蛋白质-蛋白质分子对接中打分函数研究进展

分子对接是研究分子间相互作用与识别的有效方法.其中,用于近天然构象挑选的打分函数的合理设计对于对接中复合物结构的成功预测至关重要.本文回顾了蛋白质-蛋白质分子对接组合打分函数中一些主要打分项,包括几何互补项、界面接触面积、范德华相互作用能、静电相互作用能以及统计成对偏好势等打分项的计算方法.结合本研究小组的工作,介绍了目前普遍使用的打分方案以及利用与结合位点有关的信息进行结构筛选的几种策略,比较并总结了常用打分函数的特点.最后,分析并指出了当前蛋白质-蛋白质对接打分函数所存在的主要问题,并对未来的工作进行了展望.     

14种结合自由能评价函数的比较

采用LigandFit作为构象采样工具，以230个蛋白质-配体复合物组成的预测集，系统地比较了14种自由能评价函数（Ligscore1、Ligscore2、Plp1、Plp2、Jain、Pmf、Ludi1、Ludi2、Ludi3、D-score、Pmf-score、G-score、Chemscore以及Xscore）对蛋白质和小分子之间的结合模式以及结合自由能的预测能力. Plp1、Plp2、G-score、Pmf和Xscore在预测测试集结合自由能时得到的分数同实验测定的结合自由能的线性相关系数大于50%. 在识别配体分子实验结合构象的能力方面， 选择测试构象与实际构象间的位置均方根偏差rmsd≤0.20 nm作为评价标准，14种评价函数的成功率从46%到77%不等，其中Ligscore1、Ligscore2、Plp1、Plp2以及Xscore的成功率都在70%以上. 将评价函数中的2个或者3个组合得到一组共同评价函数可以进一步提高实验构象的预测能力， 其预测成功率可以达到80%. 实验表明Xscore、Plp1和Plp2在对接和评价方面都得到较好的结果.     

针对蛋白质复合物Other类型的打分函数

在不同类型复合物结合界面的物理化学特征不同的基础上, 针对较难预测的Other 类型复合物设计出特异性打分函数, 用于在对接过程中挑选出有效结构. 该函数由原子接触能(EACE)、范德华和静电相互作用能组成,通过多元线性回归方法获得各项的权重系数. 对来自CAPRI benchmark1 中17 个Other 类复合物例子进行打分测试. 结果表明,组合打分能够刻画出Other 类型复合物单体间相互作用的特征, 反映出复合物形成前后的能量变化, 具备一定的从众多样本中筛选出有效结构的能力. 相对于残基成对势(RP), 该组合打分获得了更高的打分成功率. 对CAPRI 第八轮竞赛中两个结构预测模型进行打分排序, 该组合打分也体现出强于RP 的鉴别有效结合模式潜力.     

A_1腺苷受体的同源模建及其结构验证

采用同源模建的方法构建了A1腺苷受体的三维结构,并与拮抗剂分子DPCPX对接,将得到的复合物结构进行5 ns的分子动力学模拟,以最后2 ns的平均结构和平衡后抽取的11帧构象共12个蛋白结构为研究对象,用包含52个活性分子和1000个诱饵分子的测试库,分别通过DOCK、VINA和GOLD三种对接软件进行评价,最终得出合理的蛋白质模型.根据top10%的富集因子(EF)和ROC曲线下面积(AU-ROC)的计算结果,我们认为GOLD是最适合A1腺苷受体的对接软件,而12个蛋白质结构中F5和Favg的三维结构模型比较合理,可以作为进一步大规模虚拟筛选的模型.     

芬太尼类化合物与阿片μ受体相互作用的分子对接与分子动力学模拟

采用分子对接和分子动力学(MD)模拟方法研究了芬太尼类化合物与阿片μ受体的相互作用机制.先用AutoDock4.0程序将芬太尼类化合物对接到同源模建的阿片μ受体结构中,再用GROMACS程序包在水溶液体系中分别对12个芬太尼激动剂和阿片μ受体蛋白复合物进行了MD模拟研究,优化对接复合物的结构,最后利用MM-PBSA方法,在APBS程序中计算芬太尼类衍生物与阿片μ受体的结合自由能,计算出的受体配合物结合常数(Ki)与其实验值吻合较好,并预测了化合物的活性排序.结果表明,复合物蛋白结构与空载受体蛋白结构有较大差异,特别是胞内区IL2、IL3和跨膜区段TM4骨架构象变化较大,不同的化合物对受体结构影响也有差异,活性较好的化合物会增加蛋白特定区域结构的柔性.芬太尼类化合物可能是通过和受体结合后诱导阿片μ受体构象转变为活性构象,引起一系列的信号传导激活G蛋白,从而引发生理效应.     

毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟, 将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接, 形成非特异性激动和特异性激动的受体-配体复合物. 用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰胆碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns. 将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分, 以top5%富集因子(EF)作为评价依据, 用占诺美林优化后的M1受体模型的EF为8.0, 用乙酰胆碱优化后M1受体模型的EF为6.5, 非复合物的EF为1.5. 说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理, 可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选, 为找到新的选择性M1受体激动剂奠定了基础.     

毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟,将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接,形成非特异性激动和特异性激动的受体-配体复合物.用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰且H碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns.将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分,以top5%富集因子(EF)作为评价依据,用占诺美林优化后的M1受体模型的EF为8.0,用乙酰胆碱优化后M1受体模型的EF为6.5,非复合物的EF为1.5.说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理,可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选,为找到新的选择性M1受体激动剂奠定了基础.     

鳞状上皮细胞癌抗原2与组蛋白G的柔性对接研究

蛋白质与蛋白质间的相互作用在生物体内具有极其重要的意义 .确定蛋白质间的相互作用的机制对生物调节和药物设计都是关键的步骤 .近年来 ,随着计算机和软件技术的发展 ,使建立蛋白质 -蛋白质复合物模型成为可能 ,为研究蛋白质间的相互作用提供了直观和比较准确的参考 .目前的蛋白质 -蛋白质对接软件将对接的两个蛋白质看成刚性 ,并不考虑蛋白质对接时的柔性 ,即没有考虑由于界面氨基酸间的相互作用而导致的界面结构的变化 .本文在直接的蛋白质 -蛋白质刚性对接的基础上 ,应用动力学模拟的方法 ,对刚性对接的结构进行蛋白质界面的构象搜索 ,…     

NX(a 1Δ, X=F、Cl、Br)分子结构与ωe～Re关联模型

基于不少双原子分子的稳定激发态系列中存在已知ωe而未给出Re的现象,本文提出了ωe～Reα=C的理论模型,对近60个双原子分子的光谱数据进行了论证,并与量子力学计算结果进行了比较.结果表明,该模型具有通用性与可靠性.结合NX(a 1Δ)替代O2(a 1Δg)的新激光系统可能性研究需要,应用CIS、B3LYP与MCSCF方法,在6-311＋g(3df)基水平计算了NX(X=F、Cl、Br)第一激发态（a 1Δ）的结构,导出了解析势能函数.     

2'-羟基-2,4-二溴二苯醚与人血清白蛋白作用机制的光谱研究与计算模拟

采用分子对接、分子动力学模拟和光谱法研究了2'-羟基-2,4-二溴二苯醚(2'-OH-BDE-7)与人血清白蛋白(HSA)的相互作用.同步荧光光谱研究表明,2'-OH-BDE-7诱导HSA的构象产生变化,并与分子动力学模拟的结果相吻合.荧光光谱表明,2'-OH-BDE-7能引起HSA荧光猝灭,且其作用机制为静态猝灭和非辐射能量转移.热力学分析得出ΔG0,ΔH0和ΔS0,表明氢键和范德华力在HSA-2'-OH-BDE-7的体系中起着重要作用.竞争实验和分子对接结果表明2'-OH-BDE-7主要结合在HSA的位点Ⅰ.将计算模拟和光谱实验研究相结合,为研究小分子和蛋白质相互作用机制提供更多的信息.     

SDOCK: a global protein-protein docking program using stepwise force-field potentials

Fast Fourier transform (FFT) method limits the forms of scoring functions in global protein-protein docking. On the other hand, force field potentials can effectively describe the energy hyper surface of biological macromolecules. In this study, we developed a new protein-protein docking program, SDOCK, that incorporates van der Waals attractive potential, geometric collision, screened electrostatic potential, and Lazaridis-Karplus desolvation energy into the scoring function in the global searching process. Stepwise potentials were generated from the corresponding continuous forms to treat the structure flexibility. After optimization of the atom solvation parameters and the weights of different potential terms based on a new docking test set that contains 142 cases with small or moderate conformational changes upon binding, SDOCK slightly outperformed the well-known FFT based global docking program ZDOCK3.0. Among the 142 cases tested, 52.8% gave at least one near-native solutions in the top 100 solutions. SDOCK was also tested on six blind testing cases in Critical Assessment of Predicted Interactions rounds 13 to 18. In all six cases, the near-native solutions could be found within the top 350 solutions. Because the SDOCK approach performs global docking based on force-field potentials, one of its advantages is that it provides global binding free energy surface profiles for further analysis. The efficiency of the program is also comparable with that of other FFT based protein-protein docking programs. SDOCK is available for noncommercial applications at http://mdl.ipc.pku.edu.cn/cgi-bin/down.cgi.     

The Structure of Some Phenol-Formaldehyde Condensates for Wood Adhesives

The probable minimum-energy structures of three dimeric, three tri-meric, and two hexameric phenol-formaldehyde (PF) condensates were determined by using a simplified model where the total energy was calculated as the sum of the van der Waals, hydrogen bonding, torsional, and electrostatic energies. The minimum-energy conformation was defined as that with internal angles of rotation that correspond to the overall minimum in the total energy. Representations of the structures of these PF condensates in the minimum-energy conformations were obtained. The results obtained indicated that the van der Waals energy, and in some cases the hydrogen bonding energy, make the most important contributions to the total energy. The phenol-formaldehyde dimers and trimers are predicted by this model to have nonplanar structures, and the longer phenol-formaldehyde condensates, such as the hexamers, probably exist in helical conformations.     

Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function

Based on a statistical mechanics-based iterative method, we have extracted a set of distance-dependent, all-atom pairwise potentials for protein-ligand interactions from the crystal structures of 1300 protein-ligand complexes. The iterative method circumvents the long-standing reference state problem in knowledge-based scoring functions. The resulted scoring function, referred to as ITScore 2.0, has been tested with the CSAR (Community Structure-Activity Resource, 2009 release) benchmark of 345 diverse protein-ligand complexes. ITScore 2.0 achieved a Pearson correlation of R(2) = 0.54 in binding affinity prediction. A comparative analysis has been done on the scoring performances of ITScore 2.0, the van der Waals (VDW) scoring function, the VDW with heavy atoms only, and the force field (FF) scoring function of DOCK which consists of a VDW term and an electrostatic term. The results reveal several important factors that affect the scoring performances, which could be helpful for the improvement of scoring functions.     

Prediction of protein–protein complexes using replica exchange with repulsive scaling

The realistic prediction of protein–protein complex structures is import to ultimately model the interaction of all proteins in a cell and for the design of new protein–protein interactions. In principle, molecular dynamics (MD) simulations allow one to follow the association process under realistic conditions including full partner flexibility and surrounding solvent. However, due to the many local binding energy minima at the surface of protein partners, MD simulations are frequently trapped for long times in transient association states. We have designed a replica-exchange based scheme employing different levels of a repulsive biasing between partners in each replica simulation. The bias acts only on intermolecular interactions based on an increase in effective pairwise van der Waals radii (repulsive scaling (RS)-REMD) without affecting interactions within each protein or with the solvent. For a set of five protein test cases (out of six) the RS-REMD technique allowed the sampling of near-native complex structures even when starting from the opposide site with respect to the native binding site for one partner. Using the same start structures and same computational demand regular MD simulations sampled near native complex structures only for one case. The method showed also improved results for the refinement of docked structures in the vicinity of the native binding geometry compared to regular MD refinement.     

The Structure of Cellulose by Conformational Analysis. 1. Cellobiose and Methyl-β-cellobioside

Conformational analysis studies on the tertiary structure of cellobiose and methyl-β-cellobioside were carried out by using calculations of van der Waals, H-bond, electrostatic, and torsional energy interactions between the atoms and groups of the molecules. Energy maps as functions of the rotational anglesΨo and Φ° of the glucosidic bond were obtained in increments of 20° and refined in increments of 1°. Two “primary” and one “secondary” conformations of minimum energy were obtained for both cellobiose and methyl-β-cellobioside, some of which are equivalent to results obtained by x-ray diffraction. The H-bond forces are shown to be, together with the van der Waals forces, the predominant factors in the fixation of the conformations of minimum energy. The position and energy contributions of the H-bonds patterns for the favored conformations are identified.     

Soft energy function and generic evolutionary method for discriminating native from nonnative protein conformations

We have developed a soft energy function, termed GEMSCORE, for the protein structure prediction, which is one of emergent issues in the computational biology. The GEMSORE consists of the van der Waals, the hydrogen-bonding potential and the solvent potential with 12 parameters which are optimized by using a generic evolutionary method. The GEMSCORE is able to successfully identify 86 native proteins among 96 target proteins on six decoy sets from more 70,000 near-native structures. For these six benchmark datasets, the predictive performance of the GEMSCORE, based on native structure ranking and Z-scores, was superior to eight other energy functions. Our method is based solely on a simple and linear function and thus is considerably faster than other methods that rely on the additional complex calculations. In addition, the GEMSCORE recognized 17 and 2 native structures as the first and the second rank, respectively, among 21 targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). These results suggest that the GEMSCORE is fast and performs well to discriminate between native and nonnative structures from thousands of protein structure candidates. We believe that GEMSCORE is robust and should be a useful energy function for the protein structure prediction.     

A Gibbs free energy correlation for automated docking of carbohydrates

Thermodynamic information can be inferred from static atomic configurations. To model the thermodynamics of carbohydrate binding to proteins accurately, a large binding data set has been assembled from the literature. The data set contains information from 262 unique protein-carbohydrate crystal structures for which experimental binding information is known. Hydrogen atoms were added to the structures and training conformations were generated with the automated docking program AutoDock 3.06, resulting in a training set of 225,920 all-atom conformations. In all, 288 formulations of the AutoDock 3.0 free energy model were trained against the data set, testing each of four alternate methods of computing the van der Waals, solvation, and hydrogen-bonding energetic components. The van der Waals parameters from AutoDock 1 produced the lowest errors, and an entropic model derived from statistical mechanics produced the only models with five physically and statistically significant coefficients. Eight models predict the Gibbs free energy of binding with an error of less than 40% of the error of any similar models previously published.