Pharmacoinformatics and molecular dynamic simulation studies to identify potential small-molecule inhibitors of WNK-SPAK/OSR1 signaling that mimic the RFQV motifs of WNK kinases

The WNK-SPAK/OSR1 signaling is a complex of serine and threonine protein kinases that involves in the regulation of human blood pressure. The WNK kinases phosphorylate and activate SPAK and OSR1 kinases through the interaction of RFQV motifs of WNK kinases with the C-terminal domains of SPAK and OSR1. Upon phosphorylation, SPAK and OSR1 phosphorylate key ion co-transporters such as Na⁺-[K⁺]-2Cl⁻ (NKCC_1-2) and K⁺-Cl⁻ (KCC_1-4), which are essential for electrolytes balance and blood pressure regulation. Targeting the binding site of the RFQV motifs of WNK kinases on the C-terminal domain (CTD) of SPAK and OSR1 has emerged as a valuable approach to inhibit the WNK-SPAK/OSR1 signaling pathway. Herein, an effort has been intended to pinpoint non-peptidic small-molecules that could disrupt the binding of SPAK/OSR1 to WNK kinases, hence, inhibit the SPAK and OSR1 phosphorylation and activation by WNK kinases through pharmacoinformatics and molecular dynamic simulation methodologies. A sequential structure-based virtual screening of a focus protein-protein interaction chemical library composed of 11,870 compounds lead to the identification of three compounds having good lead-compound properties with respect to their predicted inhibitory constants, pharmacophore fit scores, binding affinities, ADME-T parameters, drug-likeness properties and ligand efficiency metrics. The mechanism of interaction and binding stability of these compounds to OSR1-CTD were confirmed using molecular docking and dynamic simulation studies. Hence, the identified compounds may have therapeutic potential as novel antihypertensive agents subjected to experimental validation.     

Ultrafast de novo docking combining pharmacophores and combinatorics

We report on a successful de novo design approach which relies on the combination of multi-million compound combinatorial docking under receptor-based pharmacophore constraints. Inspired by a rationale by A.R. Leach et al., we document on the unification of two steps into one for ligand assembly. In the original work, fragments known to bind in protein active sites were connected forming novel ligand compounds by means of generic skeleton linkers and following a combinatorial approach. In our approach, the knowledge of fragments binding to the protein has been expressed in terms of a receptor-based pharmacophore definition. The combinatorial linking step is performed in situ during docking, starting from combinatorial libraries. Three sample scenarios growing in size and complexity (combinatorial libraries of 1 million, 1.3 million, and 22.4 million compounds) have been created to illustrate the method. Docking could be accomplished between minutes and several hours depending on the outset; the results were throughout promising. Technically, a module compatibility between FlexX(C) and FlexX-Pharm has been established. The background is explained, and the crucial points from an information scientist's perspective are highlighted.     

Structure-based virtual screening of natural products as potential stearoyl-coenzyme a desaturase 1 (SCD1) inhibitors

Stearyl coenzyme A desaturase enzyme 1 (SCD1) is a key enzyme that catalyzes the conversion of saturated fatty acids (SFA) into monounsaturated fatty acids (MUFA) and plays a vital role in lipid metabolism of tumor cells. SCD1 is overexpressed in a variety of malignant tumors, and its related inhibitors showed significant anti-tumor activity in vitro and in vivo experiments, which is a new target for tumor therapy. The focus of this study is to identify novel SCD1 inhibitors from natural products through computer simulations. First, 176,602 compounds from natural product databases were virtually screened. By molecular dynamics (MD) simulations, the ligand-protein interactions of 5 compounds with high docking manifestation were analyzed accurately. Then, MM-GBSA and MM-PBMA methods were used to verify the results. Finally, ADMET prediction was performed for the 5 compounds. As a result, two natural products with potential inhibition towards SCD1 were identified, which had the excellent docking manifestation, binding mode within SCD1 pocket and stability during molecular dynamics simulation. This study provides a meaningful model for the development and optimization of new inhibitors and anti-tumor drugs targeting SCD1.     

Structure-based virtual screening of influenza virus RNA polymerase inhibitors from natural compounds: Molecular dynamics simulation and MM-GBSA calculation

The resistances of matrix protein 2 (M2) protein inhibitors and neuraminidase inhibitors for influenza virus have attracted much attention and there is an urgent need for new drug. The antiviral drugs that selectively act on RNA polymerase are less prone to resistance and possess fewer side effects on the patient. Therefore, there is increased interest in screening compounds that can inhibit influenza virus RNA polymerase. Three natural compounds were found by using molecular docking-based virtual screening, which could bind tightly within the polymerase acidic protein-polymerase basic protein 1 (PA-PB1) subunit of influenza virus polymerase. Firstly, their drug likeness properties were evaluated, which showed that the hepatotoxicity values of all the three compounds indicating they had less or no hepatotoxicity, and did not have the plasma protein biding (PPB) ability, the three compounds needed to be modified in some aspects, like bulky molecular size. The stability of the complexes of PA-hits was validated through molecular dynamics (MD) simulation, revealing compound 2 could form more stable complex with PA subunit. The torsional conformations of each rotatable bond of the ligands in PA subunit were also monitored, to investigate variation in the ligand properties during the simulation, compound 3 had fewer rotatable bonds, indicating that the molecule had stronger rigidity. The bar charts of protein–ligand contacts and contacts over the course of trajectory showed that four key residues (Glu623, Lys643, Asn703 and Trp706) of PA subunit that participated in hydrogen-bond, water bridge and hydrophobic interactions with the hit compounds. Finally, the binding free energy and contributed energies were calculated by using MM-GBSA method. Out of the three compounds, compound 1 showed the lowest total binding free energy. Among all the interactions, the contribution of the covalent binding and the van der Waals energy were more than other items, compound 1 formed more stable hydrogen bonds with the residues of PA subunit binding pocket. This study smoothed the path for the development of novel lead compounds with improved binding properties, high drug likeness, and low toxicity to humans for the treatment of influenza, which provided a good basis for further research on novel and effective influenza virus PA-PB1 interaction inhibitors.     

