否则,属于动态淬火。
所有具有HSA的农药的Stern-Volmer图显示在图2中,其他药物的KsvandKq计算在表1中。Kq值大于最大分数碰撞淬灭常数(2.0×1010L·mol-1·s-1)。在这三个温度下,生物大分子的淬灭性质应该是静态的。另一方面,计算得出的Ksv值随温度的升高而增加,这表明氨基甲酸酯农药对HSA的淬灭存在动态淬灭。因此,氨基甲酸酯类农药与HSA的相互作用机理是一种动态和静态的混合机制。
表1. 15种氨基甲酸酯农药与HSA之间的信息相互作用
(3)氨基甲酸酯类农药与人血清白蛋白结合位点数及结合常数的确定
在这一部分中,计算了氨基甲酸酯农药与HSA之间的结合位点和结合常数信息。使用以下公式计算氨基甲酸酯农药和HSA的结合位点数(n)和结合常数(Ka):
在等式中,F0是HSA的相对荧光信号,F是HSA与久效威砜的相对荧光信号,Ka是结合常数,n是结合位点的数量,[Q]是久效威砜的浓度砜。
通过图3和公式3的求得log [(F0-F)/ F] -log [Q]曲线的截距和斜率,得到不同温度下的结合位点数和结合常数。结合位点的数量大约为1,表1列出了氨基甲酸酯农药与HSA的结合常数。
(4)热力学参数和结合力
生物分子与药物分子之间的相互作用是弱的分子间相互作用,包括氢键,范德华力,静电力和疏水性相互作用。使用Van’t Hoff方程,获得药物-蛋白质相互作用的热力学常数,并根据不同条件下结合常数的变化判断药物-蛋白质相互作用的主要类型。
常数K类似于在相应温度下的结合常数。
图4显示了根据Van’t Hoff方程得出的Ln K与1 / T的关系图。可以分别根据斜率和截距获得∆G0,∆H0和∆S0。从表1可以看出,所有氨基甲酸酯农药与HSA的相互作用的ΔG0均为负值,ΔH0和ΔS0为正值。根据判断大分子与小分子之间结合力的热力学规则,当∆H0>0,∆S0> 0时,是典型的疏水力可以判断出氨基甲酸酯农药与HSA的相互作用是自发的,主要通过典型的疏水相互作用。
(5)非放射性能量转移与结合距离
用Förster能量转移理论解释了氨基甲酸酯农药与HSA之间的非辐射能量转移,并确定了氨基甲酸酯农药与氨基酸残基之间的结合距离。
能量传递效率E,能量供体和受体之间的距离r和能量传递距离R0之间存在关系。
E表示供体与受体之间的转移效率,r为供体与受体之间的平均距离,R0为转移效率为50%时的临界距离。
K2是与偶极子的供体和受体的几何形状有关的取向,对于流体中的随机取向,K2 =2/3; 34N是在光谱重叠明显的波长范围内介质的平均折射率,并且水和有机物的折射率值为1.336;φ是供体的量子产率,色氨酸在HSA中的量子产率约为0.118; J是供体的发射光谱和受体的吸收光谱之间光谱重叠的影响,可以通过以下公式计算:
F(λ)是供体在从λ到λ+Δλ的波长范围内的校正荧光信号,而ε(λ)是受体在λ处的消光系数。
人血清白蛋白的荧光光谱与氨基甲酸酯农药的吸收光谱重叠,见图5,结合距离记录在表1中。结果表明,由于r <8由于氨基甲酸酯农药与HSA之间发生了非辐射能量转移,这与静态淬灭机理的发生是一致的。
通过上述步骤测得不同农药的Ksv(淬灭常数)、Ka(结合常数)、n(结合位点数)、r(结合距离)和ΔG(自由能)如下表:
利用上述数值构建BP神经网络模型:
