蛋白质-RNA相互作用界面预测与设计

蛋白质-RNA之间的相互作用是蛋白质在细胞里面行使功能的重要方式之一. 结构生物学家利用实验手段可以得到蛋白质-RNA复合物的三维结构, 通过原子水平的晶体结构来解释蛋白质与RNA的识别过程. 但实验取得蛋白质-RNA的复合物结构非常困难, 耗钱、耗时, 同时受限于其相互作用强度. 因而利用理论的方法对蛋白质-RNA相互作用界面进行预测与设计在生物医学研究中十分重要. 本文主要综述了近期蛋白质-RNA相互作用界面预测与设计方面的进展, 包括以下几个方面: (1) 蛋白质-RNA分子对接算法以及对接前后存在的构象变化的处理; (2) 蛋白质-RNA 识别机制的研究; (3) 基于蛋白质-RNA 相互作用界面的分子设计. 蛋白质-RNA分子对接算法逐步完善将有助于我们对大量未知功能的蛋白质与RNA进行功能注释, 而基于生物大分子相互作用界面的分子设计将在药物设计领域中有广阔的应用前景.     

蛋白质相互作用: 界面分析, 结合自由能计算与相互作用设计

蛋白质相互作用在生命活动中起着重要作用. 研究蛋白质间相互作用的本质有助于了解生命活动中这些基本单元的作用. 本文主要综述了近期蛋白质相互作用研究的进展, 包括蛋白质相互作用界面的基本性质, 蛋白质结合自由能的计算方法, 不同相互作用在蛋白质结合/解离中的角色和差异, 以及上述知识在蛋白质相互作用设计中的应用. 蛋白质相互作用界面的特性, 例如界面大小、保守性以及结构的动态性质, 使得具有生物功能的蛋白质相互作用界面区别于非特异性的晶体堆积界面. 生物功能界面的一个重要结构特征是界面上存在着关键残基以及相对独立的相互作用模块. 利用多种方法, 如MM-PBSA、统计平均势以及不同的相互作用自由能模型, 可以在不同的精度上计算蛋白质相互作用自由能. 利用蛋白质相互作用界面的特点, 从不同的角度进行蛋白质相互作用对的设计与改造, 近年来已经有了不少成功的例子, 但还存在着很大的挑战. 我们认为在今后的蛋白质相互作用设计中, 考虑各种因素对蛋白质结合与解离的动力学过程的影响将有助于提高人类控制蛋白质相互作用的能力.     

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.     

蛋白质-蛋白质相互作用界面统计分析

以作者开发的从蛋白质结合部位推导出其界面所具有的疏水性质和氢键性质的计算程序PP_SITE为基础,利用蛋白质结构数据库(PDB),对蛋白质-蛋白质相互作用界面进行了统计分析.从PDB中挑出非冗余的链间相互作用对,计算出这个数据集中所有链间界面的疏水和氢键相互作用特征.对得到的界面特征进行统计分析,寻找能够明显聚类的界面特征.结果表明，界面大小、氢键和疏水相互作用在界面所占比例以及疏水相互作用的集中程度可以作为分类的依据.     

基于液质联用技术的蛋白质-蛋白质相互作用研究进展※

蛋白质-蛋白质相互作用调控着细胞内众多生物过程, 蛋白质间相互作用网络的绘制对解析复杂的生物过程至关重要. 面对生物体中复杂的蛋白质-蛋白质相互作用, 液质联用技术不仅具有灵敏度高的鉴定优势, 还可以对数以千计的蛋白质进行定量分析. 因此, 对目标蛋白质进行富集、标记或共分级的处理后, 结合液质联用技术对蛋白质准确而灵敏的鉴定, 这类技术已被广泛应用于复杂样本中蛋白质-蛋白质相互作用网络的解析. 目前基于液质联用技术的几种常用的方式, 包括亲和纯化质谱方法(AP-MS)、近程标记质谱方法(PDB-MS)、化学交联质谱方法(XL-MS)和共分级偶联质谱方法(CF-MS)等. 本综述讨论了这些方法的基本原理、优点和在细胞内解析蛋白质间相互作用的应用.     

无机纳米晶-生物界面作用的分子机制

无机纳米晶材料以其独特的光、电、磁、力学性质,成为疾病诊断与治疗功能的关键材料.本文总结了无机纳米晶的表面化学活性、离子释放性、晶相结构、晶格缺陷、表面吸附和表面修饰等与尺寸相关的理化性质与生物效应之间的关系.综述了无机纳米晶与蛋白质、磷脂生物膜间的界面相互作用,探讨了纳米晶-生物界面作用的分子机理.这有助于理解无机纳米晶的生物行为和毒理性质,指导设计安全、高效的纳米晶生物医学材料.     

