三肽和四肽构象空间的可视化方法

研究蛋白质寡肽构象在构象空间中的分布情况,对提取寡肽模式并构建短肽库具有重要意义。通过构建一个保距映射,将以主链原子均方根距离(root mean square distance,RMSD)为距离测度的三肽构象空间变换为一维直线上的欧氏距离空间,从而直观地展现三肽构象的聚集情况,表明三肽主链构象可以用单一变量编码。应用该特性对四肽的构象空间加以分析,将四肽构象映射到三维空间中,从而以可视的方式描述四肽构象空间的聚集情况。对短肽构象空间的初步分析表明,短肽的聚集性和二级结构有着密切的联系。在四肽构象空间中存在有自然边界的离散区域(与螺旋等结构相关),也有一些区域(与折叠等结构有关)难以进一步划分。这种方法也为以可视方式分析高维空间中肽段的聚集性给出了一种可能的方案。     

蛋白质的结构转换

许多不相关的蛋白质含有相同的短肽序列却形成不同的空间构象. 结构转换广泛存在于蛋白质折叠和功能过程中, 具有重要的生物学意义. 综述了Serpin和EF-Tu的失活、血细胞凝集素的激活、蛋白酶成熟、亚基装配和蛋白质淀粉样化等过程中肽链同源肽段的结构转换模式, 并讨论了它在理解蛋白质折叠机理和“构象病”病因中的应用.     

P53蛋白质N末端的二级结构预测及其三维构象

以编码Ｐ５３Ｎ末端１２０个残基的ｍＲＮＡ二级结构为基础,结合Ｃｈｏｕ－Ｆａｓｍａｎ蛋白质二级结构预测原则,预测出Ｐ５３蛋白质Ｎ端的９３个残基包含四段α螺旋结构（１４－２６;３８－４６;５１－５６;６８－７０）,没有发现β片层。与四种以多重序列联配为基础的蛋白质二级结构预测方法（准确率均为７３．２０％左右）相对照,结果十分相近。在ＳＧＩ工作站上以此为初始结构建立的三维构象提示,Ｐ５３Ｎ末端前８０个氨基酸肽段呈弧型板块结构,其转录激活区由两段主要螺旋组成,呈上下构形,占据弧型板块的顶部及底部外侧缘。Ｃ端１３个富含脯氨酸肽段则呈弯曲松散状。这些构象与Ｐ５３Ｎ末端的生物功能是相吻合的     

三联体密码子在mRNA二级结构中的分布

对编码成熟肽的ｍＲＮＡ二级结构的分析显示,每个密码子在ｍＲＮＡ二级结构中的位置有一定的倾向性,这种倾向性似乎与相应氨基酸的构象性质相一致。大多数编码疏水氨基酸的密码子位于ｍＲＮＡ二级结构中较稳定的茎区;反之,大多数编码亲水氨基酸的密码子位于柔性的环区。这个结果支持了最近得到的关于ｍＲＮＡ与蛋白质之间存在丰三维结构信息传递的结论。     

新型平均势函数在蛋白质反向折叠中的应用

利用蛋白质主链的极性分数及主链二面角为参量,构建了一种基于蛋白质结构数据库的势函数。将该势函数应用于蛋白质反向折叠研究中,发现该函数可成功地将蛋白质分子的天然构象从构建的构象库中识别出来;将一目标序列与构象库的每一可能的构象匹配,并用该势函数计算相应的能量,结果表明对绝大多数蛋白质分子来说,天然的构象的能量值总是最低。此外,该函数还将一些序列相似性较低,而结构相似性较高的蛋白质分子识别出来。我们认     

