蛋白质-蛋白质分子对接方法研究进展

蛋白质分子间相互作用与识别是21世纪生命科学研究的前沿和热点.分子对接方法是研究这一课题有效的计算机模拟手段.通常,蛋白质-蛋白质分子对接包括四个阶段:搜索受体与配体分子间的结合模式,过滤对接结构以排除不合理的结合模式,优化结构,用精细的打分函数评价、排序对接模式并挑选近天然构象.结合国内外研究蛋白质-蛋白质分子对接方法进展和本研究小组的工作,对以上四个环节做了详细的综述.另外,还分析了目前存在的主要问题,并提出对未来工作的展望.     

引入显式水分子对接提高蛋白质配体复合物结构的预测精度

在蛋白质复合物界面一般都会存在着一定量的水分子,这些水分子通过空间占据和氢键方式影响蛋白质与配体的位置关系。应用现有的计算机方法研究蛋白质-配体对接时,一般不会显式地考虑水分子的作用。本文显式地将水分子引入蛋白质-配体对接过程,考虑水分子空间占据和氢键能量对复合物对接结构的影响,提出了一种包含水分子的蛋白质-配体对接算法。实验结果表明引入水分子使蛋白质-配体对接质量有明显提高。     

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．     

蛋白质-蛋白质分子对接方法中分子柔性处理与近天然结构筛选的研究

蛋白质-蛋白质分子对接方法是研究蛋白质分子间相互作用与识别的重要理论方法。该方法主要涉及复合物结合模式的构象搜索和近天然结构的筛选两个问题。在构象搜索中,分子柔性的处理是重点也是难点,围绕这一问题,近年来提出了许多新的方法。针对近天然结构的筛选问题,目前主要采用三种解决策略:结合位点信息的利用、相似结构的聚类和打分函数对结构的评价。本文围绕以上问题,就国内外研究进展和本研究小组的工作作详细的综述,并对进一步的研究方向进行了展望。     

蛋白质-配体柔性对接综述

蛋白质与配体的相互作用在药物发现与筛选、功能食品开发等方面,都具有重要的科学意义。研究蛋白质-配体对接的目的在于通过已知配体和目标蛋白质的三维结构,运用计算手段来预测和评估蛋白质-配体复合物的三维构象,从而更好地理解蛋白质-配体之间的相互作用。随着已解析蛋白质单体结构的数量和计算能力的不断增加,蛋白质-配体对接也越来越现实并可靠。作者对蛋白质-配体柔性对接过程中所涉及的蛋白质的柔性、配体的准备、构象空间的采样方法、打分函数及其后期处理等方面进行了综述。     

蛋白质-蛋白质、蛋白质-核酸相互作用研究方法及在人肠道病毒A71型研究中的应用

研究蛋白质-蛋白质相互作用的方法主要有酵母双杂交、噬菌体展示、免疫共沉淀、谷光苷肽巯基转移酶沉淀、细胞内共定位、亲和印迹、病毒铺覆蛋白结合技术、表面等离子共振、荧光共振能量转移等技术。检测蛋白质-核酸相互作用的方法主要包括酵母单杂交、染色质免疫沉淀、电泳迁移率实验、DNA-蛋白质印迹、报告基因、免疫共沉淀、谷光苷肽巯基转移酶沉淀、噬菌体展示等技术。在我们实验室对人肠道病毒A71型(EV-A71)的研究中经常会用到这些方法,但在该研究领域尚未有中文综述发表,因此本文对EV-A71研究中这些方法的应用进行了综述,该综述对蛋白质-蛋白质、蛋白质-核酸在其他病毒研究中的应用同样具有重要启示。     

蛋白质-蛋白质、蛋白质-核酸相互作用研究方法及在人肠道病毒A71型研究中的应用北大核心CSCD

研究蛋白质-蛋白质相互作用的方法主要有酵母双杂交、噬菌体展示、免疫共沉淀、谷光苷肽巯基转移酶沉淀、细胞内共定位、亲和印迹、病毒铺覆蛋白结合技术、表面等离子共振、荧光共振能量转移等技术。检测蛋白质-核酸相互作用的方法主要包括酵母单杂交、染色质免疫沉淀、电泳迁移率实验、DNA-蛋白质印迹、报告基因、免疫共沉淀、谷光苷肽巯基转移酶沉淀、噬菌体展示等技术。在我们实验室对人肠道病毒A71型(EV-A71)的研究中经常会用到这些方法,但在该研究领域尚未有中文综述发表,因此本文对EV-A71研究中这些方法的应用进行了综述,该综述对蛋白质-蛋白质、蛋白质-核酸在其他病毒研究中的应用同样具有重要启示。     

