Virtual Screening and Hit Selection of Natural Compounds as Acetylcholinesterase Inhibitors

Acetylcholinesterase (AChE) is one of the classical targets in the treatment of Alzheimer’s disease (AD). Inhibition of AChE slows down the hydrolysis of acetycholine and increases choline levels, improving the cognitive function. The achieved success of plant-based natural drugs acting as AChE inhibitors, such as galantamine (GAL) from Galanthus genus and huperzine A from Huperzia serrate (approved drug in China), in the treatment of AD, and the fact that natural compounds (NCs) are considered as safer and less toxic compared to synthetic drugs, led us to screen the available NCs (almost 150,000) in the ZINC12 database for AChE inhibitory activity. The compounds were screened virtually by molecular docking, filtered for suitable ADME properties, and 32 ligands from 23 structural groups were selected. The stability of the complexes was estimated via 1 μs molecular dynamics simulation. Ten compounds formed stable complexes with the enzyme and had a vendor and a reasonable price per mg. They were tested for AChE inhibitory and antioxidant activity. Five compounds showed weak AChE inhibition and three of them exhibited high antioxidant activity.     

Interfacial Adhesion between Fatty Acid Collectors and Hydrophilic Surfaces: Implications for Low-Rank Coal Flotation

Fatty acids, which are enriched in vegetable oil, have attracted much attention in low-rank coal flotation because of their unique chemical structure. In this study, density functional theory calculations, molecular dynamics simulations, and atomic force microscopy were employed to investigate the adsorption structure and forces between collectors and hydrophilic surfaces. The results show that fatty acids can be easily adsorbed onto surfaces through hydrogen bonds, and can cover the oxygen sites. The existence of hydration film on hydrophilic surfaces prevented nonpolar molecules from being able to adsorb, while polar fatty acids could adsorb and expel water molecules. The adhesion force between the RCOOH-terminated probe and the surface appeared in the retraction process, which differed significantly from that of the RCH₃-terminated probe, indicating that polar fatty acids are more suitable as flotation collectors for low-rank coal than nonpolar hydrocarbon oil. The simulation and AFM test revealed the mechanisms of polar fatty acids, and can provide guidance for low-rank coal flotation applications.     

Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study

Human dihydroorotate dehydrogenase (hDHODH) is an enzyme belonging to a flavin mononucleotide (FMN)-dependent family involved in de novo pyrimidine biosynthesis, a key biological pathway for highly proliferating cancer cells and pathogens. In fact, hDHODH proved to be a promising therapeutic target for the treatment of acute myelogenous leukemia, multiple myeloma, and viral and bacterial infections; therefore, the identification of novel hDHODH ligands represents a hot topic in medicinal chemistry. In this work, we reported a virtual screening study for the identification of new promising hDHODH inhibitors. A pharmacophore-based approach combined with a consensus docking analysis and molecular dynamics simulations was applied to screen a large database of commercial compounds. The whole virtual screening protocol allowed for the identification of a novel compound that is endowed with promising inhibitory activity against hDHODH and is structurally different from known ligands. These results validated the reliability of the in silico workflow and provided a valuable starting point for hit-to-lead and future lead optimization studies aimed at the development of new potent hDHODH inhibitors.     

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

In Silico Drug Repurposing of FDA-Approved Drugs Highlighting Promacta as a Potential Inhibitor of H7N9 Influenza Virus

Influenza virus infections continue to be a significant and recurrent public health problem. Although vaccine efficacy varies, regular immunisation is the most effective method for suppressing the influenza virus. Antiviral drugs are available for influenza, although two of the four FDA-approved antiviral treatments have resulted in significant drug resistance. Therefore, new treatments are being sought to reduce the burden of flu-related illness. The time-consuming development of treatments for new and re-emerging diseases such as influenza and the high failure rate are increasing concerns. In this context, we used an in silico-based drug repurposing method to repurpose FDA-approved drugs as potential therapies against the H7N9 virus. To find potential inhibitors, a total of 2568 drugs were screened. Promacta, tucatinib, and lurasidone were identified as promising hits in the DrugBank database. According to the calculations of MM-GBSA, tucatinib (−54.11 kcal/mol) and Promacta (−56.20 kcal/mol) occupied the active site of neuraminidase with a higher binding affinity than the standard drug peramivir (−49.09 kcal/mol). Molecular dynamics (MD) simulation studies showed that the C-α atom backbones of the complexes of tucatinib and Promacta neuraminidase were stable throughout the simulation period. According to ADME analysis, the hit compounds have a high gastrointestinal absorption (GI) and do not exhibit properties that allow them to cross the blood–brain barrier (BBB). According to the in silico toxicity prediction, Promacta is not cardiotoxic, while lurasidone and tucatinib show only weak inhibition. Therefore, we propose to test these compounds experimentally against the influenza H7N9 virus. The investigation and validation of these potential H7N9 inhibitors would be beneficial in order to bring these compounds into clinical settings.     