蛋白质构象病

蛋白质结构生物学既从蛋白质一级结构序列 ,也从蛋白质空间结构及其动态变化去研究蛋白质的性质和功能。生物医学研究表明蛋白质空间构象发生异常变化会引起疾病发生 ,形成了蛋白质构象病 (Ｐｒｏｔｅｉｎｃｏｎｆｏｒｍａ ｔｉｏｎａｌｄｉｓｅａｓｅｓ)这一新的病理学概念[1] 。1 .蛋白质构象病及其分子构象病理学一般讲 ,引起构象疾病的蛋白质分子与正常蛋白质同时存在于机体内 ,至少部分蛋白质具有正常折叠的空间构象 ,并以正常形态释放。当蛋白质构象异常变化时可导致其生物功能丧失 ,或者引起其后发生的蛋白质聚集与沉积 ,使组织结构…     

肽键的统计分析和蛋白质的二级结构

 本文对蛋白质序列的肽键进行了统计分析,计算了二肽构象参数P_α、P_β、P_c和三肽构象参数Q_α、Q_β、Q_c。在此基础上提出了由氨基酸序列预测二级结构的规则。预测的正确率达90％,优于Chou-Fasman方法。这个结果表明二肽(三肽)关联在形成蛋白质二级结构中具有明显的重要性。     

p53蛋白质C端的三维结构及生物功能区

利用p53 C端118个氨基酸的mRNA二级结构和Chou-Fasman蛋白质二级结构预测原则,预测p53蛋白质C端289～325为卷曲肽段,368～393段包括两段螺旋结构: α₁ 368～373, α₂ 381～388.其中三段已知的蛋白质二级结构与此mRNA二级结构单元间有准确的对应关系.与四种以多重序列联配为基础的蛋白质二级结构预测方法（准确率均为73.20%左右）相对照,预测结果基本一致.结合单体聚合区31个氨基酸晶体结构,在SGI INDIGO²工作站上构建了p53 C端108个残基的三维结构.进一步揭示了p53 C端诸多生物功能区之间的空间构象关系.     

玉米大斑病抗性基因Ht1定位区域内候选序列的生物信息学分析

为获得玉米大斑病抗性基因Ht1候选序列, 文章采用生物信息学方法对与玉米大斑病抗性基因Ht1紧密连锁的分子标记umc22和umc122定位区域内候选序列进行了分析, 其中得到的63条ORF序列中有14条序列可编码蛋白质结构域。将14条核苷酸酸序列预测出的氨基酸序列与已克隆的24条抗性基因编码氨基酸序列进行Blast比对及进化树构建。结果发现, 候选序列gpm565a具有植物抗性基因编码产物的高度保守结构域, 而且与抗性基因Xal相似性高、亲缘关系近, 推测可能与抗性基因Ht1有关。其他候选序列由于不具有植物抗性基因编码产物的高度保守结构域或者相似性低、亲缘关系远等原因, 不能确定与抗性基因Ht1有关。通过对候选序列gpm565a进行二级结构及三维结构分析, 发现有大量构成蛋白质特异功能结构组件的无规则卷曲存在, 推测gpm565a可能是Ht1功能域的一部分。     

噬菌体展示随机十肽库的构建及抗WSSV单链抗体A1的模拟抗原表位的淘选和鉴定

本实验以pCANTAB5E噬菌粒为载体,成功构建了较高容量的噬菌体展示随机十肽库,并将其应用于抗原模拟表位的淘选和鉴定。将一种特异识别对虾白斑综合症病毒(Whitespotsyndromevirus,WSSV)的单链抗体A1对十肽库和十五肽库分别进行淘选,结果得到一系列能与单链抗体A1特异性结合的阳性克隆。将这些阳性克隆所编码的多肽氨基酸序列与已知的单链抗体A1的抗原WSSV388片段氨基酸序列做比对,发现多数阳性多肽序列都与WSSV388片段序列的C端一处K????R??R?QS的氨基酸片段相似,由此推论单链抗体A1的模拟抗原表位可能是由该不连续氨基酸片段所构成的构象表位,而非线性表位。研究结果表明,噬菌体展示随机肽库技术是一种用于研究抗原表位结构的有效方法,有助于进一步探讨WSSV的结构蛋白的构象及功能,以及相应单链抗体与细胞受体相互作用的机理。     

