超大规模药物虚拟筛选的实现与应用

基于分子对接的药物虚拟筛选技术通过评估多个配体化合物与受体的结合强度来筛选最强结合的分子。在新冠病毒疫情全球蔓延形势下,超大规模快速药物虚拟筛选对于从海量配体结构中筛选出潜药分子至关重要。超级计算机的强大算力为药物虚拟筛选提供了硬件保障,但超大规模的药物虚拟筛选还面临着很多挑战,影响了计算的有效进行。在对挑战进行分析的基础上,提出了以中央数据库进行集中任务分发的方案,设计了多层级任务分发框架,并通过多层级智能调度、海量小分子文件多层级压缩处理、动态负载均衡、高容错管理等技术有效应对了面临的各种挑战,开发了简单易用的“树”形多层级任务分发系统,实现了快速高效稳定的药物虚拟筛选任务分发、计算和结果处理功能,计算效率近线性。在此基础上,采用异构计算技术在国产先进计算系统上针对新冠病毒两种不同活性位点快速完成了超过20亿化合物的药物虚拟筛选,为应对暴发性恶性传染病的超大规模快速虚拟筛选提供了强大计算保障。     

DDGrid:一种大规模药物虚拟筛选网格

化合物活性筛选是创新药物研究的起点和具有决定意义的步骤,利用网格计算技术进行药物虚拟筛选能够极大提高药物筛选的有效性,同时可以大量减少新药研制的成本和时间。新药研发网格DDGrid是中国国家网格CNGrid的重要支持项目,通过实施主从模式架构并在网格资源监控中使用适配器模式,DDGrid可以对超过10万规模的化合物分子数据库进行虚拟筛选。     

大规模虚拟场景渐进式传输的研究进展

随着网上三维虚拟场景的急剧扩张,来自用户端的即时下载请求和有限带宽之间的矛盾亦变得日益突出。现已有大量研究者致力于通过渐进式传输来实现网上大规模虚拟场景的快速下载,因此有必要对现有研究成果进行综述。首先介绍了渐进式传输的基本原理和过程,给出了一个用于评价不同传输策略的公式,接下来分别从(1)潜在可视场景集及潜在可视场景集增量的判定,(2)三维物体模型的简化技术和流式编码,(3)不同体系结构的分布式虚拟环境中的场景传输机制,(4)虚拟场景的预下载策略等方面论述了针对大规模虚拟场景的渐进式传输策略,最后展望了网上大规模虚拟场景渐进式下载技术的未来研究方向。     

基于深度神经网络的药物蛋白虚拟筛选

药物的研发是一种投入成本高、耗费时间长且成功率较低的一种研究,为了在药物开发阶段可以快速获得潜在的化合物,针对性地提出一种基于深度神经网络的药物蛋白虚拟筛选的方法。首先从给定数据集中学习如何提取相关特征,获取配体原子和残基类型进行特征分析,快速识别活性分子和非活性分子,然后使用降维方式和K折验证等方法对药物筛选的模型进行处理,最后通过分析富集因子和AUC值验证诱饵化合物与分子蛋白的互相作用验证模型的可靠程度,实验结果表明所提出的筛选方法具有很好的可行性和有效性,有效地加快了虚拟筛选过程。     

多尺度表达在虚拟视景仿真系统中的应用研究

现在分布式虚拟实验技术与视景仿真技术深入结合,使虚拟试验系统得到了广泛的应用;传统的虚拟视景仿真,在系统的各个节点上均采用单一的模型进行运算;一方面,在小规模视景仿真中,使系统资源不能得到充分利用;另一方面,在大规模视景仿真中,又无法满足系统实时性要求;为了解决这种情况,将多尺度表达的方法和原理应用于视景仿真系统中计算节点和显示节点的设计,并对两类节点运行不同尺度模型的负载匹配进行研究,对多尺度原理的应用效果进行了验证;试验结果表明,采用粗尺度模型进行大规模视景仿真时,在保证实时性的前提下,能很好地满足真实性要求;试验证明视景仿真系统中应用多尺度表达原理将使系统运行更有效率,将为大规模虚拟视景仿真系统的设计提供了一种可行的思路。     

