A Novel Electron-Conformational Approach to Molecular Modeling for QSAR by Identification of Pharmacophore and Anti-Pharmacophore Shielding

Abstract

A novel method of pharmacophore identification and activity prediction in structure-activity (structure-property) relationships is worked out as an essential extension and improvement of previous publications. In this method each conformation of the molecular systems in the training set of the SAR problem is presented by both electronic structure and geometry parameters arranged in a matrix form. Multiple comparisons of these matrices for the active and inactive compounds allows one to separate a smaller number of matrix elements that are common for all the active compounds and are not present in the same arrangement in the inactive ones. This submatrix of activity represents the pharmacophore (Pha).

By introducing the Anti-Pharmacophore Shielding (APS) defined as molecular groups and competing charges outside the Pha that hinder the proper docking of the Pha with the bioreceptor, the procedure of Pha identification is essentially reduced to the treatment of a smaller number of simplest in structure most active and inactive compounds. A simple empirical scheme is suggested to estimate the APS numerically, while the contributions of different conformations of the same compound are taken into account by means of Boltzmann distribution. This enables us to make approximate quantitative predictions of activities.

In application to rice blast activity we reached an approximately 100% (within experimental error) prediction probability of the activity qualitatively (yes, no), and with r ² = 70% quantitatively.     

Improved electron-conformational method of pharmacophore identification and bioactivity prediction. Application to angiotensin converting enzyme inhibitors

The electron-conformational (EC) method of pharmacophore (Pha) identification and bioactivity prediction, suggested earlier, is given here two major improvements. First, an atomic index of orbital and charge controlled interaction is introduced to better represent the ligand (substrate) in its interaction with the bioreceptor. Second, the multiconformational problem is considered in view of ligand-receptor binding states, resulting in essential simplification of the expression of bioactivity. The details of the improved EC method are demonstrated in application to the problem of angiotensin converting enzyme (ACE) inhibitors. The Pha of the latter is identified by separation of the heavily populated conformations of the chosen 51 compounds (the training set), calculation of the electronic structure, construction of their EC matrixes of congruity, and processing of the latter in comparison with the activities to reveal a common submatrix of all the active only compounds that describes the Pha. The latter contains three oxygen atoms plus a fourth atom X = S, N, O at certain interatomic distances and with restricted electronic parameters (within assumed tolerances), the position of the atom X being more changeable from one active compound to another. For quantitative prediction of the bioactivity, an expression is deduced which takes into account the duly parametrized influence of auxiliary groups (AG) which, being positioned outside the Pha, either diminish the activity (antipharmacophore shielding) or enhance it. It is shown that in case of many conformations of the same compound only one of them, that of the lowest energy which has the Pha, should be parametrized. The 15 parameters chosen to represent the AG in case of ACE inhibitors are weighted by variational (adjustable) coefficients which are determined from a regression treatment of the calculated versus known activities in the training set. Then the formulas with known coefficients are used to validate the method by calculating the bioactivity of other compounds not used in the training set. The prediction of the activity proved to be more than 90% (within experimental error and available compounds) qualitatively (yes, no) and about 60%-70% quantitatively.     

Construction and Evaluation of Merged Pharmacophore Based on Peroxisome Proliferator Receptor-Alpha Agonists

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.     

Development of a quasi-dynamic pharmacophore model for anti-complement peptide analogues

Three quasi-dynamic pharmacophore models have been constructed for the complement inhibitor peptide compstatin, using first principles. Uniform sampling along 5-ns molecular dynamics trajectories provided dynamic conformers that are thought to represent the entire conformational space for nine training set molecules, compstatin, four active analogues, and four inactive analogues. The pharmacophore models were built using mixed physicochemical and structural properties of residues indispensable for structural stability and activity. Owing to the size and flexibility of compstatin, one-dimensional probability distributions of intrapharmacophore point distances, angles, and dihedral angles of different analogues spread over wide and overlapping ranges. More robust two-dimensional distance-angle probability distributions for two pharmacophore models discriminated individual analogues in terms of specific distance-angle pairs, but overall failed to identify the active and the inactive analogues as two distinct groups. Two-dimensional distance-dihedral angle probability distributions in a third pharmacophore model allowed discrimination of the groups of active and inactive analogues more effectively, with the highest-activity analogue having distinct behavior. The present study indicates that more stringent structural constraints should be used for a set of structurally similar but flexible peptides, as opposed to organic molecules, to convert dynamic conformers into pharmacophore models. Flexibility is a general aspect of the structure and function of peptides and should be taken into account in ligand-based pharmacophore design. However, the discrimination of activity using multidimensional probability surfaces depends on the peptide system, the selection of the training set, the molecular dynamics protocol, and the selection of the type and number of pharmacophore points.     

