确定蛋白质-短肽复合物结构的新方法

大部分蛋白质 -蛋白质复合物的三维结构在接触表面都显示出很好的几何匹配 .由于蛋白质的表面几何形状和其它的一些物理化学性质在分子的专一性相互作用中起了主要作用 ,所以 ,接触表面几何形状的互补常常被认为是蛋白质分子识别的基础 .一般来说 ,蛋白质接触表面的几何匹配只涉及 5到 1 0几个紧密堆积的氨基酸残基 ,因此 ,蛋白质与蛋白质配体之间的识别计算可以通过蛋白质与突变周围的或与蛋白质表面紧密接触的配体肽段的识别计算来实现 . Stoddard等 [1] 已经利用从 MBP上选取的八肽成功地计算出接近晶体结构的 MBP-受体复合物 .许多研…     

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.     

建立短肽抑制剂与酶识别的模式──基于表面静电互补和溶剂化能的Docking计算方法

提出了把简化的接触表面静电互补模型与溶剂化能模型相结合的新的Docking计算方法.编写了计算模拟程序ESCOLD,并用于酶-短肽体系的Docking计算.对3个已知X射线晶体结构的酶-抑制剂肽链复合物中的六肽用ESCOLD重新进行了Docking计算.用溶剂化能作为评价函数,正确地预测出接近实验结果的六肽取向.     

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 …     

关于静电作用对酶活性部位残基pKa影响的定量计算

随着基因技术的发展,人们几乎已经可以任意替换蛋白质序列中的特定残基。由于理论上对蛋白质性能的预测工作还远远跟不上实验的发展,只能花费大量的人力、物力利用实验方法筛选突变体。这就迫切需要建立一套能够正确预测蛋白质性质的方法。静电作用在酶促反应中起到了重要作用。基于表面带电残基对酶活性部位残基pK_α的影响,可利用替换表面残基的方法改变酶促反应随pH的变化趋势,是蛋白质工程研究中的一种有效方法。由于表面极性残基处于高介电常数的溶剂(水)与低介电常数的蛋白质的界面上,对此问题尚未有圆满的理论方法进行解决。我们利用Kirkwood理论定量地表述了表面荷电残基的替换对酶活性部位残基pK_α的影响,为研究酶突变体的性质,如突变体的电化学性质、酶催化反应随pH变化趋势的改变,酶专一性的改变等提供了一种简便的方法。     

富勒烯与β淀粉样肽低聚体结合的分子动力学模拟

本文通过分子动力学模拟研究富勒烯在Aβ42低聚体表面的结合过程.在结合过程中,C60在Aβ表面经历一系列尝试过程,最终找到某个稳定的结合位点.根据结合的残基不同,这些结合位点可以分为六类,其中核心疏水区域(CHC)位点(17LVFFA21)及Turn 27-31位点(27NKGAI31)具有最强的结合稳定性.二者的结合主要通过范德华作用稳定,而溶剂化效应则起相反作用.在这六类位点中的两个位点,观察到C60会对Aβ二级结构起破坏作用.其一位于核心疏水区域,C60有挤入多肽β片层中间的趋势;另外一位点位于N端,C60能够破坏外侧Aβ的3-5号残基的主链氢键,瓦解其末端的β片结构.这两个过程对理解富勒烯抑制Aβ聚集的微观机制提供了帮助.此外,在Turn 27-31位点以及Y10-H14位点,发现了富勒烯与Aβ纤维样聚集体结合的沟槽滚动机制,即富勒烯能够在淀粉样低聚体表面形成的特定沟槽内滚动.这一特征有助于预测富勒烯在其它淀粉样多肽表面的结合位点及结合行为.     