首先,通过Logistic回归建立了一个相对简单的线性回归模型,并获得了一个多元线性回归公式。由于R2 <0,因此证明该线性模型不适用于研究交互信息和LD50值之间的关系。然后,执行非线性回归建模。在这里,我们建立了七个常用的非线性模型,分别是SVR回归,RandomForest回归,AdaBoost回归,xgboost.XGBRF回归,GradientBoosting回归,DecisionTree回归和反向传播回归。在表2中发现R2值DecisionTree回归和反向传播回归的均值均高于0.8,显示出相对较好的拟合效果。
表2.回归模型的名称和值
因此,通过调整参数分别优化决策树回归决策树模型和BP神经网络模型。从表3可以看出,决策树模型和BP神经网络模型的R2分别达到0.949和0.952,表明拟合效果大大提高。通过对多元回归模型的分析,确定决策树模型和BP神经网络模型可能具有最好的毒性预测效果。
表3.通过调整参数优化后的R2值。
Algorithm name | R<sup>2</sup> |
DecisionTree Regression | 0.949 |
Backpropagation Regression | 0.952 |
决策树模型是一种具有由多个节点组成的树结构的简单算法,可以处理离散数据集和连续数据集。本研究中的所有参数均采用python中的默认值。决策树是一种树结构,其中每个内部节点代表一个属性的测试,每个分支代表一个测试输出,每个叶子节点代表一个类别。决策树学习使用自顶向下的递归方法,其基本思想是构建熵下降最快的树,作为信息熵的度量。
BP神经网络是一种多层前馈网络,通过误差反向传播(简称误差反向传播)进行训练。它的算法称为BP算法,使用梯度搜索技术来构成网络。使用反向传播来调节网络的权重值和阈值,以实现平方的最小误差和。本研究使用了Matlab2019a中的默认参数。训练集的反向传播神经网络模型的拟合效果如图6所示。
为了验证该模型的预测效果,选择了其他三种氨基甲酸酯农药进行测试。它们光谱检测数据和LD 50值示于表4中。通过荧光光谱研究了这三种农药与HSA的相互作用。表4计算了5个建模指标,包括淬灭常数,结合位点数,结合常数,结合距离和310 K下的自由能。通过将这些数据输入最佳的两个模型中,获得了预测的LD50值,并显示了在表5和表6中可以看出,BP模型的预测值和真实的LD50值之间的差异最小,这表明BP神经网络模型的预测效果要优于决策树模型。测试集的反向传播神经网络模型的拟合如图14所示。
决策树模型的R2值与BP神经网络模型相近,但预测效果远不如BP神经网络模型。这是由于决策树模型显示出相对较低的分类精度,尽管该方法能够处理高维和多样的特征,但在许多化学信息学数据集中也是如此。BP模型的最大特征是它比传统学习算法在保证学习准确性的前提下。对于单个隐藏层神经网络,可以随机初始化输入权重和偏差,并可以获得相应的输出权重。根据BP神经网络模型,可以看出荧光光谱信息与毒性之间存在很强的相关性,表明该方法可用于预测毒性。
表4.用于验证和HSA的三种农药的相互作用信息
表5.决策树模型的预测结果
表6. BP神经网络模型的预测结果
SSE =预测的LD50-实际的LD50
可见,BP神经网络模型在所有数据建模中效果最好,拟合程度最高。预测时,将通过荧光光谱检测的待测农药的上述五数值输调参后的BP模型中,可以快速拟合农药的LD50值,用于在早期阶段快速预测新农药的毒性,并为该化合物是否具有进一步的开发价值提供数据参考。其得到的LD50,世界卫生组织的农药危害分级标准,得到毒性等级,其数值越大,毒性越小;数值越小,毒性越大。
2、利用同步荧光光谱的测定氨基甲酸酯类农药对人血清白蛋白质构象变化的影响,其具体操作方法为:
