从对接结构中挑选近天然构象的新方法

分析了包含静电能(ΔEele)、去水化自由能(ΔGACE)以及范德华能(ΔEvdw)的打分函数在蛋白质－蛋白质对接中评价近天然构象的能力.对17种蛋白质复合物对接体系进行打分的结果表明，包含范德华能的打分函数（ΔEele＋ΔGACE＋ΔEvdw）比通常的打分函数（ΔEele、ΔGACE、ΔEele＋ΔGACE、ΔEele＋ΔEvdw、ΔGACE＋ΔEvdw）具有更好的区分近天然构象的能力.进一步的研究表明，优化（EM）对接体系后再进行打分，上面几种打分函数对对接结构的评价效果都有不同程度的改善，其中打分函数（ΔEele＋ΔGACE＋ΔEvdw）有更明显的改善.为了进一步确定候选结构中的近天然构象，以一种蛋白质复合物为例，对候选结构进行分子动力学（MD）模拟，根据MD轨迹中构象相对于初始构象的平方平均偏差（MSD）随时间的变化来辅助打分函数排除错误构象，得到了较好的结果.     

基于几何约束的蛋白质-配体准确结合自由能计算

蛋白质-配体的结合过程伴随着复杂的结构变化,在分子模拟可及的时间尺度内难以完全捕获,这使得准确估计蛋白质-配体的结合自由能十分困难.一种有效的解决途径是采用几何约束减小需要采样的构象空间,再通过后处理方式扣除约束的影响.本文综述了三种几何约束策略——漏斗状约束、球形约束和七自由度约束与自由能计算算法结合准确计算结合自由能的原理和进展,重点概述理论严谨的七自由度约束的最新进展以及与Alchemistry或重要性采样方法的联用策略,最后,讨论了如何针对不同体系选择合适的计算策略以及蛋白质-配体准确结合自由能计算在药物设计等领域中的挑战和前景,并提出了将上述方法进一步运用于研究更复杂的蛋白质-蛋白质问题的可能性.     

一组和AMBER力场配合的普适波恩模型参数

提出了一套和AMBER力场相匹配的普适波恩模型参数.新的参数集包含21种原子类型的初始半径和屏蔽因子.参数通过遗传算法拟合359个小分子水合自由能的实验值得到.采用新的参数，预测了44个小分子的水合自由能，预测值和实验值能很好地吻合，而且大大优于采用Jayaram参数得到的结果.此外，采用新的参数，还预测了15个蛋白质的水合自由能，预测值和PB/SA预测得到的结果能很好地吻合.     

酶-配体复合物亲和性的计算

描述了一种新的计算酶－配体复合物亲和性的方法．它考虑了酶－配体结合过程中自由能变化的各主要因素，并利用经验公式加以计算、从蛋白质结构数据库中选取了66个酶－配体复合物作为训练集，利用回归分析得出最后的模型．此模型通用于各种类型的酶－配体复合物，计算结果比技准确，预测算合物解离常数的平均偏差小于一个数量级．此方法还可以定量评价配体分子中每个部分对结合过程的贡献大小，可以为先导他合物的优化提供非常直接的信息     

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.     

q~2引导构象选择CoMFA方法研究HIV－1RT抑制剂

对未知受体结构的药物设计其主导方法CoMFA来说，柔性目标分子的多种构象 造成了问题的复杂性。本文介绍交叉验证参数R~2(q~2)引导的构象选择CoMFA方法 ，选择化合物的最佳构象。将一组47个HIV－1 RT抑制剂进行有、无构象选择的 CoMFA分析来作评价。根据化合物的活性、毒性、选择性指数（毒性／活性比）等 实验数据得到的模型，其交叉验证参数q~2为0.7以上，非交叉验证的相应参数为0. 94以上，最后，还经过试验集化合物验证该模型的预测能力，置信度（1－α）＞ 0.99。     

用分子对接方法预测HIV-1整合酶与金精三羧酸抑制剂的相互作用

用分子对接方法(Docking)研究了HIV-1整合酶与其抑制剂金精三羧酸的结合过程.为弄清金属离子在结合中所起的作用,选择含有一个Mg+2或不含Mg+2的两种不同的整合酶受体分别与金精三羧酸对接.结果表明, Mg+2对稳定配体与受体的结合起了重要作用. 金精三羧酸配体与含有一个金属Mg+2的整合酶受体对接,最优结合自由能为-45.19 kJ/mol. 当Mg+2失去后,整合酶的活性中心构象将发生变化,使金精三羧酸抑制剂与整合酶的结合自由能(-24.35 kJ/mol)明显增加. 预测了未知的HIV-1整合酶与其抑制剂金精三羧酸的复合物结构, 并可对基于结构的抗HIV-1整合酶的药物设计提供重要信息.     

