Chinese Journal of Chemical Physics  2016, Vol. 29 Issue (4): 508-516

The article information

Lian-sheng Qiao, Yu-su He, Xiao-qian Huo, Lu-di Jiang, Yan-kun Chen, Xi Chen, Yan-ling Zhang, Gong-yu Li
乔连生, 贺昱甦, 霍晓乾, 蒋芦荻, 陈艳昆, 陈茜, 张燕玲, 李贡宇
Construction and Evaluation of Merged Pharmacophore Based on Peroxisome Proliferator Receptor-Alpha Agonists
Chinese Journal of Chemical Physics, 2016, 29(4): 508-516
化学物理学报, 2016, 29(4): 508-516

Article history

Received on: February 22, 2016
Accepted on: May 18, 2016
Construction and Evaluation of Merged Pharmacophore Based on Peroxisome Proliferator Receptor-Alpha Agonists
Lian-sheng Qiao, Yu-su He, Xiao-qian Huo, Lu-di Jiang, Yan-kun Chen, Xi Chen, Yan-ling Zhang, Gong-yu Li     
Dated: Received on February 22, 2016; Accepted on May 18, 2016
Beijing Key Laboratory of TCM Foundation and New Drug Research, School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing 100102, China
Author: Yan-ling Zhang,
Abstract: Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore (LBP) and structure-based pharmacophore (SBP). LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distinguishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore (MP) is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of peroxisome proliferator receptor-alpha (PPARα) agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.

Virtual screening (VS) is an essential computer aided-drug design method to discover and optimize lead compounds with better efficiency than random screening and high-throughput screening. In recent years, amounts of lead compounds were found and demonstrated by VS, and numerous VS methods have also been created for different cases, including structure-based and ligand-based methods [1]. VS method has become one of the best methods available to discover lead compounds. However, some pitfalls and technical obstructions have also remained in the process of VS, especially the reality of VS is often questioned due to the differences of VS models constructed by various modelling methods [2, 3]. Therefore, it is an important for VS model to have better ability and screening efficiency to predict compounds activity [4].

Pharmacophore is commonly and illustriously used for drug design and VS. Pharmacophore model is a set of the important atoms or groups as well as corresponding spatial arrangement, which can be regarded as an abstract representation for the binding features of ligands and receptors. It is have indicated that pharmacophore-based method might outperform docking-based method for VS [5].

Depending on the construction method, pharmacophore can be categorized as ligand-based pharmacophore (LBP) and structure-based pharmacophore (SBP). LBP are formed by extracting the common pharmacological features from a series of active compounds for a specific target. SBP is obtained by analysing the receptor-ligand interactions. As previously described in Ref.[6, 7], on the basis of the comparison between LBP and SBP in screening efficiency, LBP shows lower accuracy, higher hit rates of active compounds, and better correlation relationship between structure and biological activity, whereas SBP has higher accuracy but probably misses plenty of active compounds due to the shortage of their frame information. Additionally, though LBP provides most common features for active compounds, it is not the sufficient condition for ligands identification. It is partial for characteristics of LBP models, as the drug contained optimized properties beyond binding affinity, $e$.$g$. ADME, toxicity, $etc$. Therefore, considering all the conditions mentioned above, LBP and SBP should be designed to complement each other and integrated for optimal performance. Combinatorial screening of LBP and SBP has been a common method, but it does not provide an accurate analysis about how pharmacological features affect pharmacodynamics and what the better binding mode of molecules and targets is. Meanwhile, the prediction results of LBP and SBP might be inconsistent, which is unbenificial for computing the bioactivities of compounds [8, 9]. Merged pharmacophore (MP) is presented to integrate advantages of LBP and SBP and avoid shortcomings in this work to obtain better universality, accuracy, and efficiency of screening.

Peroxisome proliferator receptor-alpha (PPAR$\alpha$) is an important target for dyslipidemia. As PPAR$\alpha$ agonists, fibrates have been used for many years in clinical, which have explicit pathway and better efficacy in modulating lipids. According to reducing synthesis of hepatic lipoprotein, PPAR$\alpha$ agonists can mainly reduce blood levels of triglycerides, moderately decline LDL-C and improve HDL-C [10, 11]. A number of molecular simulation models have already been reported on this target, many chemicals, active data and models can be used to construct and compare with MP models [12, 13].

