聚酰胺-胺型树枝状化合物与细胞色素C的结合作用

制备具有分子识别功能的材料 ,特别是设计合成某种分子 ,使其能够识别蛋白质表面 ,并干扰或促进蛋白质的特定生理功能 ,是生物有机化学中一个尚待解决的重要问题 ,也是揭开蛋白质分子识别与相互作用机理的重要问题 .人们对生物大分子———蛋白质分子之间的识别和相互作用进行了广泛的研究 ,总结出了一些规律 .( 1 )蛋白质复合物中最直接相互作用的残基数目共为 2 7～44个 ,相对与总的残基数来说很少 ,但是对分子间的识别和稳定作用却起决定性作用 ;( 2 )蛋白质复合物的接触面积为 6～ 1 0ｎｍ2 ,既需要比较大的接触面积 ,复合物才比较稳定 …     

蛋白质表面模块划分及其在结合位点预测中的应用

蛋白质-蛋白质复合物的结合位点预测是计算分子生物学的一个难题. 本文对蛋白质-蛋白质复合物数据集Benchmark 3.0 中的双链蛋白质复合物进行了研究, 计算了单体的残基溶剂可接近表面积和残基间的接触面积, 并据此提出了蛋白质表面模块划分方法. 发现模块的溶剂可接近表面积与其内部接触面积的乘积(PSAIA)值能够提供结合位点的信息. 在78 个双链蛋白质复合物中, 有74 个体系其受体或配体上具有最大或次大PSAIA值的模块是界面模块. 将该方法获得的结合位点信息应用在CAPRI竞赛Target 39 的复合物结构预测中取得了较好的结果. 本文提出的基于模块的蛋白质结合位点预测方法不同于以残基为基础且仅考虑表面残基的传统预测方法, 为蛋白质-蛋白质复合物结合位点预测提供了新思路.     

(α/β)_8桶状蛋白质中紧密接触对的统计性质研究

在计算蛋白质分子中的氨基酸紧密接触对时采用以氨基酸的重心代替Cα 原子的新方法 ,通过计算(α/β) 8桶状蛋白质中的中程和远程紧密接触对的数目 ,研究了不同氨基酸在形成紧密接触对时所具有的不同能力以及在蛋白质结构稳定性中所起的不同作用 .发现在 (α β) 8桶状蛋白质中氨基酸形成的远程紧密接触对数目与它的Fauchere Pliska疏水性实验数值 (FPH)存在着非常好的线性关系 ,而对于氨基酸所形成的中程紧密接触对数目不存在这种关系 .同时还研究了 (α β) 8桶状蛋白质分子大小 ,发现其回转半径大小与表示远程接触对数目的远程次序值LRO存在着关系 .表明这一类 (α β) 8桶状蛋白质具有相似的结构 ,分子的远程接触对数目越多 ,分子越紧密 ,分子尺寸就越小 .同时还研究了不同氨基酸之间形成紧密接触对的能力 .     

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.     

分子印迹技术在蛋白质分离分析中的研究进展

蛋白质结构复杂,种类多样,与各种生命活动密切相关。大部分蛋白质在生物体中含量极低,对其分析检测带来极大困难。因此实现复杂生物样品中蛋白质的选择性识别与分离,对实现蛋白质的分离分析意义重大。通过分子印迹技术制备的分子印迹聚合物含有与模板分子大小、形状一致,官能团相互匹配的三维印迹空穴,在蛋白质的选择性识别与分离领域显示出了巨大的发展潜力。但是,由于蛋白质具有尺寸较大、构型易变、结构复杂等特点,分子印迹技术在蛋白质印迹中面临着巨大挑战。该文在介绍几种新型分子印迹技术包括表面印迹、抗原决定基印迹和金属螯合物印迹的基础上,综述了近3年分子印迹技术在蛋白质分离分析方面的应用,并对其发展进行了总结与展望。     

无机化学中空间效应的定量研究——Ⅱ、分子结构在成键前的几何框架

本文提出分子结构在成键前的几何框架概念。根据座位—配体匹配方法计算四体配位型分子的几何框架,并与实测结构对比,结果相近,表明在弱轨道作用下,配体的大小是决定分子构型的主要因素。同时讨论了引起几何框架扭曲的原因。     