纳米材料-蛋白质界面相互作用的分子机制

纳米材料由于其优异的性能在化工、电子、机械、环境、能源、航天等各个领域已经得到了广泛的应用,并且在生物医学方面的应用越来越受到重视。纳米材料-蛋白质界面相互作用是纳米生物医学领域重要的科学问题,对于纳米材料的生物医学应用以及生物安全性评价至关重要。蛋白质分子与纳米材料在界面的相互作用,一方面可以诱导蛋白质的构象、组装结构甚至功能的改变,另一方面可以引起纳米材料的表面亲疏水性、电荷性质等表面物理化学性质的改变。基于蛋白质与纳米材料相互作用检测技术及结果,本文从分子水平阐述了纳米材料与蛋白质分子在界面之间的相互作用机理及相应的结构与性质的变化,从而可以深化对两者之间复杂的相互作用机制的理解,对于推进纳米材料在生物医学的应用及健康、安全、持续发展具有重要意义。     

3,4,5-三羟基苯甲酸二聚体氢键结构性质

分子间相互作用在生物和材料等科学中发挥着关键作用,研究分子间相互作用的本质意义重大。氢键是分子间相互作用的一种主要形式,在确定分子构象和晶体结构以及生物分子尤其是核酸和蛋白质的结构功能中起着重要作用[1-3]。苯甲酸衍生物广泛存在于生物大分子内,与生物活性离子通过氢键作用等改变生物活性分子的活性功能,研究苯甲酸衍生物分子间氢键相互作用对于了解生物体内的化学现象具有重要意义。研究表明菱角的抗肿瘤作用明显,实验上已经从菱角中成功提取了活性单体化合物:3,4,5-三羟基苯甲酸二聚体[4],理论研究标题化合物的氢键结构与氢…     

双偏振干涉测量技术在生物分析方面研究应用进展

双偏振干涉测量(dual-polarization interferometry,DPI)技术是2000年以后出现的一种灵敏、实时、无需标记的新型表面状态分析方法,它能够在亚秒尺度精确测量界面层厚度、密度和质量的绝对值.所以,DPI在固/液界面上蛋白质结构、功能表征,蛋白质之间或与其它分子间的相互作用研究,核酸固定化及杂交检测研究,模拟生物膜研究以及表面超微结构表征等方面都有着广泛的应用.本文介绍了DPI的测量原理和特点,着重评述了近年来DPI在生物分析方面的应用进展.     

阳离子-π作用的研究进展

阳离子-π作用是一种存在于阳离子和芳香性体系之间的相互作用,与一些经典的作用(如氢键、静电和疏水相互作用)相比,被认为是一种新型分子间作用,普遍存在于生物体系中,对分子识别、蛋白质和核酸的结构与功能起着十分重要的作用.本文结合我们课题组的研究工作,对生物体内存在的典型的阳离子-π作用,以及有关阳离子-π作用的实验与理论计算研究进行综述.     

Accelerating molecular docking calculations using graphics processing units

The generation of molecular conformations and the evaluation of interaction potentials are common tasks in molecular modeling applications, particularly in protein-ligand or protein-protein docking programs. In this work, we present a GPU-accelerated approach capable of speeding up these tasks considerably. For the evaluation of interaction potentials in the context of rigid protein-protein docking, the GPU-accelerated approach reached speedup factors of up to over 50 compared to an optimized CPU-based implementation. Treating the ligand and donor groups in the protein binding site as flexible, speedup factors of up to 16 can be observed in the evaluation of protein-ligand interaction potentials. Additionally, we introduce a parallel version of our protein-ligand docking algorithm PLANTS that can take advantage of this GPU-accelerated scoring function evaluation. We compared the GPU-accelerated parallel version to the same algorithm running on the CPU and also to the highly optimized sequential CPU-based version. In terms of dependence of the ligand size and the number of rotatable bonds, speedup factors of up to 10 and 7, respectively, can be observed. Finally, a fitness landscape analysis in the context of rigid protein-protein docking was performed. Using a systematic grid-based search methodology, the GPU-accelerated version outperformed the CPU-based version with speedup factors of up to 60.     

