Globin-like蛋白质折叠类型识别

蛋白质折叠类型识别是蛋白质结构研究的重要内容.以SCOP中的Globin-like折叠为研究对象,选择其中序列同一性小于25%的17个代表性蛋白质为训练集,采用机器和人工结合的办法进行结构比对,产生序列排比,经过训练得到了适合Globin-like折叠的概形隐马尔科夫模型(profile HMM)用于该折叠类型的识别.以Astrall.65中的68057个结构域样本进行检验,识别敏感度为99.64%,特异性100%.在折叠类型水平上,与Pfam和SUPERFAMILY单纯使用序列比对构建的HMM相比,所用模型由多于100个归为一个,仍然保持了很高的识别效果.结果表明:对序列相似度很低但具有相同折叠类型的蛋白质,可以通过引入结构比对的方法建立统一的HMM模型,实现高准确率的折叠类型识别.     

基于功能域组分的蛋白质折叠类型识别

蛋白质空间结构研究是分子生物学、细胞生物学、生物化学以及药物设计等领域的重要课题.折叠类型反映了蛋白质核心结构的拓扑模式,对折叠类型的识别是蛋白质序列与结构关系研究的重要内容.选取LIFCA数据库中样本量较大的53种折叠类型,应用功能域组分方法进行折叠识别.将Astral 1.65中序列一致性小于95%的样本作为检验集,全库检验结果中平均敏感性为96.42%,特异性为99.91%,马修相关系数(MCC)为0.91,各项统计结果表明:功能域组分方法可以很好地应用在蛋白质折叠识别中,LIFCA相对简单的分类规则可以很好地集中蛋白质的大部分功能特性,反映了结构与功能的对应关系.     

蛋白质折叠类型分类方法及分类数据库

蛋白质折叠规律研究是生命科学重大前沿课题,折叠分类是蛋白质折叠研究的基础。目前的蛋白质折叠类型分类基本上靠专家完成,不同的库分类并不相同,迫切需要一个建立在统一原理基础上的蛋白质折叠类型数据库。本文以ASTRAL-1.65数据库中序列同源性在25%以下、分辨率小于2.5的蛋白为基础,通过对蛋白质空间结构的观察及折叠类型特征的分析,提出以蛋白质折叠核心为中心、以蛋白质结构拓扑不变性为原则、以蛋白质折叠核心的规则结构片段组成、连接和空间排布为依据的蛋白质折叠类型分类方法,建立了低相似度蛋白质折叠分类数据库——LIFCA,包含259种蛋白质折叠类型。数据库的建立,将为进一步的蛋白质折叠建模及数据挖掘、蛋白质折叠识别、蛋白质折叠结构进化研究奠定基础。     

基于特征片段信息的PH domain-like barrel 蛋白质折叠类型分类方法

蛋白质折叠类型分类是蛋白质分类研究的重要内容。以SCOP数据库中的 PH domain-like barrel 折叠类型为研究对象,选择序列相似度小于25%的61个样本为检验集,通过结构特征分析,确定了该折叠类型的模板及其对应的特征参数,利用模板与待测蛋白的空间结构比对信息,提出了一个新的折叠类型打分函数Fscore,建立了基于Fscore的蛋白质折叠类型分类方法并用于该折叠类型的分类。用此方法对Astral1.75中序列相似度小于95%的16711个样本进行检验,分类结果的特异性为99.97%。结果表明:特征参数抓住了折叠类型的本质,打分函数Fscore及基于Fscore建立的分类方法可用于 PH domain-like barrel 蛋白质折叠类型自动分类。     

蛋白质折叠类型识别方法研究

蛋白质折叠类型识别是一种分析蛋白质结构的重要方法.以序列相似性低于25%的822个全B类蛋白为研究对象,提取核心结构二级结构片段及片段问氢键作用信息为折叠类型特征参数,构建全B类蛋白74种折叠类型模板数据库.定义查询蛋白与折叠类型模板间二级结构匹配函数SS、氢键作用势函数BP及打分函数P,P值最小的模板所对应的折叠类型为查询蛋白的折叠类型.从SCOP1.69中随机抽取三组、每组50个全β类蛋白结构域进行预测,分辨精度分别为56%、56%和42%;对Ding等提供的检验集进行预测,总分辨精度为61.5%.结果和比对表明,此方法是一种有效的折叠类型识别方法.     