Folding Free Energy Determination of an RNA Three-Way Junction Using Fluctuation Theorems

Nonequilibrium work relations and fluctuation theorems permit us to extract equilibrium information from nonequilibrium measurements. They find application in single-molecule pulling experiments where molecular free energies can be determined from irreversible work measurements by using unidirectional (e.g., Jarzynski’s equality) and bidirectional (e.g., Crooks fluctuation theorem and Bennet’s acceptance ratio (BAR)) methods. However, irreversibility and the finite number of pulls limit their applicability: the higher the dissipation, the larger the number of pulls necessary to estimate ΔG within a few kBT. Here, we revisit pulling experiments on an RNA three-way junction (3WJ) that exhibits significant dissipation and work-distribution long tails upon mechanical unfolding. While bidirectional methods are more predictive, unidirectional methods are strongly biased. We also consider a cyclic protocol that combines the forward and reverse work values to increase the statistics of the measurements. For a fixed total experimental time, faster pulling rates permit us to efficiently sample rare events and reduce the bias, compensating for the increased dissipation. This analysis provides a more stringent test of the fluctuation theorem in the large irreversibility regime.     

非稳态油膜力作用下偏角不对中碰摩转子系统振动特性分析

建立了非稳态油膜力、碰摩力作用下,偏角不对中轴系的横向振动模型,将Jeffcott转子模型推广至约束条件下的轴系振动模型,通过引入转子间的位移约束,由Lagrange方程导出了不对中轴系系统的振动方程.仿真研究表明:由于不对中的存在,轴系横向振动是强非线性、非定常、同时含有自激励、参数激励和外激励的三自由度振动系统;由于从动轴与主动轴的不对中,导致了转轴间的运动约束,使轴系横向振动系统比Jeffcott转子动力学模型所描述的振动系统少了一个自由度.随着偏角不对中量、转轴质量比的变化,系统出现周期、概周期、混沌运动特征.不对中轴系处于概周期运动时,呈现出自激振动特征.其结果可为旋转机械故障诊断、轴系对中及设备的安全运行提供更为准确的理论参考.     

Identification of Mtb GlmU Uridyltransferase Domain Inhibitors by Ligand-Based and Structure-Based Drug Design Approaches

Targeting enzymes that play a role in the biosynthesis of the bacterial cell wall has long been a strategy for antibacterial discovery. In particular, the cell wall of Mycobacterium tuberculosis (Mtb) is a complex of three layers, one of which is Peptidoglycan, an essential component providing rigidity and strength. UDP-GlcNAc, a precursor for the synthesis of peptidoglycan, is formed by GlmU, a bi-functional enzyme. Inhibiting GlmU Uridyltransferase activity has been proven to be an effective anti-bacterial, but its similarity with human enzymes has been a deterrent to drug development. To develop Mtb selective hits, the Mtb GlmU substrate binding pocket was compared with structurally similar human enzymes to identify selectivity determining factors. Substrate binding pockets and conformational changes upon substrate binding were analyzed and MD simulations with substrates were performed to quantify crucial interactions to develop critical pharmacophore features. Thereafter, two strategies were applied to propose potent and selective bacterial GlmU Uridyltransferase domain inhibitors: (i) optimization of existing inhibitors, and (ii) identification by virtual screening. The binding modes of hits identified from virtual screening and ligand growing approaches were evaluated further for their ability to retain stable contacts within the pocket during 20 ns MD simulations. Hits that are predicted to be more potent than existing inhibitors and selective against human homologues could be of great interest for rejuvenating drug discovery efforts towards targeting the Mtb cell wall for antibacterial discovery.     

基于孔口出流模型的实验研究及理论分析

基于孔口出流模型对底部有小孔的圆柱体容器中液体液面高度随时间的变化规律进行了实验研究,并通过计入液体表面张力引起的孔口收缩和流体黏滞力导致的能量损失对实验结果进行了理论分析,与实验结果一致.     

Novel Inhibitors of 2′-O-Methyltransferase of the SARS-CoV-2 Coronavirus

The COVID-19 pandemic is still affecting many people worldwide and causing a heavy burden to global health. To eliminate the disease, SARS-CoV-2, the virus responsible for the pandemic, can be targeted in several ways. One of them is to inhibit the 2′-O-methyltransferase (nsp16) enzyme that is crucial for effective translation of viral RNA and virus replication. For methylation of substrates, nsp16 utilizes S-adenosyl methionine (SAM). Binding of a small molecule in the protein site where SAM binds can disrupt the synthesis of viral proteins and, as a result, the replication of the virus. Here, we performed high-throughput docking into the SAM-binding site of nsp16 for almost 40 thousand structures, prepared for compounds from three libraries: Enamine Coronavirus Library, Enamine Nucleoside Mimetics Library, and Chemdiv Nucleoside Analogue Library. For the top scoring ligands, semi-empirical quantum-chemical calculations were performed, to better estimate protein–ligand binding enthalpy. Relying upon the calculated binding energies and predicted docking poses, we selected 21 compounds for experimental testing.