基于TENA思想的分布式靶场虚拟试验系统设计

在分析试验与训练使能体系结构（TENA）思想的基础上,提出了1种分布式虚拟试验体系结构,该体系架构是1个集试验方案编辑、过程控制、综合显示、数据存储与处理功能于一体的综合化、通用化的虚拟试验支撑平台,介绍了该体系结构的特点和组成。根据该体系结构设计了分布式虚拟试验系统,详细阐述了分布式虚拟试验系统的基本组成及通信机制,可为类似系统的构建提供方法借鉴。     

整合药效团的分子对接方法研究

通过对计算机药物虚拟筛选技术、Dock评分函数体系和PocketV.2评分方法的分析,研究Dock评分函数体系及其源代码、PocketV.2评分方法及其源代码,提出基于药效团的计算机药物虚拟筛选方法。利用模拟配体与受体真实相互作用的分子间距离及性质的点模式匹配算法,实现了基于药效团的计算机药物虚拟筛选。在超性能计算集群环境下运行,并对该算法进行了测试,结果表明具有较强的可靠性和较高的准确性。     

基于结构的利用分布式计算的虚拟筛选（英文）

药物虚拟筛选的方法被广泛应用于从百万数量级的类药分子数据库中挑选出潜在的活性候选化合物,相比较于传统的流程可以显著降低研发成本和时间。为此,我们建立了一个大型分布式计算阵列,本论文详细说明了阵列系统的建立方法和对应的虚拟筛选流程。通过对以极光激酶A为药物筛选靶点的测试,我们从数据库中得到了包含已知的高活性分子等多个候选化合物,验证了系统的可行性。     

基于XML的协作虚拟样机通信技术

协作虚拟样机技术是在分布式环境下多领域专家协作建立和应用虚拟样机的方法和技术。如何实现系统中数字模型的同步以及相互操作是一个复杂的课题。目前协作虚拟样机通信的主流技术仍然使用传统的专有的通信协议和特定的数据格式通信，不能扩展，很难构建异构的、基于Intenct的协同设计环境。文中介绍了一种使用XMI，作为数据格式、HTTP作为传输协议的用于协作虚拟样机的通信模式,它可以很好地解决上述问题,进而构建一个面向对象的、可扩展的、基于Intemet的、跨平台的分布式协作虚拟样机系统。     

基于XML的协作虚拟样机通信技术

协作虚拟样机技术是在分布式环境下多领域专家协作建立和应用虚拟样机的方法和技术.如何实现系统中数字模型的同步以及相互操作是一个复杂的课题.目前协作虚拟样机通信的主流技术仍然使用传统的专有的通信协议和特定的数据格式通信,不能扩展,很难构建异构的、基于Intemet的协同设计环境.文中介绍了一种使用XML作为数据格式、HTTP作为传输协议的用于协作虚拟样机的通信模式,它可以很好地解决上述问题,进而构建一个面向对象的、可扩展的、基于Internet的、跨平台的分布式协作虚拟样机系统.     

Identification of a high affinity selective inhibitor of Polo-like kinase 1 for cancer chemotherapy by computational approach

Polo-like kinase (Plk)1 is a key regulator of the cell cycle during mitotic phase and is an attractive anti-mitotic drug target for cancer. Plk1 is a member of Ser/Thr kinase family which also includes Plk2-4 in human. Plk1 promotes the cell division whereas Plk2 and Plk3 are reported to act as tumour suppressors. The available inhibitors of Plk1 also suppress Plk2 and Plk3 activity significantly resulting in the cell death of normal cells in addition to the cancer cells. Hence, it is imperative to explore Plk1 specific inhibitors as anti-cancer drugs. In this work, a selective potential inhibitor of Plk1 has been identified by molecular docking based high throughput virtual screening. The identified compound exploits the subtle differences between the binding sites of Plk1 and other Ser/Thr kinases including Plk2-4. The predicted binding affinity of identified inhibitor is higher than available inhibitors with a 100-fold selectivity towards Plk1 over Plk2-4 and several cell cycle kinases. It also satisfies the Lipinski's criteria of drug-like molecules and passes the other ADMET filters. This triazole compound with aryl substituent belongs to a novel class of potential inhibitor for Plk1. The suggested potential lead molecule can thus be tested and developed further as a potent and selective anti-cancer drug.     

