表达蛋白质质谱分析

对基因编码的蛋白质进行系统分析可以为注释基因组信息和研究疾病发生机理提供参考.质谱因其高通量、高灵敏度和高精度等特点成为蛋白质表达谱研究的核心技术.过去10年,质谱技术的发展大大促进了蛋白质表达谱的研究.本文综述了蛋白质表达谱的定性和定量研究进展,并展望了进一步的研究方向.     

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.     

一种无染料基于序列标签结合焦测序解码的多基因表达量同时测定技术及在大肠癌诊断中的应用

随着人类基因组计划的深入,从基因组微观分子水平研究疾病已经日趋成熟.在基因组的构造解析已完成的基础上进一步对基因功能进行解析,即mRNA及翻译产物蛋白质的情况,也是现今的研究重点之一.目前认为多数疾病都存在着基因表达的变化,如癌症的发生和发展的过程中伴随着基因表达的异常,因此基因表达量的分析能提高治疗的质量及病人的存活率~([1]),已逐渐成为基因分析的主要课题.     

蛋白质的结构预测和分子设计

蛋白质的结构预测及分子设计是适应基因工程的需要发展起来的对蛋白质的空间结构进行研究并在此基础上提供蛋白质改造方案的方法。通过对已知的蛋白质结构数据进行总结、分析,结合分子力学、分子动力学等计算方法可以进行某些种类的结构预测。在对蛋白质的结构与功能研究的基础上可以提出改造方案,从而进行有目的的分子设计。     

天花粉蛋白基因的克隆、序列测定及在大肠杆菌和烟草中的表达

本文利用DNA多聚酶链式反应(PCR)技术,从括楼基因组DNA中扩增并克隆了天花粉蛋白基因,核苷酸序列分析结果表明,我们克隆的是天花粉蛋白的成熟肽及N端23个氨基酸的信号的编码序列,与前人从基因组或cDNA中克隆的该基因的比较发现,其同源性为99.25％,并且证实与发表的蛋白质一级结构的序列有较大差异,将该基因克隆到大肠杆菌高效表达质粒pJLA_(502)的P_RP_L启动子下游,通过温度诱导,得到了表达产物,进一步将该基因克隆到植物中间载体pE_3的花椰菜花叶病毒35S启动子下游,应用根癌农杆菌Ti质粒介导的遗传转化系统,成功地将该基因导入了烟草基因组,获得了转基因植株,Western blotting分析结果证实,天花粉蛋白基因已在大肠杆菌和转基因烟草中表达。     

紫外圆二色光谱预测蛋白质结构的研究方法

介绍了蛋白质紫外圆二色性(CD)产生的原理及其与蛋白质结构的关系。评述了用远紫外CD预测蛋白质二级结构的方法原理、参考蛋白、拟合算法和拟合程序,以及方法存在的问题。近紫外CD与蛋白质的三级结构密切相关,近紫外蛋白质CD反映芳香氨基酸残基、二硫键等微环境的变化,表征着丰富的蛋白质三级结构的信息。     

泉生热袍菌结构基因组的选靶研究

对泉生热袍菌进行了结构基因组的选靶研究，从泉生热袍菌的蛋白组中挑选了20个蛋白质作为第一批进行结构测定的目标，以发现新的蛋白质折叠模式. 选靶研究主要使用了BLAST搜索， PSI-BLAST搜索和ProtoNet数据库搜索等方法. 另外，还用PredictProtein程序对选中的蛋白质进行了二级结构和外形预测. 选中的20个蛋白质中有8个被克隆、表达和纯化，其中2个得到了单晶并收集了X衍射数据. 实验结果和最近一些文献报道的结果表明，挑选的一些蛋白质具有新的折叠模式，表明了这种选靶策略的有效性.     