In this work, LBP and SBP were constructed for PPAR$\alpha$ agonist, respectively. The optimal LBP and SBP models were separately selected by several evaluation indexes. According to comparasion of the difference of the optimal LBP and SBP, the identical and non-identical pharmacological features were obtained as mainly and secondarily pharmacological features. Thereafter, MP was constructed by the best weight and tolerance values of mainly and secondarily pharmacological features, based on single-factor explorations and orthogonal experimental design. Moreover, docking and literatures were applied to verify the reliability and validity of MP model. The best MP model was used to predict the PPAR$\alpha$ activity of natural compounds. In order to compare the screening efficiency, two testing sets and traditional Chinese medicine database (TCMD, version 2009) were computed by final LBP, final SBP, optimal LBP, optimal SBP, MP, docking model and the combinatorial screening of two type models. Technology road map is shown in Fig. 1.

Figure 1 Technology road map of building MP.
Ⅱ MATERIALS AND METHODS A Ligand preparation

Twenty five active compounds of training set were derived from the Binding Database ( EC$_{50}$ values of 25 compounds were obtained by luciferase reporter gene assay for transactivation of PPAR$\alpha$ expressed in human HepG2 cells (Fig. S1 in supplementary materials). The range of EC$_{50}$ in training set was across 10 orders of magnitude, and the structural diversity of compounds was also considered. Then 100 active compounds were randomly obtained from the Binding Database, and 300 inactive compounds were selected from MDDR databases (MDL Drug Data Report: Version 2007.2) for first testing set. Three-dimensional databases and energy optimizations of training set and first testing set were constructed by Discovery Studio 4.0, with business EDP system technique conformation generation method, CHARMm force field, and 255 maximum numbers of conformations.

Figure S1 Structures and EC50 of compounds in training set.

In order to evaluate the similarities and differences of compounds in training set and first test set, chemical space of two sets was analyzed by principal component analysis (PCA) and clustering algorithm based on molecular properties [14, 15]. Prior to modeling, chemical space was defined by calculating a given set of molecular descriptors for each compound. Initially, 43 topological descriptors commonly used 2D descriptors were calculated for analyzing the basic properties of compounds. PCA was then implemented to map the distribution of the compounds in chemical space. Thereafter, a total of 1481 molecular descriptors were computed for compounds in training set and first testing set. BestFirst and CfsSubsetEval algorithm with 10 fold cross-validation were performed in Weka 3.6.10 for selecting the descriptors, which were highly correlated with classification [16, 17]. And the compounds were clustered based on these descriptors in training set and first test set.

B LBP generation

LBP models were built by Hypogen (3D QSAR Pharmacophore Mapping). According to the feature analysis of compounds in training set, hydrogen bond acceptor (A), hydrogen bond acceptor lipid (Ali), hydrophobic feature (H), ionizable negative feature (N), and ring aromatic feature (R) were found in these compounds. In order to improve the representativeness of pharmacophore, three optimum proportions of pharmacological features were set for pharmacophore generation. In proportion 1, features of A and ALi were required to be generated. Features of A and N in proportion 2 and that of N in proportion 3 were required to be generated. Then Fisher cross-validation and cost function were used to evaluate each model.

Ligand profiler module was used to externally verify pharmacophore models by the first testing set. The number of total compounds ($N_\mathrm{TD}$) and active compounds ($N_\mathrm{TA}$) were 400 and 100 in testing set. Hits of active compounds (Ha) and hits of total compound (Ht) were counted by pharmacophore profiling. Based on the values of Ha and Ht, hit rate of active compounds (HRA), identify effective index (IEI) and comprehensive appraisal index (CAI) were computed by Eq.(1) to Eq.(3):

$ \mathrm{HRA}= \frac{\mathrm{Ha}}{N_\mathrm{TA}}\times 100\% $ (1)
$ \mathrm{IEI}=\frac{\mathrm{Ha}}{\mathrm{Ht}}\left(\frac{N_\mathrm{TA}}{N_\mathrm{TD}}\right)^{-1} $ (2)
$ \mathrm{CAI}=\mathrm{IEI}\times\mathrm{HRA} $ (3)

