CN103207947A - Method for predicting activity of angiotensin converting enzyme inhibitor - Google Patents
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- 229940079593 drug Drugs 0.000 claims abstract description 5
- 125000003275 alpha amino acid group Chemical group 0.000 claims abstract 2
- 238000012360 testing method Methods 0.000 claims description 19
- UUUHXMGGBIUAPW-UHFFFAOYSA-N 1-[1-[2-[[5-amino-2-[[1-[5-(diaminomethylideneamino)-2-[[1-[3-(1h-indol-3-yl)-2-[(5-oxopyrrolidine-2-carbonyl)amino]propanoyl]pyrrolidine-2-carbonyl]amino]pentanoyl]pyrrolidine-2-carbonyl]amino]-5-oxopentanoyl]amino]-3-methylpentanoyl]pyrrolidine-2-carbon Chemical compound C1CCC(C(=O)N2C(CCC2)C(O)=O)N1C(=O)C(C(C)CC)NC(=O)C(CCC(N)=O)NC(=O)C1CCCN1C(=O)C(CCCN=C(N)N)NC(=O)C1CCCN1C(=O)C(CC=1C2=CC=CC=C2NC=1)NC(=O)C1CCC(=O)N1 UUUHXMGGBIUAPW-UHFFFAOYSA-N 0.000 claims description 12
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- CZGUSIXMZVURDU-JZXHSEFVSA-N Ile(5)-angiotensin II Chemical compound C([C@@H](C(=O)N[C@@H]([C@@H](C)CC)C(=O)N[C@@H](CC=1NC=NC=1)C(=O)N1[C@@H](CCC1)C(=O)N[C@@H](CC=1C=CC=CC=1)C([O-])=O)NC(=O)[C@@H](NC(=O)[C@H](CCCNC(N)=[NH2+])NC(=O)[C@@H]([NH3+])CC([O-])=O)C(C)C)C1=CC=C(O)C=C1 CZGUSIXMZVURDU-JZXHSEFVSA-N 0.000 description 2
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- MENAYYMPBRSAAE-AWEZNQCLSA-N 3-[[5-[[(2s)-1-carboxy-3-oxopropan-2-yl]carbamoyl]pyridin-2-yl]methylsulfamoyl]benzoic acid Chemical compound N1=CC(C(=O)N[C@@H](CC(=O)O)C=O)=CC=C1CNS(=O)(=O)C1=CC=CC(C(O)=O)=C1 MENAYYMPBRSAAE-AWEZNQCLSA-N 0.000 description 1
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Abstract
一种预测血管紧张素转化酶抑制剂活性的方法，选取天然氨基酸的量子化学参数，做主成分分析得到19个主成分，将主成分作为描述每个氨基酸的主成分氨基酸量化参数，用氨基酸量子化学参数得分对血管紧张素转化酶抑制剂氨基酸序列进行表征，以血管紧张素转化酶抑制剂的药物活性值作为建立模型的因变量，用逐步线性回归方法进行变量筛选，找出与因变量显著相关的氨基酸量子化学参数得分，利用逐步线性回归处理每步得到的氨基酸所有量子化学参数得分，按照其重要性顺序，依次用偏最小二乘方法构建血管紧张素转化酶抑制剂的药物活性模型，并采用留一法交叉检验以及外部检验评价模型的预测能力，具有操作简单、形式统一、数据容易获取等特点。
A method for predicting the activity of angiotensin-converting enzyme inhibitors. The quantum chemical parameters of natural amino acids are selected, and 19 principal components are obtained by principal component analysis. The principal components are used as the principal components to describe each amino acid. The parameter score is used to characterize the amino acid sequence of angiotensin-converting enzyme inhibitors, and the drug activity value of angiotensin-converting enzyme inhibitors is used as the dependent variable of the model, and the stepwise linear regression method is used for variable screening to find out the significant correlation with the dependent variable Amino acid quantum chemical parameter scores, using stepwise linear regression to process all the amino acid quantum chemical parameter scores obtained in each step, according to their order of importance, using the partial least squares method to construct the drug activity model of angiotensin converting enzyme inhibitors, and The prediction ability of the evaluation model is evaluated by leave-one-out cross-check and external inspection, which has the characteristics of simple operation, unified form, and easy access to data.
Description
Technical field
The present invention relates to the D-M (Determiner-Measure) construction-character/active technical field of peptide, particularly a kind of method of predicting the angiotensin-converting enzyme inhibitor activity.