基因组中硒蛋白的信息学

人类已经步入"后基因组"时代,基因组研究的重心将由测定基因的DNA序列转移到解释生命的所有遗传信息,从分子整体水平对生物学功能的研究,在分子层面上探索人类健康和疾病的奥秘.硒蛋白基因是各种基因组中一类重要的蛋白质基因,从基因组中寻找新的硒蛋白基因,对于硒蛋白生物功能的探索具有十分重要的意义.本文就硒代半胱氨酸插入元件(SECIS)结构特征、从基因组中寻找硒蛋白的生物信息学方法及其研究进展作了简要介绍,并对未来的发展趋势进行了展望.     

深度学习在蛋白质结构预测中的应用及启示

蛋白质结构预测通常指借助计算机计算模拟方法从氨基酸序列推断其三维空间结构.而空间结构决定其生理功能,故结构预测问题尤为重要.基于单纯物理学的预测仅能应对较短蛋白质且精度不高.而基于数据驱动和生物信息学的方法近十多年备受重视.本文主要回顾近十多年来深度学习在蛋白质预测领域的应用,重点介绍Deepmind团队的AlphaFold方法,此方法预测在单域蛋白质达到了中低分辨率实验精度,一定程度上解决了困扰人们五十多年的蛋白质结构预测难题.     

真菌三萜及甾体的生物合成研究进展

三萜和甾体是由六个异戊二烯单元组成的一大类天然产物,具有复杂多样的化学结构和广泛的生物活性.真菌是三萜和甾体化合物发现的重要源泉,但与植物相比,目前从真菌中发现的三萜骨架类型仍然很少,仍有较大的研究空间.基因组挖掘已成为后基因组时代发现新颖天然产物的重要手段,其主要通过与相似功能基因的比较来发现新功能基因.随着高通量测序技术和生物信息技术的飞速发展,一些具有重要生物活性的真菌三萜或甾体的生物合成途径逐渐被阐明,这为利用基因组挖据手段从真菌中发现新颖三萜或甾体化合物奠定了基础.重点介绍近年来在真菌三萜或甾体生物合成方面的研究进展情况.     

蛋白质结构的“限域下最低能量结构片段”假说与蛋白质进化的“石器时代”

蛋白质折叠问题被称为第二遗传密码,至今未破译;蛋白质序列的天书仍然是"句读之不知,惑之不解"。在最近工作的基础上,我们提出了蛋白质结构的"限域下最低能量结构片段"假说。这一假说指出,蛋白质中存在一些关键的长程强相互作用位点,这些位点相当于标点符号,将蛋白质序列的天书变成可读的句子(多肽片段)。这些片段的天然结构是在这些强长程相互作用位点限域下的能量最低状态。完整的蛋白质结构由这些"限域下最低能量结构片段"拼合而成,而蛋白质整体结构并不一定是全局性的能量最低状态。在蛋白质折叠过程中,局部片段的天然结构倾向性为强长程相互作用的形成提供主要基于焓效应的驱动力,而天然强长程相互作用的形成为局部片段的天然结构提供主要基于熵效应的稳定性。在蛋白质进化早期,可能存在一个"石器时代",即依附不同界面(比如岩石)的限域作用而稳定的多肽片段先进化出来,后由这些片段逐步进化(包括拼合)而成蛋白质。     

Alpha-helix and beta-hairpin Folding from experiment,analytical theory and molecular dynamics simulations

The alpha-helix and beta-hairpin are the minimal secondary structure elements of proteins. Identification of the factors governing the formation of these structures independently of the rest of the protein is important for understanding the determinants and rules driving the folding process to a unique native structure. It has been shown that some alpha-helices and beta-hairpins can fold autonomously into native-like structures, either in aqueous solution or in the presence of an organic co-solvent; possible mechanisms of these processes have been considered in literature. The characteristic times for folding of alpha and beta structures are estimated from experiments, simple analytical theories and more detailed computer models. Our aim is to review recent experimental and theoretical studies of folding of alpha and beta structures focusing much attention on beta-hairpins.     

PreSSAPro: a software for the prediction of secondary structure by amino acid properties

PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in smaller but homogeneous data sets corresponding to protein structural classes, i.e. all-alpha, all-beta, or alpha–beta proteins. Predictions result improved by the use of propensities evaluated for the right protein class. PreSSAPro predicts the secondary structure according to the right protein class, if known, or gives a multiple prediction with reference to the different structural classes. The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations. The service is available at the URL http://bioinformatica.isa.cnr.it/PRESSAPRO/.     