在283 K~232 K下对溶液体系预热2~5分钟,扫描速度设300 nm/min;在200至400 nm范围内同时扫描激发和发射光谱,得到氨基甲酸酯类农药时HSA的同步荧光光谱,激发波长和发射波长之间的间隔分别设置为15 nm和60 nm,当Δλ= 15 nm时,HSA的浓度保持在2×10-6 M、农药在0~42×10-6 M;当Δλ=60nm时,HSA的浓度保持在5×10-7 M、农药在0~105×10-7 M;首先测HSA,再与农药比例需要具体确定,初步按1:1、1:2、1:3的比例来确定;
结论:Δλ= 15 nm的同步荧光光谱只能显示酪氨酸残基的荧光、如图8所示,而Δλ= 60 nm的同步荧光光谱只能显示色氨酸残基的荧光、如图9所示。蛋白质构象的变化可以通过发射波长的变化来判断,因为氨基酸残基的最大发射波长与其环境的疏水性有关。如实验结果所示,随着药物浓度的增加,在Δλ= 15 nm和Δλ= 60 nm的光谱下,蛋白质的最大荧光发射峰减小。这表明酪氨酸和色氨酸周围的微环境发生了轻微变化。
3、利用三维荧光光谱测定氨基甲酸酯类农药对人血清白蛋白质构象变化的影响,其具体操作方法为:
在283 K~232 K下对溶液体系预热2~5分钟,扫描速度设300 nm/min;对8×10-8 M HAS和对8×10-8 M HSA与8×10-7M农药混配的样品分别进行了三维荧光光谱分析,如图10、图11所示,记录了发射波长为250~500 nm,初始激发波长设置为210 nm,激发间隔10nm,激发和发射狭缝宽度均为15nm;首先测HSA,再与农药比例需要具体确定,初步按1:1、1:2、1:3的比例来确定;
结论:根据测得结果,峰1主要表现出多肽骨架结构的荧光光谱行为,峰2主要表现出色氨酸和酪氨酸残基的光谱特征。加入农药后,峰1的荧光信号显着下降,表明多肽骨架结构周围的微环境,已经改变了。峰2的信号也下降但不明显,这表明色氨酸和酪氨酸周围的微环境略有变化。
4、利用圆二色性光谱测定氨基甲酸酯类农药对人血清白蛋白质二级结构的变化,其具体操作方法为:
在283 K~232 K下对溶液体系预热2~5分钟,在200~500 nm波长范围内记录了HSA的CD光谱,HSA浓度保持在2×10-6 M,农药在0~20×10-6 M;首先测HSA,再与农药比例需要具体确定,初步按1:1、1:2、1:3的比例来确定;
结论:测试了游离HSA和HSA农药系统的CD光谱。HSA在208nm和222nm处显示出负的科顿效应,这是典型的α螺旋结构。加入农药后,HSA对棉花在208 nm和222 nm处的负面影响有所减轻。 HSA的圆二色性形状没有改变,表明系统中蛋白质的二级结构仍以α-螺旋结构为主,如图12所示。下表计算了不同农药对α-螺旋含量的变化:
表7是不同农药对α-螺旋含量的变化
根据以下方程式,CD光谱的结果通常表示为平均残留摩尔椭圆数(MRE),单位为deg·cm2·dmol-1。
其中Cp是蛋白质的摩尔浓度,n是氨基酸残基的数量(585),l是路径长度(1 mm)。使用以下公式从MRE值计算出游离和结合的HSA的α螺旋度:
其中,MRE208是在208 nm处观察到的MRE值; 4000是208 nm处随机线圈构象的MRE值; 33000是纯α螺旋在208 nm处的MRE值。
表7中示出了在添加农药之前和之后α-螺旋含量的变化。随着化合物含量的增加,HSA的α-螺旋含量略有降低,表明加入化合物后蛋白构象发生了变化。
5、利用UV-Vis吸收光谱测定氨基甲酸酯类农药对HSA的影响,具体操作步骤如下:
在283 K~232 K下对溶液体系预2~5分钟,在190~500 nm范围内记录了添加了各种药物的HSA的UV-Vis光谱,波长间隔设置为0.5 nm,狭缝宽度设置为2nm;系统中的HSA固定为5×10-6M的浓度,并用0~175×10-6M农药滴定;