评估石杉碱甲杂合体衍生物作为AD症药物活性顺序的简单方法

为了合理化设计、指导具有高活性、低毒性的石杉碱甲杂合体(Huprine X)衍生物的合成, 建立了一个计算结合自由能的新方案, 以实现对该类衍生物活性排列顺序的预测. 该结合自由能由三部分组成: 抑制剂和靶酶的相互作用能; 活性位点残基在结合前后的构象绝对自由能的变化; 抑制剂从稳定构象变为活性构象的自由能增加值. 通过计算已合成的14个杂合体衍生物的结合自由能, 结果显示理论值和实验测定的生物活性值有很好的等级相关性, 其斯皮尔曼相关系数为0.85, 证明了该方法的可行性.     

不对称反应的对映体过量值预测

建立了手性催化剂结构与反应产物对映体过量值的定量结构-活性相关性模型, 以辅助苯乙酮不对称氢转移反应的催化剂筛选. 手性催化剂由手性氨醇配体与金属络合物合成, 故采用构象独立手性指数或构象依赖手性指数描述手性氨醇配体, 采用指示变量区分金属络合物, 然后合并2个指数来描述手性催化剂. 通过遗传算法进行变量选择, 采用随机森林法建立数学模型以预测对映体过量值, 测试集的相关系数R²=0.769, 整个数据集的OOB(Out-of-bag)交叉验证结果为R²=0.785.     

分子亲脂-亲水性的量子化学描述II. 氨基酸侧链的亲水指标和亲脂指标

在启发式亲脂势HMLP(heuristicmolecularlipophilicitypotential)的基础上提出了分子、分子片段和原子的亲水指标和亲脂指标.计算出了20个天然氨基酸侧链的亲水、亲脂指标和亲水、亲脂表面积,并用线性自由能函数表达氨基酸侧链的溶剂化自由能,?Gsol,=b0 b1Li b2Hi b3Si b4Si.应用线性自由能函数和氨基酸侧链的亲水和亲脂! -i指标,计算了20个氨基酸残基的3种相转移自由能(蒸气-水、蒸气-正辛醇、正辛醇-水)和正辛醇-水分配系数logPow,取得了与实验值高度一致的良好效果.HMLP的亲水和亲脂指标是HMLP的指标化,扩展了这一方法的使用范围.氨基酸侧链的亲水、亲脂指标和线性自由能函数有望用于生物大分子受体与配体的结合自由能的估算、蛋白质的结构与功能、蛋白-蛋白相互作用和识别的研究.     

An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes

Fourteen popular scoring functions, i.e., X-Score, DrugScore, five scoring functions in the Sybyl software (D-Score, PMF-Score, G-Score, ChemScore, and F-Score), four scoring functions in the Cerius2 software (LigScore, PLP, PMF, and LUDI), two scoring functions in the GOLD program (GoldScore and ChemScore), and HINT, were tested on the refined set of the PDBbind database, a set of 800 diverse protein-ligand complexes with high-resolution crystal structures and experimentally determined Ki or Kd values. The focus of our study was to assess the ability of these scoring functions to predict binding affinities based on the experimentally determined high-resolution crystal structures of proteins in complex with their ligands. The quantitative correlation between the binding scores produced by each scoring function and the known binding constants of the 800 complexes was computed. X-Score, DrugScore, Sybyl::ChemScore, and Cerius2::PLP provided better correlations than the other scoring functions with standard deviations of 1.8-2.0 log units. These four scoring functions were also found to be robust enough to carry out computation directly on unaltered crystal structures. To examine how well scoring functions predict the binding affinities for ligands bound to the same target protein, the performance of these 14 scoring functions were evaluated on three subsets of protein-ligand complexes from the test set: HIV-1 protease complexes (82 entries), trypsin complexes (45 entries), and carbonic anhydrase II complexes (40 entries). Although the results for the HIV-1 protease subset are less than desirable, several scoring functions are able to satisfactorily predict the binding affinities for the trypsin and the carbonic anhydrase II subsets with standard deviation as low as 1.0 log unit (corresponding to 1.3-1.4 kcal/mol at room temperature). Our results demonstrate the strengths as well as the weaknesses of current scoring functions for binding affinity prediction.     

