基于容错机制的精细药物分子对接网格

针对药物分子对接的搜索空间大、耗费时间长、对计算环境要求高等问题,给出一种将网格技术应用于药物分子对接的方案。基于遗传算法多种群竞争机制的对接演化模型GasDock,以信息熵控制空间的收缩,增强进化的目的性,提高对接效率。提出局部类树型结构及结点失效的容错机制,提高了精细药物分子对接任务的效率及容错性。测试结果证明了精细药物分子对接与网格技术相结合的合理性和有效性。     

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。     

tPSODock:基于化学得分函数和两层粒子群算法的计算机分子对接程序

药物分子对接是计算机辅助药物设计的主要方法之一。利用化学得分函数(Chemscor)作为能量函数,以及将一种新的优化算法一两层粒子群算法作为搜索算法,得到了一种新的计算机分子对接程序：tPSODock。利用tPSODock计算了100个蛋白质一配体的复合物,并且与Consdock和Autodock3．0计算结果进行了对比,结果显示88％的计算结果RMSD小于2．0A,优于Consdock以及Autodock的计算结果。说明tPSODock在是一种高效的分子对接软件,可以用于大规模数据库的筛选工作,适合新药的开发和研制。     

隐式曲面的多分辨率法向网格逼近

法向网格是一种新型的曲面多分辨率描述方式，其中每个层次都可以表示为其前一个粗糙层次的法向偏移．文中提出一种基于法向网格表示的隐式曲面多分辨率网格逼近算法．首先通过基于空间剖分技术的多边形化算法获得隐式曲面的粗糙逼近网格，并利用网格均衡化方法对粗糙网格进行优化，消除其中的狭长三角形；然后利用法向细分规则迭代地对网格中的三角面片进行细分，并利用区间算术技术沿法向方向对隐式曲面进行逼近．最终生成的隐式曲面分片线性逼近网格为法向网格．该逼近网格为隐式曲面提供了一种多分辨率表示，网格具有细分连通性，其数据量较传统的多边形化算法所生成的网格有大幅度的压缩．该算法可用于隐式曲面的多级绘制、累进传输及相关数字几何处理．     

一种基于免疫遗传算法的分子对接构象搜索策略

分子对接是计算机辅助药物分子优化设计中的一种重要方法,搜索算法和评分函数是当前分子对接研究的难点与热点.在借鉴当前分子对接构象搜索策略的基础上,提出了一个基于免疫遗传算法的分子对接药物设计方法AutoDockIGA.首先建立了基于最优化方法的分子对接数学模型AutoDockModel,并设计了基于免疫遗传算法的构象搜索策略,运用此方法对布克海文蛋白质数据库中(Brookhaven Protein Data Bank)的6种蛋白质一配体复合物进行了实验测试,并将实验结果与AutoDock3.0、模拟退火算法的对接时间和精度进行比较和分析,实例测试表明AutoDockIGA具有更高的寻优能力.     

流感病毒神经氨酸酶抑制剂的理性筛选

介绍了理性筛选流感病毒神经氨酸酶抑制剂的全过程，共分4个阶段：1)化合物数据库类药性处理；2)建立神经氨酸酶抑制剂三维药效团并对目标数据库进行构象搜索；3)分子对接及对接后分析；4)神经氨酸酶抑制模型的建立及待测化合物的活性检测。活性检测后发现4个活性化合物，其中Ic。为10。M的化合物1个．Ic，。为10^-6M的化合物2个，IC50为10^-7M的化合物1个。应用理性筛选方法，从化合物数据库中挑选出部分化合物进行神经氨酸酶抑制活性的筛选，减少了药物筛选的盲目性，提高了药物发现的机率。     

云环境下药物分子对接动态任务迁移的应用

基于传统动态任务迁移方法,针对药物分子对接任务,提出了云环境下药物分子对接任务优化迁移策略。通过分析各结点自身中断事件发生次数的数学期望和方差,得出即时可靠性评价参数,从而选取最可靠的结点作为迁移目标。在源结点和可靠的目标结点之间实施增量数据预迁移,缩短了停机拷贝时间。最后的实例表明任务迁移策略能够在云环境下选择可靠目标结点,药物分子对接安全性得到了提高。     