基于框架核酸的细胞表面受体配体相互作用研究进展

细胞表面受体与配体之间的特异性相互作用在细胞生物学过程中起着重要作用。然而,与均相溶液不同,受体分子在细胞膜上的分布是非连续的、动态的,因此细胞表面的受体配体相互作用通常呈现复杂的非线性结合模式。框架核酸作为一类具有确定几何形状的DNA纳米支架,可用于多价配体的偶联,为深入揭示受体配体相互作用机制提供了可靠的工具。利用框架核酸纳米分辨率的可寻址特性,可实现对配体数目、间距及空间构象等参数的精确调控,进而研究细胞表面受体配体的结合特性及影响因素,优化结合条件最终实现高效的分子识别及靶向治疗。本文综述了基于框架核酸的细胞表面受体配体相互作用研究进展,通过探讨细胞表面受体配体相互作用的重要影响因素及生物学应用,对该研究领域的发展前景和未来趋势予以展望。     

(α/β)8桶状蛋白质中紧密接触对的统计性质研究

在计算蛋白质分子中的氨基酸紧密接触对时采用以氨基酸的重心代替C^α原子的新方法，通过计算（α/β）8桶状蛋白质中的中程和远程紧密接触对的数目，研究了不同氨基酸在形成紧密接触对时所具有的不同能力以及在蛋白质结构稳定性中所起的不同作用．发现在（α/β）8桶状蛋白质中氨基酸形成的远程紧密接触对数目与它的Fauchere—Pliska疏水性实验数值(FPH)存在着非常好的线性关系，而对于氨基酸所形成的中程紧密接触对数目不存在这种关系．同时还研究了（α/β）8桶状蛋白质分子大小，发现其回转半径大小与表示远程接触对数目的远程次序值LRO存在着关系．表明这一类（α/β）8桶状蛋白质具有相似的结构，分子的远程接触对数目越多，分子越紧密，分子尺寸就越小．同时还研究了不同氨基酸之间形成紧密接触对的能力．     

抗原肽与MHC分子相互作用QM/MM分子动力学模拟研究

在MHC I类(major histocompatibility complex class I)分子抗原加工提呈过程中抗原蛋白在抗原提呈细胞(antigen-presenting cells, APC)的胞浆中被蛋白酶体(proteasome)裂解成短肽peptide, 由转运相关蛋白(transporter associated with antigen processing, TAP)将蛋白酶体裂解产生的短肽片段从胞浆转运至内质网腔. 短肽peptide在内质网中与新生成的MHC I类分子结合, 形成peptide-MHC复合体被提呈到APC细胞表面, 与T细胞表面抗原受体(T cell receptor, TCR)特异性识别结合, 使得CTL细胞开始活化、增殖、分化, 进而对肿瘤细胞进行特异性杀伤. 目前对CTL细胞如何识别抗原肽-MHC复合物分子及抗原短肽peptide如何与主要组织相容性复合体MHC分子的相互作用识别结合的机理还不是很清楚. 传统的预测CTL细胞表位的方法没有考虑受体与配体结合过程中电子结构的变化, 电子结构的变化需要用量子力学方法来处理. 本文采用QM/MM多尺度生物大分子的分子动力学模拟方法, 以天然抗原肽TAX (LLFGYPVYVYU)与HLA-A*0201分子结合的晶体结构为模板, 替换抗原肽“锚点”氨基酸, 将口袋氨基酸残基的原子极化电荷在空间形成的静电势用电多极矩分量表示. 用箱线图分析每个口袋氨基酸分子静电势变化和功能, 确定Pocket B的Glu63和Lys66的功能是精细识别氨基酸和一级结合氨基酸, Pocket F的Asp77, Tyr84的功能是精细识别氨基酸, 而Asp77, Lys146是一级结合氨基酸, 表明QM/MM方法在提取抗原肽与MHC I类分子识别结合特异性信息是可行的, 这对了解免疫识别机理和指导肿瘤疫苗的开发都具有指导意义.     

茜素与AcO~-阴离子的超分子作用及紫外-可见吸收光谱的理论研究

采用密度泛函方法(DFT/B3LYP),在6-31+G水平上分别优化茜素以及AcO-阴离子复合物的几何构型.从几何结构参数、电荷布居和前线轨道能量等方面探讨了复合物形成过程中主体分子的构象变化,以及主客体间的超分子作用.用含时密度泛函方法(TD-B3LYP/6-31+G)分别计算了主体分子以及与阴离子形成复合物的紫外-可见吸收光谱.根据所得复合物的特征吸收峰波长红移情况,从理论上较好地解释了主体分子通过氢键与AcO-形成稳定阴离子复合物的实验事实.结果表明,乙腈溶剂中茜素对AcO-具有较强的超分子作用和选择性识别能力.     