IEI denoted the ability to distinguishing active compounds and inactive compounds, and CAI was used to evaluate the screening efficiency of pharmacophore [18]. Meanwhile, compounds in training set were matched with pharmacophore, and maximum omitted features were set to-1 with flexible fitting methods. Fit value for every compound was computed to indicate the overlap degree of pharmacophore and molecule [19]. The higher fit values indicated that the better conformational coverage. Then correlation values of testing (corr$_\mathrm{test}$) was calculated by correlation function of the rank of fit values and the rank of biological activities from compounds in training set [20]. To some extent, corr$_\mathrm{test}$ represented the reliability of pharmacophore. According to the experienced criterion for the first testing set in which the proportion of active and inactive compounds was 1:3, the cutoff values of IEI, CAI, and corr$_{\mathrm{test}}$ were 3, 2, and 0.6. The corresponding values of excellent pharmacophore model should be higher than three cutoff values [18]. However, as this criterion was difficult to achieve, an optimal model should possess two values over the cutoff values at least.

C SBP generation

Eight crystal structures of PPAR$\alpha$ were obtained from PDB (, and the basic information was shown in Table S1 (supplementary materials). These crystal structures were all combined with agonists, which were directly related to anti-dyslipidemia. And the binding sites within these complexes had been clearly defined. According to these binding sites, preliminary SBP models were generated with more redundant features, which were detrimental for the screening efficiency with direct VS. Preliminary SBP models was simplified based on clustering methods, and sub-pharmacophore models were generated by random combination of 4-7 pharmacological features from the clustering results. In order to evaluate the SBP models, evaluation criteria of SBP including HRA, IEI, CAI, and corr$_{\mathrm{test}}$ were also computed by VS of the first testing set.

Table S1 Ligand-receptor complexes of PPARα
D MP construction

MP was constructed based on adjusting the tolerance and weight values of mainly and secondarily pharmacological features of pharmacophore model. The process of MP modelling included two steps, $i$.$e$. identification of mainly and secondarily pharmacological features and optimization of tolerance and weight values of these features. The mode diagram of MP generation was shown in Fig. 2.

Figure 2 The mode diagram of MP modelling. Red features indicate mainly pharmacological features and black features represent secondarily pharmacological features. The changes of feature shape suggest the variation of tolerance and weight value.

Initially, the mainly and secondarily pharmacological features needed to be identified based on the comparative analysis of pharmacological features from SBP and LBP. Accordingly, one optimal SBP and one optimal LBP were mapped with the same ligand by implemented comparative analysis, respectively, so as to implement comparative analysis. To be specific, on the basis of the ranking results of SBP evaluation, four optimal SBPs associated with four corresponding complexes were selected, and the ligands within these complexes were extracted to match with four optimal LBPs. If a pharmacological feature was discovered in both SBP and LBP at the same time, this feature was considered as mainly pharmacological feature. On the contrary, the other features were regarded as secondarily pharmacological features. A combination of LBP and SBP, which had the most number of mainly pharmacological features, was regarded as a set of final LBP, final SBP, and the material of MP.

Further, MP models were constructed by optimizing the weight and tolerance values of mainly and secondarily pharmacological features of SBP, because excluded volumes of SBP were more reliable than LBP. Tolerance represented radius of pharmacological features, meanwhile weight represented contribution of biological activity by features. In order to determine the weight and tolerance values of features, single-factor explorations were implemented to analyse the impact of four factors, including tolerance of main feature, tolerance of secondary feature, weight of main feature, and weight of secondary feature. The corresponding curves about four factors were utilized to select level of orthogonal experiments. Through single-factor exploration, four factors and four levels of orthogonal experiments were designed and implemented. Wherein, CAI, and corr$_{\mathrm{test}}$ were chosen as evaluated indexes during optimization experiments. By further orthogonal analysis and variance analysis, the mean, range, squares variance, and confidence level were calculated to identify MP [21].

E Molecular docking generation

Libdock was utilized to generate docking model based on ligand-receptor complex of PDB: 3KDU, which was also the original material of final SBP model. Firstly, crystal complex used for docking has been changed the ionization state in pH=6.75. Then, original ligand (NKS) was re-docked into active site, and RMSD values were calculated. In general, RMSD, less than 2.00 Å, expressed the high reliability of docking model. Docking model with the smallest RMSD value was considered as the optimal docking models. Meanwhile, in order to evaluate the screening efficiency and reliability of docking model, corr$_{\mathrm{test}}$ and CAI were also calculated based on the screening of first testing set and TCMD. In order to distinguish hit and non-hit compounds, threshold of hit compounds was defined greater than 80% of score of NKS [22].

