Identification of novel HDAC8 selective inhibitors through ligand and structure based studies: Exploiting the acetate release channel differences among class I isoforms

Histone deacetylases (HDACs) are key regulators of gene expression and have emerged as crucial therapeutic targets for cancer. Among the HDACs, inhibition of HDAC8 enzyme has been reported to be a novel strategy in the treatment of female-specific cancers. Most of the HDAC inhibitors discovered so far inhibit multiple HDAC isoforms causing toxicities in the clinic thus limiting their potential. Therefore, the discovery of isoform-selective HDAC8 inhibitors is highly desirable. In the present study, a combination of ligand and structure based drug design tools were utilized to build a statistically significant pharmacophore based 3D QSAR model with statistical parameters R²: 0.9964, and Q²: 0.7154, from a series of 31 known HDAC8 inhibitors. Top 1000 hits obtained from Virtual screening of Phase database were subjected to docking studies against HDAC8. Top 100 hits obtained were redocked into HDAC Class I (HDAC 1,2,3) and Class II isoforms (HDAC 4, 6) and rescored with XP Glide Score. Based on fitness score, XP glide score and interacting amino acid residues, five HDAC8 inhibitors (1–5) were selected for in vitro studies. The HDAC8 activity assay followed by enzyme kinetics clearly indicated Compounds 1, 2 and 3 to be potent HDAC8 selective inhibitors with IC₅₀ of 126 pM, 112 nM, and 442 nM respectively. These compounds were cytotoxic to HeLa cells where HDAC8 is overexpressed but not to normal cells, HEK293. Also, they were able to induce apoptosis by modulating Bax/Bcl2, cleavage of PARP and release of Cytochrome C. Molecular Dynamics simulations observed most favorable interaction patterns and presented a rationale for the activities of the identified compounds. Selectivity against HDAC8 was due to exploitation of the architectural difference in the acetate release channel among class I HDAC isoforms.     

Analysis of a high-throughput screening data set using potency-scaled molecular similarity algorithms

Molecular similarity methods for ligand-based virtual screening (VS) generally do not take compound potency as a variable or search parameter into account. We have incorporated a logarithmic potency scaling function into two conceptually distinct VS algorithms to account for relative compound potency during search calculations. A high-throughput screening (HTS) data set containing cathepsin B inhibitors was analyzed to evaluate the effects of potency scaling. Sets of template compounds were randomly selected from the HTS data and used to search for hits having varying potency levels in the presence or absence of potency scaling. Enrichment of potent compounds in small subsets of the HTS data set was observed as a consequence of potency scaling. In part, observed enrichments could be rationalized as a result of recentering chemical reference space on a subspace populated by potent compounds. Our findings suggest that VS calculations using multiple reference compounds can be directed toward the preferential detection of potent database hits by scaling compound contributions according to potency differences.     

同源模建和分子对接研究HDAC1/HDAC8的选择性

组蛋白去乙酰化酶(HDACs)是近年来治疗肿瘤的重要靶标之一.由于HDACs包含多种亚型,且各亚型的生理功能存在一定的差异,其选择性抑制剂的开发已成为当前的研发热点.我们通过同源模建的HDAC1结构,与已有的HDAC8晶体结构的活性位点进行比较分析,探讨了对两者选择性有重要影响的残基,为基于受体的选择性抑制剂研究提供重要信息.同时选择了52个HDAC抑制剂,分别建立了HDAC1、HDAC8的活性值与对接打分值的线性回归模型.所建的HDAC1和HDAC8的线性构效关系模型的非交叉验证系数R2分别为0.82和0.80,表明具有一定的统计学意义.利用所建模型对已设计合成的化合物进行了预测,预测结果对HDAC1、HDAC8选择性抑制剂的优化改造提供了一定的指导意义.     

基于分子描述符和机器学习方法预测和虚拟筛选MMP-13对MMP-1的选择性抑制剂

基质金属蛋白酶-13 (MMP-13)为预防和治疗骨关节炎(OA)提供了充满希望的靶标. 通过抑制剂来阻断MMP-13的活性将会对治疗OA疾病产生潜在的作用. 然而,宽谱抑制剂同样抑制MMP家族的其它成员,特别是MMP-1,这将会导致肌与骨的综合症. 因此,设计和发现潜在的MMP-13 相对于MMP-1 的高效选择性抑制剂,在对治疗OA新型药物的研发中具有相当重要的现实意义. 本研究通过两种机器学习方法(ML)：支持向量机(SVM)和随机森林(RF)来建立分类模型,用于预测不同结构的MMP-13 对MMP-1 的选择性抑制剂. 所建这些模型的预测效果都已经达到了令人满意的精度. 在这两种ML模型中,RF对于MMP-13选择性抑制剂和非抑制剂的精度分别达到97.58%和100%. 同时,与MMP-13对MMP-1的选择性抑制最相关的分子描述符也基于不同的特征选择方法被两种模型挑选出来. 最后,用预测效果最好的RF模型虚拟筛选了ZINC数据库的“fragment-like”子集,从而得到了一系列潜在的候选药物. 研究表明,机器学习方法,特别是RF方法,对于发现潜在的MMP-13选择性抑制剂十分有效. 同时还得到了一些与MMP-13的选择性抑制相关的分子描述符.     

