分子对接与全局极小化方法

全局极小化方法及其在结构生物学中的应用近年来取得了显著的进展.适当简化的分子对接问题是全局极小化方法的一个很好目标,并且是当前一个相当活跃的研究领域.对接可分为两类：主要用于从头配体设计的细致对接和用于已知化合物数据库筛选以发现药物的粗略对接,它们对全局极小化算法的要求是不同的.简要评述了新出现的适合于对接问题的随机和确定性全局极小化算法,其中势能平滑算法看来很有希望,值得密切关注.     

预测蛋白质—蛋白质复合物结构的软对接算法

提出了一种有效的软对接算法 ,用于在已知受体和配体三维结构的条件下预测蛋白质 蛋白质复合物的结构。该算法的分子模型基于Janin提出的简化蛋白质模型 ,并在此基础上有所改进。对蛋白质分子表面的柔性氨基酸残基Arg、Lys、Asp、Glu和Met进行了特殊处理 ,通过软化分子表面的方式考虑了它们的侧链柔性。采用双重过滤技术来排除不合理的对接结构 ,此过滤技术是以复合物界面几何互补性和残基成对偏好性为标准提出的。对所得到的构象进行能量优化 ,之后用打分函数对这些结构进行排序 ,挑选出与复合物天然结构接近的构象。该打分函数包括静电、疏水和范德华相互作用能。用此算法对 2 6个复合物进行了结构预测 ,均找到了近天然结构 ,其中有 2 0个复合物的近天然结构排在了前 10位。改进的分子模型可以在一定程度上描述蛋白质表面残基侧链的柔性 ;双重过滤技术使更多的近天然结构保留下来 ,从而提高了算法成功预测的可能性 ;打分函数可以较合理地评价对接结构。总之 ,此种软对接算法能够对蛋白质分子识别的研究提供有益的帮助。     

蛋白质与类药分子的柔性对接

本文利用“禁忌搜索”算法和Ｇｅｈｌｈａａｒ简化能量势函数实现蛋白质与类药分子之间的柔性对接。对包含１００个复合物的检验集进行了计算检验,得到了满意的结果,８９％预测复合物结构的误差小于０．２５ｎｍ。与利用遗传算法进行柔性对接的ＧＯＬＤ程序相比,本方法的成功率高,局限性小,计算时间也短。     

小波变换及滚球算法在单分子荧光能量共振转移图像分析中的应用

单分子荧光共振能量转移技术是通过检测单个分子内的荧光供体及受体间荧光能量转移的效率来研究分子构象的变化．要得到这些生物大分子的信息就需要对大量的单分子信号进行统计分析,人工分析这些信息,既费时费力又不具备客观性和可重复性,因此本文将小波变换及滚球算法应用到单分子荧光能量共振转移图像中对单分子信号进行统计分析．在保证准确检测到单分子信号的前提下,文章对滚球算法和小波变换算法处理图像后的线性进行了分析,结果表明,滚球算法和小波变换算法不但能够很好地去除单分子FRET图像的背景噪声,同时还能很好地保持单分子荧光信号的线性．最后本文还利用滚球算法处理单分子FRET图像及统计15 bp DNA的FRET效率的直方图,通过计算得到了15 bp DNA的FRET效率值．     

小波变换及滚球算法在单分子荧光能量共振转移图像分析中的应用（英文）

单分子荧光共振能量转移技术是通过检测单个分子内的荧光供体及受体间荧光能量转移的效率来研究分子构象的变化.要得到这些生物大分子的信息就需要对大量的单分子信号进行统计分析,人工分析这些信息,既费时费力又不具备客观性和可重复性,因此本文将小波变换及滚球算法应用到单分子荧光能量共振转移图像中对单分子信号进行统计分析.在保证准确检测到单分子信号的前提下,文章对滚球算法和小波变换算法处理图像后的线性进行了分析,结果表明,滚球算法和小波变换算法不但能够很好地去除单分子FRET图像的背景噪声,同时还能很好地保持单分子荧光信号的线性.最后本文还利用滚球算法处理单分子FRET图像及统计15 bp DNA的FRET效率的直方图,通过计算得到了15 bp DNA的FRET效率值.     

利用回归模型筛选出近天然的抗原-抗体对接模拟结构

在抗原-抗体分子对接模拟所生成的大量计算生成构象中筛选出近天然结构,即接近真实情况的抗原-抗体结合模式。借鉴QSAR原理,定义抗原-抗体接触面描述符并利用Discovery Studio 4.5软件平台计算出各对接模拟构象的接触面描述符和能量参数。构造训练集数据进行回归分析,建立预测对接模拟构象是否是近天然结构的数学模型。通过测试集和实际应用情况检验该数学模型。通过回归分析所建立的数学模型能够在成百上千的抗原-抗体对接模拟构象中有效筛选出其中的近天然结构,在测试集验证和4G7抗体结合模式预测应用中具有良好的表现,验证了该数学模型的有效性和实用性。经验性的抗原-抗体接触面特征如氢键密度、氨基酸对偏好性指数等以及能量参数能够共同有效表征近天然结构,所建立的数学模型有效增强了通过分子对接预测抗原-抗体结合模式的可行性。     