结论:紫外-可见光谱显示HSA有两个特征吸收峰,分别在212 nm和278 nm左右。212 nm附近的强吸收峰反映了HSA对肽骨架结构的吸收,278 nm左右的弱吸收峰是由芳香族氨基酸(Trp、Tyr和Phe)产生的。结果显示,添加农药后,呈现出相同的趋势,见图13显示,HSA 212 nm的紫外-可见吸收降低,278 nm附近的弱吸收没有显著变化。以上结果表明,肽骨架附近的微环境极性增加,芳香族氨基酸附近的微环境略有改变。
此外,紫外-可见光谱可用于确定动态淬灭和静态淬灭之间的淬灭机制。动态淬灭只影响荧光团的激发态,而不影响荧光团的吸收光谱。然而,由于络合物的形成,荧光团的吸收不断变化。不同浓度农药对HSA的吸收光谱,可以确定该类农药与HSA的反应机理为静态淬灭。
芳香族氨基酸(色氨酸残基、酪氨酸残基和苯丙氨酸残基)是HSA中唯一能发出荧光的氨基酸残基。HSA的固有荧光主要是由于214位的色氨酸引起的。在310 K下,没有和存在久效威砜时的荧光发射光谱和紫外光谱图。可见HSA的荧光信号随农药的滴定而减弱,并伴有蓝移现象,说明HSA的荧光被久效威砜淬灭,同样经过检测,HSA也被其他14种氨基甲酸酯类农药淬灭。
本发明的蛋白在预测药物性能上的应用,通过紫外-可见光谱、荧光、同步荧光、三维荧光、圆二色性(CD)光谱和分子对接技术系统研究了药物与HSA的相互作用信息。为了用最少的设备和最少的时间更容易地预测毒性,通过荧光光谱和毒性获得了五个指标(淬灭常数,结合位点数,结合常数,自由能,结合距离)之间的8个回归模型和8个分类模型。成立。通过比较从该模型获得的预测LD50值与其他三种氨基甲酸酯农药的实际LD50值,进一步验证了最好的两个新模型。这项研究构建了一种预测氨基甲酸酯类农药毒性的新方法(CPBITR),更重要的是,它首次为预测药物毒性提供了一个新的独特视角(与载体蛋白结合)。该研究对药物研发产业的发展具有重要的现实意义。
利用上述方法将药物与载体蛋白结合,可对前述所有氨基甲酸酯类农药、人用药和兽药的毒性及其它性能的预测。
Claims (1)
1.蛋白在预测药物性能上的应用,其特征在于是通过下列步骤实现的:
(1)制备缓冲液:制备pH值为7.4的0.02 M磷酸盐缓冲液;
(2)制备蛋白溶液:用步骤(1)制得的缓冲液根据信号值对蛋白液进行溶解并稀释;
(3)配制检测液:将步骤(2)制得的蛋白稀释液与待测定药物以1:1~300的摩尔比进行混合,并利用荧光光谱对其性能进行预测,其具体操作方法为:
在283 K~232 K下对溶液体系预热3分钟,在300~500 nm之间测量了不同药物的情况下人血清白蛋白的发射光谱,激发波长设定为280 nm,激发和发射的狭缝宽度均为15 nm;HSA浓度保持恒定在5×10-7 M,农药浓度为0~105×10-7 M;
根据以下公式校正荧光信号的内部滤波效果:
在该公式中,Fcor和Fobs分别是校正后的荧光信号和观察到的荧光信号,Aex 和Aem分别代表溶液体系在激发和发射波长处的吸光度;
通过荧光发射光谱得到不同药物的淬灭常数、结合常数、结合位点数、结合距离和自由能后,并利用BP神经网络完成建模;预测时,将通过荧光光谱检测的待测农药的上述五数值输模型中,即可得到农药的LD50值,根据世界卫生组织的农药危害分级标准,得到毒性等级,完成其毒性的预测。
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