PROSIGN: a method for protein secondary structure assignment based on three-dimensional coordinates of consecutive C(alpha) atoms

The automatic assignment of secondary structure from three-dimensional atomic coordinates of proteins is an essential step for the analysis and modeling of protein structures. So different methods based on different criteria have been designed to perform this task. We introduce a new method for protein secondary structure assignment based solely on C(alpha) coordinates. We introduce four certain relations between C(alpha) three-dimensional coordinates of consecutive residues, each of which applies to one of the four regular secondary structure categories: alpha-helix, 3(10)-helix, pi-helix and beta-strand. In our approach, the deviation of the C(alpha) coordinates of each residue from each relation is calculated. Based on these deviation values, secondary structures are assigned to all residues of a protein. We show that our method agrees well with popular methods as DSSP, STRIDE and assignments in PDB files. It is shown that our method gives more information about helix geometry leading to more accurate secondary structure assignment.     

SeqFold – fully automated fold recognition and modeling software – evaluation and application

SeqFold is a fold recognition program based on sequence-similarity detection aided by predicted secondary structure [1–3]. Critical validation and evaluation of SeqFold fold recognition performance based on the latest Critical Assessment of protein Structure Prediction (CASP2) targets has been performed. It has revealed that four out of seven CASP2 threading targets were assigned a correct fold using this method. SeqFold has also been applied to the problem of fold recognition for leptin. Mice with a defective leptin gene are extremely obese and diabetic. Leptin does not exhibit clear sequence homology to any protein with known structure. SeqFold predicts that leptin belongs to the class of short-chain four-helical cytokines. The structure of leptin, which has recently been solved by X-ray crystallography, reveals that leptin is a long-chain four-helical cytokine. The 3D model of leptin demonstrates that SeqFold alignment-based homology modeling captures essential features of the leptin structure. Received: 25 May 1998 / Accepted: 4 August 1998 / Published online: 2 November 1998     

Deciphering globular protein sequence–structure relationships: from observation to prediction

Careful comparison of proteins sharing a same fold but only low or no sequence identity should allow a better understanding of the coding of three-dimensional structures by amino acid sequences. It has already been shown that positions of a given fold occupied mainly by hydrophobic residues in the different proteins of a structural family share very specific physical properties and participate in stabilization of the protein domain. They probably also play a crucial role in the very first steps of folding [ Poupon A, Mornon J.-P (1999) FEBS Lett. 452: 283–289; Mirny LA, Shaknovich EI (1999) J. Mol. Biol. 291: 177–196]. To further understand the sequence–structure relationship, we studied the correlation between allowed mutations at a given three-dimensional position and some of its physical properties. The different amino acids were divided in three groups (hydrophobic, nonpolar or weakly polar and polar or charged), and a correlation was established between the occupation rate of each group at a given position in the fold and the burying, the side-chain dispersion, the interposition distances and the ability to form a network of directly interacting residues. The results are then applied to predict some solvent accessibility. We show that this property can be accurately predicted for about 70% of the residues, providing precious information concerning the corresponding three-dimensional structures. The results are used to predict other structural features, as secondary structures, compactness or long-range interactions between residues remote in sequence. This information will allow the number of possible structures for a given sequence to be reduced considerably, simplifying the ab initio modelling problem to a level where it might be solved by computing methods. Received: 7 October 2000 / Accepted: 5 January 2001 / Published online: 3 April 2001     

Key issues in the computational simulation of GPCR function: representation of loop domains

