丝氨酸蛋白酶超家族分子结构进化研究

采用刚体结构比较法进行蛋白质的结构比较,根据结构比较分数构建分子进化树, 研究丝氨酸蛋白酶超家族分子的进化规律。对分子进化树进行了一些初步分析,得到了一些有意义的结果。根据蛋白质的进化,可以比较精确的确定某物种的进化地位,对于物种的分类具有重要意义。通过对超家族分子进化的研究可以了解蛋白质超家族不同蛋白质之间的亲缘关系和蛋白质之间的进化差异,对于蛋白质工程分子设计提供帮助,对蛋白质结构预测具有一定意义     

趋化因子及其受体基因家族的系统进化分析

通过分析现有的趋化因子和趋化因子受体的氨基酸序列,用距离法和最简约法构建了聚类图,探讨了趋化因子和趋化因子受体基因家族的系统演化特征。可见基因家族成员的分化早于脊椎动物的分化。不同物种的同一种基因的聚类关系能较好地反映物种经因子受体的进化速度不同,其中ＣＸＣＲ４的进化速率最低。趋化因子和趋化因子受体可能都起源于少数几个原始的基因,病毒编码与寄主相似的趋化因子或受体是进化过程中分子模拟的结果。     

节肢动物血蓝蛋白家族的组成与演化

血蓝蛋白是动物界的三类呼吸功能蛋白之一,目前仅发现于节肢动物和软体动物等少数动物类群中。不同亚型的血蓝蛋白有不同的理化性质和序列,但均结合氧分子,并以六聚体,甚至更复杂的聚合体结构存在。血蓝蛋白与酚氧化酶、拟血蓝蛋白、昆虫储存蛋白以及昆虫储存蛋白受体等结构类似、进化上近缘的分子共同组成了血蓝蛋白超家族。该文主要介绍了血蓝蛋白家族成员在节肢动物四大类群（螯肢动物、多足动物、甲壳动物和六足动物）中已知的分布、结构和功能,并重点综述了血蓝蛋白家族成员在节肢动物系统演化研究中发挥的独特而有效的作用,进一步强调了在更多节肢动物类群中研究血蓝蛋白家族的功能和演化的重要性。     

植物类型Ⅲ聚酮合酶超家族基因结构、功能及代谢产物

植物聚酮类化合物主要包括酚类、芪类及类黄酮化合物等,在植物花色、防止紫外线伤害、预防病原菌、昆虫危害以及作为植物与环境互作信号分子方面行使着重要的生物学功能。该类化合物具有显著多样的生物学活性,对人体保健及疾病治疗有显著意义。植物类型Ⅲ聚酮化合物合酶(PKS)在该类化合物生物合成起始反应中行使着关键作用,决定该类化合物基本分子骨架建成和代谢途径碳硫走向,为合成途径关键酶和限速酶。以查尔酮合酶为原型酶的植物类型Ⅲ PKS超家族是研究系统进化和蛋白结构与功能关系的模式分子家族,目前已经分离得到14种植物类型Ⅲ PKS基因,这些同祖同源基因及其表达产物既有共性,也表现出许多独特个性,这些个性赋予此类次生代谢产物结构上的多样性。以下综述了植物类型Ⅲ PKS超家族基因结构、功能及代谢产物研究进展。     

植物类型III聚酮合酶超家族基因结构、功能及代谢产物

植物聚酮类化合物主要包括酚类、芪类及类黄酮化合物等,在植物花色、防止紫外线伤害、预防病原菌、昆虫危害以及作为植物与环境互作信号分子方面行使着重要的生物学功能。该类化合物具有显著多样的生物学活性,对人体保健及疾病治疗有显著意义。植物类型III 聚酮化合物合酶 (PKS) 在该类化合物生物合成起始反应中行使着关键作用,决定该类化合物基本分子骨架建成和代谢途径碳硫走向,为合成途径关键酶和限速酶。以查尔酮合酶为原型酶的植物类型III PKS超家族是研究系统进化和蛋白结构与功能关系的模式分子家族,目前已经分离得到14种植物类型III PKS基因,这些同祖同源基因及其表达产物既有共性,也表现出许多独特个性,这些个性赋予此类次生代谢产物结构上的多样性。以下综述了植物类型III PKS超家族基因结构、功能及代谢产物研究进展。     