Background technology
(Angiotensin-converting enzyme, ACE) inhibitor is a kind of compound that suppresses hypertensin conversion enzyme activity to angiotensin converting enzyme.(rennin-angiotensin system RAS) plays key effect to the plain – hypertensin system of kidney in regulating human blood-pressure.The proangiotensin that is produced by liver is fractured into nonactive decapeptide angiotensin I through feritin catalysis, passing through angiotensin converting enzyme (ACE) catalytic pyrolysis again is the octapeptide Angiotensin II with extremely strong vessel retraction function, so ACE just becomes the action target spot of blood-pressure drug research.The ACE inhibitor suppresses the effective biologically active purpose of ACE (J.Chem.Soc.Perkin Trans.I.1984,23:155 – 162) by simulation ACE substrate angiotensin I activity part structure feature thereby reach competition.Therefore the ACE inhibitor also becomes disease medicament precursors such as treatment hypertension, heart disease, diabetes and ephrosis.The peptides that the ACE inhibitor generally is made up of amino acid residue, yet to the research and development of peptide medicament and the discovery of lead compound, so far be still costly but a job that efficient is very low, therefore, press for the new theoretical method of development and peptide quasi-molecule designing technique and instruct the peptide medicament exploitation.In recent years, be the design of based computer accessory molecule with various theoretical calculation methods and Molecular Simulation Technique, in the research and development of peptides, be used widely.Carry out conformational analysis with computing machine molecular modeling, molecular dynamics and quantum chemistry etc., seek the pharmacophore of polypeptide and analog, carry out QSAR research, the design that ins all sorts of ways has peptide class and the non-peptide mimics of greater activity, has become in the world very active research field.But, in the QSAR of peptide medicament research, on the one hand, because the relative complexity of peptide matters structure and high flexibility, make its parameter based on whole peptide molecule be difficult to determine that on the other hand, the functional characteristic of peptide relates to factors such as amino acid position in the sequence, formation and physicochemical property thereof, so cause the QSAR research of peptide also not have comparatively ripe methodology guidance at present, among the whole bag of tricks and technology all also are in and constantly attempt and develop.
Summary of the invention
In order to overcome the shortcoming of above-mentioned prior art, the purpose of this invention is to provide a kind of method of predicting the angiotensin-converting enzyme inhibitor activity, have simple to operate, unity of form, data and characteristics such as obtain easily.
In order to achieve the above object, the present invention solves by the following technical programs:
A kind of method of predicting the angiotensin-converting enzyme inhibitor activity may further comprise the steps:
1) choose 2236 Quantum chemical parameters of 20 kinds of natural amino acids, 2236 kinds of Quantum chemical parameters of 20 kinds of natural amino acids specifically comprise: highest occupied molecular orbital energy, minimum track energy, energy gap, overall flex, the highest minimum orbital energy that occupies of occupying are than, the final frontier electron density of heat, gross energy, internuclear repulsion, ionization potential, electronic characteristic value (EEVA) descriptor, close electric atom, the frontier electron density of nucleophilic atom, total close electric superdelocalizability, average close electric superdelocalizability, total nucleophilic superdeloca lizability, the average nucleophilic superdeloca lizability of generating;
2) utilize SPSS13.0 software to do principal component analysis (PCA) to 2236 Quantum chemical parameters of 20 kinds of natural amino acids, obtain 19 major components, as shown in table 1,
Table 1 is 19 major components of 2236 kinds of Quantum chemical parameters of 20 kinds of natural amino acids
a20 kinds of natural amino acids represent with conventional single English alphabet,
3) 19 major components are quantized parameter as describing each amino acid whose 19 major component amino acid, be called amino acid quantum chemical parameters score;
4) with 19 amino acid quantum chemical parameters scores angiotensin converting enzyme (ACE) inhibitor amino acid sequence is characterized, wherein each amino acid residue characterizes with 19 amino acid quantum chemical parameters scores, and with the independent variable of characterization result as active forecast model;
5) with the pharmaceutically active value of angiotensin converting enzyme (ACE) inhibitor as the dependent variable of setting up model, carry out the variable screening with progressively linear regression (SMR) method, find out the amino acid quantum chemical parameters score with the dependent variable significant correlation, be specially: the level of signifiance value P with inclined to one side F test value correspondence is foundation: when the maximum level of signifiance value P of F test value partially in candidate's variable≤0.99, then introduce this amino acid quantum chemical parameters score and carry out modeling; In the variable of introducing equation, if its minimum level of signifiance value P of F test value partially 〉=1.00 o'clock, then remove this amino acid quantum chemical parameters score and carry out modeling;
6) all the Quantum chemical parameters scores of amino acid that per step of progressively linear regression (SMR) obtained, according to its sequence of importance, use offset minimum binary (PLS) method to make up the pharmaceutically active model of angiotensin converting enzyme (ACE) inhibitor successively, and adopt the predictive ability of leaving-one method crosscheck and external inspection evaluation model.