Pharmacophore modeling,virtual screening and molecular docking of ATPase inhibitors of HSP70

Heat shock protein 70 is an effective anticancer target as it influences many signaling pathways. Hence the study investigated the important pharmacophore feature required for ATPase inhibitors of HSP70 by generating a ligand based pharmacophore model followed by virtual based screening and subsequent validation by molecular docking in Discovery studio V4.0. The most extrapolative pharmacophore model (hypotheses 8) consisted of four hydrogen bond acceptors. Further validation by external test set prediction identified 200 hits from Mini Maybridge, Drug Diverse, SCPDB compounds and Phytochemicals. Consequently, the screened compounds were refined by rule of five, ADMET and molecular docking to retain the best competitive hits. Finally Phytochemical compounds Muricatetrocin B, Diacetylphiladelphicalactone C, Eleutheroside B and 5-(3-{[1-(benzylsulfonyl)piperidin-4-yl]amino}phenyl)- 4-bromo-3-(carboxymethoxy)thiophene-2-carboxylic acid were obtained as leads to inhibit the ATPase activity of HSP70 in our findings and thus can be proposed for further in vitro and in vivo evaluation.     

基于吡咯烷与正丁烷类衍生物CCR5拮抗剂的药效团模型构建

吡咯烷与正丁烷类CCR5(化学趋化因子受体5)拮抗剂可通过抑制人类免疫缺陷病毒(HIV-1)包膜蛋白与CCR5的相互作用而阻断病毒进入细胞. 本文使用已知拮抗剂结构和活性信息构建了一个三维药效团模型. 按照Catalyst/HypoGen模块的要求, 选择了25个结构和活性均具备差异性的分子作为药效团产生的训练集. 其中训练集分子以IC50值表示的生物活性值跨度为0.06到10000 nmol·L-1. 最好的药效团模型(Hypo 1)由两个正离子化特征以及三个疏水特征组成, 训练集预测相关系数为0.924, 均方根偏差为1.068. 模型用于预测由74个分子组成的测试集化合物活性, 结果表明模型可以提供较好的活性预测结果并用于新的拮抗剂的设计.     

Pharmacophore Model Generation Based on Pyrrolidine- and Butane-derived CCR5 Antagonists

A three-dimensional pharmacophore model was developed for a considerable number of pyrrolidine-based and butane-based chemokine (C-C motif) receptor 5 (CCR5) antagonists, which can block the entry of human immunodeficiency virus type 1 (HIV-1) by inhibiting the interaction of HIV-1 envelope protein and CCR5. The pharmacophore model was generated using a training set consisting of 25 carefully selected antagonists with the diverse molecular architecture and bioactivity, as required by the Catalyst/HypoGen program. The activity of the training set molecules expressed in IC₅₀ (half-inhibitory concentration) covered from 0.06 to 10000 nmol·L^–1. The most predictive pharmacophore model (Hypo 1), consisting of two positive ionizable points and three hydrophobic groups, had a correlation of 0.924 and a root mean square of 1.068, and a cost difference of 63.67 bits between the null cost and the total cost. The model was applied in predicting the activity of 74 compounds as a test set. The results indicated that the model was able to provide clear guidelines and accurate activity prediction for novel antagonist design.     

基于药效团的BRD4靶向小分子抑制剂虚拟筛选

BRD4靶点和多种肿瘤密切相关,是具有良好成药性的热门靶点.本文选取活性较好且结构差异较大的BRD4小分子抑制剂作为训练集分子,基于配体小分子共同特征(HipHop)方法使用Discovery Studio 3.0分子模拟软件构建了药效团.药效团通过测试集验证、ROC曲线验证(SE(sensitivity)=0.937...     