心脏肌球蛋白结合蛋白C的生物信息学分析

MYBPC3基因突变是家族性肥厚型心肌病的原因之一。本文对心脏肌球蛋白结合蛋白C基因(cardic myosin binding protein C,MYBPC3)及其编码蛋白(c My BP-C)进行生物信息学分析。运用生物信息学相关数据库和在线生物学软件分析MYBPC3基因的结构与突变位点,对c My BP-C蛋白分子物种间的序列同源性、蛋白质空间结构、理化性质、组织特异性、蛋白质翻译后修饰、蛋白质相互作用网络进行分析。结果表明人MYBPC3基因mRNA全长为4 217 bp,编码区为3 825 bp,MYBPC3基因编码1 274个氨基酸组成的多肽,与物种进化程度一致,属于免疫球蛋白超家族,是酸性亲水蛋白,稳定性不高,其主要二级结构元件为随机卷曲。与c My BP-C存在相互作用的基因和蛋白主要是磷酸激酶与肌小节组成成分。本文对MYBPC3基因进行生物信息学分析,为深入研究MYBPC3基因的分子功能以及靶向治疗遗传性心肌病提供一定的依据。     

细胞因子受体超家族的结构保守性及生物学意义

细胞因子受体超家族是目前数量最大的受体家族,其成员在序列与空间结构上都有一定的保守性,并存在着共用链现象,说明此超家族各成员可能从同一始祖基因进化而来,研究其结构保守性,对于寻找此超家族新的成员,进行同源蛋白质结构预测,探讨其信号转导机制,都有重要意义。     

基于全基因组结构域信息的进化树构建

重建生物进化树一直以来都是进化生物学家的梦想。大量物种全基因组的测序使得我们可以从全基因组水平上构建进化树,来研究各个物种之间的进化关系。本文采用2种统计方法和3种距离计算方法,在全基因组水平上建立基于蛋白质结构的进化树。选取93个物种的全基因组作为分析对象,涵盖了3个超界:真核生物,细菌和古细菌。而结果也正确地将这些物种分为三个大类,每个大分支内部的物种聚类情况也基本和这些物种的形态学分类相吻合。并将这些方法的聚类结果与物种分类的结果相比较,得出丰度的统计方法和基于两向量夹角的距离计算方法这种组合在构建进化树上比其他组合更好。     

蛋白质序列和结构的保守性与其功能的关系

以蛋白质酪氨酸磷酸酶为模型,通过序列和结构的比较,对蛋白质序列、结构的保守性与其和进化的关系问题进行了研究。结果显示,虽然在酪氨酸磷酸酶超家族分子的序列中,仅有３个与其催化功能密切相关的残基是高度保守的,但它们功能结构域的核心拓扑结构却明显类似,其中存在着βａβ和βａβａ。２个保守的结构单元;此外,它们活性位点的拓扑结构也极其相似,因此,在保持蛋白南功能方面具有重要作用的残基是高度保守的,而具维持     

阳离子-π相互作用在两种典型蛋白质折叠型中的偏好性

阳离子-π相互作用是一种在阳离子和芳香性体系之间形成的一种作用力。在蛋白质中,带正电荷的氨基酸(Lys、Arg)和芳香族氨基酸(Phe、Tyr、Trp)之间可以形成阳离子-π相互作用。对α/β类蛋白中两种典型折叠类型――单绕和双绕的研究表明:(1)单绕结构中阳离子-π相互作用的分布密度是双绕结构的2.3倍。(2)Arg-Phe组合偏好在双绕中出现,Arg-Tyr组合偏好在单绕中出现。(3)在单绕中除Lys-Phe组合外,其余5种组合的阳离子-π相互作用能量高于双绕的对应组合,其中以Arg-Trp组合的能量最高。(4)在单绕结构中,样本所含氨基酸残基数量和样本中阳离子-π的数量有明显的相关性,在双绕结构中没有发现类似的相关性。(5)在单绕和双绕结构当中,把阳离子-π相互作用能量分解为静电能和范德华力能揭示出静电能与范德华力能之比接近2∶1,静电作用在阳离子-π相互作用中起主要作用。     