用界面几何约束的多起点蒙特卡罗方法对蛋白质/ 短肽识别体系的对接计算

提出了一个界面几何约束的多起点蒙特卡罗构象搜索方法,并把这个方法用于三个丝氨酸蛋白酶/短肽抑制剂体系的刚性、部分柔性和全部柔性对接计算中。我们的方法成功地预测出了接近晶体结构的配体构象。与没有几何约束相比,我们的几何约束蒙特卡罗方法显示出了更好的收敛性质。     

PepPro: A Nonredundant Structure Data Set for Benchmarking Peptide–Protein Computational Docking

We present a nonredundant benchmark, coined PepPro, for testing peptide–protein docking algorithms. Currently, PepPro contains 89 nonredundant experimentally determined peptide–protein complex structures, with peptide sequence lengths ranging from 5 to 30 amino acids. The benchmark covers peptides with distinct secondary structures, including helix, partial helix, a mixture of helix and β-sheet, β-sheet formed through binding, β-sheet formed through self-folding, and coil. In addition, unbound proteins' structures are provided for 58 complexes and can be used for testing the ability of a docking algorithm handling the conformational changes of proteins during the binding process. PepPro should benefit the docking community for the development and improvement of peptide docking algorithms. The benchmark is available at http://zoulab.dalton.missouri.edu/PepPro_benchmark . © 2019 Wiley Periodicals, Inc.     

Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase

Many proteins undergo small side chain or even backbone movements on binding of different ligands into the same protein structure. This is known as induced fit and is potentially problematic for virtual screening of databases against protein targets. In this report we investigate the limits of the rigid protein approximation used by the docking program, GOLD, through cross-docking using protein structures of influenza neuraminidase. Neuraminidase is known to exhibit small but significant induced fit effects on ligand binding. Some neuraminidase crystal structures caused concern due to the bound ligand conformation and GOLD performed poorly on these complexes. A `clean' set, which contained unique, unambiguous complexes, was defined. For this set, the lowest energy structure was correctly docked (i.e. RMSD < 1.5 Å away from the crystal reference structure) in 84% of proteins, and the most promiscuous protein (1mwe) was able to dock all 15 ligands accurately including those that normally required an induced fit movement. This is considerably better than the 70% success rate seen with GOLD against general validation sets. Inclusion of specific water molecules involved in water-mediated hydrogen bonds did not significantly improve the docking performance for ligands that formed water-mediated contacts but it did prevent docking of ligands that displaced these waters. Our data supports the use of a single protein structure for virtual screening with GOLD in some applications involving induced fit effects, although care must be taken to identify the protein structure that performs best against a wide variety of ligands. The performance of GOLD was significantly better than the GOLD implementation of ChemScore and the reasons for this are discussed. Overall, GOLD has shown itself to be an extremely good, robust docking program for this system.     

Molecular Dynamics Scoring of Protein–Peptide Models Derived from Coarse-Grained Docking

One of the major challenges in the computational prediction of protein–peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein–peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor–ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein–peptide complexes show that the proposed MD-based scoring approach can be used to identify protein–peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.     

Energy design for protein-protein interactions

Proteins bind to other proteins efficiently and specifically to carry on many cell functions such as signaling, activation, transport, enzymatic reactions, and more. To determine the geometry and strength of binding of a protein pair, an energy function is required. An algorithm to design an optimal energy function, based on empirical data of protein complexes, is proposed and applied. Emphasis is made on negative design in which incorrect geometries are presented to the algorithm that learns to avoid them. For the docking problem the search for plausible geometries can be performed exhaustively. The possible geometries of the complex are generated on a grid with the help of a fast Fourier transform algorithm. A novel formulation of negative design makes it possible to investigate iteratively hundreds of millions of negative examples while monotonically improving the quality of the potential. Experimental structures for 640 protein complexes are used to generate positive and negative examples for learning parameters. The algorithm designed in this work finds the correct binding structure as the lowest energy minimum in 318 cases of the 640 examples. Further benchmarks on independent sets confirm the significant capacity of the scoring function to recognize correct modes of interactions.     