采用电活性标记物N-(2-乙基-二茂铁)马来酰亚胺检测与表面固定的 一致性双链DNA特异性作用的p53蛋白质

建立了电化学检测表面固定捕获的野生型p53蛋白质的方法. 首先在金电极表面形成巯基化的单链DNA探针/己硫醇(HT)混合自组装膜, 随后巯基化的单链DNA探针与溶液中序列匹配的靶点DNA杂交, 所形成的一致性双链DNA捕获溶液中的野生型p53蛋白质. p53分子表面的半胱氨酸残基采用巯基特异性试剂N-(2-乙基-二茂铁)马来酰亚胺(Fc-Mi)进行衍生. 通过检测二茂铁的电化学信号来指示p53与一致性双链DNA之间的特异性相互作用. p53蛋白质与双链DNA的键合程度取决于双链DNA的序列. 该方法可检测的p53最低浓度为1.33 nmol•L－1.     

胰岛素样生长因子及其结合蛋白的作用机理研究

胰岛素样生长因子(insulin-like growth factor,IGF),主要由IGF1和IGF2、胰岛素样生长因子受体(IGF receptor,IGFR)I型和II型,以及胰岛素样生长因子结合蛋白(IGF binding protein,IGFBP)家族组成.实验中发现IGFBP可以抑制IGF2促肿瘤生长的活性,但二者之间的结合位点并不清晰.计算化学可以对蛋白质的结合亲和力进行预测,在药物设计领域中具有非常重要的作用.因此使用分子动力学(molecular dynamics,MD)模拟对IGFBP6与IGF2之间的结合位点进行了扫描式搜索,通过对RMSD(Root Mean-Square Displacement)、相互作用能、有效吸附残基、接触面积等分析,发现IGF2与IGFBP6的结合位点主要集中于C端结构域,其中Arg、Gln、His、Asp、Glu、Asn等极性残基贡献最多,它们主要通过静电相互作用来促进IGFBP6-IGF2复合体系之间的结合.这些结果不仅有助于构建IGFBP6与IGF2二者相互作用的网络图谱,还可以用于推测IGF家族中蛋白质的未知功能,对了解生命活动的本质具有重要意义.     

基于原子表面的蛋白质水合自由能预测模型

提出了一种计算蛋白质水合自由能的简化模型（SAWSA 2）.模型把蛋白质分子中的原子分为20种不同的原子类型,通过每类原子的溶剂可及化表面以及相应的溶剂化参数,就可以得到分子的水合自由能.不同原子类型的溶剂化参数通过110个蛋白质分子水合自由能拟合得到,水合自由能的标准值采用了基于求解Possion-Boltzmann方程（PB）以及分子表面计算（SA） 相结合的方法.采用得到的模型,预测了20个蛋白质分子的水合自由能,预测值的相对值和绝对值都能和PB/SA的计算值很好地吻合,大大优于两种已报导的水合自由能模型.     

富勒烯与β淀粉样肽低聚体结合的分子动力学模拟

本文通过分子动力学模拟研究富勒烯在Aβ42 低聚体表面的结合过程. 在结合过程中,C₆₀在Aβ表面经历一系列尝试过程,最终找到某个稳定的结合位点. 根据结合的残基不同,这些结合位点可以分为六类,其中核心疏水区域（CHC）位点（¹⁷LVFFA²¹）及Turn 27-31 位点（²⁷NKGAI³¹）具有最强的结合稳定性. 二者的结合主要通过范德华作用稳定,而溶剂化效应则起相反作用. 在这六类位点中的两个位点,观察到C₆₀会对Aβ二级结构起破坏作用. 其一位于核心疏水区域,C₆₀有挤入多肽β片层中间的趋势;另外一位点位于N端,C₆₀能够破坏外侧Aβ的3-5 号残基的主链氢键,瓦解其末端的β片结构. 这两个过程对理解富勒烯抑制Aβ聚集的微观机制提供了帮助. 此外,在Turn 27-31 位点以及Y10-H14 位点,发现了富勒烯与Aβ纤维样聚集体结合的沟槽滚动机制,即富勒烯能够在淀粉样低聚体表面形成的特定沟槽内滚动. 这一特征有助于预测富勒烯在其它淀粉样多肽表面的结合位点及结合行为.     