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。     

侧链能量在蛋白质对接优化中的应用

一般的蛋白质对接程序能够提供大量的待选构象,但其中仅含有少量的正确构象。现在对接的主要工作在于如何从这些大量构象中挑出正确构象。我们先前的研究工作证明蛋白质界面比非界面表面具有更高的能量。在这里,我们使用由chen等人提出的一个用于检验、设计对接程序的蛋白质复合物标准库中的非抗原-抗体复合物,将侧链能量运用到对接中,并比较了侧链能量和残基配对倾向性、残基组成倾向性、残基保守性在对接中的表现。单独使用这四项的正确构象的平均百排分位排序分别为：38．6±19．6、26．3±20．8、22．7±16．6和37．8±26．1,但是对于个别蛋白,侧链能量的表现要优于其它的三个参数。我们将四个参数综合起来考虑,发展了一个新的打分函数,平均百排分位排序为22．2±7．8,并且提高了筛选效率。     

基于网络药理学探讨丹参-丹皮配伍抗脑缺血损伤的作用机制

本文旨在通过网络药理学和分子对接方法探讨丹参-丹皮活性成分治疗脑卒中的潜在分子机制。首先基于中药系统药理学分析平台筛选丹参、丹皮的活性成分及其作用靶点,利用CTD、TTD和GeneCards数据库收集脑卒中相关靶点。然后将药物和疾病靶点取交集,借助STRING数据库获取靶点间相互作用关系,利用R语言的ClusterProfiler包对其进行生物功能和通路富集分析。最后,通过Cytoscape软件构建蛋白质-蛋白质相互作用和成分-靶点-通路网络图,并利用AutoDock Vina软件对网络中的关键靶点及对应成分进行分子对接验证。结果显示丹参-丹皮成分作用于脑卒中的靶点67个,GO分析显示其主要参与脂多糖应答,细菌来源的分子反应,氧化应激等生物学过程。KEGG通路富集共得到149条通路(P<0.05),主要涉及AGE-RAGE信号通路、IL-17信号通路、TNF信号通路等。分子对接结果显示,筛选的主要活性成分与其对应靶蛋白均具有较好的结合活性。综上,本研究通过网络药理学预测了丹参-丹皮治疗脑卒中可能的药效物质基础及其作用机制,为进一步挖掘其药效成分和临床扩大使用范围提供科学依据。     

Domain-Based Protein Docking with Extremely Large Conformational Changes

Proteins are key components in many processes in living cells, and physical interactions with other proteins and nucleic acids often form key parts of their functions. In many cases, large flexibility of proteins as they interact is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D structures of such protein complexes. When such structures are not yet experimentally determined, protein docking has long been present to computationally generate useful structure models. However, protein docking has long had the limitation that the consideration of flexibility is usually limited to very small movements or very small structures. Methods have been developed which handle minor flexibility via normal mode or other structure sampling, but new methods are required to model ordered proteins which undergo large-scale conformational changes to elucidate their function at the molecular level. Here, we present Flex-LZerD, a framework for docking such complexes. Via partial assembly multidomain docking and an iterative normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein–protein and protein-nucleic acid complexes.     

Modeling protein–nucleic acid complexes with extremely large conformational changes using Flex-LZerD

Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein–nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.     

FLIPDock: docking flexible ligands into flexible receptors

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.     

Applying conformational selection theory to improve crossdocking efficiency in 3‐phosphoinositide dependent protein kinase‐1

The emerging picture of biomolecular recognition is that of conformational selection followed by induced‐fit. Conformational selection theory states that binding partners exist in various conformations in solution, with binding involving a “selection” between complementary conformers. In this study, we devise a docking protocol that mimics conformational selection in protein–ligand binding and demonstrate that it significantly enhances crossdocking accuracy over Glide's flexible docking protocol, which is widely used in the pharmaceutical industry. Our protocol uses a pregenerated conformational ensemble to simulate ligand flexibility. The ensemble was generated by thorough conformational sampling coupled with conformer minimization. The generated conformers were then rigidly docked in the active site of the protein along with a postdocking minimization step that allows limited induced fit effects to be modeled for the ligand. We illustrate the improved performance of our protocol through crossdocking of 31 ligands to cocomplexed proteins of the kinase 3‐phosphoinositide dependent protein kinase‐1 extracted from the crystal structures 1H1W (ATP bound), 1OKY (staurosporine bound) and 3QD0 (bound to a potent inhibitor). Consistent with conformational selection theory, the performance of our protocol was the best for crossdocking to the cognate protein bound to the natural ligand, ATP. Proteins 2014; 82:436–451. © 2013 Wiley Periodicals, Inc.     