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes, X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.     

An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials

Using a novel iterative method, we have developed a knowledge-based scoring function (ITScore) to predict protein-ligand interactions. The pair potentials for ITScore were derived from a training set of 786 protein-ligand complex structures in the Protein Data Bank. Twenty-six atom types were used based on the atom type category of the SYBYL software. The iterative method circumvents the long-standing reference state problem in the derivation of knowledge-based scoring functions. The basic idea is to improve pair potentials by iteration until they correctly discriminate experimentally determined binding modes from decoy ligand poses for the ligand-protein complexes in the training set. The iterative method is efficient and normally converges within 20 iterative steps. The scoring function based on the derived potentials was tested on a diverse set of 140 protein-ligand complexes for affinity prediction, yielding a high correlation coefficient of 0.74. Because ITScore uses SYBYL-defined atom types, this scoring function is easy to use for molecular files prepared by SYBYL or converted by software such as BABEL.     

An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function

We have developed an iterative knowledge-based scoring function (ITScore) to describe protein-ligand interactions. Here, we assess ITScore through extensive tests on native structure identification, binding affinity prediction, and virtual database screening. Specifically, ITScore was first applied to a test set of 100 protein-ligand complexes constructed by Wang et al. (J Med Chem 2003, 46, 2287), and compared with 14 other scoring functions. The results show that ITScore yielded a high success rate of 82% on identifying native-like binding modes under the criterion of rmsd < or = 2 A for each top-ranked ligand conformation. The success rate increased to 98% if the top five conformations were considered for each ligand. In the case of binding affinity prediction, ITScore also obtained a good correlation for this test set (R = 0.65). Next, ITScore was used to predict binding affinities of a second diverse test set of 77 protein-ligand complexes prepared by Muegge and Martin (J Med Chem 1999, 42, 791), and compared with four other widely used knowledge-based scoring functions. ITScore yielded a high correlation of R2 = 0.65 (or R = 0.81) in the affinity prediction. Finally, enrichment tests were performed with ITScore against four target proteins using the compound databases constructed by Jacobsson et al. (J Med Chem 2003, 46, 5781). The results were compared with those of eight other scoring functions. ITScore yielded high enrichments in all four database screening tests. ITScore can be easily combined with the existing docking programs for the use of structure-based drug design.     

Ligand Identification Scoring Algorithm (LISA)

A central problem in de novo drug design is determining the binding affinity of a ligand with a receptor. A new scoring algorithm is presented that estimates the binding affinity of a protein-ligand complex given a three-dimensional structure. The method, LISA (Ligand Identification Scoring Algorithm), uses an empirical scoring function to describe the binding free energy. Interaction terms have been designed to account for van der Waals (VDW) contacts, hydrogen bonding, desolvation effects, and metal chelation to model the dissociation equilibrium constants using a linear model. Atom types have been introduced to differentiate the parameters for VDW, H-bonding interactions, and metal chelation between different atom pairs. A training set of 492 protein-ligand complexes was selected for the fitting process. Different test sets have been examined to evaluate its ability to predict experimentally measured binding affinities. By comparing with other well-known scoring functions, the results show that LISA has advantages over many existing scoring functions in simulating protein-ligand binding affinity, especially metalloprotein-ligand binding affinity. Artificial Neural Network (ANN) was also used in order to demonstrate that the energy terms in LISA are well designed and do not require extra cross terms.     

Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy

The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.     

Robust scoring functions for protein-ligand interactions with quantum chemical charge models

Ordinary least-squares (OLS) regression has been used widely for constructing the scoring functions for protein-ligand interactions. However, OLS is very sensitive to the existence of outliers, and models constructed using it are easily affected by the outliers or even the choice of the data set. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. In the development of the AutoDock4 scoring function, only OLS was conducted, and the simple Gasteiger method was adopted. It is therefore of considerable interest to see whether more rigorous charge models could improve the statistical performance of the AutoDock4 scoring function. In this study, we have employed two well-established quantum chemical approaches, namely the restrained electrostatic potential (RESP) and the Austin-model 1-bond charge correction (AM1-BCC) methods, to obtain atomic partial charges, and we have compared how different charge models affect the performance of AutoDock4 scoring functions. In combination with robust regression analysis and outlier exclusion, our new protein-ligand free energy regression model with AM1-BCC charges for ligands and Amber99SB charges for proteins achieve lowest root-mean-squared error of 1.637 kcal/mol for the training set of 147 complexes and 2.176 kcal/mol for the external test set of 1427 complexes. The assessment for binding pose prediction with the 100 external decoy sets indicates very high success rate of 87% with the criteria of predicted root-mean-squared deviation of less than 2 ?. The success rates and statistical performance of our robust scoring functions are only weakly class-dependent (hydrophobic, hydrophilic, or mixed).     