FDS: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function

The docking of flexible small molecule ligands to large flexible protein targets is addressed in this article using a two-stage simulation-based method. The methodology presented is a hybrid approach where the first component is a dock of the ligand to the protein binding site, based on deriving sets of simultaneously satisfied intermolecular hydrogen bonds using graph theory and a recursive distance geometry algorithm. The output structures are reduced in number by cluster analysis based on distance similarities. These structures are submitted to a modified Monte Carlo algorithm using the AMBER-AA molecular mechanics force field with the Generalized Born/Surface Area (GB/SA) continuum model. This solvent model is not only less expensive than an explicit representation, but also yields increased sampling. Sampling is also increased using a rotamer library to direct some of the protein side-chain movements along with large dihedral moves. Finally, a softening function for the nonbonded force field terms is used, enabling the potential energy function to be slowly turned on throughout the course of the simulation. The docking procedure is optimized, and the results are presented for a single complex of the arabinose binding protein. It was found that for a rigid receptor model, the X-ray binding geometry was reproduced and uniquely identified based on the associated potential energy. However, when side-chain flexibility was included, although the X-ray structure was identified, it was one of three possible binding geometries that were energetically indistinguishable. These results suggest that on relaxing the constraint on receptor flexibility, the docking energy hypersurface changes from being funnel-like to rugged. A further 14 complexes were then examined using the optimized protocol. For each complex the docking methodology was tested for a fully flexible ligand, both with and without protein side-chain flexibility. For the rigid protein docking, 13 out of the 15 test cases were able to find the experimental binding mode; this number was reduced to 11 for the flexible protein docking. However, of these 11, in the majority of cases the experimental binding mode was not uniquely identified, but was present in a cluster of low energy structures that were energetically indistinguishable. These results not only support the presence of a rugged docking energy hypersurface, but also suggest that it may be necessary to consider the possibility of more than one binding conformation during ligand optimization.     

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.     

Ligand-specific scoring functions: improved ranking of docking solutions1

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.     

DOX: A new computational protocol for accurate prediction of the protein–ligand binding structures

Molecular docking techniques have now been widely used to predict the protein–ligand binding modes, especially when the structures of crystal complexes are not available. Most docking algorithms are able to effectively generate and rank a large number of probable binding poses. However, it is hard for them to accurately evaluate these poses and identify the most accurate binding structure. In this study, we first examined the performance of some docking programs, based on a testing set made of 15 crystal complexes with drug statins for the human 3‐hydroxy‐3‐methylglutaryl coenzyme A reductase (HMGR). We found that most of the top ranking HMGR–statin binding poses, predicted by the docking programs, were energetically unstable as revealed by the high theoretical‐level calculations, which were usually accompanied by the large deviations from the geometric parameters of the corresponding crystal binding structures. Subsequently, we proposed a new computational protocol, DOX, based on the joint use of molecular Docking, ONIOM, and eXtended ONIOM (XO) methods to predict the accurate binding structures for the protein–ligand complexes of interest. Our testing results demonstrate that the DOX protocol can efficiently predict accurate geometries for all 15 HMGR‐statin crystal complexes without exception. This study suggests a promising computational route, as an effective alternative to the experimental one, toward predicting the accurate binding structures, which is the prerequisite for all the deep understandings of the properties, functions, and mechanisms of the protein–ligand complexes. © 2015 Wiley Periodicals, Inc.     

AcquaAlta: a directional approach to the solvation of ligand-protein complexes

Water molecules mediating polar interactions in ligand-protein complexes can substantially contribute to binding affinity and specificity. To account for such water molecules in computer-aided drug design, we performed an extensive search in the Cambridge Structural Database (CSD) to identify the geometrical criteria defining interactions of water molecules with ligand and protein. In addition, with ab initio calculations the propensity of ligand hydration was evaluated. Based on this information, we developed an algorithm (AcquaAlta) to reproduce water molecules bridging polar interactions between ligand and protein moieties. This approach was validated with 20 crystal structures and yielded a match of 76% between experimental and calculated water positions. When water molecules establishing only weak interactions with the protein were neglected, the match could be improved to 88%. Supported by a pharmacophore-based alignment tool, the solvation algorithm was then applied to the docking of oligopeptides to the periplasmic oligopeptide binding protein A (OppA). Calculated waters based on the crystal poses matched an average of 66% of the experimental waters. With water molecules calculated based on the docked ligands, the average match with the experimental waters dropped to 53%.