The (S−1,S) inventory model and its counterparts in queueing theory

We explore the relationship between the ($S?1,S$) inventory model and three well-known queueing models: the Erlang loss system, the machine-repair model and a two-node Jackson network. Exploiting this relationship allows us to obtain key performance measures of the ($S?1,S$) model, like the so-called virtual outdating time, the number of items on the shelf in steady state, the long-run rate of unsatisfied demands and the distribution of the empty shelf period.     

Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines

A mixed parallel scheme that combines message passing interface (MPI) and multithreading was implemented in the AutoDock Vina molecular docking program. The resulting program, named VinaLC, was tested on the petascale high performance computing (HPC) machines at Lawrence Livermore National Laboratory. To exploit the typical cluster‐type supercomputers, thousands of docking calculations were dispatched by the master process to run simultaneously on thousands of slave processes, where each docking calculation takes one slave process on one node, and within the node each docking calculation runs via multithreading on multiple CPU cores and shared memory. Input and output of the program and the data handling within the program were carefully designed to deal with large databases and ultimately achieve HPC on a large number of CPU cores. Parallel performance analysis of the VinaLC program shows that the code scales up to more than 15K CPUs with a very low overhead cost of 3.94%. One million flexible compound docking calculations took only 1.4 h to finish on about 15K CPUs. The docking accuracy of VinaLC has been validated against the DUD data set by the re‐docking of X‐ray ligands and an enrichment study, 64.4% of the top scoring poses have RMSD values under 2.0 Å. The program has been demonstrated to have good enrichment performance on 70% of the targets in the DUD data set. An analysis of the enrichment factors calculated at various percentages of the screening database indicates VinaLC has very good early recovery of actives. © 2013 Wiley Periodicals, Inc.     

Targeting tribbles homolog 3 (TRIB3) protein against type 2 diabetes for the identification of potential inhibitors by in silico screening

Tribbles homolog 3 (TRIB3) protein is inhibiting the insulin signaling by directly binding to the Akt/PKB leading to insulin resistance in the pancreas causing type 2 diabetes mellitus. Hence, TRIB3 protein is considered as a possible drug target for the new lead identification against type 2 diabetes. In the present study, the homology model of TRIB3 protein was generated to explore its biochemical function and molecular interactions in the new lead identification. The energy minimization of TRIB3 protein was carried out and evaluated by validation protocols for structure reliability. The druggable binding site of TRIB3 protein was identified for the virtual screening and molecular docking studies. The Asinex-fragments library of 22634 small molecules was docked at TRIB3 active site using the Glide module to identify new chemical entities. A total of 9 molecules were identified as final hits from virtual screening and their potency was ranked using Glide score, Glide energies, and residues interactions. The 6 prioritized lead molecules were further optimized using AutoDock, Prime MM/GBSA, and percentage of human oral absorption for the identification of potential leads. The molecules L2, L5, and L6 are identified as lead inhibitors and are showing consistent interactions with key residues Glu194 and Lys196 of TRIB3 protein. The identified potential leads were analyzed by ADME properties for their drug likeness and HergIC50 values are predicted for the prevention of preclinical failures. The present work sheds light on the identification of the best lead molecules against TRIB3 protein and offers a route to design as novel potential drug candidates for T2DM.     

In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling,molecular docking,and dynamics simulations

The rapid increase of HIV-1 infection throughout the globe has a high demand for a superior drug with lesser side effects. LEDGF/p75, the human Lens Epithelium-Derived Growth Factor is identified as a promising cellular cofactor with integrase in facilitating the viral replication in an early stage by acting as a tethering factor in the pre-integration to the chromatin. Therefore, the present study was designed to identify a potent inhibitor by applying an E-pharmacophore based virtual screening, molecular docking, and dynamics simulation approaches. Finally, ZINC22077550 and ZINC32124441 were best identified potent molecules with the efficient binding affinity, strong hydrogen bonding, and acceptable pharmacological properties to hamper the interaction between integrase and LEDGF/p75. Further, the DFT and MDS studies were also analyzed, and shown a favorable energetic state and dynamic stability then reference compound. In conclusion, we suggest that these findings could be novel therapeutics in the future and may increase the lifespan of individuals suffering from viral infection.     

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.     

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.     

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.     

移相器微位移旋转误差的分析及测试

以压电陶瓷(PZT)微位移器为主要研究对象, 引入一种处理静态干涉图的新方法--虚光栅移相叠栅条纹法,设计实验对一台实际使用的移相器微位移旋转误差进行测试研究,对其引起的波面旋转情况进行了定量的计算分析,并给出测试结果.用虚光栅移相叠栅条纹法处理实验中加有载频的干涉图时,不需要使用任何移相器件,可以进行动态位相的检测,整个移相过程用计算机进行控制,避免了引入额外的移相误差.     

Multiple QSAR and molecular modelling for identification of potent human adenovirus inhibitors

This study has investigated docking-based 2D- and 3D-quantitative structure-activity relationships (QSARs) for a range of 53 hydroxybenzamide analogues as anti- Human adenoviruses (HAdVs). The best 3D-QSAR (Schrodinger, LLC, NY, 2020) and 2D-QSAR models were obtained for the training set and were found to be statistically significant, with cross-validated coefficients (q²) of 0.6775 and 0.7875, and coefficients of determination (r²) of 0.8106 and 0.8122, respectively. Our in-silico docking and virtual screening studies revealed significant higher binding affinity of dataset molecule 34 (-141.444 ​kcal/mol) and hit ZINC01088642 (-114.357 ​kcal/mol) with 4PIE protein than the standard drugs. In in-silico ADME/toxicity studies, molecule 34 and proposed hit ZINC01088642 were found safe with good intestinal absorption, aqueous solubility, medium blood–brain barrier (BBB), no eye corrosion, no skin irritancy, and non-mutagenic profiles. Molecular dynamics analysis showed good stability of complex, hit ZINC01088642 with protein, 4PIE over the simulation period of 20 ns. We believe that further experimental, as well as in-vitro investigation, will shed more lights on the identification of ZINC01088642 as a potential human adenovirus agent.