Monoamine Oxidase (MAO) as a Potential Target for Anticancer Drug Design and Development

Monoamine oxidases (MAOs) are oxidative enzymes that catalyze the conversion of biogenic amines into their corresponding aldehydes and ketones through oxidative deamination. Owing to the crucial role of MAOs in maintaining functional levels of neurotransmitters, the implications of its distorted activity have been associated with numerous neurological diseases. Recently, an unanticipated role of MAOs in tumor progression and metastasis has been reported. The chemical inhibition of MAOs might be a valuable therapeutic approach for cancer treatment. In this review, we reported computational approaches exploited in the design and development of selective MAO inhibitors accompanied by their biological activities. Additionally, we generated a pharmacophore model for MAO-A active inhibitors to identify the structural motifs to invoke an activity.     

距离比较法(DISCO)构建原卟啉原氧化酶抑制剂药效团模型

在原卟啉原氧化酶(Protox)三维结构未知情况下,利用距离比较法(DISCO),将8个具有代表性结构特征的Protox抑制剂,与其作用底物(基质)原卟啉原IX(ProtogenIX)的分子构象进行迭合,建立了可能的药效团模型;并据此确定了ProtogenIX与Protox相互作用时,可能采取的活性构象。这些信息将有助于设计、开发新型Protox抑制剂。     

Identification of Novel Antagonists Targeting Cannabinoid Receptor 2 Using a Multi-Step Virtual Screening Strategy

The endocannabinoid system plays an essential role in the regulation of analgesia and human immunity, and Cannabinoid Receptor 2 (CB2) has been proved to be an ideal target for the treatment of liver diseases and some cancers. In this study, we identified CB2 antagonists using a three-step “deep learning–pharmacophore–molecular docking” virtual screening approach. From the ChemDiv database (1,178,506 compounds), 15 hits were selected and tested by radioligand binding assays and cAMP functional assays. A total of 7 out of the 15 hits were found to exhibit binding affinities in the radioligand binding assays against CB2 receptor, with a pK_i of 5.15–6.66, among which five compounds showed antagonistic activities with pIC₅₀ of 5.25–6.93 in the cAMP functional assays. Among these hits, Compound 8 with the 4H-pyrido[1,2-a]pyrimidin-4-one scaffold showed the best binding affinity and antagonistic activity with a pK_i of 6.66 and pIC₅₀ of 6.93, respectively. The new scaffold could serve as a lead for further development of CB2 drugs. Additionally, we hope that the model in this study could be further utilized to identify more novel CB2 receptor antagonists, and the developed approach could also be used to design potent ligands for other therapeutic targets.     

GSK-3抑制剂药效团模型的构建

选择活性跨越0.08～100 000nmol.L-1的8类共30个GSK-3竞争性抑制剂小分子作为训练集,其余490个不同结构和活性的小分子作为测试集.采用Accelrys Discovery Stutio Client 2.5软件,筛选得到由1个氢键受体、1个氢键供体、1个疏水中心、1个环芳香性和1个排除体积组成的最佳模型(Correl=0.900,Config=13.962,Δcost=90.725),采用测试集与分子对接方法对模型进行评价,表明该模型具有较强的预测能力,为进一步的数据库搜索及寻找新型GSK-3抑制剂先导物提供了依据.     

De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks

An evolutionary algorithm was developed for fragment-based de novo design of molecules (TOPAS, TOPology-Assigning System). This stochastic method aims at generating a novel molecular structure mimicking a template structure. A set of 25,000 fragment structures serves as the building block supply, which were obtained by a straightforward fragmentation procedure applied to 36,000 known drugs. Eleven reaction schemes were implemented for both fragmentation and building block assembly. This combination of drug-derived building blocks and a restricted set of reaction schemes proved to be a key for the automatic development of novel, synthetically tractable structures. In a cyclic optimization process, molecular architectures were generated from a parent structure by virtual synthesis, and the best structure of a generation was selected as the parent for the subsequent TOPAS cycle. Similarity measures were used to define `fitness', based on 2D-structural similarity or topological pharmacophore distance between the template molecule and the variants. The concept of varying library `diversity' during a design process was consequently implemented by using adaptive variant distributions. The efficiency of the design algorithm was demonstrated for the de novo construction of potential thrombin inhibitors mimicking peptide and non-peptide template structures.     