F Verification and application of MP

In order to verify the screening efficiency of MP, the second testing set was constructed based on the first testing set to enlarge the screening efficiency of MP. The 100 active compounds were also used in the second testing set. In addition, 16992 random compounds were gained from MDDR, and the second testing set was composed of 17092 compounds. The second testing set had the better effect on judging the ability of pharmacophore and more inactive compounds. Search 3D database module were used to VS by final LBP (L1), final SBP (S1), optimal LBP (L2), and optimal SBP (S2), MP, and docking model. Thus, seven screening groups composed by different models were set up. Wherein, the hits obtained from the groups of L1 plus S1, and L2 plus S2 were intersected to obtain the combinatorial screening hits. Corresponding indexes were computed to compare screening efficiency. The existing molecular simulation work of PPAR$\alpha$ agonist could be compared to MP by literature validation. The proved MP model was utilized to predict PPAR$\alpha$ activity of TCM, and the VS results were selected and analysed by reported biological experiments.

Ⅲ RESULTS AND DISCUSSION A Chemical space analysis of ligands

Accroding to PCA analysis, chemical space of 25 compounds in training set, 100 active and 300 inactive compounds in first test set were obtained by basic topological properties in Fig. 3. Chemical space of active compounds was overlapped by that of inactive compounds completely. It indicated that active compounds were extremely similar to inactive compounds based on the basic drug-like properties.

Figure 3 Chemical space distribution of the first two principal components of 25 compounds in training set, 100 active and 300 inactive compounds in first test set. The red, green, and blue points represent the chemical space of compounds in training set, active, and inactive compounds in first testing set, respectively. Chemical space of compounds in training set and active compounds in first testing set were both overlapped by inactive compounds in first test set.

In order to cluster the compounds, 30 molecular descriptors, which can distinguish active compound and inactive compound, were selected by using two feature selection algorithms (Table S2 in supplementaty materials). Types of descriptors mainly contained typical 2D and 3D descriptors. Then clustering algorithm was used to group active and inactive compounds. In total, compounds were classified into four groups and nearly all the active compounds were classified into group 2. The distribution of the various clusters suggested that there were difference between active and inactive compounds based on the structural descriptors (Fig. 4). However, clustering algorithm might not have a good ability to distinguish active compounds from inactive compounds in group 2. Therefore, 3D structure of compounds should be further studied by advanced modeling methods such as pharmacophore and docking.

Table S2 Details of the selected descriptors for compounds in training and first test set
Figure 4 Cluster distribution of active and inactive compounds.
B LBP evaluation

Thirty Hypogen pharmacophore models derived from the three optimum proportions were constructed. Hypogen algorithm provided adequately internal evaluation methods. Firstly, the internal correlation coefficient (corr$_{\mathrm{train}}$) of these pharmacophore represented the correlation value between estimated and practical activities of compounds in training set. The corr$_\mathrm{train}$ value greater than 0.8 indicated better reliability. Secondly, cost function ($\Delta$cost) was utilized to distinguish different pharmacophores. $\Delta$cost was the difference value of NullCost and TotalCost, and $\Delta$cost of these pharmacophore models was farther greater than 60, indicating these models had more than 90% possibility of reflecting the real situation. Meanwhile, higher $\Delta$cost reflected the reliability of pharmacophore with higher degree of confidence. Thirdly, configuration (config) value was fixed, it depended on the complexity of pharmacophore. Config less than 17 indicated lower chance and better reliability of pharmacophore models. The FixedCost values of these pharmacophores were between 70 and 100, which were expected. More details of cost function could be found in Osmans work [23, 24]. Internal evaluation of partial LBP was shown in Table S3 (supplementaty materials). External test was carried out in order to further evaluate the LBP and build MP. Four optimal LBP models among 30 Hypogen models were selected with two basic principles: (i) The models with high CAI values were selected, like train1106204, train1105207, and train1105110; (ii) the models with high IEI values are also selected, like train1106204, train1105209, and train1105110. The results of optimal LBPs are shown in Table Ⅰ. Train1105207 with the highest CAI in four LBP models was defined as optimal LBP (L2).

