结构基因组学研究与核磁共振

各种生物的基因组DNA测序计划的完成,将结构生物学带入了结构基因组学时代.结构基因组学是对所有基因组产物结构的系统性测定,它运用高通量的选择、表达、纯化以及结构测定和计算分析手段,为基因组的每个蛋白质产物提供实验测定的结构或较好的理论模型,这将加速生命科学各个领域的研究.生物信息学、基因工程、结构测定技术等的发展为结构基因组学研究提供了保证.近年来核磁共振在技术方法上的进展,使其成为结构基因组学高通量结构分析中的一个关键方法.     

核磁共振波谱法在蛋白质三维结构解析中的应用

蛋白质特定的三维结构与其生物功能密切相关,因此,研究蛋白质的三维结构有助于揭示其生物功能机制。将核磁共振（NMR）波谱法应用于研究溶液状态下蛋白质的三维结构,能够更加准确地揭示蛋白质结构与生物功能之间的关系。本文综述了NMR解析蛋白质三维结构的理论和技术方法,以及NMR结合其他生物物理手段,并辅以分子建模计算法研究蛋白质三维结构的研究进展和最新方法,为精准解析蛋白质的三维结构提供思路及策略。     

结构基因组学研究概述

随着蛋白质数据库、基因组序列数据库和自动化技术的迅速发展,结构基因组学作为新的预测蛋白质结构的技术显得越发重要。本文对结构基因组学的产生发展进行概述,分别介绍结构基因组技术的功能、实验流程、蛋白质功能的预测方法,并对该学科发展进行了展望。     

蛋白质晶体学技术的发展与展望

从50年前英国科学家解析出第一个蛋白质晶体结构以来,蛋白质晶体学历经数个里程碑式的发展,已经成为一门成熟的高科技学科,是结构生物学的主要研究手段。近年来结构生物学发展迅速并和其他学科相互渗透交叉,特别是受到结构基因组学等热点学科的极大带动。作为结构生物学的基本手段和技术,蛋白质晶体学从解析简单的蛋白质三维结构延伸到解决各类生物大分子及复合物结构,并更加注重研究结构与功能之间的相互关系,派生出诸如基于结构的药物设计等应用性很强的分支。生物技术及计算机技术的飞速发展,尤其是高通量技术在生物学领域的应用,为蛋白质晶体学带来了全新的概念和更加广阔的前景。文章将主要介绍蛋白质晶体学技术的一些历史发展以及对未来的展望。     

结构生物学研究中的eIF-5A的表达与纯化

真核细胞翻译官始因子eIF-5A（eukaryotic initiation factor 5A）是迄今发现的惟一含有特殊氨基酸hypusine残基的蛋白质,其具体生物学功能仍不明确。为了推进对其功能的研究,拟从结构生物学入手,对其结构进行核磁共振（NMR）结构解析。利用GST融合蛋白原核表达系统,将eIF-5A进行原核表达,经过优化表达与纯化条件,得到了高产率与高纯度的可溶性eIF-5A用以进行NMR测试：经过!1H-^15N HSQC NMR实验,发现其适合应用NMR方法进行结构解析,从而为溶液中eIF-5A三维构象的研究奠定了基础.     

工业应用导向的蛋白质结构与功能研究进展

在蛋白质工程、绿色生物制造以及合成生物学等研究领域中,对重要催化反应的重塑和合成路径的优化搭建,都依赖于对相关蛋白质结构与功能的深入了解。合成生物技术近年来的飞速发展对关键菌种及生物催化过程中的蛋白质的性能提出了更高要求,相关研究的关键是获得大批量、高纯度目的蛋白,并进行快速、准确的构效关系研究。中国科学院天津工业生物技术研究所建所10年来,在工业蛋白质领域进行了多年的积累,成功搭建成了蛋白质结构生物学平台;并在植物天然产物合成相关萜类合成酶、白色污染降解的聚对苯二甲酸乙二酯(polyethylene terephthalate, PET)塑料降解酶以及生物质转化利用相关酶等方面获得了一些进展,通过对这些蛋白进行结构和功能的研究,为许多研究工作提供了理论依据。蛋白质结构功能研究相关技术的不断发展,将加速合成生物学的学术和工业应用研究,推动我国生物制造领域的科技创新升级。     