Protein surface conservation in binding sites

An algorithm is described which uses the conservation of the 3D structure of protein surfaces, as opposed to their sequences, to detect protein-protein binding sites. The protein in which protein-protein binding sites are sought is compared with structures of multiple structurally related proteins and the surface that is conserved at least once is considered to be a part of the binding site. The binding site predictions obtained in this way for a set of protein-protein complexes correspond well with the actual protein-protein binding sites. A comparison of this method with an algorithm using the support vector machine approach for predicting protein-protein binding sites shows structural conservation to be an important characteristic that distinguishes binding sites from the remainder of protein surfaces.     

On the nonpolar hydration free energy of proteins: surface area and continuum solvent models for the solute-solvent interaction energy

Implicit solvent hydration free energy models are an important component of most modern computational methods aimed at protein structure prediction, binding affinity prediction, and modeling of conformational equilibria. The nonpolar component of the hydration free energy, consisting of a repulsive cavity term and an attractive van der Waals solute-solvent interaction term, is often modeled using estimators based on the solvent exposed solute surface area. In this paper, we analyze the accuracy of linear surface area models for predicting the van der Waals solute-solvent interaction energies of native and non-native protein conformations, peptides and small molecules, and the desolvation penalty of protein-protein and protein-ligand binding complexes. The target values are obtained from explicit solvent simulations and from a continuum solvent van der Waals interaction energy model. The results indicate that the standard surface area model, while useful on a coarse-grained scale, may not be accurate or transferable enough for high resolution modeling studies of protein folding and binding. The continuum model constructed in the course of this study provides one path for the development of a computationally efficient implicit solvent nonpolar hydration free energy estimator suitable for high-resolution structural and thermodynamic modeling of biological macromolecules.     

Pattern recognition strategies for molecular surfaces: III. Binding site prediction with a neural network

An algorithm for the identification of possible binding sites of biomolecules, which are represented as regions of the molecular surface, is introduced. The algorithm is based on the segmentation of the molecular surface into overlapping patches as described in the first article of this series.1 The properties of these patches (calculated on the basis of physical and chemical properties) are used for the analysis of the molecular surfaces of 7821 proteins and protein complexes. Special attention is drawn to known protein binding sites. A binding site identification algorithm is realized on the basis of the calculated data using a neural network strategy. The neural network is able to classify surface patches as protein-protein, protein-DNA, protein-ligand, or nonbinding sites. To show the capability of the algorithm, results of the surface analysis and the predictions are presented and discussed with representative examples.     

Exploiting three kinds of interface propensities to identify protein binding sites

Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. In this study, we present a building block of proteins called order profiles to use the evolutionary information of the protein sequence frequency profiles and apply this building block to produce a class of propensities called order profile interface propensities. For comparisons, we revisit the usage of residue interface propensities and binary profile interface propensities for protein binding site prediction. Each kind of propensities combined with sequence profiles and accessible surface areas are inputted into SVM. When tested on four types of complexes (hetero-permanent complexes, hetero-transient complexes, homo-permanent complexes and homo-transient complexes), experimental results show that the order profile interface propensities are better than residue interface propensities and binary profile interface propensities. Therefore, order profile is a suitable profile-level building block of the protein sequences and can be widely used in many tasks of computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the protein remote homology detection.     

Maintaining solvent accessible surface area under rotamer substitution for protein design

Although quantities derived from solvent accessible surface areas (SASA) are useful in many applications in protein design and structural biology, the computational cost of accurate SASA calculation makes SASA-based scores difficult to integrate into commonly used protein design methodologies. We demonstrate a method for maintaining accurate SASA during a Monte Carlo search of sequence and rotamer space for a fixed protein backbone. We extend the fast Le Grand and Merz algorithm (Le Grand and Merz, J Comput Chem, 14, 349), which discretizes the solvent accessible surface for each atom by placing dots on a sphere and combines Boolean masks to determine which dots are exposed. By replacing semigroup operations with group operations (from Boolean logic to counting dot coverage) we support SASA updates. Our algorithm takes time proportional to the number of atoms affected by rotamer substitution, rather than the number of atoms in the protein. For design simulations with a one hundred residue protein our approach is approximately 145 times faster than performing a Le Grand and Merz SASA calculation from scratch following each rotamer substitution. To demonstrate practical effectiveness, we optimize a SASA-based measure of protein packing in the complete redesign of a large set of proteins and protein-protein interfaces.     