Effective handling of induced-fit motion in flexible docking

For structure-based drug design, where various ligand structures need to be docked to a target protein structure, a docking method that can handle conformational flexibility of not only the ligand, but also the protein, is indispensable. We have developed a simple and effective approach for dealing with the local induced-fit motion of the target protein, and implemented it in our docking tool, ADAM. Our approach efficiently combines the following two strategies: a vdW-offset grid in which the protein cavity is enlarged uniformly, and structure optimization allowing the motion of ligand and protein atoms. To examine the effectiveness of our approach, we performed docking validation studies, including redocking in 18 test cases and foreign-docking, in which various ligands from foreign crystal structures of complexes are docked into a target protein structure, in 22 cases (on five target proteins). With the original ADAM, the correct docking modes (RMSD < 2.0 A) were not present among the top 20 models in one case of redocking and four cases of foreign-docking. When the handling of induced-fit motion was implemented, the correct solutions were acquired in all 40 test cases. In foreign-docking on thymidine kinase, the correct docking modes were obtained as the top-ranked solutions for all 10 test ligands by our combinatorial approach, and this appears to be the best result ever reported with any docking tool. The results of docking validation have thus confirmed the effectiveness of our approach, which can provide reliable docking models even in the case of foreign-docking, where conformational change of the target protein cannot be ignored. We expect that this approach will contribute substantially to actual drug design, including virtual screening.     

Protein-protein docking with a reduced protein model accounting for side-chain flexibility

A protein-protein docking approach has been developed based on a reduced protein representation with up to three pseudo atoms per amino acid residue. Docking is performed by energy minimization in rotational and translational degrees of freedom. The reduced protein representation allows an efficient search for docking minima on the protein surfaces within. During docking, an effective energy function between pseudo atoms has been used based on amino acid size and physico-chemical character. Energy minimization of protein test complexes in the reduced representation results in geometries close to experiment with backbone root mean square deviations (RMSDs) of approximately 1 to 3 A for the mobile protein partner from the experimental geometry. For most test cases, the energy-minimized experimental structure scores among the top five energy minima in systematic docking studies when using both partners in their bound conformations. To account for side-chain conformational changes in case of using unbound protein conformations, a multicopy approach has been used to select the most favorable side-chain conformation during the docking process. The multicopy approach significantly improves the docking performance, using unbound (apo) binding partners without a significant increase in computer time. For most docking test systems using unbound partners, and without accounting for any information about the known binding geometry, a solution within approximately 2 to 3.5 A RMSD of the full mobile partner from the experimental geometry was found among the 40 top-scoring complexes. The approach could be extended to include protein loop flexibility, and might also be useful for docking of modeled protein structures.     

The relationship between the flexibility of proteins and their conformational states on forming protein-protein complexes with an application to protein-protein docking

We investigate the extent to which the conformational fluctuations of proteins in solution reflect the conformational changes that they undergo when they form binary protein-protein complexes. To do this, we study a set of 41 proteins that form such complexes and whose three-dimensional structures are known, both bound in the complex and unbound. We carry out molecular dynamics simulations of each protein, starting from the unbound structure, and analyze the resulting conformational fluctuations in trajectories of 5 ns in length, comparing with the structure in the complex. It is found that fluctuations take some parts of the molecules into regions of conformational space close to the bound state (or give information about it), but at no point in the simulation does each protein as whole sample the complete bound state. Subsequent use of conformations from a clustered MD ensemble in rigid-body docking is nevertheless partially successful when compared to docking the unbound conformations, as long as the unbound conformations are themselves included with the MD conformations and the whole globally rescored. For one key example where sub-domain motion is present, a ribonuclease inhibitor, principal components analysis of the MD was applied and was also able to produce conformations for docking that gave enhanced results compared to the unbound. The most significant finding is that core interface residues show a tendency to be less mobile (by size of fluctuation or entropy) than the rest of the surface even when the other binding partner is absent, and conversely the peripheral interface residues are more mobile. This surprising result, consistent across up to 40 of the 41 proteins, suggests different roles for these regions in protein recognition and binding, and suggests ways that docking algorithms could be improved by treating these regions differently in the docking process.     

Characterizing the noncovalent binding behavior of tartrazine to lysozyme: A combined spectroscopic and computational analysis

Tartrazine is a stable water‐soluble azo dye widely used as a food additive, which could pose potential threats to humans and the environment. In this paper, we evaluated the response mechanism between tartrazine and lysozyme under simulated conditions by means of biophysical methods, including multiple spectroscopic techniques, isothermal titration calorimetry (ITC), and molecular docking studies. From the multispectroscopic analysis, we found that tartrazine could effectively quench the intrinsic fluorescence of lysozyme to form a complex and lead to the conformational and microenvironmental changes of the enzyme. The ITC measurements suggested that the electrostatic forces played a major role in the binding of tartrazine to lysozyme with two binding sites. Finally, the molecular docking indicated that tartrazine had specific interactions with the residues of Trp108. The study provides an important insight within the binding mechanism of tartrazine to lysozyme in vitro.     

Principles of docking: An overview of search algorithms and a guide to scoring functions

The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.