Discovery of a Novel 5-HT_(2A) Inhibitor by Pharmacophore-based Virtual Screening

The serotonin 2A(5-HT2A) receptor has been implicated in several neurological conditions and potent 5-HT2A antagonists have therapeutic effects in the treatment of schizo phrenia and depression.In this study,a potent novel 5-HT2A inhibitor 05245768 with a Ki value of (593.89±34.10) nmol/L was discovered by integrating a set of computational approaches and experiments(protein structure prediction,pharmacophore-based virtual screening,automated molecular docking and pharmacological bioassay).The 5-HT2A recept...     

Analysis and use of fragment-occurrence data in similarity-based virtual screening

Current systems for similarity-based virtual screening use similarity measures in which all the fragments in a fingerprint contribute equally to the calculation of structural similarity. This paper discusses the weighting of fragments on the basis of their frequencies of occurrence in molecules. Extensive experiments with sets of active molecules from the MDL Drug Data Report and the World of Molecular Bioactivity databases, using fingerprints encoding Tripos holograms, Pipeline Pilot ECFC_4 circular substructures and Sunset Molecular keys, demonstrate clearly that frequency-based screening is generally more effective than conventional, unweighted screening. The results suggest that standardising the raw occurrence frequencies by taking the square root of the frequencies will maximise the effectiveness of virtual screening. An upper-bound analysis shows the complex interactions that can take place between representations, weighting schemes and similarity coefficients when similarity measures are computed, and provides a rationalisation of the relative performance of the various weighting schemes.     

Robust optimization of scoring functions for a target class

Target-specific optimization of scoring functions for protein–ligand docking is an effective method for significantly improving the discrimination of active and inactive molecules in virtual screening applications. Its applicability, however, is limited due to the narrow focus on, e.g., single protein structures. Using an ensemble of protein kinase structures, the publically available directory of useful decoys ligand dataset, and a novel multi-factorial optimization procedure, it is shown here that scoring functions can be tuned to multiple targets of a target class simultaneously. This leads to an improved robustness of the resulting scoring function parameters. Extensive validation experiments clearly demonstrate that (1) virtual screening performance for kinases improves significantly; (2) variations in database content affect this kind of machine-learning strategy to a lesser extent than binary QSAR models, and (3) the reweighting of interaction types is of particular importance for improved screening performance. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

基于分子描述符和机器学习方法预测和虚拟筛选乳腺癌靶向蛋白HEC1抑制剂

HEC1(癌症高表达蛋白)是纺锤体检查点控制、着丝粒功能、细胞存活的关键的有丝分裂调节器,与原发性乳腺癌的不良预后有关.筛选具有高亲和力的HEC1新型抑制剂对探索乳腺癌的靶向治疗具有重要意义.本文从结构多样性的化合物库中筛选HEC1抑制剂.通过对分子描述符的特征筛选,采用支持向量机(SVM)和随机森林(RF)方法分别对HEC1抑制剂和非抑制剂建立了分类模型.经对比, RF模型显示了更好的预测精度.我们采用RF模型对HEC1抑制剂进行了虚拟筛选,从“in-house”实体库筛选得到2个潜在的HEC1抑制剂分子.随后对筛出的化合物进行了体外活性实验,发现对乳腺癌细胞株MDA-MB-468和MDA-MB-231均有一定程度的抗肿瘤活性.研究结果表明,机器学习方法对于设计和虚拟筛选HEC1抑制剂有良好的效果.     

ω-芋螺毒素的定量构效关系与虚拟筛选

ω-芋螺毒素属于海洋生物活性多肽,由24-31个氨基酸残基组成.特异性作用于电压敏感的钙离子通道(VGCCs),能够直接开发成药物或作为先导化合物进行新药开发.本文应用新型氨基酸残基结构描述符cscales和遗传偏最小二乘算法,对ω-芋螺毒素进行定量构效关系(QSAR)研究,并设计、构建了容量为2244个化合物的N-型和P/Q-型VGCC拮抗剂虚拟组合多肽库,然后分别采用QSAR模型预测和相似性搜索方法对组合多肽库进行了虚拟筛选.研究结果表明,建立的N-型和P/Q-型VGCC拮抗剂QSAR模型均具有较好的预测能力,交叉验证相关系数(CV-r2)均大于0.89.主成分分析和聚类分析结果表明,虚拟组合多肽库中化合物具有较好的结构多样性和差异性.通过虚拟筛选,得到了具有高预测活性的6个N-型和19个P/Q-型钙离子通道拮抗剂,为进一步的合成和活性评价奠定了理论基础.同时,本文建立的多肽QSAR预测模型和虚拟筛选策略,为其它多肽类化合物的定量构效关系研究和虚拟筛选提供了参考.