Hot spot occlusion from bulk water: a comprehensive study of the complex between the lysozyme HEL and the antibody FVD1.3

Alanine scanning of protein-protein interfaces has shown that there are some residues in the protein-protein interfaces, responsible for most of the binding free energy, which are called hot spots. Hot spots tend to exist in densely packed central clusters, and a hypothesis has been proposed that considers that inaccessibility to the solvent must be a necessary condition to define a residue as a binding hot spot. This O-ring hypothesis is mainly based on the analysis of the accessible surface area (ASA) of 23 static, crystallographic structures of protein complexes. It is known, however, that protein flexibility allows for temporary exposures of buried interfacial groups, and even though the ASA provides a general trend of the propensity for hydration, protein/solvent-specific interactions or hydrogen bonding cannot be considered here. Therefore, a microscopic level, atomistic picture of hot spot solvation is needed to support the O-ring hypothesis. In this study, we began by applying a computational alanine-scanning mutagenesis technique, which reproduces the experimental results and allows for decomposing the binding free energy difference in its different energetic factors. Subsequently, we calculated the radial distribution function and residence times of the water molecules near the hot/warm spots to study the importance of the water environment around those energetically important amino acid residues. This study shows that within a flexible, dynamic protein framework, the warm/hot spot residues are, indeed, kept sheltered from the bulk solvent during the whole simulation, which allows a better interacting microenvironment.     

Develop and test a solvent accessible surface area-based model in conformational entropy calculations

It is of great interest in modern drug design to accurately calculate the free energies of protein-ligand or nucleic acid-ligand binding. MM-PBSA (molecular mechanics Poisson-Boltzmann surface area) and MM-GBSA (molecular mechanics generalized Born surface area) have gained popularity in this field. For both methods, the conformational entropy, which is usually calculated through normal-mode analysis (NMA), is needed to calculate the absolute binding free energies. Unfortunately, NMA is computationally demanding and becomes a bottleneck of the MM-PB/GBSA-NMA methods. In this work, we have developed a fast approach to estimate the conformational entropy based upon solvent accessible surface area calculations. In our approach, the conformational entropy of a molecule, S, can be obtained by summing up the contributions of all atoms, no matter they are buried or exposed. Each atom has two types of surface areas, solvent accessible surface area (SAS) and buried SAS (BSAS). The two types of surface areas are weighted to estimate the contribution of an atom to S. Atoms having the same atom type share the same weight and a general parameter k is applied to balance the contributions of the two types of surface areas. This entropy model was parametrized using a large set of small molecules for which their conformational entropies were calculated at the B3LYP/6-31G* level taking the solvent effect into account. The weighted solvent accessible surface area (WSAS) model was extensively evaluated in three tests. For convenience, TS values, the product of temperature T and conformational entropy S, were calculated in those tests. T was always set to 298.15 K through the text. First of all, good correlations were achieved between WSAS TS and NMA TS for 44 protein or nucleic acid systems sampled with molecular dynamics simulations (10 snapshots were collected for postentropy calculations): the mean correlation coefficient squares (R2) was 0.56. As to the 20 complexes, the TS changes upon binding; TΔS values were also calculated, and the mean R2 was 0.67 between NMA and WSAS. In the second test, TS values were calculated for 12 proteins decoy sets (each set has 31 conformations) generated by the Rosetta software package. Again, good correlations were achieved for all decoy sets: the mean, maximum, and minimum of R2 were 0.73, 0.89, and 0.55, respectively. Finally, binding free energies were calculated for 6 protein systems (the numbers of inhibitors range from 4 to 18) using four scoring functions. Compared to the measured binding free energies, the mean R2 of the six protein systems were 0.51, 0.47, 0.40, and 0.43 for MM-GBSA-WSAS, MM-GBSA-NMA, MM-PBSA-WSAS, and MM-PBSA-NMA, respectively. The mean rms errors of prediction were 1.19, 1.24, 1.41, 1.29 kcal/mol for the four scoring functions, correspondingly. Therefore, the two scoring functions employing WSAS achieved a comparable prediction performance to that of the scoring functions using NMA. It should be emphasized that no minimization was performed prior to the WSAS calculation in the last test. Although WSAS is not as rigorous as physical models such as quasi-harmonic analysis and thermodynamic integration (TI), it is computationally very efficient as only surface area calculation is involved and no structural minimization is required. Moreover, WSAS has achieved a comparable performance to normal-mode analysis. We expect that this model could find its applications in the fields like high throughput screening (HTS), molecular docking, and rational protein design. In those fields, efficiency is crucial since there are a large number of compounds, docking poses, or protein models to be evaluated. A list of acronyms and abbreviations used in this work is provided for quick reference.     