一个识别蛋白质折叠模式的SVM分类器

蛋白质折叠模式识别是一种分析蛋白质结构的重要方法。以序列相似性较低的蛋白质为训练集,提取蛋白质序列信息频数及疏水性等信息作为折叠类型特征,从SCOP数据库中已分类蛋白质构建1 393种折叠模式的数据集,采用SVM预测蛋白质1 393种折叠模式。封闭测试准确率达99.612 2%,基于SCOP的开放测试准确率达79.632 9%。基于另一个权威测试集的开放测试折叠准确率达64.705 9%,SCOP类准确率达76.470 6%,可以有效地对蛋白质折叠模式进行预测,从而为蛋白质从头预测提供参考。     

α类蛋白质折叠类型自动化分类研究

蛋白质折叠类型识别是蛋白质结构研究的重要内容,折叠类型分类是折叠识别的基础。通过对ASTRAL-1.65数据库α类蛋白质所属折叠类型进行系统研究,建立蛋白质折叠类型模板数据库,提取反映折叠类型拓扑结构的模板特征参数,根据模板特征参数和TM-align结构比对结果,建立基于特征参数的打分函数Fdscore,并实现α类蛋白质折叠类型自动化分类。使用相同数据集样本,将Fdscore分类方法与TM-score分类方法对比,Fdscore分类方法的平均敏感性、平均特异性、MCC值分别为71.86%、99.49%、0.69,均高于TM-score分类方法相对应结果。上述结果表明该分类方法可用于α类蛋白质折叠类型的自动化分类。     

基于序列疏水值震荡的折叠速率预测

蛋白质折叠速率的正确预测对理解蛋白质的折叠机理非常重要。本文从伪氨基酸组成的方法出发,提出利用序列疏水值震荡的方法来提取蛋白质氨基酸的序列顺序信息,建立线性回归模型进行折叠速率预测。该方法不需要蛋白质的任何二级结构、三级结构信息或结构类信息,可直接从序列对蛋白质折叠速率进行预测。对含有62个蛋白质的数据集,经过Jack．knife交互检验验证,相关系数达到0．804,表示折叠速率预测值与实验值有很好的相关性,说明了氨基酸序列信息对蛋白质折叠速率影响重要。同其他方法相比,本文的方法具有计算简单,输入参数少等特点。     

运用PDB中的同源信息提高NetTurnP的蛋白质β转角预测精度

β转角作为一种蛋白质二级结构类型在蛋白质折叠、蛋白质稳定性、分子识别等方面具有重要作用．现有的β转角预测方法,没有将PDB等结构数据库中先前存在的同源序列的结构信息映射到待预测的蛋白质序列上．PDB存储的结构已超过70 000,因此对一条新确定的序列,有较大可能性从PDB中找到其同源序列．本文融合PDB中提取的同源结构信息(对每一待测序列,仅使用先于该序列存储于PDB中的同源信息)与NetTurnP预测,提出了一种新的β转角预测方法BTMapping,在经典的BT426数据集和本文构建的数据集EVA937上,以马修斯相关系数表示的预测精度分别为0.56、0.52,而仅使用NetTurnP的为0.50、0.46,以Q_total表示的预测精度分别为81.4%、80.4%,而仅使用NetTurnP的为78.2%、77.3%．结果证实同源结构信息结合先进的β转角预测器如NetTurnP有助于改进β转角识别．BTMapping程序及相关数据集可从http://www.bio530.weebly.com获得．     

SCOP数据库蛋白质折叠类型的自动分类分析

蛋白质折叠规律研究是生命科学领域重要的前沿课题之一,蛋白质折叠类型分类是折叠规律研究的基础。本研究以SCOP数据库的蛋白质折叠类型分类为基础、以Astral SCOPe 2.05数据库中相似性小于40%的α、β、α+β及α/β类所属的折叠类型为研究对象,完成了989种蛋白质折叠类型的模板构建并形成模板数据库;基于折叠类型设计模板建立了蛋白质折叠类型分类方法,实现了SCOP数据库蛋白质折叠类型的自动化分类。家族模板自洽性检验与独立性检验所得的敏感性、特异性以及MCC的平均值分别为:95.00%、99.99%、0.94与90.00%、99.97%、0.92,折叠类型模板自洽性检验与独立性检验所得的敏感性、特异性以及MCC的平均值分别为:93.71%、99.97%、0.91与86.00%、99.93%、0.87。结果表明:模板设计合理,可有效用于对已知结构的蛋白质进行分类。     

