蛋白质相互作用预测、设计与调控

蛋白质相互作用是生命活动在分子水平上的基本事件. 蛋白质相互作用的三维图像可以给出关键生命活动过程的分子细节. 了解蛋白质相互作用的原理有助于揭示生命活动的机制, 并在此基础上开展有重要价值的蛋白质设计. 本文对于蛋白质相互作用预测、设计和调控研究的近期进展进行了总结归纳, 介绍了作者实验室在相关领域的研究进展, 并对今后的研究方向进行了展望. 主要包括: (1) 蛋白质相互作用网络、蛋白质相互作用机制和蛋白质复合物结构计算分析; (2) 基于序列、结合位点以及复合物结构的蛋白质相互作用预测; (3)蛋白质相互作用设计方法; (4) 利用化学分子调控蛋白质相互作用的方法; (5) 针对蛋白质相互作用的蛋白质药物设计方法.     

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.     

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.     

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。     

分析蛋白质-蛋白质相互作用界面的新方法

疏水性和氢键是蛋白质-蛋白质相互作用中的主要因素.提出一种新的计算方法，从蛋白受体的结合部位推导出蛋白配体相应部位应该具有的疏水性质和氢键性质.应用这种方法可以很容易地找出影响相互作用的关键残基，并且将界面的这两种特征用图形软件显示出来.在应用到实际蛋白-蛋白相互作用中时，发现它的用途并不限于此.它可以作为研究蛋白相互作用的一个基本工具.     

基于磷脂膜的界面相互作用研究

磷脂是一类非常重要的生物分子,它是细胞膜的主要组成部分,同时也在诸多生命活动（如细胞激活、代谢维持和激素分泌等）中发挥着不可替代的作用。磷脂种类繁多,且具有自组装能力强、生物相容性好、无细胞毒性、易于获得等一系列优点。作为一种最典型的界面材料,磷脂膜特殊的双层结构及出色的生物学性能引起了科研人员的广泛关注,其能够模拟生物膜结构,有助于研究界面上的分子特征及作用行为。此外,磷脂可被用作生物医学材料,改性磷脂以及磷脂与纳米颗粒的复合物在肿瘤成像技术、药物靶向递送系统等方面具有良好的发展前景,显著促进了新型生物材料的开发与进步。本文先归纳了磷脂的分类,并比较了磷脂在不同基底的吸附行为;之后重点分析了磷脂膜界面的选择性识别功能以及与多肽、酶、蛋白质等生物分子之间的相互作用;最后对基于磷脂膜的生物材料在生物传感、药物研究和成像技术中的应用作出了展望。     

量子点与蛋白质相互作用热力学

量子点(QDs)具有宽激发窄发射、量子产率高、发射波长可调和抗光漂白等优异的光学性质,因而在生物医学成像与示踪、生物传感等方面有着广泛的应用前景。量子点进入生命体系后,首先会遇到蛋白质,量子点与蛋白质之间发生相互作用后,蛋白质的结构和功能会因此发生变化,量子点的性能及应用也会发生改变。研究量子点与蛋白质的相互作用规律,可以为量子点的精细设计、高效应用以及生物安全性评价提供理论依据。本文在总结国内外相关文献和本课题组工作的基础上,介绍了量子点与蛋白质相互作用的热力学方法;重点从热力学角度揭示量子点与蛋白质相互作用的机制。     

蛋白质相互作用的研究方法

生物体的生理功能主要由细胞中的蛋白质控制和调节。其中,多数蛋白质是通过与配体结合或是作为蛋白质复合物中的一部分参与细胞的代谢过程。因此,研究蛋白质间的相互作用是理解生命活动的基础。本文对现有蛋白质相互作用的研究方法和技术进行了评述。     

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.     

蛋白质分子的电学性质、结构与生物活性*

在生命体内,蛋白质通常固着在膜载体上与其它分子相互作用而参与生命活动,所以承受各向异性压力的蛋白质是其存在和功能化的基本形式。设计和研究蛋白质分子在各向异性压力下的分子结构、力学性质和电学/电化学性质不仅对深入理解蛋白质的生物活性至关重要,而且有助于促进蛋白质分子在分子电子器件中的应用。本文综述了利用导电原子力显微镜对蛋白质分子的电学性质的研究进展。在不同的探针压力下,蛋白质分子发生不同程度的形变,表现了不同的电子输运机理。由此可以进一步推测蛋白质分子的生物活性。     

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.     

Structure‐Based Design of Inhibitors of Protein–Protein Interactions: Mimicking Peptide Binding Epitopes

Protein–protein interactions (PPIs) are involved at all levels of cellular organization, thus making the development of PPI inhibitors extremely valuable. The identification of selective inhibitors is challenging because of the shallow and extended nature of PPI interfaces. Inhibitors can be obtained by mimicking peptide binding epitopes in their bioactive conformation. For this purpose, several strategies have been evolved to enable a projection of side chain functionalities in analogy to peptide secondary structures, thereby yielding molecules that are generally referred to as peptidomimetics. Herein, we introduce a new classification of peptidomimetics (classes A–D) that enables a clear assignment of available approaches. Based on this classification, the Review summarizes strategies that have been applied for the structure‐based design of PPI inhibitors through stabilizing or mimicking turns, β‐sheets, and helices.     