Table S3 Internal evaluation of LBP
Table Ⅰ External evaluation of pharmacophore by first testing set.
C SBP evaluation

468 sub-pharmacophore models were obtained by simplifying initial SBP models from eight complexes. The first testing set was used to carry out external testing for sub-pharmacophore. The four optimal SBP models were chosen for further MP generation by two basic principles, the same as LBP. As a result, two sub-pharmacophores with high CAI values, namely 1.2 P54 (17) and 1.2 ZNN (14), were reserved. Moreover, two sub-pharmacophores containing feature N, $i$.$e$. 2.3 FEI (33) and 3 KDU (15), were selected with high IEI values. wherein, 1.2 ZNN (14) with the highest CAI in SBP model was defined as optimal SBP.

D Design and evaluation of MP

According to the evaluation of SBPs, ligands of NKS, 735, S44, and CTM with structural optimization were extracted from four complexes based on the top four optimal SBPs. Then, the four optimal LBP models and four optimal SBP models were compared with each other, and 16 combinations mapping with initial ligands were obtained in order to count the number of mainly pharmacological features. Wherein, 3KDU (15) and train1105110 had the most mainly pharmacological features. Thus, these two pharmacophores were selected as materials for MP generation. The mainly pharmacological features contained one feature A, one feature N and two features H. Final SBP (S1) and Final LBP (L1) are shown in Fig. S2 (supplementaty materials).

Figure S2 Final SBP (a) and final LBP (b) mapped with NKS

During the construction of MP, the reliability of MP should be firstly ensured. In this work, corr$_{\mathrm{test}}$ value greater than 0.6 was regarded more reliable, and screening efficiency was further considered by CAI. Therefore, during the selection of the level of single factor, both corr$_{\mathrm{test}}$ and CAI were considered to ensure the reliability and efficiency of pharmacophore. With the single-factor explorations, corr$_{\mathrm{test}}$ and CAI were generally consistent. The effect curves of tolerance main feature (Ⅰ), tolerance of secondary feature (Ⅱ), weight of main feature (Ⅲ) and weight of secondary feature (Ⅳ) are shown in Fig. 5.

Figure 5 Effect curves of (a) tolerance of main feature, (b) tolerance of secondary feature, (c) weight of main feature, and (d) weight of secondary feature. Red lines represent the trend lines of CAI and blue lines represent the trend lines of corrtest.

In Fig. 5(a), CAI was considered as the prior evaluation index. Two times of tolerance had the maximum CAI, while corr$_{\mathrm{test}}$ had stabilized after 1.6 times. In Fig. 5(b), the prior evaluation index was correlation value. 1.6 times of tolerance had the maximum corr$_{\mathrm{test}}$, and CAI had stabilized after 1.4 times. This meant that the predictive ability of the pharmacophore was acceptable after 1.4 times. In Fig 5(c), two times of weight for pharmacophore seemed to be more reliable and efficient, as corr$_{\mathrm{test}}$ and CAI were in reasonable steady state. For the weight value in Fig. 5(d), 0.2 times of weight had the best corr$_{\mathrm{test}}$ and the CAI.

According to the results of single-factor explorations, four factors and four levels of orthogonal experiments, and shown in Table Ⅱ. The values of the four levels for each factor were listed in Table S4 (supplementaty materials) and variance analysis of orthogonal experiment was shown inTable S5 (supplementaty materials). Based on the variance analysis, the tolerance of main feature had a significant impact on CAI value, which indicated that it might be the most essential factor for MP generation. Besides, four factors for corr$_{\mathrm{test}}$ were not significant, for we first ensured that the corr$_{\mathrm{test}}$ values were stable during the above selection of single factor explorations. For CAI, the impact of four factors were Ⅰ>Ⅱ>Ⅲ>Ⅳ so the best combination was Ⅰ4442. For correlation, the impact of four factors was Ⅰ>Ⅲ>Ⅱ>Ⅳ, so the best combination was Ⅰ3333. Comprehensive evaluation of two indexes was performed. The best MP should possess the highest CAI value, of which the corr$_{\mathrm{test}}$ was greater than 0.6. Therefore, zonghe-13 (Ⅰ4142) was determined as the best MP, which was matched with CHEMBL 1813006.