Context-based identification of protein-protein interfaces and "hot-spot" residues

Reliable determination of protein-protein interaction sites is of critical importance for structure-based design of small molecules modulating protein function through macromolecular interfaces. We present an alignment-free computational method for prediction of protein-protein interface residues. The method ("iPred") is based on a knowledge-based scoring function adapted from the field of protein folding and small molecule docking. Based on a training set of 394 hetero-dimeric proteins iPred achieves sustained accuracy on an external unbound test set. Prediction robustness was assessed from more than 1500 diverse complexes containing homo- and hetero-dimers. The technique does not rely on sequence conservation, so that rapid interface identification is possible even for proteins for which homologs are unknown or lack conserved residue patterns in interface region. Functional "hot-spot" residues are enriched among the predicted interface residues, rendering the method predestined for macromolecular binding site identification and drug design studies aiming at modulating protein-protein interaction that might influence protein function. For a comparative structural model of peptidase HtrA from Helicobacter pylori, we performed mutation studies for predicted hot-spot residues, which were confirmed as functionally relevant for HtrA activity or oligomerization.     

Brownian dynamics simulation of protein association

Summary The Brownian Dynamics (BD) method is applied to study the diffusive dynamics and interaction of two proteins, cytochrome c (CYTC) and cytochrome c peroxidase (CYP). We examine the role of protein electrostatic charge distribution in the facilitation of protein-protein docking prior to the electron transfer step, assessing the influence of individual charged amino acid residues. Accurate interaction potentials are computed by iterating the linearized Poisson-Boltzmann (PB) equation around the larger protein CYP. The low dielectric constant inside proteins, electrolyte screening effects and irregular protein surface topography are taken into account. We observe a large ensemble of electrostatically stable encounter complexes seemingly with acceptable geometric requirements for electron transfer rather than a single dominant complex. Stabilities of the large variety of docking complexes are rationalized in terms of generalized charged residue complementarities. However, it is found that the electrostatic interactions giving rise to complex stabilities are somewhat nonspecific in nature. A large series of additional simulations are performed in which individual charged residues on CYTC have been chemically modified. Resulting perturbations of the association rate are significant and qualitatively similar to results observed in comparable kinetics experiments. We therefore demonstrate the potential of the Brownian dynamics method to estimate the effects of site-directed mutagenesis on protein-protein and protein-ligand diffusional association rates.     

Comparison of two implementations of the incremental construction algorithm in flexible docking of thrombin inhibitors

A set of 32 known thrombin inhibitors representing different chemical classes has been used to evaluate the performance of two implementations of incremental construction algorithms for flexible molecular docking: DOCK 4.0 and FlexX 1.5. Both docking tools are able to dock 10–35% of our test set within 2 Å of their known, bound conformations using default sampling and scoring parameters. Although flexible docking with DOCK or FlexX is not able to reconstruct all native complexes, it does offer a significant improvement over rigid body docking of single, rule-based conformations, which is still often used for docking of large databases. Docking of sets of multiple conformers of each inhibitor, obtained with a novel protocol for diverse conformer generation and selection, yielded results comparable to those obtained by flexible docking. Chemical scoring, which is an empirically modified force field scoring method implemented in DOCK 4.0, outperforms both interaction energy scoring by DOCK and the Böhm scoring function used by FlexX in rigid and flexible docking of thrombin inhibitors. Our results indicate that for reliable docking of flexible ligands the selection of anchor fragments, conformational sampling and currently available scoring methods still require improvement.     

Simulation of conformational changes occurring when a protein interacts with its receptor

In order to simulate the conformational changes occurring when a protein interacts with its receptor, we firstly evaluated the structural differences between the experimental unbound and bound conformations for selected proteins and created theoretical complexes by replacing, in each experimental complex, the protein-bound with the protein-unbound chain. The theoretical models were then subjected to additional modeling refinements to improve the side chain geometry. Comparing the theoretical and experimental complexes in term of structural and energetic factors is resulted that the refined theoretical complexes became more similar to the experimental ones. We applied the same procedure within an homology modeling experiment, using as templates the experimental structures of human interleukin-1beta (IL-1beta) unbound and bound with its receptor, to build models of the homologous proteins from mouse and trout in unbound and bound conformations and to simulate the interaction with the related receptors. Our results suggest that homology modeling techniques are sensitive to differences between bound and unbound conformations, and that modeling with accuracy the side chains in the complex improves the interaction and molecular recognition. Moreover, our refinement procedure could be used in protein-protein interaction studies and, also, applied in conjunction with rigid-body docking when is not available the protein-bound conformation.     

