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- 核酸对接方法研究进展

分子对接技术作为预测蛋白质-核酸复合物结构的有效方法,为研究在生物学过程中蛋白质-核酸的相互作用提供了重要的工具。本文首先分析了当前蛋白质-核酸对接研究中的主要困难,例如构象变化和核糖磷酸骨架的带电性问题。然后从构象搜索、打分函数、柔性策略三个方面比较和总结了蛋白质-核酸对接中主要的计算方法。最后回顾了蛋白质-核酸对接计算模型的应用,并对未来的工作进行了展望。     

蛋白质晶体中的分子堆砌与晶体生长

根据蛋白质晶体结构研究的结果,阐述了蛋白质晶体中分子堆砌模式和分子间的相互作用;并讨论了某些因素对分子堆砌及结晶的影响。     

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。     

蛋白质氨基酸网络研究进展

氨基酸网络是运用复杂网络工具对蛋白质结构-功能关系研究的新方法。本文回顾了氨基酸网络中常用网络参量的计算方法,如：度分布,聚集系数,平均最短路径等。结合本研究小组的工作,介绍了常用的网络构建和分析方法,并总结了氨基酸网络在蛋白质折叠以及蛋白质分子对接问题中的应用。最后,分析了氨基酸网络研究目前存在的主要问题,并对未来的工作进行了展望。     

蛋白质氨基酸网络研究进展

氨基酸网络是运用复杂网络工具对蛋白质结构-功能关系研究的新方法。本文回顾了氨基酸网络中常用网络参量的计算方法,如:度分布,聚集系数,平均最短路径等。结合本研究小组的工作,介绍了常用的网络构建和分析方法,并总结了氨基酸网络在蛋白质折叠以及蛋白质分子对接问题中的应用。最后,分析了氨基酸网络研究目前存在的主要问题,并对未来的工作进行了展望。     

白藜芦醇靶点蛋白质的研究

为探讨白藜芦醇在肿瘤细胞中的结合靶点蛋白质.采用亲和甄别磁珠法生物淘洗白藜芦醇的靶点蛋白质.在构建并优化了白藜芦醇结构模型的基础上,通过分子动力学优化分析白藜芦醇与其靶点蛋白质的结构模型,并且使用分子对接分析验证两者的结合作用.结果表明,亲和甄X别磁珠法直接筛选到的能与白藜芦醇的特异性结合的蛋白质是Myosin蛋白质和Actin蛋白质,并且成功构建得到了合理的白藜芦醇分子与Actin蛋白质的复合物的三维结构.通过分析白藜芦醇分子与Actin蛋白活性氨基酸残基结合模式发现,残基Val30,Phe31,Pro32,Thr203,Ala204,Glu205,Pro243,Asp244,等对两者的结合都有重要贡献.白藜芦醇是通过作用于肿瘤细胞的骨架结构蛋白来干扰细胞的有丝分裂过程,从而导致肿瘤细胞的体外增殖受到抑制.     

鹅膏毒肽与RNA聚合酶II相互作用的分子机制研究

研究鹅膏毒肽与RNA聚合酶Ⅱ相互作用的分子机制,利用分子对接方法获得了9种鹅膏毒肽与RNA聚合酶Ⅱ相互作用的结合模式、结合位点、对接能和抑制常数等信息,并对鹅膏毒肽的毒性与结构间的构效关系进行了考察。结果表明:利用分子对接方法获得的鹅膏毒肽与RNA聚合酶Ⅱ相互作用的信息与实验结果相一致;不同R2取代基引起毒素与聚合酶II结合能力强弱不同,从而导致鹅膏毒肽分子间的毒性差异。结果证实了运用分子对接方法探索多肽分子与蛋白质相互作用的可行性,为在分子水平上研究多肽与蛋白质的相互作用开拓了新的思路。     

蛋白质分子表面高质量网格构建技术的研究进展

分子表面即分子边界,在一定程度上蕴含了分子的生物化学属性信息,对分子表面进行分析将有助于理解分子对接、识别和相互作用等问题。由于蛋白质分子表面的构造相对复杂,尤其是分子表面的网格化,因此寻求高效的算法构建高质量的蛋白质分子表面网格对生成光滑的分子表面、分子可视化及分子模拟都有着重要的意义。本文主要根据现有定义的蛋白质分子表面,针对近年来几种高质量分子表面网格构建的新技术进行了阐述,同时介绍了几款蛋白质分子表面可视化软件,并对它们的性能进行了简单的分析。     

