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.     

应用死端排除法考虑分子对接中的侧链柔性

假设分子对接面的紧密堆积类似于蛋白质内部的紧密堆积,因此用于蛋白质内部的侧链构象预测方法,如死端排除法,可应用于分子对接面内的侧链构象预测。应用９个晶体结构对这一假设进行检验。结果表明假设基本正确。对２个蛋白酶和抑制剂的应用比较成功。９个配体中的７个有正确的均方根差的趋势。还发现受体结构的柔性较小,说明由于对接面的紧密堆积产生的侧链构象变化很小。根据这些结果,提出一个新的分子对接流程图,即在刚体对     

应用死端排除法考虑分子对接中的侧链柔性

假设分子对接面的紧密堆积类似于蛋白质内部的紧密堆积,因此用于蛋白质内部的侧链构象预测方法,如死端排除法,可应用于分子对接面内的侧链构象预测。应用９个晶体结构对这一假设进行检验,结果表明假设基本正确。对２个蛋白酶与抑制剂的应用比较成功。９个配体中的７个有正确的均方根差的趋势。还发现受体结构的柔性较小,说明由于对接面的紧密堆积产生的侧链构象变化很小。根据这些结果,提出一个新的分子对接流程图,即在刚体对接后加入对接面中氨基酸残基的侧链构象预测。对一个蛋白酶与抑制剂的复合结构的应用表明对接中的正确解的信号与噪音比相对错误解增加了。     

FitDockApp：基于模板的分子对接PyMOL插件图形化计算软件

目的 分子对接在预测分子之间的结合模式和亲和力方面起着至关重要的作用,是计算结构生物学和计算机辅助药物设计研究的重要方法。本研究团队近期开发了一款基于模板的新型对接方法FitDock,当存在近似的蛋白质配体模板时,它在准确性和速度方面都超过了业界常用的分子对接方法。为了增强FitDock方法的可用性,使其在分子模拟领域得到更广泛的应用,很有必要发展图像化的软件工具。方法 基于Python图像化编程,本文开发了FitDockApp,这是分子可视化软件PyMOL的插件软件。结果 FitDockApp能够通过操作窗口界面,实现基于模板的分子对接和配体结构比对,实时显示预测三维结构,并提供将对接文件上传到实验室服务器获取最优模板的便利。此外,FitDockApp还具备批量对接功能。结论 FitDockApp通过用户友好的界面简化了对接过程,并提供丰富的功能,帮助研究人员获得精确的对接结果。FitDockApp是一款免费软件,兼容Windows和Linux系统,可在http://cao.labshare.cn/fitdock/下载。     

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。     

面向虚拟筛选的GPU加速的分子对接方法

虚拟筛选是在计算机上对化合物分子进行模拟预筛选,找出容易和药物靶标结合的小分子(配体),从而降低实际实验测试次数,提高药物先导化合物的发现效率。常用的分子对接软件可以用于基于结构的虚拟筛选,寻找配体与靶标的最佳的作用模式和结合构象,并通过打分函数来筛选出潜在的配体。现有的对接软件如AutoDock Vina等在分子对接过程中需要耗费大量时间和计算资源,特别是面对大规模分子对接时,过长的筛选时间不能满足应用需求,因此,本文在最高效的QVina2对接软件基础上,提出一种基于GPU的QVina 2并行化方法QVina2-GPU,利用GPU硬件高度并行体系加速分子对接。具体包括增加初始化分子构象数量,以扩展蒙特卡罗的迭代局部搜索中线程的并行规模,增加蒙特卡罗的迭代搜索的广度以减少每次蒙特卡罗迭代搜索深度,并利用Wolfe-Powell准则改进局部搜索算法,提高了对接精度,进一步减少蒙特卡罗迭代搜索深度,最后,在NVIDIA Geforce RTX 3090平台上在公开的配体数据库上验证了QVina2-GPU的性能,实验表明在保证分子对接精度的基础上,我们提出的QVina2-GPU对Qvina2的平均加速比达到5.18倍,最大加速比达到12.28倍。     

分子对接的随机聚点搜索算法

随机聚点搜索算法是一种普遍的全局极小化方法,在目标函数自变量数目不很大时,计算效率较高。将该算法应用于分子对接,首先要通过模型分子对接,反复调整算法各控制参数使效率最高。对于HIV－1蛋白酶与苯甲醚配体的刚性对接,算法成功的找到了相互作用能量全局极小,与晶体结构的均方根偏差(RMSD)仅0.2?。这表明,该算法可高效率找到分子对接的能量最适构型。     

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．     

以EGFR为靶点的肿瘤分子靶向药物研究进展

随着分子生物学研究的进展,分子靶向治疗已成为除手术、放疗、化疗之外的第4种治疗方法,越来越多的用于临床治疗恶性肿瘤。分子靶向药物进入体内能够特异地选择致癌位点,杀伤肿瘤细胞,而不会波及周围正常的组织细胞,因此分子靶向治疗又被称为"生物导弹"。与传统化疗药物相比,分子靶向药物具有特异性强、疗效明显、副作用少等优点。按照分子靶向药物的性质主要归为两大类:一类是单克隆抗体,如西妥昔单抗等;另一类是单靶点或多靶点的小分子抑制剂,如吉非替尼等。表皮生长因子受体(EGFR)对肿瘤的生长、发展以及肿瘤干细胞的维持都有着非常重要的作用,并且在多种实体瘤中存在过表达或异常表达,因此在肿瘤治疗中,EGFR成为一个非常重要的用药靶点。现主要对目前国内已上市的针对EGFR的分子靶向药物最新的临床研究进展作一简要综述。     

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。     

ConsDock: A new program for the consensus analysis of protein-ligand interactions

Protein-based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein-bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein-ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top-ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 A RMSD of the X-ray structure. It can be applied as a postprocessing filter to either single- or multiple-docking programs to prioritize three-dimensional guided lead optimization from the most likely docking solution.     