Wavelet transforms for the characterization and detection of repeating motifs

The role of repeating motifs in protein structures is thought to be as modular building blocks which allow an economic way of constructing complex proteins. In this work novel wavelet transform analysis techniques are used to detect and characterize repeating motifs in protein sequence and structure data, where the Kyte-Doolittle hydrophobicity scale (Eta Phi) and relative accessible surface area (rASA) data provide residue information about the protein sequence and structure, respectively. We analyze a variety of repeating protein motifs, TIM barrels, propellor blades, coiled coils and leucine-rich repeat structures. Detection and characterization of these motifs is performed using techniques based on the continuous wavelet transform (CWT). Results indicate that the wavelet transform techniques developed herein are a promising approach for the detection and characterization of repeating motifs for both structural and in some instances sequence data.     

SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary information

Knowledge of three dimensional structure is essential to understand the function of a protein. Although the overall fold is made from the whole details of its sequence, a small group of residues, often called as structural motifs, play a crucial role in determining the protein fold and its stability. Identification of such structural motifs requires sufficient number of sequence and structural homologs to define conservation and evolutionary information. Unfortunately, there are many structures in the protein structure databases have no homologous structures or sequences. In this work, we report an SVM method, SMpred, to identify structural motifs from single protein structure without using sequence and structural homologs. SMpred method was trained and tested using 132 proteins domains containing 581 motifs. SMpred method achieved 78.79% accuracy with 79.06% sensitivity and 78.53% specificity. The performance of SMpred was evaluated with MegaMotifBase using 188 proteins containing 1161 motifs. Out of 1161 motifs, SMpred correctly identified 1503 structural motifs reported in MegaMotifBase. Further, we showed that SMpred is useful approach for the length deviant superfamilies and single member superfamilies. This result suggests the usefulness of our approach for facilitating the identification of structural motifs in protein structure in the absence of sequence and structural homologs. The dataset and executable for the SMpred algorithm is available at http://www3.ntu.edu.sg/home/EPNSugan/index_files/SMpred.htm.     

Discovery of local packing motifs in protein structures

We present a language for describing structural patterns of residues in protein structures and a method for the discovery of such patterns that recur in a set of protein structures. The patterns impose restrictions on the spatial position of each residue, their order along the amino acid chain, and which amino acids are allowed in each position. Unlike other methods for comparing sets of protein structures, our method is not based on the use of pairwise structure comparisons which is often time consuming and can produce inconsistent results. Instead, the method simultaneously takes into account information from all structures in the search for conserved structure patterns which are potential structure motifs. The method is based on describing the spatial neighborhoods of each residue in each structure as a string and applying a sequence pattern discovery method to find patterns common to subsets of these strings. Finally it is checked whether the similarities between the neighborhood strings correspond to spatially similar substructures. We apply the method to analyze sets of very disparate proteins from the four different protein families: serine proteases, cuprodoxins, cysteine proteinases, and ferredoxins. The motifs found by the method correspond well to the site and motif information given in the annotation of these proteins in PDB, Swiss-Prot, and PROSITE. Furthermore, the motifs are confirmed by using the motif data to constrain the structural alignment of the proteins obtained with the program SAP. This gave the best superposition/alignment of the proteins given the motif assignment.     

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.     

Construction of a dictionary of sequence motifs that characterize groups of related proteins.

An automatic procedure is proposed to identify, from the protein sequence database, conserved amino acid patterns (or sequence motifs) that are exclusive to a group of functionally related proteins. This procedure is applied to the PIR database and a dictionary of sequence motifs that relate to specific superfamilies constructed. The motifs have a practical relevance in identifying the membership of specific superfamilies without the need to perform sequence database searches in 20% of newly determined sequences. The sequence motifs identified represent functionally important sites on protein molecules. When multiple blocks exist in a single motif they are often close together in the 3-D structure. Furthermore, occasionally these motif blocks were found to be split by introns when the correlation with exon structures was examined.     