The PDB is a covering set of small protein structures

Structure comparisons of all representative proteins have been done. Employing the relative root mean square deviation (RMSD) from native enables the assessment of the statistical significance of structure alignments of different lengths in terms of a Z-score. Two conclusions emerge: first, proteins with their native fold can be distinguished by their Z-score. Second and somewhat surprising, all small proteins up to 100 residues in length have significant structure alignments to other proteins in a different secondary structure and fold class; i.e. 24.0% of them have 60% coverage by a template protein with a RMSD below 3.5 Å and 6.0% have 70% coverage. If the restriction that we align proteins only having different secondary structure types is removed, then in a representative benchmark set of proteins of 200 residues or smaller, 93% can be aligned to a single template structure (with average sequence identity of 9.8%), with a RMSD less than 4 Å, and 79% average coverage. In this sense, the current Protein Data Bank (PDB) is almost a covering set of small protein structures. The length of the aligned region (relative to the whole protein length) does not differ among the top hit proteins, indicating that protein structure space is highly dense. For larger proteins, non-related proteins can cover a significant portion of the structure. Moreover, these top hit proteins are aligned to different parts of the target protein, so that almost the entire molecule can be covered when combined. The number of proteins required to cover a target protein is very small, e.g. the top ten hit proteins can give 90% coverage below a RMSD of 3.5 Å for proteins up to 320 residues long. These results give a new view of the nature of protein structure space, and its implications for protein structure prediction are discussed.     

The FSSP database of structurally aligned protein fold families.

FSSP (families of structurally similar proteins) is a database of structural alignments of proteins in the Protein Data Bank (PDB). The database currently contains an extended structural family for each of 330 representative protein chains. Each data set contains structural alignments of one search structure with all other structurally significantly similar proteins in the representative set (remote homologs, < 30% sequence identity), as well as all structures in the Protein Data Bank with 70-30% sequence identity relative to the search structure (medium homologs). Very close homologs (above 70% sequence identity) are excluded as they rarely have marked structural differences. The alignments of remote homologs are the result of pairwise all-against-all structural comparisons in the set of 330 representative protein chains. All such comparisons are based purely on the 3D co-ordinates of the proteins and are derived by automatic (objective) structure comparison programs. The significance of structural similarity is estimated based on statistical criteria. The FSSP database is available electronically from the EMBL file server and by anonymous ftp (file transfer protocol).     

A database of protein structure families with common folding motifs.

The availability of fast and robust algorithms for protein structure comparison provides an opportunity to produce a database of three-dimensional comparisons, called families of structurally similar proteins (FSSP). The database currently contains an extended structural family for each of 154 representative (below 30% sequence identity) protein chains. Each data set contains: the search structure; all its relatives with 70-30% sequence identity, aligned structurally; and all other proteins from the representative set that contain substructures significantly similar to the search structure. Very close relatives (above 70% sequence identity) rarely have significant structural differences and are excluded. The alignments of remote relatives are the result of pairwise all-against-all structural comparisons in the set of 154 representative protein chains. The comparisons were carried out with each of three novel automatic algorithms that cover different aspects of protein structure similarity. The user of the database has the choice between strict rigid-body comparisons and comparisons that take into account interdomain motion or geometrical distortions; and, between comparisons that require strictly sequential ordering of segments and comparisons, which allow altered topology of loop connections or chain reversals. The data sets report the structurally equivalent residues in the form of a multiple alignment and as a list of matching fragments to facilitate inspection by three-dimensional graphics. If substructures are ignored, the result is a database of structure alignments of full-length proteins, including those in the twilight zone of sequence similarity.(ABSTRACT TRUNCATED AT 250 WORDS)     

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.     

Ensemble classifier for protein fold pattern recognition

MOTIVATION: Prediction of protein folding patterns is one level deeper than that of protein structural classes, and hence is much more complicated and difficult. To deal with such a challenging problem, the ensemble classifier was introduced. It was formed by a set of basic classifiers, with each trained in different parameter systems, such as predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, as well as different dimensions of pseudo-amino acid composition, which were extracted from a training dataset. The operation engine for the constituent individual classifiers was OET-KNN (optimized evidence-theoretic k-nearest neighbors) rule. Their outcomes were combined through a weighted voting to give a final determination for classifying a query protein. The recognition was to find the true fold among the 27 possible patterns. RESULTS: The overall success rate thus obtained was 62% for a testing dataset where most of the proteins have <25% sequence identity with the proteins used in training the classifier. Such a rate is 6-21% higher than the corresponding rates obtained by various existing NN (neural networks) and SVM (support vector machines) approaches, implying that the ensemble classifier is very promising and might become a useful vehicle in protein science, as well as proteomics and bioinformatics. AVAILABILITY: The ensemble classifier, called PFP-Pred, is available as a web-server at http://202.120.37.186/bioinf/fold/PFP-Pred.htm for public usage.     