Applications of the ArgusLab4/AScore protocol in the structure-based binding affinity prediction of various inhibitors of group-1 and group-2 influenza virus neuraminidases (NAs)

Using the crystal structures of inhibitors bound to either group-2 or group-1 neuraminidases (NAs), AScore/ShapeDock (GaDock) scoring was shown to identify the binding modes in agreement with the experiment for all inhibitors docked in their own NA/inhibitor crystal structures. To investigate the effect of small changes in protein structure on predicted binding modes, in a set of 132 docking experiments (11 inhibitors docked in 12 group-2 NA structures), AScore/ShapeDock (GaDock) identified the correct binding modes of 116 complexes. In a total of 88 docking experiments (8 inhibitors docked in 11 group-1 NA structures), AScore/ShapeDock predicted 80 binding modes correctly. Flexible AScore/ShapeDock docking, as quite reproducible, is suggested to be convenient for designing novel H5N1 inhibitors.     

Protein-protein docking by simulating the process of association subject to biochemical constraints

We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures.     

Examination of shape complementarity in docking of unbound proteins.

Here we carry out an examination of shape complementarity as a criterion in protein-protein docking and binding. Specifically, we examine the quality of shape complementarity as a critical determinant not only in the docking of 26 protein-protein "bound" complexed cases, but in particular, of 19 "unbound" protein-protein cases, where the structures have been determined separately. In all cases, entire molecular surfaces are utilized in the docking, with no consideration of the location of the active site, or of particular residues/atoms in either the receptor or the ligand that participate in the binding. To evaluate the goodness of the strictly geometry-based shape complementarity in the docking process as compared to the main favorable and unfavorable energy components, we study systematically a potential correlation between each of these components and the root mean square deviation (RMSD) of the "unbound" protein-protein cases. Specifically, we examine the non-polar buried surface area, polar buried surface area, buried surface area relating to groups bearing unsatisfied buried charges, and the number of hydrogen bonds in all docked protein-protein interfaces. For these cases, where the two proteins have been crystallized separately, and where entire molecular surfaces are considered without a predefinition of the binding site, no correlation is observed. None of these parameters appears to consistently improve on shape complementarity in the docking of unbound molecules. These findings argue that simplicity in the docking process, utilizing geometrical shape criteria may capture many of the essential features in protein-protein docking. In particular, they further reinforce the long held notion of the importance of molecular surface shape complementarity in the binding, and hence in docking. This is particularly interesting in light of the fact that the structures of the docked pairs have been determined separately, allowing side chains on the surface of the proteins to move relatively freely. This study has been enabled by our efficient, computer vision-based docking algorithms. The fast CPU matching times, on the order of minutes on a PC, allow such large-scale docking experiments of large molecules, which may not be feasible by other techniques. Proteins 1999;36:307-317.     

A holistic approach to protein docking

Docking of unbound protein structures into a complex has gained significant progress in recent years, but nonetheless still poses a great challenge. We have pursued a holistic approach to docking which brings together effective methods at different stages. First, protein-protein interaction sites are predicted or obtained from experimental studies in the literature. Interface prediction/experimental data are then used to guide the generation of docked poses or to rank docked poses generated from an unbiased search. Finally, selected models are refined by lengthy molecular dynamics (MD) simulations in explicit water. For CAPRI target T27, we used information on interaction sites as input to drive docking and as a filter to rank docked poses. Lead candidates were then clustered according to RMSD among them. From the clustering, 10 models were selected and subject to refinement by MD simulations. Our Model 7 is rated number one among all submissions according to L_rmsd. Six of our other submissions are rated acceptable. As scorer, eight of our submissions are rated acceptable.     

Protein-protein association kinetics and protein docking

Rigid body protein docking methods frequently yield false positive structures that have good surface complementarity, but are far from the native complex. The main reason for this is the uncertainty of the protein structures to be docked, including the positions of solvent-exposed sidechains. Substantial efforts have been devoted to finding near-native structures by rescoring the docked conformations and employing various filters. An alternative approach emulates the process of protein-protein association, that is, first finding the region in which binding is likely to occur and then refining the complex while allowing for flexibility.     