药物分子对接中的构象搜索策略

药物分子对接设计是大规模数据库筛选的理想途径。本文在介绍分子对接理论的基础上，建立了一个数学规划模型，将分子对接中的构象搜索转化为约束极小化问题，并采用带有空间收缩的小种群遗传算法进行求解。在遗传算法中还引入了信息熵的概念，用熵控制各种群搜索空间的收缩。本方法用种群的多样性避免了遗传进化的早熟现象，以空间收缩尺度作为停机判据，有效地控制了算法的收敛。在多种群进化机制上，采用小种群策略，极大程度地减少了计算量，提高了分子对接的效率。实例表明本方法适用于药物分子对接设计。     

CUDA下受体评分网格生成并行算法

针对分子对接中生成评分网格需要花费很多的计算时间这一问题, 提出了一种基于统一计算设备架构（CUDA）的评分网格生成并行算法。该算法把传统计算方法中三维计算空间中的一维通过在图形处理单元（GPU）上进行并行处理, 使得总生成时间得到了降低, 提高了评分网格的生成效率。实验结果表明, 借助于GPU的浮点计算能力, 提出的并行算法对比传统的计算方法可以显著缩短评分网格的生成时间, 为评分网格的生成提供一种新的方式。     

金莲花碱的潜在作用靶标预测及分子对接研究

利用Pharm Mapper sever的药效团匹配方法对金莲花碱(trolline)的潜在作用靶标进行筛选,虚拟筛选得到的靶标所涉及的疾病与实验报道的金莲花碱的抗菌活性一致。进而利用分子对接技术对金莲花碱与潜在靶标的作用模式进行了研究,发现它们在形状、电负性和疏水性方面匹配良好,能有效抑制靶标的活性。本研究结果表明,Pharm Mapper sever的药效团匹配和分子对接方法可作为预测药物潜在靶标的有效手段,为探讨金莲花碱的分子机制提供线索,并为深入揭示金莲花碱抗菌活性的分子机制提供新思路。     

网格计算技术在药物分子对接中的应用

药物分子筛选所涉及的搜索空间非常巨大,需要耗费大量的时间,并且对计算环境也有较高的要求。将网格技术应用于药物分子筛选中,能有效解决上述问题。通过引入网格结点计算性能评价参数,使性能较好的结点承担计算量较大的任务。按照结点间传输环境的不同进行区域划分,从而减少了由于传输环境变化而造成的筛选服务质量的波动;最后的实例测试表明了药物分子筛选服务与网格技术相结合的合理性及有效性。     

高性能计算在药物设计中的应用

计算机药物设计是高性能计算应用的一个重要领域。本文针对药物设计领域,简要介绍了高性能计算技术在蛋白质分子动力学模拟、药物虚拟筛选、药靶反向对接、个性化药物设计中的应用。     

Multisensory VR interaction for protein-docking in the CoRSAIRe project

Proteins take on their function in the cell by interacting with other proteins or biomolecular complexes. To study this process, computational methods, collectively named protein docking, are used to predict the position and orientation of a protein ligand when it is bound to a protein receptor or enzyme, taking into account chemical or physical criteria. This process is intensively studied to discover new biological functions for proteins and to better understand how these macromolecules take on these functions at the molecular scale. Pharmaceutical research also employs docking techniques for a variety of purposes, most notably in the virtual screening of large databases of available chemicals to select likely molecular candidates for drug design. The basic hypothesis of our work is that Virtual Reality (VR) and multimodal interaction can increase efficiency in reaching and analysing docking solutions, in addition to fully a computational docking approach. To this end, we conducted an ergonomic analysis of the protein–protein current docking task as it is carried out today. Using these results, we designed an immersive and multimodal application where VR devices, such as the three-dimensional mouse and haptic devices, are used to interactively manipulate two proteins to explore possible docking solutions. During this exploration, visual, audio, and haptic feedbacks are combined to render and evaluate chemical or physical properties of the current docking configuration.     

MOLECULAR DOCKING: An example of grid enabled applications

The molecular docking web interface was developed to execute Autodock3.05 molecular docking program in the Grid environment. The nature of the application which allows the whole docking jobs to be broken up into multiple small independent tasks, has the potential of utilizing the availability of the Grid computing. Using the web interface, the whole docking procedures can be automated from the start to the end. Automation includes the preparation of the target receptor, creation of parameter files (gpf and dpf), calculation of grid energy, and docking of molecules. Once the job is split into small tasks, the tasks are submitted to Globus GRAM that submits the tasks to the resources available in the Grid environment. The execution of the grid-enabled AutoDock 3.05 is tested and the results showed that the process of molecular docking are faster compared if the execution is run on sequential computing resources.     