Table Ⅱ Results and analysis of orthogonal experiment.
Table S4 Values of the Four Levels for Each Factor
Table S5 Variance Analysis of Orthogonal Experiment
E Verification of MP 1 Molecular docking

The radius of the active pocket was determined as 9.5 Å in molecular docking model from B chain of 3KDU which is the template of SBP and MP models. Moreover, the RMSD value of optimal pose was 1.53 Å. The LibDock Score of this pose was 174.669 and the docking results were shown in Fig. S3 (supplementaty materials). According to analyses of the key amino acid between NKS and receptor, Tyr 464 and Cys 276 were regarded as essential hydrogen interaction in B chain, which has a little difference in A chain reported by Jun $et$. $al$. [25]. In B chain, Cys 276 and Cys 275 could produce pi-sulfur interaction with benzene ring of ligand which might be beneficial to biological activity and proved by H2 and H3 features in MP. With the first testing set evaluation, the scores of 119 hits were greater than 80% of score of NKS. 81 active compounds were included in 119 hits. Then 989 hits were obtained by TCMD VS.

Figure S3 The docking results of ligands and PPARα. (A) 3D interaction between NKS and 3KDU; (B) 2D interaction between NKS and 3KDU; (C) 3D interaction between salvianolic acid C and 3KDU; (D) 2D interaction between salvianolic acid C and 3KDU.
2 Comparison of screening efficiency

Through the first testing set (400), screening efficiency of MP and combined screening was estimated. As Table Ⅱ shows, the optimal SBP, MP, docking, and combinatorial screening between optimal LBP and optimal SBP had similar screening efficiency. However, due to the small size of the first testing set, results of screening efficiency might be a bit contingent. In order to make the evaluation of the screening efficiency more reasonable and further exclude interference from inactive compounds, the second testing set (17092) was utilized for re-evaluating the above-mentioned models. The evaluation results are shown in Table Ⅲ. It shows MP and combinatorial screening had better screening efficiency. The combination of Train1105207 and 1.2ZNN(14) had the highest screening efficiency, which meant that a combinatorial screening by SBP and LBP had a better efficiency. SBP and LBP might be complementary techniques for VS and had individual advantages. Meanwhile, similar screening efficiency between MP and the combination of train1105110 and 3KDU(15) proved the correction and reliability of merged method and mainly pharmacological features. Besides, all above-mentioned models were also used to compute TCMD, and screening efficiency was evaluated once more (Fig. 7). MP model could obtain a moderate number of lead compounds from an unacquainted database whereas the combination model could not, because of the inconsistency of SBP and LBP. Generally, the screening efficiency of MP was similar to that of combinatorial model, based on the comparison of the CAI values between the first testing set, the second testing set, and TCMD. Compared with SBP and molecular docking, MP had better correlation value, which was helpful for further drug design. In addition, compared with original pharmacophore, MP had higher efficiency because of the high CAI value.

Table Ⅲ External evaluation of pharmacophore by second testing set.
Figure 7 VS of TCMD.
3 Literature verification

Molecular simulation studies of PPAR$\alpha$ had been implemented more in-depth, and plenty of literatures were available to verify the characteristic of MP. Dhoke et al. have constructed the LBP and SBP for PPAR$\alpha$ [26]. Compound active X (Fig. 8(a)) in their training set was used to map with MP model. Compared with the results of their LBP, mainly pharmacological features of feature A within ethoxy oxygen and feature N within carbonyl oxygen of carboxylic acid were at the same position in compound active X, which proved the validity of mainly pharmacological features of MP model. Moreover, Chen et al. has also reported the SBP model of PPAR$\alpha$, and two hydrophobic feature were adjacent, which proved the necessity of up-regulating the tolerance of mainly pharmacological feature H2 and H3 [27]. Further, 3D-QSAR analysis of selective agonist for PPAR$\alpha$ has been reported by Huang et al. [28]. According to the CoMFA contour maps, electronegative favored carboxyl was an essential distinction for selective PPAR$\alpha$ agonist against the $\delta$ and $\gamma$ subtype. Moreover the HipHop pharmacophore model of pan PPAR$\alpha$ agonist indicated that feature H2 within alkyl side chain and feature N within carboxyl were crucial to the specificity of pharmacophore by Sandeep et al. [29].

Figure 8 MP mapping with (a) compound active X and (b) salvianolic acid C.
4 VS by MP model

According to VS TCMD by MP, a set of 328 potential TCM compounds were obtained in Fig. 7. Salvianolic acid C derived from $Salvia$ $miltiorrhiza$ could match with MP model completely (Fig. 8(b)). Then, molecular docking was used to analyse the interaction between salvianolic acid C and PPAR$\alpha$. 2D and 3D docking graphs were shown in Fig. S3 (supplementary materials). It was indicated that $Salvia$ $miltiorrhiza$ had superior effect on hyperlipidemia [30], which could significantly decrease total cholesterol, LDL and triglycerides in rat plasma. Combined with the predicted results, salvianolic acid C might be the main pharmacological component as PPAR$\alpha$ agonist in $Salvia$ $miltiorrhiza$.