Incremental generation of summarized clustering hierarchy for protein family analysis

MOTIVATION: Protein sequence clustering has been widely exploited to facilitate in-depth analysis of protein functions and families. For some applications of protein sequence clustering, it is highly desirable that a hierarchical structure, also referred to as dendrogram, which shows how proteins are clustered at various levels, is generated. However, as the sizes of contemporary protein databases continue to grow at rapid rates, it is of great interest to develop some summarization mechanisms so that the users can browse the dendrogram and/or search for the desired information more effectively. RESULTS: In this paper, the design of a novel incremental clustering algorithm aimed at generating summarized dendrograms for analysis of protein databases is described. The proposed incremental clustering algorithm employs a statistics-based model to summarize the distributions of the similarity scores among the proteins in the database and to control formation of clusters. Experimental results reveal that, due to the summarization mechanism incorporated, the proposed incremental clustering algorithm offers the users highly concise dendrograms for analysis of protein clusters with biological significance. Another distinction of the proposed algorithm is its incremental nature. As the sizes of the contemporary protein databases continue to grow at fast rates, due to the concern of efficiency, it is desirable that cluster analysis of a protein database can be carried out incrementally, when the protein database is updated. Experimental results with the Swiss-Prot protein database reveal that the time complexity for carrying out incremental clustering with k new proteins added into the database containing n proteins is O(n2betalogn), where beta congruent with 0.865, provided that k < n. AVAILABILITY: The Linux executable is available on the following supplementary page.     

试用聚类方法探讨中国雪雀的分类地位

应用聚类方法对雪雀属、种及部分亚种的分类地位进行初步探索,选取一定的数量性状,取平均值组成原始数据矩阵。原始数据矩阵再进一步标准化,然后应用距离系数和相关系数进行聚类分析。比较了数量性状的聚类结果与传统的形态特征分类之间的异同,并探讨了雪雀属种级分类单元的亲缘关系和分类地位。     

Clustering and visualizing similarity networks of membrane proteins

We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence–structure–function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence–structure–function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. Proteins 2015; 83:1450–1461. © 2015 Wiley Periodicals, Inc.     

Collective dynamics differentiates functional divergence in protein evolution

Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynamic evolution have largely been left out of molecular evolution studies. Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins. We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA (ZAMF). Our predictions are within ∼2.7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors. Beyond static structure prediction, a particular feature of ZAMF is that it generates protein dynamics information. We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics. Strikingly, our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace, while those sharing the same function are simultaneously clustered together and distant from those, that have functionally diverged. Dynamic analysis also enables those mutations that most affect dynamics to be identified. It correctly predicts all mutations (functional and permissive) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function.     

Human Protein Cluster Analysis Using Amino Acid Frequencies

The paper focuses on the development of a software tool for protein clustering according to their amino acid content. All known human proteins were clustered according to the relative frequencies of their amino acids starting from the UniProtKB/Swiss-Prot reference database and making use of hierarchical cluster analysis. Results were compared to those based on sequence similarities. Results: Proteins display different clustering patterns according to type. Many extracellular proteins with highly specific and repetitive sequences (keratins, collagens etc.) cluster clearly confirming the accuracy of the clustering method. In our case clustering by sequence and amino acid content overlaps. Proteins with a more complex structure with multiple domains (catalytic, extracellular, transmembrane etc.), even if classified very similar according to sequence similarity and function (aquaporins, cadherins, steroid 5-alpha reductase etc.) showed different clustering according to amino acid content. Availability of essential amino acids according to local conditions (starvation, low or high oxygen, cell cycle phase etc.) may be a limiting factor in protein synthesis, whatever the mRNA level. This type of protein clustering may therefore prove a valuable tool in identifying so far unknown metabolic connections and constraints.     