Gaussian mapping of chemical fragments in ligand binding sites

We present a new approach to automatically define a quasi-optimal minimal set of pharmacophoric points mapping the interaction properties of a user-defined ligand binding site. The method is based on a fitting algorithm where a grid of sampled interaction energies of the target protein with small chemical fragments in the binding site is approximated by a linear expansion of Gaussian functions. A heuristic approximation selects from this expansion the smallest possible set of Gaussians required to describe the interaction properties of the binding site within a prespecified accuracy. We have evaluated the performance of the approach by comparing the computed Gaussians with the positions of aromatic sites found in experimental protein-ligand complexes. For a set of 53 complexes, good correspondence is found in general. At a 95% significance level, approximately 65% of the predicted interaction points have an aromatic binding site within 1.5 A. We then studied the utility of these points in docking using the program DOCK. Short docking times, with an average of approximately 0.18 s per conformer, are obtained, while retaining, both for rigid and flexible docking, the ability to sample native-like binding modes for the ligand. An average 4-5-fold speed-up in docking times and a similar success rate is estimated with respect to the standard DOCK protocol.     

Pharmacophore and receptor models for neurokinin receptors

Three neurokinin (NK) antagonist pharmacophore models (Models 1-3) accounting for hydrogen bonding groups in the 'head' and 'tail' of NK receptor ligands have been developed by use of a new procedure for treatment of hydrogen bonds during superimposition. Instead of modelling the hydrogen bond acceptor vector in the strict direction of the lone pair, an angle is allowed between the hydrogen bond acceptor direction and the ideal lone pair direction. This approach adds flexibility to hydrogen bond directions and produces more realistic RMS values. By using this approach, two novel pharmacophore models were derived (Models 2 and 3) and a hydrogen bond acceptor was added to a previously published NK2 pharmacophore model [Poulsen et al., J. Comput.-Aided Mol. Design, 16 (2002) 273] (Model 1). Model 2 as well as Model 3 are described by seven pharmacophore elements: three hydrophobic groups, three hydrogen bond acceptors and a hydrogen bond donor. Model 1 contains the same hydrophobic groups and hydrogen bond donor as Models 2 and 3, but only one hydrogen bond acceptor. The hydrogen bond acceptors and donor are represented as vectors. Two of the hydrophobic groups are always aromatic rings whereas the other hydrophobic group can be either aromatic or aliphatic. In Model 1 the antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. Model 2 has the same two aromatic rings in a parallel displaced conformation whereas Model 3 has the rings in an edge to face conformation. The pharmacophore models were evaluated using both a structure (NK receptor homology models) and a ligand based approach. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 21 non-peptide antagonists from several structurally diverse classes were fitted to the pharmacophore models. More antagonists could be fitted to Model 2 with a low RMS and a low conformational energy penalty than to Models 1 and 3. Pharmacophore Model 2 was also able to explain the NK1, NK2 and NK3 subtype selectivity of the compounds fitted to the model. Three NK 7TM receptor models were constructed, one for each receptor subtype. The location of the antagonist binding site in the three NK receptor models is identical. Compounds fitted to pharmacophore Model 2 could be docked into the NK1, NK2 and NK3 receptor models after adjustment of the conformation of the flexible linker connecting the head and tail. Models I and 3 are not compatible with the receptor models.     

Development of a common 3D pharmacophore for δ-opioid recognition from peptides and non-peptides using a novel computer program

A unified three-dimensional (3D) pharmacophore for recognition of the -opioid receptorby families of structurally diverse -opioid ligands, including peptides and non-peptides,has been determined. An additional structural feature required for -selectivity was alsocharacterized using a subset of these ligands that are highly selective for the -opioidreceptor. To obtain these pharmacophores, we have used a recently developed computerprogram that performs systematic and automated comparisons of molecules to determinewhether any common 3D relationships exist among candidate recognition moieties in high-affinity analogs. All the low-energy conformations of each ligand are included in thesecomparisons. The program developed should be applicable in general to molecular super-imposition problems in rational drug design and to develop both 3D recognition and activationpharmacophores for any receptor for which high- and low-affinity analogs and agonists andantagonists have been identified.     

A pharmacophore model for NK2 antagonist comprising compounds from several structurally diverse classes

A neurokinin 2 (NK2) antagonist pharmacophore model has been developed on the basis of five non-peptide antagonists from several structurally diverse classes. To evaluate the pharmacophore model, another 20 antagonists were fitted to the model. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 23 of the 25 antagonists were fitted to the model in a low energy conformation with a low RMS value. The pharmacophore model is described by four pharmacophore elements: Three hydrophobic groups and a hydrogen bond donor represented as a vector. The hydrophobic groups are generally aromatic rings, but this is not a requirement. The antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. The model was able to explain the enantioselectivity of SR48968 and GR159897.