Recognizing protein folds by cluster distance geometry

Cluster distance geometry is a recent generalization of distance geometry whereby protein structures can be described at even lower levels of detail than one point per residue. With improvements in the clustering technique, protein conformations can be summarized in terms of alternative contact patterns between clusters, where each cluster contains four sequentially adjacent amino acid residues. A very simple potential function involving 210 adjustable parameters can be determined that favors the native contacts of 31 small, monomeric proteins over their respective sets of nonnative contacts. This potential then favors the native contacts for 174 small, monomeric proteins that have low sequence identity with any of the training set. A broader search finds 698 small protein chains from the Protein Data Bank where the native contacts are preferred over all alternatives, even though they have low sequence identity with the training set. This amounts to a highly predictive method for ab initio protein folding at low spatial resolution.     

Evolutionary networks in the formatted protein sequence space.

In our recent work, a new approach to establish sequence relatedness, by walking through the protein sequence space, was introduced. The sequence space is built from 20 amino acid long fragments of proteins from a very large collection of fully sequenced prokaryotic genomes. The fragments, points in the space, are connected, if they are closely related (high sequence identity). The connected fragments form variety of networks of sequence kinship. In this research the networks in the formatted sequence space and their topology are analyzed. For lower identity thresholds a huge network of complex structure is formed, involving up to 10% points of the space. When the threshold is increased, the major network splits into a set of smaller clusters with a wide diversity of sizes and topologies. Such "evolutionary networks" may serve as a powerful sequence annotation tool that allows one to reveal fine details in the evolutionary history of proteins.     

Protein threading using PROSPECT: design and evaluation

The computer system PROSPECT for the protein fold recognition using the threading method is described and evaluated in this article. For a given target protein sequence and a template structure, PROSPECT guarantees to find a globally optimal threading alignment between the two. The scoring function for a threading alignment employed in PROSPECT consists of four additive terms: i) a mutation term, ii) a singleton fitness term, iii) a pairwise-contact potential term, and iv) alignment gap penalties. The current version of PROSPECT considers pair contacts only between core (alpha-helix or beta-strand) residues and alignment gaps only in loop regions. PROSPECT finds a globally optimal threading efficiently when pairwise contacts are considered only between residues that are spatially close (7 A or less between the C(beta) atoms in the current implementation). On a test set consisting of 137 pairs of target-template proteins, each pair being from the same superfamily and having sequence identity 相似文献   

Structure-dependent sequence alignment for remotely related proteins

MOTIVATION: The quality of a model structure derived from a comparative modeling procedure is dictated by the accuracy of the predicted sequence-template alignment. As the sequence-template pairs are increasingly remote in sequence relationship, the prediction of the sequence-template alignments becomes increasingly problematic with sequence alignment methods. Structural information of the template, used in connection with the sequence relationship of the sequence-template pair, could significantly improve the accuracy of the sequence-template alignment. In this paper, we describe a sequence-template alignment method that integrates sequence and structural information to enhance the accuracy of sequence-template alignments for distantly related protein pairs. RESULTS: The structure-dependent sequence alignment (SDSA) procedure was optimized for coverage and accuracy on a training set of 412 protein pairs; the structures for each of the training pairs are similar (RMSD< approximately 4A) but the sequence relationship is undetectable (average pair-wise sequence identity = 8%). The optimized SDSA procedure was then applied to extend PSI-BLAST local alignments by calculating the global alignments under the constraint of the residue pairs in the local alignments. This composite alignment procedure was assessed with a testing set of 1421 protein pairs, of which the pair-wise structures are similar (RMSD< approximately 4A) but the sequences are marginally related at best in each pair (average pair-wise sequence identity = 13%). The assessment showed that the composite alignment procedure predicted more aligned residues pairs with an average of 27% increase in correctly aligned residues over the standard PSI-BLAST alignments for the protein pairs in the testing set.