Analysis of structural motifs of proteins using sets of codes, describing local structures]

An amino acid sequence pattern conserved among a family of proteins is called motif. It is usually related to the specific function of the family. On the other hand, functions of proteins are achieved by their 3D structures. Specific local structures, called structural motifs, are considered related to their functions. However, searching for common structural motifs in different proteins is much more difficult than for common sequence motifs. We are attempting in this study to convert the information about the structural motifs into a set of one-dimensional digital strings, i.e., a set of codes, to compare them more easily by computer and to investigate their relationship to functions more quantitatively. By applying the Delaunay tessellation to a 3D structure of a protein, we can assign each local structure to a unique code that is defined so as to reflect its structural feature. Since a structural motif is defined as a set of the local structures in this paper, the structural motif is represented by a set of the codes. In order to examine the ability of the set of the codes to distinguish differences among the sets of local structures with a given PROSITE pattern that contain both true and false positives, we clustered them by introducing a similarity measure among the set of the codes. The obtained clustering shows a good agreement with other results by direct structural comparison methods such as a superposition method. The structural motifs in homologous proteins are also properly clustered according to their sources. These results suggest that the structural motifs can be well characterized by these sets of the codes, and that the method can be utilized in comparing structural motifs and relating them with function.     

Prediction of complex super-secondary structure βαβ motifs based on combined features

Prediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins.     

Gibbs sampling and helix-cap motifs

Protein backbones have characteristic secondary structures, including α-helices and β-sheets. Which structure is adopted locally is strongly biased by the local amino acid sequence of the protein. Accurate (probabilistic) mappings from sequence to structure are valuable for both secondary-structure prediction and protein design. For the case of α-helix caps, we test whether the information content of the sequence–structure mapping can be self-consistently improved by using a relaxed definition of the structure. We derive helix-cap sequence motifs using database helix assignments for proteins of known structure. These motifs are refined using Gibbs sampling in competition with a null motif. Then Gibbs sampling is repeated, allowing for frameshifts of ±1 amino acid residue, in order to find sequence motifs of higher total information content. All helix-cap motifs were found to have good generalization capability, as judged by training on a small set of non-redundant proteins and testing on a larger set. For overall prediction purposes, frameshift motifs using all training examples yielded the best results. Frameshift motifs using a fraction of all training examples performed best in terms of true positives among top predictions. However, motifs without frameshifts also performed well, despite a roughly one-third lower total information content.     

Analysis of Protein Structural Motifs in Terms of Sets of Codes Representing Local Structures

An amino acid sequence pattern conserved among a family of proteins is called motif. It is usually related to the specific function of the family. On the other hand, functions of proteins are realized through their 3D structures. Specific local structures, called structural motifs, are considered as related to their functions. However, searching for common structural motifs in different proteins is much more difficult than for common sequence motifs. We are attempting in this study to convert the information about the structural motifs into a set of one-dimensional digital strings, i.e., a set of codes, to compare them more easily by computer and to investigate their relationship to functions more quantitatively. By applying the Delaunay tessellation to a 3D structure of a protein, we can assign each local structure to a unique code that is defined so as to reflect its structural feature. Since a structural motif is defined as a set of the local structures in this paper, the structural motif is represented by a set of the codes. In order to examine the ability of the set of the codes to distinguish differences among the sets of local structures with a given PROSITE pattern that contain both true and false positives, we clustered them by introducing a similarity measure among the set of the codes. The obtained clustering shows a good agreement with other results by direct structural comparison methods such as a superposition method. The structural motifs in homologous proteins are also properly clustered according to their sources. These results suggest that the structural motifs can be well characterized by these sets of the codes, and that the method can be utilized in comparing structural motifs and relating them with function.