蛋白质复合物结构预测的集成分子对接方法

蛋白质分子间相互作用与识别是当前生命科学研究的热点,分子对接方法是研究这一问题的有效手段．为了推进分子对接方法的发展,欧洲生物信息学中心组织了国际蛋白质复合物结构预测（CAPRI）竞赛．通过参加CAPRI竞赛,逐步摸索出了一套用于蛋白质复合物结构预测的集成蛋白质一蛋白质分子对接方法HoDock,它包括结合位点预测、初始复合物结构采集、精细复合物结构采集、结构成簇和打分排序以及最终复合物结构挑选等主要步骤．本文以最近的CAPRI Target 39为例,具体说明该方法的主要步骤和应用．该方法在CAPRI Target 39竞赛中取得了比较好的结果,预测结构Model 10是所有参赛小组提交的366个结构中仅有的3个正确结构之一,其配体均方根偏差（L_Rmsd）为0．25nm．在对接过程中,首先用理论预测和实验信息相结合的方法来寻找蛋白质结合位点残基,确认CAPRI Target 39A链的A31TRP和A191HIS,B链的B512ARG和B531ARG为可能结合位点残基．同时,用ZDock程序做不依赖结合位点的初步全局刚性对接．然后,根据结合位点信息进行初步局部刚性对接,从全局和局部对接中挑出了11个初始对接复合物结构．进而,用改进的Rosetta Dock程序做精细位置约束对接,并对每组对接中打分排序前200的结构进行成簇聚类．最后,综合分析打分、成簇和结合位点三方面的信息,得到10个蛋白质复合物结构．竞赛结果表明,A191HIS,B512ARG和B531ARG三个结合位点残基预测正确,提交的10个蛋白质复合物结构中有5个复合物受体一配体界面残基预测成功率较高．与其他参赛小组的对接结果比较,表明HoDock方法具有一定优势．这些结果说明我们提出的集成分子对接方法有助于提高蛋白质复合物结构预测的准确率．     

一种在格点模型上模拟蛋白质折叠结构的优化算法

蛋白质折叠问题是生物信息学中一个经典的多项式复杂程度的非确定性(non-deterministic polynomial,NP)难度问题.势能曲面变平法(ELP)是一种启发式的全局优化算法.通过对ELP方法中的直方图函数提出一种新的更新机制,并将基于贪心策略的初始构象的产生,基于牵引移动的邻域搜索策略与ELP方法相结合,为面心立方体(FCC)格点模型的蛋白质折叠问题提出一种改进的势能曲面变平(ELP+)算法.采用文献中9条常用序列作为测试集.对于每条序列,ELP+算法均能找到与文献中的算法所得到的最低能量相等或更低的能量.实验结果表明,ELP+算法是求解FCC格点模型的蛋白质折叠问题的一种有效算法.     

人白介素6受体结合功能域的构效关系研究

以分子对接（docking）方法研究人白介素6受体胞外区配基结合功能域“WSXWS”区氨基酸残基定点突变对受体与配基人白介素6结合时的相互作用能量、分子间相互作用的影响,从分子力学、分子动态学分析了人白介素6受体胞外区功能域关键氨基酸残基在受体与配基结合中的构象变化以及与人白介素6间的相互作用.     

反向虚拟筛选平台及应用

靶标确证是老药新用、药物毒副作用研究的关键。基于分子对接方法 Auto Dock Vina和内部构建的疾病靶标数据库,采用分布式架构,构建了反向虚拟筛选平台。应用该平台对药物吡斯的明进行靶标确证,最终成功找到其靶标乙酰胆碱酯酶,验证了平台的实用性和准确性。     

A hierarchical method for molecular docking using cloud computing

Discovering small molecules that interact with protein targets will be a key part of future drug discovery efforts. Molecular docking of drug-like molecules is likely to be valuable in this field; however, the great number of such molecules makes the potential size of this task enormous. In this paper, a method to screen small molecular databases using cloud computing is proposed. This method is called the hierarchical method for molecular docking and can be completed in a relatively short period of time. In this method, the optimization of molecular docking is divided into two subproblems based on the different effects on the protein–ligand interaction energy. An adaptive genetic algorithm is developed to solve the optimization problem and a new docking program (FlexGAsDock) based on the hierarchical docking method has been developed. The implementation of docking on a cloud computing platform is then discussed. The docking results show that this method can be conveniently used for the efficient molecular design of drugs.     