Some key concerns raised by molecular modeling and computational simulation of functional mechanisms for membrane proteins are discussed and illustrated for members of the family of G protein coupled receptors (GPCRs). Of particular importance are issues related to the modeling and computational treatment of loop regions. These are demonstrated here with results from different levels of computational simulations applied to the structures of rhodopsin and a model of the 5-HT2A serotonin receptor, 5-HT2AR. First, comparative Molecular Dynamics (MD) simulations are reported for rhodopsin in vacuum and embedded in an explicit representation of the membrane and water environment. It is shown that in spite of a partial accounting of solvent screening effects by neutralization of charged side chains, vacuum MD simulations can lead to severe distortions of the loop structures. The primary source of the distortion appears to be formation of artifactual H-bonds, as has been repeatedly observed in vacuum simulations. To address such shortcomings, a recently proposed approach that has been developed for calculating the structure of segments that connect elements of secondary structure with known coordinates, is applied to 5-HT2AR to obtain an initial representation of the loops connecting the transmembrane (TM) helices. The approach consists of a simulated annealing combined with biased scaled collective variables Monte Carlo technique, and is applied to loops connecting the TM segments on both the extra-cellular and the cytoplasmic sides of the receptor. Although this initial calculation treats the loops as independent structural entities, the final structure exhibits a number of interloop interactions that may have functional significance. Finally, it is shown here that in the case where a given loop from two different GPCRs (here rhodopsin and 5-HT2AR) has approximately the same length and some degree of sequence identity, the fold adopted by the loops can be similar. Thus, in such special cases homology modeling might be used to obtain initial structures of these loops. Notably, however, all other loops in these two receptors appear to be very different in sequence and structure, so that their conformations can be found reliably only by ab initio, energy based methods and not by homology modeling.     

A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology. In this article a combined classifier based on the information content of extracted features from the primary structure of protein has been introduced to face this challenging problem. In the first stage of our proposed two-tier architecture, there are several classifiers each of which is trained with a different sequence based feature vector. Apart from the application of the predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, and different dimensions of pseudo-amino acid composition vectors in similar studies, the position specific scoring matrix (PSSM) has also been used to improve the correct classification rate (CCR) in this study. Using K-fold cross validation on training dataset related to 27 famous folds of SCOP, the 28 dimensional probability output vector from each evidence theoretic K-NN classifier is used to determine the information content or expertness of corresponding feature for discrimination in each fold class. In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class. The expertness factor of each classifier in each fold class has been used to calculate the fuzzy integral operator weights. Results make it possible to provide deeper interpretation about the effectiveness of each feature for discrimination in target classes for query proteins.     

Bacterial protein structures reveal phylum dependent divergence

Protein sequence space is vast compared to protein fold space. This raises important questions about how structures adapt to evolutionary changes in protein sequences. A growing trend is to regard protein fold space as a continuum rather than a series of discrete structures. From this perspective, homologous protein structures within the same functional classification should reveal a constant rate of structural drift relative to sequence changes. The clusters of orthologous groups (COG) classification system was used to annotate homologous bacterial protein structures in the Protein Data Bank (PDB). The structures and sequences of proteins within each COG were compared against each other to establish their relatedness. As expected, the analysis demonstrates a sharp structural divergence between the bacterial phyla Firmicutes and Proteobacteria. Additionally, each COG had a distinct sequence/structure relationship, indicating that different evolutionary pressures affect the degree of structural divergence. However, our analysis also shows the relative drift rate between sequence identity and structure divergence remains constant.     

Structure-based identification and clustering of protein families and superfamilies

Summary We describe an approach to protein structure comparison designed to detect distantly related proteins of similar fold, where the procedure must be sufficiently flexible to take into account the elasticity of protein folds without losing specificity. Protein structures are represented as a series of secondary structure elements, where for each element a local environment describes its relations with the elements that surround it. Secondary structures are then aligned by comparing their features and local environments. The procedure is illustrated with searches of a database of 468 protein structures in order to identify proteins of similar topology to porcine pepsin, porphobilinogen deaminase and serum amyloid P-component. In all cases the searches correctly identify protein structures of similar fold as the search proteins. Multiple cross-comparisons of protein structures allow the clustering of proteins of similar fold. This is exemplified with a clustering of /- and -class protein structures. We discuss applications of the comparison and clustering of three-dimensional protein structures to comparative modelling and structure-based protein design.