Ligand-specific scoring functions: improved ranking of docking solutions

Molecular docking is a powerful computational method that has been widely used in many biomolecular studies to predict geometry of a protein-ligand complex. However, while its conformational search algorithms are usually able to generate correct conformation of a ligand in the binding site, the scoring methods often fail to discriminate it among many false variants. We propose to treat this problem by applying more precise ligand-specific scoring filters to re-rank docking solutions. In this way specific features of interactions between protein and different types of compounds can be implicitly taken into account. New scoring functions were constructed including hydrogen bonds, hydrophobic and hydrophilic complementarity terms. These scoring functions also discriminate ligands by the size of the molecule, the total hydrophobicity, and the number of peptide bonds for peptide ligands. Weighting coefficients of the scoring functions were adjusted using a training set of 60 protein-ligand complexes. The proposed method was then tested on the results of docking obtained for an additional 70 complexes. In both cases the success rate was 5-8% better compared to the standard functions implemented in popular docking software.     

An improved PMF scoring function for universally predicting the interactions of a ligand with protein, DNA, and RNA

An improved potential mean force (PMF) scoring function, named KScore, has been developed by using 23 redefined ligand atom types and 17 protein atom types, as well as 28 newly introduced atom types for nucleic acids (DNA and RNA). Metal ions and water molecules embedded in the binding sites of receptors are considered explicitly by two newly defined atom types. The individual potential terms were devised on the basis of the high-resolution crystal and NMR structures of 2,422 protein-ligand complexes, 300 DNA-ligand complexes, and 97 RNA-ligand complexes. The optimized atom pairwise distances and minima of the potentials overcome some of the disadvantages and ambiguities of current PMF potentials; thus, they more reasonably explain the atomic interaction between receptors and ligands. KScore was validated against five test sets of protein-ligand complexes and two sets of nucleic-acid-ligand complexes. The results showed acceptable correlations between KScore scores and experimentally determined binding affinities (log K i's or binding free energies). In particular, KScore can be used to rank the binding of ligands with metalloproteins; the linear correlation coefficient ( R) for the test set is 0.65. In addition to reasonably ranking protein-ligand interactions, KScore also yielded good results for scoring DNA/RNA--ligand interactions; the linear correlation coefficients for DNA-ligand and RNA-ligand complexes are 0.68 and 0.81, respectively. Moreover, KScore can appropriately reproduce the experimental structures of ligand-receptor complexes. Thus, KScore is an appropriate scoring function for universally ranking the interactions of ligands with protein, DNA, and RNA.     

Role of binding entropy in the refinement of protein-ligand docking predictions: analysis based on the use of 11 scoring functions

We present results of testing the ability of eleven popular scoring functions to predict native docked positions using a recently developed method (Ruvinsky and Kozintsev, J Comput Chem 2005, 26, 1089) for estimation the entropy contributions of relative motions to protein-ligand binding affinity. The method is based on the integration of the configurational integral over clusters obtained from multiple docked positions. We use a test set of 100 PDB protein-ligand complexes and ensembles of 101 docked positions generated by (Wang et al. J Med Chem 2003, 46, 2287) for each ligand in the test set. To test the suggested method we compared the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10-21% when used in conjunction with the AutoDock scoring function, by 2-25% with G-Score, by 7-41% with D-Score, by 0-8% with LigScore, by 1-6% with PLP, by 0-12% with LUDI, by 2-8% with F-Score, by 7-29% with ChemScore, by 0-9% with X-Score, by 2-19% with PMF, and by 1-7% with DrugScore. We also compared the performance of the suggested method with the method based on ranking by cluster occupancy only. We analyze how the choice of a clustering-RMSD and a low bound of dense clusters impacts on docking accuracy of the scoring methods. We derive optimal intervals of the clustering-RMSD for 11 scoring functions.