In silico identification of novel inhibitors against Plasmodium falciparum dihydroorate dehydrogenase

Plasmodium falciparum causes the most fatal form of malaria and accounts for over 1 million deaths annually, yet currently used drug therapies are compromised by resistance. The malaria parasite cannot salvage pyrimidines and relies on de novo biosynthesis for survival. The enzyme dihydrooratate dehydrogenase (DHODH), a mitochondrial flavoenzyme, catalyzes the rate-limiting step of this pathway and is therefore an attractive anti-malarial chemotherapeutic target. In an effort to design new and potential anti-malarials, structure-based pharmacophore mapping, molecular docking, binding energy calculations and binding affinity predictions were employed in a virtual screening strategy to design new and potent P. falciparum dihydrooratate dehydrogenase (PfDHODH) inhibitors. A structure-based pharmacophore model was generated which consist of important interactions as observed in co-crystal of PfDHODH enzyme. The developed model was used to retrieve molecules from ChemBridge database, a freely available commercial database. A total of 87 molecules mapped on the modeled pharmacophore from the database. The retrieved hits were further screened by docking simulation, binding energy calculations and biding affinity predictions using genetic optimization for ligand docking (GOLD) and MOE. Based on these results, finally 26 chemo-types molecules were predicted as new, potential and structurally diverse PfDHODH inhibitors.     

Protein tyrosine phosphatases: Ligand interaction analysis and optimisation of virtual screening

Docking-based virtual screening is an established component of structure-based drug discovery. Nevertheless, scoring and ranking of computationally docked ligand libraries still suffer from many false positives. Identifying optimal docking parameters for a target protein prior to virtual screening can improve experimental hit rates. Here, we examine protocols for virtual screening against the important but challenging class of drug target, protein tyrosine phosphatases. In this study, common interaction features were identified from analysis of protein–ligand binding geometries of more than 50 complexed phosphatase crystal structures. It was found that two interactions were consistently formed across all phosphatase inhibitors: (1) a polar contact with the conserved arginine residue, and (2) at least one interaction with the P-loop backbone amide. In order to investigate the significance of these features on phosphatase-ligand binding, a series of seeded virtual screening experiments were conducted on three phosphatase enzymes, PTP1B, Cdc25b and IF2. It was observed that when the conserved arginine and P-loop amide interactions were used as pharmacophoric constraints during docking, enrichment of the virtual screen significantly increased in the three studied phosphatases, by up to a factor of two in some cases. Additionally, the use of such pharmacophoric constraints considerably improved the ability of docking to predict the inhibitor's bound pose, decreasing RMSD to the crystallographic geometry by 43% on average. Constrained docking improved enrichment of screens against both open and closed conformations of PTP1B. Incorporation of an ordered water molecule in PTP1B screening was also found to generally improve enrichment. The knowledge-based computational strategies explored here can potentially inform structure-based design of new phosphatase inhibitors using docking-based virtual screening.     

嵌入式跨平台虚拟软件技术的研究与应用

嵌入式跨平台虚拟软件是一种将RISC硬件体系结构虚拟成CISC硬件体系结构平台。本文的研究目的是通过在RISC体系硬件平台添加一个软件层,实现一个虚拟而且兼容X86的硬件平台,从而顺利地运行X86平台下的所有操作系统、应用程序等X86系列软件。重点讲述嵌入式跨平台虚拟机CPU虚拟设计和bootloader设计。嵌入式跨平台虚拟软件技术在嵌入式行业中有较高的应用价值。     

Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach

DNA methylation is an epigenetic change that results in the addition of a methyl group at the carbon-5 position of cytosine residues. DNA methyltransferase (DNMT) inhibitors can suppress tumour growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. Additionally, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects. In the present study, we have designed a workflow by integrating various ligand-based and structure-based approaches to discover new agents active against DNMT1. We have derived a pharmacophore model with the help of available DNMT1 inhibitors. Utilising this model, we performed the virtual screening of Maybridge chemical library and the identified hits were then subsequently filtered based on the Naïve Bayesian classification model. The molecules that have returned from this classification model were subjected to ensemble based docking. We have selected 10 molecules for the biological assay by inspecting the interactions portrayed by these molecules. Three out of the ten tested compounds have shown DNMT1 inhibitory activity. These compounds were also found to demonstrate potential inhibition of cellular proliferation in human breast cancer MDA-MB-231 cells. In the present study, we have utilized a multi-step virtual screening protocol to identify inhibitors of DNMT1, which offers a starting point to develop more potent DNMT1 inhibitors as anti-cancer agents.     

Identification of estrogen receptor α ligands with virtual screening techniques

Utilization of computer-aided molecular discovery methods in virtual screening (VS) is a cost-effective approach to identify novel bioactive small molecules. Unfortunately, no universal VS strategy can guarantee high hit rates for all biological targets, but each target requires distinct, fine-tuned solutions. Here, we have studied in retrospective manner the effectiveness and usefulness of common pharmacophore hypothesis, molecular docking and negative image-based screening as potential VS tools for a widely applied drug discovery target, estrogen receptor α (ERα). The comparison of the methods helps to demonstrate the differences in their ability to identify active molecules. For example, structure-based methods identified an already known active ligand from the widely-used bechmarking decoy molecule set. Although prospective VS against one commercially available database with around 100,000 drug-like molecules did not retrieve many testworthy hits, one novel hit molecule with pIC₅₀ value of 6.6, was identified. Furthermore, our small in-house compound collection of easy-to-synthesize molecules was virtually screened against ERα, yielding to five hit candidates, which were found to be active in vitro having pIC₅₀ values from 5.5 to 6.5.     

A systematic methodology for large scale compound screening: A case study on the discovery of novel S1PL inhibitors

Decrease in sphingosine 1-phosphate (S1P) concentration induces migration of pathogenic T cells to the blood stream, disrupts the CNS and it is implicated in multiple sclerosis (MS), a progressive inflammatory disorder of the central nervous system (CNS), and Alzheimer’s disease (AD). A promising treatment alternative for MS and AD is inhibition of the activity of the microsomal enzyme sphingosine 1-phosphate lyase (S1PL), which degrades intracellular S1P. This report describes an integrated systematic approach comprising virtual screening, molecular docking, substructure search and molecular dynamics simulation to discover novel S1PL inhibitors. Virtual screening of the ZINC database via ligand-based and structure-based pharmacophore models yielded 10000 hits. After molecular docking, common substructures of the top ranking hits were identified. The ligand binding poses were optimized by induced fit docking. MD simulations were performed on the complex structures to determine the stability of the S1PL-ligand complex and to calculate the binding free energy. Selectivity of the selected molecules was examined by docking them to hERG and cytochrome P450 receptors. As a final outcome, 15 compounds from different chemotypes were proposed as potential S1PL inhibitors. These molecules may guide future medicinal chemistry efforts in the discovery of new compounds against the destructive action of pathogenic T cells.     

Allosteric pocket of the dengue virus (serotype 2) NS2B/NS3 protease: In silico ligand screening and molecular dynamics studies of inhibition

The allosteric pocket of the Dengue virus (DENV2) NS2B/NS3 protease, which is proximal to its catalytic triad, represents a promising drug target (Othman et al., 2008). We have explored this binding site through large-scale virtual screening and molecular dynamics simulations followed by calculations of binding free energy. We propose two mechanisms for enzyme inhibition. A ligand may either destabilize electronic density or create steric effects relating to the catalytic triad residues NS3-HIS51, NS3-ASP75, and NS3-SER135. A ligand may also disrupt movement of the C-terminal of NS2B required for inter-conversion between the “open” and “closed” conformations. We found that chalcone and adenosine derivatives had the top potential for drug discovery hits, acting through both inhibitory mechanisms. Studying the molecular mechanisms of these compounds might be helpful in further investigations of the allosteric pocket and its potential for drug discovery.