PIPER: an FFT-based protein docking program with pairwise potentials

The Fast Fourier Transform (FFT) correlation approach to protein-protein docking can evaluate the energies of billions of docked conformations on a grid if the energy is described in the form of a correlation function. Here, this restriction is removed, and the approach is efficiently used with pairwise interaction potentials that substantially improve the docking results. The basic idea is approximating the interaction matrix by its eigenvectors corresponding to the few dominant eigenvalues, resulting in an energy expression written as the sum of a few correlation functions, and solving the problem by repeated FFT calculations. In addition to describing how the method is implemented, we present a novel class of structure-based pairwise intermolecular potentials. The DARS (Decoys As the Reference State) potentials are extracted from structures of protein-protein complexes and use large sets of docked conformations as decoys to derive atom pair distributions in the reference state. The current version of the DARS potential works well for enzyme-inhibitor complexes. With the new FFT-based program, DARS provides much better docking results than the earlier approaches, in many cases generating 50% more near-native docked conformations. Although the potential is far from optimal for antibody-antigen pairs, the results are still slightly better than those given by an earlier FFT method. The docking program PIPER is freely available for noncommercial applications.     

Ligand-induced conformational changes: improved predictions of ligand binding conformations and affinities

A computational docking strategy using multiple conformations of the target protein is discussed and evaluated. A series of low molecular weight, competitive, nonpeptide protein tyrosine phosphatase inhibitors are considered for which the x-ray crystallographic structures in complex with protein tyrosine phosphatase 1B (PTP1B) are known. To obtain a quantitative measure of the impact of conformational changes induced by the inhibitors, these were docked to the active site region of various structures of PTP1B using the docking program FlexX. Firstly, the inhibitors were docked to a PTP1B crystal structure cocrystallized with a hexapeptide. The estimated binding energies for various docking modes as well as the RMS differences between the docked compounds and the crystallographic structure were calculated. In this scenario the estimated binding energies were not predictive inasmuch as docking modes with low estimated binding energies corresponded to relatively large RMS differences when aligned with the corresponding crystal structure. Secondly, the inhibitors were docked to their parent protein structures in which they were cocrystallized. In this case, there was a good correlation between low predicted binding energy and a correct docking mode. Thirdly, to improve the predictability of the docking procedure in the general case, where only a single target protein structure is known, we evaluate an approach which takes possible protein side-chain conformational changes into account. Here, side chains exposed to the active site were considered in their allowed rotamer conformations and protein models containing all possible combinations of side-chain rotamers were generated. To evaluate which of these modeled active sites is the most likely binding site conformation for a certain inhibitor, the inhibitors were docked against all active site models. The receptor rotamer model corresponding to the lowest estimated binding energy is taken as the top candidate. Using this protocol, correct inhibitor binding modes could successfully be discriminated from proposed incorrect binding modes. Moreover, the ranking of the estimated ligand binding energies was in good agreement with experimentally observed binding affinities.     

Principles of flexible protein-protein docking

Treating flexibility in molecular docking is a major challenge in cell biology research. Here we describe the background and the principles of existing flexible protein-protein docking methods, focusing on the algorithms and their rational. We describe how protein flexibility is treated in different stages of the docking process: in the preprocessing stage, rigid and flexible parts are identified and their possible conformations are modeled. This preprocessing provides information for the subsequent docking and refinement stages. In the docking stage, an ensemble of pre-generated conformations or the identified rigid domains may be docked separately. In the refinement stage, small-scale movements of the backbone and side-chains are modeled and the binding orientation is improved by rigid-body adjustments. For clarity of presentation, we divide the different methods into categories. This should allow the reader to focus on the most suitable method for a particular docking problem.     

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.     

Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2

This article describes and reviews our efforts using Hex 3.1 to predict the docking modes of the seven target protein-protein complexes presented in the CAPRI (Critical Assessment of Predicted Interactions) blind docking trial. For each target, the structure of at least one of the docking partners was given in its unbound form, and several of the targets involved large multimeric structures (e.g., Lactobacillus HPr kinase, hemagglutinin, bovine rotavirus VP6). Here we describe several enhancements to our original spherical polar Fourier docking correlation algorithm. For example, a novel surface sphere smothering algorithm is introduced to generate multiple local coordinate systems around the surface of a large receptor molecule, which may be used to define a small number of initial ligand-docking orientations distributed over the receptor surface. High-resolution spherical polar docking correlations are performed over the resulting receptor surface patches, and candidate docking solutions are refined by using a novel soft molecular mechanics energy minimization procedure. Overall, this approach identified two good solutions at rank 5 or less for two of the seven CAPRI complexes. Subsequent analysis of our results shows that Hex 3.1 is able to place good solutions within a list of 相似文献