Role of protein flexibility in the design of Bcl-XL targeting agents: insight from molecular dynamics

Detailed understanding of protein–ligand interactions is crucial to the design of more effective drugs. This is particularly true when targets are protein interfaces which have flexible, shallow binding sites that exhibit substantial structural rearrangement upon ligand binding. In this study, we use molecular dynamics simulations and free energy calculations to explore the role of ligand-induced conformational changes in modulating the activity of three generations of Bcl-X_L inhibitors. We show that the improvement in the binding affinity of each successive ligand design is directly related to a unique and measurable reduction in local flexibility of specific regions of the binding groove, accompanied by the corresponding changes in the secondary structure of the protein. Dynamic analysis of ligand–protein interactions reveals that the latter evolve with each new design consistent with the observed increase in protein stability, and correlate well with the measured binding affinities. Moreover, our free energy calculations predict binding affinities which are in qualitative agreement with experiment, and indicate that hydrogen bonding to Asn100 could play a prominent role in stabilizing the bound conformations of latter generation ligands, which has not been recognized previously. Overall our results suggest that molecular dynamics simulations provide important information on the dynamics of ligand–protein interactions that can be useful in guiding the design of small-molecule inhibitors of protein interfaces. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Computational alanine scanning mutagenesis--an improved methodological approach

Alanine scanning mutagenesis of protein-protein interfacial residues can be applied to a wide variety of protein complexes to understand the structural and energetic characteristics of the hot-spots. Binding free energies have been estimated with reasonable accuracy with empirical methods, such as Molecular Mechanics/Poisson-Boltzmann surface area (MM-PBSA), and with more rigorous computational approaches like Free Energy Perturbation (FEP) and Thermodynamic Integration (TI). The main objective of this work is the development of an improved methodological approach, with less computational cost, that predicts accurately differences in binding free energies between the wild-type and alanine mutated complexes (DeltaDeltaG(binding)). The method was applied to three complexes, and a mean unsigned error of 0.80 kcal/mol was obtained in a set of 46 mutations. The computational method presented here achieved an overall success rate of 80% and an 82% success rate in residues for which alanine mutation causes an increase in the binding free energy > 2.0 kcal/mol (warm- and hot-spots). This fully atomistic computational methodological approach consists in a computational Molecular Dynamics simulation protocol performed in a continuum medium using the Generalized Born model. A set of three different internal dielectric constants, to mimic the different degree of relaxation of the interface when different types of amino acids are mutated for alanine, have to be used for the proteins, depending on the type of amino acid that is mutated. This method permits a systematic scanning mutagenesis of protein-protein interfaces and it is capable of anticipating the experimental results of mutagenesis, thus guiding new experimental investigations.     

Toward quantitative estimates of binding affinities for protein–ligand systems involving large inhibitor compounds: A steered molecular dynamics simulation route

Understanding binding mechanisms between enzymes and potential inhibitors and quantifying protein – ligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled protein – ligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine‐based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of protein – ligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the Michaelis – Menten mechanism with a competitive inhibitor.     

氢氘交换质谱技术在蛋白质和蛋白复合物结构研究中的应用进展

蛋白质是生命功能的执行者,其功能的发挥受自身结构动态变化、与其他生物分子的相互作用及修饰等因素的调节。因此,对蛋白质及蛋白复合物结构的研究有助于揭示重要生命过程中的分子机理与机制。氢氘交换质谱(Hydrogen deuterium exchange mass spectrometry,HDX-MS)是研究蛋白质结构、动态变化和相互作用的强有力工具,也是传统生物物理手段的重要补充。该文综述了HDX-MS的基本原理、机制、实验方法和研究最新进展,并从蛋白质自身动态变化、蛋白质-小分子相互作用、蛋白质-蛋白质相互作用3个方面介绍了近年来HDX-MS在蛋白及蛋白复合物研究中的应用进展。     

Comparative evaluation of MMPBSA and XSCORE to compute binding free energy in XIAP-peptide complexes

Evaluation of binding free energy in receptor-ligand complexes is one of the most important challenges in theoretical drug design. Free energy is directly correlated to the thermodynamic affinity constant, and, as a first step in druglikeness, a lead compound must have this constant in the range of micro- to nanomolar activity. Many efforts have been made to calculate it by rigorous computational approaches, such as free energy perturbation or linear response approximation. However, these methods are still computationally expensive. We focus our work on XIAP, an antiapoptotic protein whose inhibition can lead to new drugs against cancer disease. We report here a comparative evaluation of two completely different methodologies to estimate binding free energy, MMPBSA (a force field based function) and XSCORE (an empirical scoring function), in seven XIAP-peptide complexes using a representative set of structures generated by previous molecular dynamics simulations. Both methods are able to predict the experimental binding free energy with acceptable errors, but if one needs to identify slight differences upon binding, MMPBSA performs better, although XSCORE is not a bad choice taking into account the low computational cost of this method.     

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine,biomedical text mining and network analysis

In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein–protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies.     

What Can We Learn from Molecular Recognition in Protein–Ligand Complexes for the Design of New Drugs?

The understanding of noncovalent interactions in protein–ligand complexes is essential in modern biochemistry and should contribute toward the discovery of new drugs. In the present review, we summarize recent work aimed at a better understanding of the physical nature of molecular recognition in protein–ligand complexes and also at the development and application of new computational tools that exploit our current knowledge on structural and energetic aspects of protein–ligand interactions in the design of novel ligands. These approaches are based on the exponentially growing amount of information about the geometry of protein structures and the properties of small organic molecules exposed to a structured molecular environment. The various contributions that determine the binding affinity of ligands toward a particular receptor are discussed. Their putative binding site conformations are analyzed, and some predictions are attempted. The similarity of ligands is examined with respect to their recognition properties. This information is used to understand and propose binding modes. In addition, an overview of the existing methods for the design and selection of novel protein ligands is given.