蛋白质NMR样品制备技术

在结构基因组学和结构生物学的研究中,核磁共振技术被广泛地用于测定蛋白质在溶液中的空间结构及其动力学,研究蛋白质与配体的相互作用。但是,核磁共振技术的应用在很大程度上受到样品制备技术的制约。该文就蛋白质NMR样品的制备技术进行综述,主要介绍几种常用的重组蛋白质表达体系、蛋白质同位素标记技术以及缓冲液的选择等方面的进展。     

26S蛋白酶体的结构生物学研究进展

26S蛋白酶体是真核细胞内负责蛋白质降解的主要分子机器,通过特异性降解目的蛋白质,几乎参与了生物体的绝大多数生命活动.26S蛋白酶体在结构上可分为19S调节颗粒和20S核心颗粒两部分.19S调节颗粒负责识别带有泛素链标记的蛋白质底物及对其进行去折叠,并最终将去折叠的蛋白质底物传送至20S核心颗粒中进行降解.由于26S蛋白酶体的结构组成复杂,分子量十分巨大,现有的X-ray技术和NMR技术对其完整结构的解析都无能为力,仅能解析出部分单个蛋白成员或分子量较低的亚复合物晶体结构.而冷冻电镜技术在相当一段时间内处于发展的初级阶段,导致其三维结构的研究进展曾经十分缓慢,严重阻碍了人们对其结构和功能的了解.近年来,随着在X-ray技术领域对大分子复合物结构解析的经验积累和冷冻电镜技术领域的技术革命,完整的26S蛋白酶体三维结构解析取得了飞速的发展.本文回顾了近几年在26S蛋白酶体结构生物学领域的重要进展,并展望了该领域未来的发展及面临的挑战.     

蛋白质结构研究

生物大分子的功能主要取决于它们的三维结构、运动及相互作用。对蛋白质结构的解析可以从根本上阐明蛋白质功能的分子机制和基础,同时也是研究蛋白质功能的一个重途径。本文简述了当前蛋白质结构研究的主要手段,如X射线晶体学、核磁共振波谱学和三维电镜重构方法学等及其优缺点和适用性,总结了目前蛋白质结构研究进展并对将来的发展方向进行了一些探讨。尽管上述三种主要的研究方法已经比较成熟,而且在适用对象和实验方法上有很好的相互补充,但还是有相当多的生物学问题在结构水平上得不到解释和支持。因此,本文对目前蛋白质结构研究的热点和难点——膜蛋白和蛋白质复合物的研究现状和方法做了简要的概述,希望能够引起广大同行的关注。     

预测蛋白质可结晶性研究进展

解析蛋白质的三维结构具有重要的生物学意义,更是蛋白质功能研究和理性药物设计的基础。目前解析蛋白质结构最重要的方法是X-射线衍射晶体学解析技术。但是运用该技术解析蛋白质结构的关键是获得高质量的蛋白质晶体。然而,据统计仅有42%的可溶纯化蛋白质能够得到晶体,即不同蛋白质的可结晶性表现不同。由于实验方法验证蛋白质的可结晶性耗时耗力,因此,有研究者运用计算机模拟的方法预测蛋白质的可结晶性,从而节省资源与成本并且提高实验的成功率。本文结合我们的研究工作,介绍了几种目前较为成功的蛋白质可结晶性预测方法及其研究途径。     

The fragment transformation method to detect the protein structural motifs

To identify functional structural motifs from protein structures of unknown function becomes increasingly important in recent years due to the progress of the structural genomics initiatives. Although certain structural patterns such as the Asp-His-Ser catalytic triad are easy to detect because of their conserved residues and stringently constrained geometry, it is usually more challenging to detect a general structural motifs like, for example, the betabetaalpha-metal binding motif, which has a much more variable conformation and sequence. At present, the identification of these motifs usually relies on manual procedures based on different structure and sequence analysis tools. In this study, we develop a structural alignment algorithm combining both structural and sequence information to identify the local structure motifs. We applied our method to the following examples: the betabetaalpha-metal binding motif and the treble clef motif. The betabetaalpha-metal binding motif plays an important role in nonspecific DNA interactions and cleavage in host defense and apoptosis. The treble clef motif is a zinc-binding motif adaptable to diverse functions such as the binding of nucleic acid and hydrolysis of phosphodiester bonds. Our results are encouraging, indicating that we can effectively identify these structural motifs in an automatic fashion. Our method may provide a useful means for automatic functional annotation through detecting structural motifs associated with particular functions.     