A new protocol for predicting novel GSK-3β ATP competitive inhibitors

Glycogen synthase kinase 3β (GSK-3β) is a potential therapeutic target for cancer, type-2 diabetes, and Alzheimer's disease. This paper proposes a new lead identification protocol that predicts new GSK-3β ATP competitive inhibitors with topologically diverse scaffolds. First, three-dimensional quantitative structure-activity relationship (3D QSAR) models were built and validated. These models are based upon known GSK-3β inhibitors, benzofuran-3-yl-(indol-3-yl) maleimides, by means of comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Second, 28?826 maleimide derivatives were selected from the PubChem database. After filtration via Lipinski's rules, 10?429 maleimide derivatives were left. Third, the FlexX-dock program was employed to virtually screen the 10?429 compounds against GSK-3β. This resulted in 617 virtual hits. Fourth, the 3D QSAR models predicted that from the 617 virtual hits, 93 compounds would have GSK-3β inhibition values of less than 15 nM. Finally, from the 93 predicted active hits, 23 compounds were confirmed as GSK-3β inhibitors from literatures; their GSK-3β inhibition ranged from 1.3 to 480 nM. Therefore, the hits rate of our virtual screening protocol is greater than 25%. The protocol combines ligand- and structure-based approaches and therefore validates both approaches and is capable of identifying new hits with topologically diverse scaffolds.     

Binding Free Energy (BFE) Calculations and Quantitative Structure–Activity Relationship (QSAR) Analysis of Schistosoma mansoni Histone Deacetylase 8 (smHDAC8) Inhibitors

Histone-modifying proteins have been identified as promising targets to treat several diseases including cancer and parasitic ailments. In silico methods have been incorporated within a variety of drug discovery programs to facilitate the identification and development of novel lead compounds. In this study, we explore the binding modes of a series of benzhydroxamates derivatives developed as histone deacetylase inhibitors of Schistosoma mansoni histone deacetylase (smHDAC) using molecular docking and binding free energy (BFE) calculations. The developed docking protocol was able to correctly reproduce the experimentally established binding modes of resolved smHDAC8–inhibitor complexes. However, as has been reported in former studies, the obtained docking scores weakly correlate with the experimentally determined activity of the studied inhibitors. Thus, the obtained docking poses were refined and rescored using the Amber software. From the computed protein–inhibitor BFE, different quantitative structure–activity relationship (QSAR) models could be developed and validated using several cross-validation techniques. Some of the generated QSAR models with good correlation could explain up to ~73% variance in activity within the studied training set molecules. The best performing models were subsequently tested on an external test set of newly designed and synthesized analogs. In vitro testing showed a good correlation between the predicted and experimentally observed IC₅₀ values. Thus, the generated models can be considered as interesting tools for the identification of novel smHDAC8 inhibitors.     

A neural network based virtual high throughput screening test for the prediction of CNS activity

A virtual high throughput screening test to identify potentially CNS-active drugs has been developed. Discrimination was based on the knowledge available in databases containing CNS-active (Cipsline from Prous Science) and inactive compounds (Chemical Directory from Sigma-Aldrich). Molecular structures were represented using 2D Unit y fingerprints and a feedforward neural network was trained to classify molecules regarding their CNS activity. The parameterized network was validated by reclassification of the training set elements, by the classification of a test set preselected from the Prous database, and also by the prediction of activity for known CNS drugs not used in the training set but available in the Medchem database (Daylight). These tests revealed that our neural net recognized at least 89% of CNS-active compounds and would be suitable for use in our virtual screening protocol.     

Identification of Novel TRPC5 Inhibitors by Pharmacophore-Based and Structure-Based Approaches

Canonical transient receptor potential-5 (TRPC5), which belongs to the subfamily of transient receptor potential (TRP) channels, is a non-selective cation channel mainly expressed in the central nervous system and shows more restricted expression in the periphery. TRPC5 plays a crucial role in human physiology and pathology, for instance, anxiety, depression, epilepsy, pain, memory and chronic kidney disease (CKD). However, due to lack of the effective and selective inhibitors, its physiological and pathological mechanism remains so far unknown. It is therefore pivotal to identify potential TRPC5 inhibitors. We have applied ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) methods. The pharmacophore models of TRPC5 antagonists generated by using the HypoGen and HipHop algorithms were used as a query model for the screening of potential inhibitors against the Specs database. The resultant hits from LBVS were further screened by SBVS. SBVS was carried out based on the homology model generation of human TRPC5, binding site identification, molecular dynamics optimization and molecular docking studies. In our systematic screening approaches, we have identified 7 hits compounds with comparable dock score after Lipinski and Veber rules, ADMET, PAINS analysis, cluster analysis, and similarity analysis. In conclusion, the current research provides novel backbones for the new-generation of TRPC5 inhibitors.     

Computational Approaches to Discover Novel Natural Compounds for SARS-CoV-2 Therapeutics

Scientists all over the world are facing a challenging task of finding effective therapeutics for the coronavirus disease (COVID-19). One of the fastest ways of finding putative drug candidates is the use of computational drug discovery approaches. The purpose of the current study is to retrieve natural compounds that have obeyed to drug-like properties as potential inhibitors. Computational molecular modelling techniques were employed to discover compounds with potential SARS-CoV-2 inhibition properties. Accordingly, the InterBioScreen (IBS) database was obtained and was prepared by minimizing the compounds. To the resultant compounds, the absorption, distribution, metabolism, excretion and toxicity (ADMET) and Lipinski's Rule of Five was applied to yield drug-like compounds. The obtained compounds were subjected to molecular dynamics simulation studies to evaluate their stabilities. In the current article, we have employed the docking based virtual screening method using InterBioScreen (IBS) natural compound database yielding two compounds has potential hits. These compounds have demonstrated higher binding affinity scores than the reference compound together with good pharmacokinetic properties. Additionally, the identified hits have displayed stable interaction results inferred by molecular dynamics simulation results. Taken together, we advocate the use of two natural compounds, STOCK1N-71493 and STOCK1N-45683 as SARS-CoV-2 treatment regime.