Flexible protein‐protein docking based on Best‐First search algorithm

We developed a new high resolution protein‐protein docking method based on Best‐First search algorithm that loosely imitates protein‐protein associations. The method operates in two stages: first, we perform a rigid search on the unbound proteins. Second, we search alternately on rigid and flexible degrees of freedom starting from multiple configurations from the rigid search. Both stages use heuristics added to the energy function, which causes the proteins to rapidly approach each other and remain adjacent, while optimizing on the energy. The method deals with backbone flexibility explicitly by searching over ensembles of conformations generated before docking. We ran the rigid docking stage on 66 complexes and grouped the results into four classes according to evaluation criteria used in Critical Assessment of Predicted Interactions (CAPRI; “high,” “medium,” “acceptable,” and “incorrect”). Our method found medium binding conformations for 26% of the complexes and acceptable for additional 44% among the top 10 configurations. Considering all the configurations, we found medium binding conformations for 55% of the complexes and acceptable for additional 39% of the complexes. Introducing side‐chains flexibility in the second stage improves the best found binding conformation but harms the ranking. However, introducing side‐chains and backbone flexibility improve both the best found binding conformation and the best found conformation in the top 10. Our approach is a basis for incorporating multiple flexible motions into protein‐protein docking and is of interest even with the current use of a simple energy function. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Design and structure of peptide and peptidomimetic antagonists of protein-protein interaction

Peptides based on the amino acid sequences found at protein-protein interaction sites make excellent leads for antagonist development. A statistical picture of amino acids involved in protein-protein interactions indicates that proteins recognize and interact with one another through the restricted set of specialized interface amino acid residues, Pro, Ile, Tyr, Trp, Asp and Arg. These amino acids represent residues from each of the three classes of amino acids, hydrophobic, aromatic and charged, with one anionic and one cationic residue at neutral pH. The use of peptides as drug leads has been successfully used to search for antagonists of cell-surface receptors. Peptide, peptidomimetic, and non-peptide organic inhibitors of a class of cell surface receptors, the integrins, currently serve as therapeutic and diagnostic imaging agents. In this review, we discuss the structural features of protein-protein interactions as well as the design of peptides, peptidomimetics, and small organic molecules for the inhibition of protein-protein interactions. Information gained from studying inhibitors of integrin functions is now being applied to the design and testing of inhibitors of other protein-protein interactions. Most drug development progress in the past several decades has been made using the enzyme binding-pocket model of drug targets. Small molecules are designed to fit into the substrate-binding pockets of proteins based on a lock-and-key, induced-fit, or conformational ensemble model of the protein binding site. Traditionally, enzymes have been used as therapeutic drug targets because it was easier to develop rapid, sensitive screening assays, and to find low molecular weight inhibitors that blocked the active site. However, for proteins which interact with other proteins, rather than with small substrate molecules, the lack of binding pockets means that this approach will not generally succeed. There exist many diseases in which the inhibition of protein-protein interactions would provide therapeutic benefit, but there are no general methods available to address such problems. The focus of the first part of this review is to discuss the features of protein-protein interactions which may serve as general guidelines for the development and design of inhibitors for protein-protein interactions. In the second part we focus on the design of peptides (lead compounds) and their conversion into peptidomimetics or small organic molecules for the inhibition of protein-protein interactions. We draw examples from the important and emerging area of integrin-based cell adhesion and show how the principles of protein-protein interactions are followed in the discovery, optimization and usage of specific protein interface peptides as drug leads.     

A strategy based on protein-protein interface motifs may help in identifying drug off-targets

Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can "attack" nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, "The Interface Attack", based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding to others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model that we call "Protein Interface and Interaction Network (P2IN)", which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces and which proteins may compete to bind the same surface region. We built the P2IN with the p53 signaling network and performed network robustness analysis. We show that (1) "hitting" frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than the attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.     

VISCANA: visualized cluster analysis of protein-ligand interaction based on the ab initio fragment molecular orbital method for virtual ligand screening

We have developed a visualized cluster analysis of protein-ligand interaction (VISCANA) that analyzes the pattern of the interaction of the receptor and ligand on the basis of quantum theory for virtual ligand screening. Kitaura et al. (Chem. Phys. Lett. 1999, 312, 319-324.) have proposed an ab initio fragment molecular orbital (FMO) method by which large molecules such as proteins can be easily treated with chemical accuracy. In the FMO method, a total energy of the molecule is evaluated by summation of fragment energies and interfragment interaction energies (IFIEs). In this paper, we have proposed a cluster analysis using the dissimilarity that is defined as the squared Euclidean distance between IFIEs of two ligands. Although the result of an ordered table by clustering is still a massive collection of numbers, we combine a clustering method with a graphical representation of the IFIEs by representing each data point with colors that quantitatively and qualitatively reflect the IFIEs. We applied VISCANA to a docking study of pharmacophores of the human estrogen receptor alpha ligand-binding domain (57 amino acid residues). By using VISCANA, we could classify even structurally different ligands into functionally similar clusters according to the interaction pattern of a ligand and amino acid residues of the receptor protein. In addition, VISCANA could estimate the correct docking conformation by analyzing patterns of the receptor-ligand interactions of some conformations through the docking calculation.