A soft docking algorithm for predicting the structure of antibody-antigen complexes

An efficient soft docking algorithm is described for predicting the mode of binding between an antibody and its antigen based on the three-dimensional structures of the molecules. The basic tools are the "simplified protein" model and the docking algorithm of Wodak and Janin. The side-chain flexibility of Arg, Lys, Asp, Glu, and Met residues on the protein surface is taken into account. A combined filtering technique is used to select candidate binding modes. After energy minimization, we calculate a scoring function, which includes electrostatic and desolvation energy terms. This procedure was applied to targets 04, 05, and 06 of CAPRI, which are complexes of three different camelid antibody VHH variable domains with pig alpha-amylase. For target 06, two native-like structures with a root-mean-square deviation < 4.0 A relative to the X-ray structure were found within the five top ranking structures. For targets 04 and 05, our procedure produced models where more than half of the antigen residues forming the epitope were correctly predicted, albeit with a wrong VHH domain orientation. Thus, our soft docking algorithm is a promising tool for predicting antibody-antigen recognition.     

A protein-protein docking algorithm dependent on the type of complexes

An efficient 'soft docking' algorithm is described to assist the prediction of protein-protein association using three-dimensional structures of molecules. The basic tools are the 'simplified protein' model and the docking algorithm of Wodak and Janin. The side chain flexibility of Arg, Lys, Asp, Glu and Met residues at the protein surface is taken into account. The complex type-dependent filtering technique on the basis of the geometric matching, hydrophobicity and electrostatic complementarity is used to select candidate binding modes. Subsequently, we calculate a scoring function which includes electrostatic and desolvation energy terms. In the 44 complexes tested including enzyme-inhibitor, antibody-antigen and other complexes, native-like structures were all found, of which 30 were ranked in the top 20. Thus, our soft docking algorithm has the potential to predict protein-protein recognition.     

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.     

Efficient molecular docking of NMR structures: application to HIV-1 protease

Docking ligands into an ensemble of NMR conformers is essential to structure-based drug discovery if only NMR structures are available for the target. However, sequentially docking ligands into each NMR conformer through standard single-receptor-structure docking, referred to as sequential docking, is computationally expensive for large-scale database screening because of the large number of NMR conformers involved. Recently, we developed an efficient ensemble docking algorithm to consider protein structural variations in ligand binding. The algorithm simultaneously docks ligands into an ensemble of protein structures and achieves comparable performance to sequential docking without significant increase in computational time over single-structure docking. Here, we applied this algorithm to docking with NMR structures. The HIV-1 protease was used for validation in terms of docking accuracy and virtual screening. Ensemble docking of the NMR structures identified 91% of the known inhibitors under the criterion of RMSD < 2.0 A for the best-scored conformation, higher than the average success rate of single docking of individual crystal structures (66%). In the virtual screening test, on average, ensemble docking of the NMR structures obtained higher enrichments than single-structure docking of the crystal structures. In contrast, docking of either the NMR minimized average structure or a single NMR conformer performed less satisfactorily on both binding mode prediction and virtual screening, indicating that a single NMR structure may not be suitable for docking calculations. The success of ensemble docking of the NMR structures suggests an efficient alternative method for standard single docking of crystal structures and for considering protein flexibility.     

Response surface methodology in docking study of small molecule BACE-1 inhibitors

Computational evaluation of ligand-receptor binding via docking strategy is a well established approach in structure-based drug design. This technique has been applied frequently in developing molecules of biological interest. However, any procedure would require an optimization set up to be more efficient, economic and time-saving. Advantages of modern statistical optimization methods over conventional one-factor-at-a-time studies have been well revealed. The optimization by experimental design provides a combination of factor levels simultaneously satisfying the requirements considered for each of the responses and factors. In this study, response surface method was applied to optimize the prominent factors (number of genetic algorithm runs, population size, maximum number of evaluations, torsion degrees for ligand and number of rotatable bonds in ligand) in AutoDock4.2-based binding study of small molecule β-secretase inhibitors as anti-alzheimer agents. Results revealed that a number of rotatable bonds in ligand and maximum number of docking evaluations were determinant variables affecting docking outputs. The interference between torsion degrees for ligand and number of genetic algorithm runs for docking procedure was found to be the significant interaction term in our model. Optimized docking outputs exhibited a high correlation with experimental fluorescence resonance energy transfer-based IC(50)s for β-secretase inhibitors (R(2)?=?0.9133).     

Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.     

Automatic identification and representation of protein binding sites for molecular docking.

Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.     

A robust and efficient automated docking algorithm for molecular recognition.

A completely automated method is described for determining the most likely mode of binding of two (macro)molecules from the knowledge of their three-dimensional structures alone. The method is based on well-known graph theoretical techniques and has been used successfully to determine and rationalize the binding of a number of known macromolecular complexes. In this article we present results for a special case of the general molecular recognition problem--given the information concerning the particular atoms involved in the binding for one of the molecules, the algorithm can correctly identify the corresponding (contacting) atoms of the other molecule. The approach used can be easily extended to the general molecular recognition problem and requires the extraction of maximal common subgraphs. In these studies the docking of the macromolecules was achieved without the aid of computer graphics or other visual aids. The algorithm has been used to determine the correct mode of binding of a protein antigen to an antibody in approximately 100 min on a DEC micro VAX 3600.