A novel approach to structural alignment using realistic structural and environmental information

In the era of structural genomics, it is necessary to generate accurate structural alignments in order to build good templates for homology modeling. Although a great number of structural alignment algorithms have been developed, most of them ignore intermolecular interactions during the alignment procedure. Therefore, structures in different oligomeric states are barely distinguishable, and it is very challenging to find correct alignment in coil regions. Here we present a novel approach to structural alignment using a clique finding algorithm and environmental information (SAUCE). In this approach, we build the alignment based on not only structural coordinate information but also realistic environmental information extracted from biological unit files provided by the Protein Data Bank (PDB). At first, we eliminate all environmentally unfavorable pairings of residues. Then we identify alignments in core regions via a maximal clique finding algorithm. Two extreme value distribution (EVD) form statistics have been developed to evaluate core region alignments. With an optional extension step, global alignment can be derived based on environment-based dynamic programming linking. We show that our method is able to differentiate three-dimensional structures in different oligomeric states, and is able to find flexible alignments between multidomain structures without predetermined hinge regions. The overall performance is also evaluated on a large scale by comparisons to current structural classification databases as well as to other alignment methods.     

Predicted role for the archease protein family based on structural and sequence analysis of TM1083 and MTH1598, two proteins structurally characterized through structural genomics efforts

Recently, the structures of two proteins belonging to the archease family, TM1083 from Thermotoga maritima and MTH1598 from Methanobacterium thermoautotrophicum, have been solved independently by two Protein Structure Initiative structural genomics pilot centers using X-ray crystallography and NMR, respectively. The archease protein family is a good example of one of the paradoxes of structural genomics: Approximately one third of protein structures produced by structural genomics centers have no known function and are still annotated as "hypothetical proteins" in the Protein Data Bank. In the case of archeases, despite the existence of two protein structures and abundant sequence information, there is still no function assigned to this protein family. Here, our group predicts, based on structural similarity, sequence conservation, and gene context analyses, that members of this protein family might function as chaperones or modulators of proteins involved in DNA/RNA processing. The conservation of genomic context for this protein family is constant from Archaea and Bacteria to humans, and suggests that unannotated open reading frames contiguous to them could be novel RNA/DNA binding proteins.     

Computer-aided NMR assay for detecting natively folded structural domains

Structural genomics projects require strategies for rapidly recognizing protein sequences appropriate for routine structure determination. For large proteins, this strategy includes the dissection of proteins into structural domains that form stable native structures. However, protein dissection essentially remains an empirical and often a tedious process. Here, we describe a simple strategy for rapidly identifying structural domains and assessing their structures. This approach combines the computational prediction of sequence regions corresponding to putative domains with an experimental assessment of their structures and stabilities by NMR and biochemical methods. We tested this approach with nine putative domains predicted from a set of 108 Thermus thermophilus HB8 sequences using PASS, a domain prediction program we previously reported. To facilitate the experimental assessment of the domain structures, we developed a generic 6-hour His-tag-based purification protocol, which enables the sample quality evaluation of a putative structural domain in a single day. As a result, we observed that half of the predicted structural domains were indeed natively folded, as judged by their HSQC spectra. Furthermore, two of the natively folded domains were novel, without related sequences classified in the Pfam and SMART databases, which is a significant result with regard to the ability of structural genomics projects to uniformly cover the protein fold space.     

Methodologies for target selection in structural genomics

As the number of complete genomes that have been sequenced keeps growing, unknown areas of the protein space are revealed and new horizons open up. Most of this information will be fully appreciated only when the structural information about the encoded proteins becomes available. The goal of structural genomics is to direct large-scale efforts of protein structure determination, so as to increase the impact of these efforts. This review focuses on current approaches in structural genomics aimed at selecting representative proteins as targets for structure determination. We will discuss the concept of representative structures/folds, the current methodologies for identifying those proteins, and computational techniques for identifying proteins which are expected to adopt new structural folds.     

MUSTANG: a multiple structural alignment algorithm

Multiple structural alignment is a fundamental problem in structural genomics. In this article, we define a reliable and robust algorithm, MUSTANG (MUltiple STructural AligNment AlGorithm), for the alignment of multiple protein structures. Given a set of protein structures, the program constructs a multiple alignment using the spatial information of the C(alpha) atoms in the set. Broadly based on the progressive pairwise heuristic, this algorithm gains accuracy through novel and effective refinement phases. MUSTANG reports the multiple sequence alignment and the corresponding superposition of structures. Alignments generated by MUSTANG are compared with several handcurated alignments in the literature as well as with the benchmark alignments of 1033 alignment families from the HOMSTRAD database. The performance of MUSTANG was compared with DALI at a pairwise level, and with other multiple structural alignment tools such as POSA, CE-MC, MALECON, and MultiProt. MUSTANG performs comparably to popular pairwise and multiple structural alignment tools for closely related proteins, and performs more reliably than other multiple structural alignment methods on hard data sets containing distantly related proteins or proteins that show conformational changes.     