The invention has the beneficial effects as follows:
A new amino acid descriptor of the present invention---amino acid quantum chemical parameters score can obtain parameter by the pharmaceutically active model of angiotensin converting enzyme (ACE) inhibitor activity that makes up, and is used for the activity of prediction angiotensin converting enzyme (ACE) inhibitor.Characteristics such as that this method has is simple to operate, unity of form, data are obtained easily are expected to become valuable structure characterization methods in the forecasting research of angiotensin converting enzyme (ACE) inhibitor activity.
Description of drawings
Fig. 1 is the predicted value of 58 ACE inhibitor dipeptides activity and the correlation scatter diagram of experiment value.
Fig. 2 is the predicted value of 55 ACE inhibitor tripeptide actives and the correlation scatter diagram of experiment value.
Embodiment
Below in conjunction with drawings and Examples the present invention is described in detail.
A kind of method of predicting the angiotensin-converting enzyme inhibitor activity may further comprise the steps:
1) choose 2236 kinds of Quantum chemical parameters of 20 kinds of natural amino acids, 2236 kinds of Quantum chemical parameters of 20 kinds of natural amino acids specifically comprise: highest occupied molecular orbital energy, minimum track energy, energy gap, overall flex, the highest minimum orbital energy that occupies of occupying are than, the final frontier electron density of heat, gross energy, internuclear repulsion, ionization potential, electronic characteristic value (EEVA) descriptor, close electric atom, the frontier electron density of nucleophilic atom, total close electric superdelocalizability, average close electric superdelocalizability, total nucleophilic superdeloca lizability, the average nucleophilic superdeloca lizability of generating;
2) utilize SPSS13.0 software to do principal component analysis (PCA) to 2236 Quantum chemical parameters of 20 kinds of natural amino acids, obtain 19 major components, as shown in table 1,
Table 1 is 19 major components of 2236 kinds of Quantum chemical parameters of 20 kinds of natural amino acids
a20 kinds of natural amino acids represent with conventional single English alphabet,
3) 19 major components are quantized parameter as describing each amino acid whose 19 major component amino acid, be called amino acid quantum chemical parameters score, these 19 scores combine the full detail of 2236 kinds of quantization parameters substantially, therefore, can use it for the structural characterization of peptide;
4) with 19 amino acid quantum chemical parameters scores angiotensin converting enzyme (ACE) inhibitor amino acid sequence is characterized, wherein each amino acid residue characterizes with 19 amino acid quantum chemical parameters scores, and with the independent variable of characterization result as active forecast model;
5) with the pharmaceutically active value of angiotensin converting enzyme (ACE) inhibitor as the dependent variable of setting up model, with the screening of progressively linear regression (SMR) method and the closely-related parameter of angiotensin converting enzyme (ACE) inhibitor structure, find out the Quantum chemical parameters score with the dependent variable significant correlation;
Carry out the variable screening with regression technique (SMR) progressively, introduce variable successively by Fischer significance test, level of signifiance value P with inclined to one side F test value correspondence is foundation: when the maximum level of signifiance value P of F test value partially in candidate's variable≤0.99, then introduce this variable; In the variable of introducing equation, if its minimum level of signifiance value P of F test value partially 〉=1.00 o'clock, then reject this variable;
6) all the Quantum chemical parameters scores of amino acid that per step of progressively linear regression (SMR) obtained, according to its sequence of importance, use offset minimum binary (PLS) method to make up the pharmaceutically active model of angiotensin converting enzyme (ACE) inhibitor successively, and adopt the predictive ability of leaving-one method crosscheck and external inspection evaluation model.
Biologically active value pIC with angiotensin converting enzyme (ACE) inhibitor
50(log(1/IC
50)) for the dependent variable of prediction angiotensin converting enzyme (ACE) inhibitor activity model, in conjunction with progressively linear regression (SMR) modeling, come the predictive ability of evaluation model with offset minimum binary (PLS) with the accumulation multiple correlation coefficient of leaving-one method crosscheck; Then, further come the predictive ability of verification model by the method for external certificate, sample is divided into training set and external certificate test set, wherein the ratio of training set sample and test set sample is 4:1, training set is used for modeling, then with the activity of the model prediction test set sample of building up.
Be that amino acid quantum chemical parameters score is for the application example of ACE inhibitor activity prediction below
Below the Quantum chemical parameters score is characterized 58 angiotensin converting enzyme (ACE) inhibitor dipeptides and two kinds of peptide class formations of 55 angiotensin converting enzyme (ACE) inhibitor tripeptides respectively, adopt progressively linear regression in conjunction with the offset minimum binary modeling, thereby checking Quantum chemical parameters score is in the validity of angiotensin converting enzyme (ACE) inhibitor activity forecasting research.