Design of a grid service-based platform for in silico protein-ligand screenings

Grid computing offers the powerful alternative of sharing resources on a worldwide scale, across different institutions to run computationally intensive, scientific applications without the need for a centralized supercomputer. Much effort has been put into development of software that deploys legacy applications on a grid-based infrastructure and efficiently uses available resources. One field that can benefit greatly from the use of grid resources is that of drug discovery since molecular docking simulations are an integral part of the discovery process. In this paper, we present a scalable, reusable platform to choreograph large virtual screening experiments over a computational grid using the molecular docking simulation software DOCK. Software components are applied on multiple levels to create automated workflows consisting of input data delivery, job scheduling, status query, and collection of output to be displayed in a manageable fashion for further analysis. This was achieved using Opal OP to wrap the DOCK application as a grid service and PERL for data manipulation purposes, alleviating the requirement for extensive knowledge of grid infrastructure. With the platform in place, a screening of the ZINC 2,066,906 compound "drug-like" subset database against an enzyme's catalytic site was successfully performed using the MPI version of DOCK 5.4 on the PRAGMA grid testbed. The screening required 11.56 days laboratory time and utilized 200 processors over 7 clusters.     

Molecular modeling, docking and ADMET studies applied to the design of a novel hybrid for treatment of Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia in adults, which is characterized by senile plaquets and cholinergic deficit as the disease progresses. Improvement of cholinergic neurotransmission is the basis of some drugs currently used in the treatment of AD. It is achieved by acetylcholinesterase (AChE) inhibition, the enzyme responsible for acetylcholine hydrolysis. Molecular modeling techniques were of utmost importance to design a new pharmaceutical against Alzheimer's disease, with potential inhibitory activity over AChE, since the inhibition of human plasma butyrylcholinesterase (BChE) may cause side effects. Some of the drugs currently used in the treatment of AD are capable of increasing the cholinergic transmission through the AChE inhibition. In this work we proposed molecular hybrids of tacrine with donepezil (fusion of these structures), in order to suggest new proposals of AChE inhibitors for future treatment of AD. We have analyzed all the structures by docking, density functional studies and drug like properties.     

基于CHARMM力场的蛋白质分子场计算及触觉感知

分子场是研究分子结构与功能的重要工具之一,已经成为药物设计和分子对接的常规方法.由于触觉设备能提供丰富的感官信息,近年来触觉交互正逐渐成为该领域的研究热点.文中研究了蛋白质分子场的触觉感知.首先从蛋白质分子结构出发,基于CHARMM力场,经过分子动力学模拟采样计算得到蛋白质分子场;然后结合触觉设备建立触觉感知模型,通过视觉与触觉相结合的方式分析分子场能量分布以丰富分子场的感知信息;最后绘制触觉场景并给用户提供相关的视觉和触觉反馈.实验结果表明,文中提出的分子场计算方法鲁棒,且触觉系统能保持较好的稳定性.     

Prediction of the receptor conformation for iGluR2 agonist binding: QM/MM docking to an extensive conformational ensemble generated using normal mode analysis

Highly flexible proteins constitute a significant challenge in molecular docking within the field of drug design. Depending on the efficacy of the bound ligand, the ligand-binding domain (LBD) of the ionotropic glutamate receptor iGluR2 adopts markedly different degrees of domain closure due to large-scale domain movements. With the purpose of predicting the induced domain closure of five known iGluR2 partial to full agonists we performed a validation study in which normal mode analysis (NMA) was employed to generate a 25-membered ensemble of iGluR2 LBD structures with gradually changing domain closures, followed by accurate QM/MM docking to the ensemble. Based on the docking scores we were able to predict the correct optimal degree of closure for each ligand within 1–3° deviation from the experimental structures. We demonstrate that NMA is a useful tool for reliable ensemble generation and that we are able to predict the ligand induced conformational change of the receptor through docking to such an ensemble. The described protocol expands and improves the information that can be obtained from computational docking when dealing with a flexible receptor.