Evaluation of AlphaFold2 structures as docking targets

AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone and global root-mean-square deviation (RMSD) when compared with experimental structures. However, it is unclear if the structures predicted by AlphaFold2 will be valuable targets of docking. To address this question, we redocked ligands in the PDBbind datasets against the experimental co-crystallized receptor structures and against the AlphaFold2 structures using AutoDock-GPU. We find that the quality measure provided during structure prediction is not a good predictor of docking performance, despite accurately reflecting the quality of the alpha carbon alignment with experimental structures. Removing low-confidence regions of the predicted structure and making side chains flexible improves the docking outcomes. Overall, despite high-quality prediction of backbone conformation, fine structural details limit the naive application of AlphaFold2 models as docking targets.     

Curcumin binds in silico to anti-cancer drug target enzyme MMP-3 (human stromelysin-1) with affinity comparable to two known inhibitors of the enzyme

In silico interaction of curcumin with the enzyme MMP-3 (human stromelysin-1) was studied by molecular docking using AutoDock 4.2 as the docking software application. AutoDock 4.2 software serves as a valid and acceptable docking application to study the interactions of small compounds with proteins. Interactions of curcumin with MMP-3 were compared to those of two known inhibitors of the enzyme, PBSA and MPPT. The calculated free energy of binding (ΔG binding) shows that curcumin binds with affinity comparable to or better than the two known inhibitors. Binding interactions of curcumin with active site residues of the enzyme are also predicted. Curcumin appears to bind in an extendended conformation making extensive VDW contacts in the active site of the enzyme. Hydrogen bonding and pi-pi interactions with key active site residues is also observed. Thus, curcumin can be considered as a good lead compound in the development of new inhibitors of MMP-3 which is a potential target of anticancer drugs. The results of these studies can serve as a starting point for further computational and experimental studies.     

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.     

Monte Carlo docking with ubiquitin.

The development of general strategies for the performance of docking simulations is prerequisite to the exploitation of this powerful computational method. Comprehensive strategies can only be derived from docking experiences with a diverse array of biological systems, and we have chosen the ubiquitin/diubiquitin system as a learning tool for this process. Using our multiple-start Monte Carlo docking method, we have reconstructed the known structure of diubiquitin from its two halves as well as from two copies of the uncomplexed monomer. For both of these cases, our relatively simple potential function ranked the correct solution among the lowest energy configurations. In the experiments involving the ubiquitin monomer, various structural modifications were made to compensate for the lack of flexibility and for the lack of a covalent bond in the modeled interaction. Potentially flexible regions could be identified using available biochemical and structural information. A systematic conformational search ruled out the possibility that the required covalent bond could be formed in one family of low-energy configurations, which was distant from the observed dimer configuration. A variety of analyses was performed on the low-energy dockings obtained in the experiment involving structurally modified ubiquitin. Characterization of the size and chemical nature of the interface surfaces was a powerful adjunct to our potential function, enabling us to distinguish more accurately between correct and incorrect dockings. Calculations with the structure of tetraubiquitin indicated that the dimer configuration in this molecule is much less favorable than that observed in the diubiquitin structure, for a simple monomer-monomer pair. Based on the analysis of our results, we draw conclusions regarding some of the approximations involved in our simulations, the use of diverse chemical and biochemical information in experimental design and the analysis of docking results, as well as possible modifications to our docking protocol.     

Flexible ligand docking using conformational ensembles.

Molecular docking algorithms suggest possible structures for molecular complexes. They are used to model biological function and to discover potential ligands. A present challenge for docking algorithms is the treatment of molecular flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given molecule are pre-calculated in the same frame of reference, so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an ensemble, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same molecule. The algorithm was tested using three organic ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand ensemble method found conformations that resembled those determined in X-ray crystal structures (RMS values typically less than 1.5 A). To test the method's usefulness for inhibitor discovery, multi-compound and multi-conformer databases were screened for compounds known to bind to dihydrofolate reductase and compounds known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those observed experimentally. The ligand ensemble method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation.     

Gaussian docking functions

A shape-based Gaussian docking function is constructed which uses Gaussian functions to represent the shapes of individual atoms. A set of 20 trypsin ligand-protein complexes are drawn from the Protein Data Bank (PDB), the ligands are separated from the proteins, and then are docked back into the active sites using numerical optimization of this function. It is found that by employing this docking function, quasi-Newton optimization is capable of moving ligands great distances [on average 7 A root mean square distance (RMSD)] to locate the correctly docked structure. It is also found that a ligand drawn from one PDB file can be docked into a trypsin structure drawn from any of the trypsin PDB files. This implies that this scoring function is not limited to more accurate x-ray structures, as is the case for many of the conventional docking methods, but could be extended to homology models.     

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.