1) prediction of angiotensin converting enzyme (ACE) inhibitor dipeptides activity
Selected 58 angiotensin converting enzyme (ACE) inhibitor dipeptides derives from (Angiotensin converting enzyme inhibitors such as Cushman, 1981, pp.3 – 25), each angiotensin converting enzyme (ACE) inhibitor dipeptides can characterize with 19 * 2 amino acid quantum chemical parameters scores, adopt SMR-PLS to select variable, with model crosscheck multiple correlation coefficient (Q
LOO 2) the descriptor number is set up final PLS model when reaching maximum.When introducing 12 amino acid quantum chemical parameters score (v
20, v
2, v
27, v
3, v
28, v
22, v
24, v
4, v
12, v
19, v
25, v
26) time, gained PLS model is explained Y variable 93.8% variance with 1 remarkable major component, and crosscheck (CV) the Y variable variance of explaining is 91.0%, and root-mean-square error (RMSE) is 0.352.Fig. 1 provides 58 angiotensin converting enzyme (ACE) inhibitor, two peptide biological activity calculated values and experimental observation value correlation circumstance, and all samples all were dispersed near 45 ° of straight lines of initial point as can be seen from Figure 1, the Non Apparent Abnormality point.In order further to check the predictive ability of amino acid quantum chemical parameters score modeling, 58 angiotensin converting enzyme (ACE) inhibitor dipeptides is divided into training set and test set two parts at random, wherein 43 samples are as training set, remaining 15 as test set.The choosing method of test set is chosen one then at first 58 angiotensin converting enzyme (ACE) inhibitor dipeptides being sorted from small to large by its activity from per four samples.Multiple correlation coefficient (the R of training set institute established model
Cum 2) and crosscheck multiple correlation coefficient (Q
LOO 2) be respectively 0.915,0.831, external samples verification multiple correlation coefficient (Q
Ext 2) be 0.767.
2) prediction of angiotensin converting enzyme (ACE) inhibitor tripeptide active
Selected 55 angiotensin converting enzyme (ACE) inhibitor tripeptides from Wu etc. (J Agric Food Chem.2006,54:732-738).At first use amino acid quantum chemical parameters score that 55 angiotensin converting enzyme (ACE) inhibitor tripeptides structure is characterized, common property is given birth to 19 * 3 descriptor variablees, and modeling method is the same.At first progressively screen modeling behind the variable with 55 sample sets, in the variable screening process, when introducing 13 amino acid quantum chemical parameters score (v
55, v
52, v
19, v
43, v
3, v
20, v
5, v
24, v
44, v
7, v
22, v
42, v
16) time, gained offset minimum binary (PLS) model is explained Y variable 99.4% variance with 2 remarkable major components, and crosscheck (CV) the Y variable variance of explaining is 97.9%, and RMSE is 0.074.Fig. 2 provides 55 angiotensin converting enzyme (ACE) inhibitor, three peptide biological activities and calculates and the observed reading correlation circumstance, can find out that most of sample all was dispersed near 45 ° of straight lines of initial point the Non Apparent Abnormality point.In order further to verify the predictive ability of amino acid quantum chemical parameters score modeling, we adopt identical method that 55 angiotensin converting enzyme (ACE) inhibitor tripeptides is divided into 41 training set samples and 14 test set samples at random.Multiple correlation coefficient (the R of training set institute established model
Cum 2) and crosscheck multiple correlation coefficient (Q
LOO 2) be respectively 0.984,0.944, external samples verification multiple correlation coefficient (Q
Ext 2)
Be 0.846.The result shows that the predictive ability of amino acid quantum chemical parameters score modeling is stronger.
The above, it only is preferred embodiment of the present invention, be not that the present invention is done any pro forma restriction, every foundation technical spirit of the present invention all still belongs in the scope of technical solution of the present invention any simple modification, equivalent variations and modification that above embodiment does.
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| CN106124659A (en) * | 2016-06-23 | 2016-11-16 | 井冈山大学 | The method of prediction sulfa antibiotics rate of photocatalytic oxidation |
| CN108763863A (en) * | 2018-05-30 | 2018-11-06 | 西北民族大学 | The quantitative structure activity relationship model of ace inhibitory peptide and its application |
| CN114496112A (en) * | 2022-01-21 | 2022-05-13 | 内蒙古工业大学 | An intelligent quantification method for anti-breast cancer drug components based on multi-objective optimization |
| CN114496112B (en) * | 2022-01-21 | 2023-10-31 | 内蒙古工业大学 | An intelligent quantification method of anti-breast cancer drug ingredients based on multi-objective optimization |
| CN115527629A (en) * | 2022-09-29 | 2022-12-27 | 北京有竹居网络技术有限公司 | Data processing method and device for quantum chemical system |
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