An analytical method for computing atomic contact areas in biomolecules

We propose a new analytical method for detecting and computing contacts between atoms in biomolecules. It is based on the alpha shape theory and proceeds in three steps. First, we compute the weighted Delaunay triangulation of the union of spheres representing the molecule. In the second step, the Delaunay complex is filtered to derive the dual complex. Finally, contacts between spheres are collected. In this approach, two atoms i and j are defined to be in contact if their centers are connected by an edge in the dual complex. The contact areas between atom i and its neighbors are computed based on the caps formed by these neighbors on the surface of i; the total area of all these caps is partitioned according to their spherical Laguerre Voronoi diagram on the surface of i. This method is analytical and its implementation in a new program BallContact is fast and robust. We have used BallContact to study contacts in a database of 1551 high resolution protein structures. We show that with this new definition of atomic contacts, we generate realistic representations of the environments of atoms and residues within a protein. In particular, we establish the importance of nonpolar contact areas that complement the information represented by the accessible surface areas. This new method bears similarity to the tessellation methods used to quantify atomic volumes and contacts, with the advantage that it does not require the presence of explicit solvent molecules if the surface of the protein is to be considered. © 2012 Wiley Periodicals, Inc.     

Extending the applicability of the O-ring theory to protein–DNA complexes

Many biological processes depend on protein-based interactions, which are governed by central regions with higher binding affinities, the hot-spots. The O-ring theory or the “Water Exclusion” hypothesis states that the more deeply buried central regions are surrounded by areas, the null-spots, whose role would be to shelter the hot-spots from the bulk solvent. Although this theory is well-established for protein–protein interfaces, its applicability to other protein interfaces remains unclear. Our goal was to verify its applicability to protein–DNA interfaces. We performed Molecular Dynamics simulations in explicit solvent of several protein–DNA complexes and measured a variety of solvent accessible surface area (SASA) features, as well as, radial distribution functions of hot-spots and null-spots. Our aim was to test the influence of water in their coordination sphere. Our results show that hot-spots tend to have fewer water molecules in their neighborhood when compared to null-spots, and higher values of ΔSASA, which confirms their occlusion from solvent. This study provides evidence in support of the O-ring theory with its applicability to a new type of protein-based interface: protein–DNA.     

Bcl-2蛋白结合底物时的诱导契合及高效抑制剂的柔性对接研究

Bcl-2蛋白是目前抗肿瘤药物研究很具前景的新靶点. Bcl-2蛋白与底物作用的活性腔生理情况下是蛋白与蛋白作用接触面大而平坦, 与底物结合时发生明显的诱导契合. 通过分析和比较Bcl-2蛋白和其高同源的Bcl-x_L蛋白的自由状态及与底物复合时的9个相关三维结构模型, 明确了蛋白及活性腔的骨架结构特征, 活性腔内的重要位点和关键残基, 及它们结合底物时发生的诱导契合. 基于以上认识, 采用柔性对接的方法得到了高亲和力的小分子抑制剂与Bcl-2蛋白的合理结合模式, 为以后设计合成新型高效Bcl-2蛋白抑制剂打下了坚实基础.