Parallel screening and activity profiling with HIV protease inhibitor pharmacophore models

Parallel Screening has been introduced as an in silico method to predict the potential biological activities of compounds by screening them with a multitude of pharmacophore models. This study presents an early application example employing a Pipeline Pilot-based screening platform for automatic large-scale virtual activity profiling. An extensive set of HIV protease inhibitor pharmacophore models was used to screen a selection of active and inactive compounds. Furthermore, we aimed to address the usually critically eyed point, whether it is possible in a parallel screening system to differentiate between similar molecules/molecules acting on closely related proteins, and therefore we incorporated a collection of other protease inhibitors including aspartic protease inhibitors. The results of the screening experiments show a clear trend toward most extensive retrieval of known active ligands, followed by the general protease inhibitors and lowest recovery of inactive compounds.     

Hydrogen-bonding effects,electrostatic potential,and the antitumor activity of flavone acetic acid and related compounds. II. Ab initio studies on the second stable conformations

We previously studied the first stable conformations of flavone acetic acid and related compounds. In this article, a similar investigation was carried out on the second stable conformation of the same compounds. Emphasis is on the conformation dependence of the hydrogen-bonding effects, the molecular electrostatic potential (MEP), and the antitumor activity shown by these compounds. The results show that the second conformation is about 7.0 kcal mol⁻¹ higher in energy and possibly is an inactive conformation as no correlation has been found between the antitumor activity and the MEP features. In addition, a detailed comparison with the first conformation, which is probably the active conformation, has been made of the geometry, the total energy, the Mulliken charges on some important atoms, hydrogen-bonding effects, and the MEP minima and isosurfaces. The role of the hydrogen-bonding effects, which was unclear in our previous work, is clarified in this work. The possible molecular basis of the antitumor activity is suggested. © 1996 John Wiley & Sons, Inc.     

Ligand-based molecular modeling study on a chemically diverse series of cholecystokinin-B/gastrin receptor antagonists: generation of predictive model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.     

Use of catalyst pharmacophore models for screening of large combinatorial libraries

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.     

Computer-aided molecular design of (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule targeted agents in prostate cancer

Microtubules are tube-shaped, filamentous and cytoskeletal proteins that are essential in all eukaryotic cells. Microtubule is an attractive and promising target for anticancer agents. In this study, three-dimensional quantitative structure activity relationships (3D-QSAR) including comparative molecular field analysis, CoMFA, and comparative molecular similarity indices analysis, CoMSIA, were performed on a set of 45 (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule-targeted anti-prostate cancer agents. Automated grid potential analysis, AutoGPA module in Molecular Operating Environment 2009.10 (MOE) as a new 3D-QSAR approach with the pharmacophore-based alignment was carried out on the same dataset. AutoGPA-based 3D-QSAR model yielded better prediction parameters than CoMFA and CoMSIA. Based on the contour maps generated from the models, some key features were identified in (E)-N-Aryl-2-arylethene-sulfonamide analogues that were responsible for the anti-cancer activity. Virtual screening was performed based on pharmacophore modeling and molecular docking to identify the new inhibitors from ZINC database. Seven top ranked compounds were found based on Gold score fitness function. In silico ADMET studies were performed on compounds retrieved from virtual screening in compliance with the standard ranges.     

Molecular modeling approaches for the prediction of the nonspecific binding of drugs to hepatic microsomes

Molecular modeling approaches for the prediction of the nonspecific binding of drugs to hepatic microsomes were examined using a published database of 56 compounds. Models generated were evaluated using an independent test set of 13 compounds. A pharmacophore approach identified structural features of drugs associated with nonspecific binding. A side-chain amino group and complementary hydrophobic domain were the principal features noted. The use of shape overlays, based on the pharmacophore, in conjunction with a chemical force field in the program ROCS, yielded discrimination between molecules classified as strong binders (experimental fraction unbound in microsomes<0.50) and those with a lower degree of binding (experimental fraction unbound in microsomes>0.50). In the initial data set of 56 molecules, 18 were classified as strong binders (on the basis of the above criteria), and all of those were recovered in the top 22 molecular hits from ROCS. Additionally, computationally generated values of log P were shown to provide a reasonable estimate of the fraction unbound in microsomes, providing the compounds were in their basic form at physiological pH.