Conserved residue clustering and protein structure prediction

Protein residues that are critical for structure and function are expected to be conserved throughout evolution. Here, we investigate the extent to which these conserved residues are clustered in three-dimensional protein structures. In 92% of the proteins in a data set of 79 proteins, the most conserved positions in multiple sequence alignments are significantly more clustered than randomly selected sets of positions. The comparison to random subsets is not necessarily appropriate, however, because the signal could be the result of differences in the amino acid composition of sets of conserved residues compared to random subsets (hydrophobic residues tend to be close together in the protein core), or differences in sequence separation of the residues in the different sets. In order to overcome these limits, we compare the degree of clustering of the conserved positions on the native structure and on alternative conformations generated by the de novo structure prediction method Rosetta. For 65% of the 79 proteins, the conserved residues are significantly more clustered in the native structure than in the alternative conformations, indicating that the clustering of conserved residues in protein structures goes beyond that expected purely from sequence locality and composition effects. The differences in the spatial distribution of conserved residues can be utilized in de novo protein structure prediction: We find that for 79% of the proteins, selection of the Rosetta generated conformations with the greatest clustering of the conserved residues significantly enriches the fraction of close-to-native structures.     

SUPFAM—a database of potential protein superfamily relationships derived by comparing sequence-based and structure-based families: implications for structural genomics and function annotation in genomes

Members of a superfamily of proteins could result from divergent evolution of homologues with insignificant similarity in the amino acid sequences. A superfamily relationship is detected commonly after the three-dimensional structures of the proteins are determined using X-ray analysis or NMR. The SUPFAM database described here relates two homologous protein families in a multiple sequence alignment database of either known or unknown structure. The present release (1.1), which is the first version of the SUPFAM database, has been derived by analysing Pfam, which is one of the commonly used databases of multiple sequence alignments of homologous proteins. The first step in establishing SUPFAM is to relate Pfam families with the families in PALI, which is an alignment database of homologous proteins of known structure that is derived largely from SCOP. The second step involves relating Pfam families which could not be associated reliably with a protein superfamily of known structure. The profile matching procedure, IMPALA, has been used in these steps. The first step resulted in identification of 1280 Pfam families (out of 2697, i.e. 47%) which are related, either by close homologous connection to a SCOP family or by distant relationship to a SCOP family, potentially forming new superfamily connections. Using the profiles of 1417 Pfam families with apparently no structural information, an all-against-all comparison involving a sequence-profile match using IMPALA resulted in clustering of 67 homologous protein families of Pfam into 28 potential new superfamilies. Expansion of groups of related proteins of yet unknown structural information, as proposed in SUPFAM, should help in identifying ‘priority proteins’ for structure determination in structural genomics initiatives to expand the coverage of structural information in the protein sequence space. For example, we could assign 858 distinct Pfam domains in 2203 of the gene products in the genome of Mycobacterium tubercolosis. Fifty-one of these Pfam families of unknown structure could be clustered into 17 potentially new superfamilies forming good targets for structural genomics. SUPFAM database can be accessed at http://pauling.mbu.iisc.ernet.in/~supfam.     

Testing for Spatial Clustering of Amino Acid Replacements Within Protein Tertiary Structure

Widely used models of protein evolution ignore protein structure. Therefore, these models do not predict spatial clustering of amino acid replacements with respect to tertiary structure. One formal and biologically implausible possibility is that there is no tendency for amino acid replacements to be spatially clustered during evolution. An alternative to this is that amino acid replacements are spatially clustered and this spatial clustering can be fully explained by a tendency for similar rates of amino acid replacement at sites that are nearby in protein tertiary structure. A third possibility is that the amount of clustering exceeds that which can be explained solely on the basis of independently evolving protein sites with spatially clustered replacement rates. We introduce two simple and not very parametric hypothesis tests that help distinguish these three possibilities. We then apply these tests to 273 homologous protein families. The null hypothesis of no spatial clustering is rejected for 102 of 273 families. The explanation of spatially clustered rates but independent change among sites is rejected for 43 families. These findings need to be reconciled with the common practice of basing evolutionary inferences on models that assume independent change among sites. [Reviewing Editior: Dr. David Pollock]