Improving the computational accuracy of LBP and reducing the missing rate of SBP are essential expectations of pharmacophore research. In this work, MP model was built to integrate merits and avoid demerits of LBP and SBP with weight and tolerance modulation. And single-factor explorations and orthogonal experimental design were performed for identifying the levels of weight and tolerance (by analysing the screening results of three databases, the screening efficiency of MP was compared with that of other models).

In general, mainly pharmacological features of MP could reflect the essential binding mode of ligands and receptor. Meanwhile, the screening efficiency between MP and the combination of SBP and LBP was equally well, and both of them were better than original pharmacophore models. Actually, combinatorial screening was constituted based on the screening efficiency of each model, so it might result in inconsistent prediction between LBP and SBP, which could produce the confusion of drug design. MP considered the consistency between LBP and SBP. Compared with combinatorial screening of LBP and SBP, MP model was the single model, which could avoid the inconsistent results from two models. The high correlation value of MP model could be utilized to predict the biological activity of compounds, which was available to guide drug design and optimization. Therefore, MP could compensate the deficiencies of LBP and SBP, which provided new ideas and methods for improving the reliability and effectiveness of pharmacophore.

Supplementary materials: Table S1 shows eight ligand-receptor complexes of PPAR$\alpha$. Table S2 shows the details of the selected descriptors for compounds in training and first test set, which are used for analysis of chemical space. Table S3 shows the internal evaluation of LBP models. Table S4 shows the values of the four levels for each factor in orthogonal experiment. Table S5 shows the variance analysis of orthogonal experiment. Figure S1 shows the structures and EC$_{50}$ of compounds in training set for LBP modeling. Table S2 shows that final SBP and final LBP models mapped with NKS. Table S3 shows the docking results of ligands and PPAR$\alpha$. 3D interaction between NKS and 3KDU, 2D interaction between NKS and 3KDU, 3D interaction between salvianolic acid C and 3KDU, 2D interaction between salvianolic acid C and 3KDU are also given.


This work was supported by the National Natural Science Foundation of China (No.81173522, No.81430094, and No.81573831) and the Joint Construction Project of the Beijing Municipal Commission of Education.

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乔连生, 贺昱甦, 霍晓乾, 蒋芦荻, 陈艳昆, 陈茜, 张燕玲, 李贡宇     
北京中医药大学中药学院, 中药基础与新药研究重点实验室, 北京 100102
摘要: 药效团是最常用的虚拟筛选方法之一,主要包括配体药效团模型和受体药效团模型两大类.配体药效团通常具有阳性化合物命中率高的优势,但却具有准确率低的特点,受体药效团通常具有较强的特异性,但却具有漏筛率高的问题.因此,合理地利用两种类型的药效团的优势,有效地规避它们的缺点,是药效团研究与应用的重要方向.本研究拟基于PPARα激动剂探讨融合药效团模型构建的思路与方法.首先,分别构建PPARα激动剂的配体和受体药效团模型.通过比较两种药效团模型的药效特征的差异,确认主要及次要的药效特征.进一步以受体药效团模型为模板,调整其主要和次要药效特征的半径及权重,通过单因素实验及正交试验设计,获得半径及权重的最优值,从而构建最优的融合药效团模型.最优融合药效团被用于进一步的中药化学成分PPARα激动活性预测.随后,通过三个化合物数据库的筛选,评价融合药效团准确性、可靠性和适用性,并以筛选效率为指标,比较融合药效团与其他分子模拟模型及筛选方式的优劣.结果发现,针对PPARα激动剂,融合药效团模型具有比单一药效团模型更好的筛选效率.同时,融合药效团也具有与药效团联合筛选相近的筛选效率,且具有更好的化合物活性评价能力,能合理的规避配体药效团与受体药效团计算预测结果不一致的问题.研究结果显示,融合药效团模型具有独特的优势,可以进一步用于化合物生物活性的计算及药物设计研究.
关键词: Merged pharmacophore     Ligand-based pharmacophore     Structure-based pharmacophore     Peroxisome proliferator receptor-alpha     Docking     Combinatorial screening