Using a library of structural templates to recognise catalytic sites and explore their evolution in homologous families

Catalytic site structure is normally highly conserved between distantly related enzymes. As a consequence, templates representing catalytic sites have the potential to succeed at function prediction in cases where methods based on sequence or overall structure fail. There are many methods for searching protein structures for matches to structural templates, but few validated template libraries to use with these methods. We present a library of structural templates representing catalytic sites, based on information from the scientific literature. Furthermore, we analyse homologous template families to discover the diversity within families and the utility of templates for active site recognition. Templates representing the catalytic sites of homologous proteins mostly differ by less than 1A root mean square deviation, even when the sequence similarity between the two proteins is low. Within these sets of homologues there is usually no discernible relationship between catalytic site structure similarity and sequence similarity. Because of this structural conservation of catalytic sites, the templates can discriminate between matches to related proteins and random matches with over 85% sensitivity and predictive accuracy. Templates based on protein backbone positions are more discriminating than those based on side-chain atoms. These analyses show encouraging prospects for prediction of functional sites in structural genomics structures of unknown function, and will be of use in analyses of convergent evolution and exploring relationships between active site geometry and chemistry. The template library can be queried via a web server at and is available for download.     

Protein surface analysis for function annotation in high-throughput structural genomics pipeline

Structural genomics (SG) initiatives are expanding the universe of protein fold space by rapidly determining structures of proteins that were intentionally selected on the basis of low sequence similarity to proteins of known structure. Often these proteins have no associated biochemical or cellular functions. The SG success has resulted in an accelerated deposition of novel structures. In some cases the structural bioinformatics analysis applied to these novel structures has provided specific functional assignment. However, this approach has also uncovered limitations in the functional analysis of uncharacterized proteins using traditional sequence and backbone structure methodologies. A novel method, named pvSOAR (pocket and void Surface of Amino Acid Residues), of comparing the protein surfaces of geometrically defined pockets and voids was developed. pvSOAR was able to detect previously unrecognized and novel functional relationships between surface features of proteins. In this study, pvSOAR is applied to several structural genomics proteins. We examined the surfaces of YecM, BioH, and RpiB from Escherichia coli as well as the CBS domains from inosine-5'-monosphate dehydrogenase from Streptococcus pyogenes, conserved hypothetical protein Ta549 from Thermoplasm acidophilum, and CBS domain protein mt1622 from Methanobacterium thermoautotrophicum with the goal to infer information about their biochemical function.     

De-orphaning the structural proteome through reciprocal comparison of evolutionarily important structural features

Function prediction frequently relies on comparing genes or gene products to search for relevant similarities. Because the number of protein structures with unknown function is mushrooming, however, we asked here whether such comparisons could be improved by focusing narrowly on the key functional features of protein structures, as defined by the Evolutionary Trace (ET). Therefore a series of algorithms was built to (a) extract local motifs (3D templates) from protein structures based on ET ranking of residue importance; (b) to assess their geometric and evolutionary similarity to other structures; and (c) to transfer enzyme annotation whenever a plurality was reached across matches. Whereas a prototype had only been 80% accurate and was not scalable, here a speedy new matching algorithm enabled large-scale searches for reciprocal matches and thus raised annotation specificity to 100% in both positive and negative controls of 49 enzymes and 50 non-enzymes, respectively-in one case even identifying an annotation error-while maintaining sensitivity ( approximately 60%). Critically, this Evolutionary Trace Annotation (ETA) pipeline requires no prior knowledge of functional mechanisms. It could thus be applied in a large-scale retrospective study of 1218 structural genomics enzymes and reached 92% accuracy. Likewise, it was applied to all 2935 unannotated structural genomics proteins and predicted enzymatic functions in 320 cases: 258 on first pass and 62 more on second pass. Controls and initial analyses suggest that these predictions are reliable. Thus the large-scale evolutionary integration of sequence-structure-function data, here through reciprocal identification of local, functionally important structural features, may contribute significantly to de-orphaning the structural proteome.