基于HNP模型及相对熵的蛋白质设计方法在不同蛋白质体系中的应用

详细考察了基于HNP(H:hydtophobic,N:neutral,P:hydrophilic)模型及相对熵的蛋白质设计方法对于不同结构类型蛋白质的适用性,并与基于HP模型的结果进行了比较.通过对190个4种不同结构类型的蛋白质进行预测,结果表明,基于HNP模型及相对熵的设计方法对于不同结构类型的蛋白质具有普适性.进一步的研究发现,对于α螺旋、β折叠等规则的二级结构,该方法的预测成功率高于无规卷曲结构预测成功率.另外,还比较了对不同氨基酸的预测差异,结果显示亲水残基的预测成功率较高.此外,研究表明该方法对于蛋白质保守残基的预测成功率高于非保守残基.在以上分析的基础上,进一步讨论了导致这些差异的原因.这些研究为基于相对熵的蛋白质设计方法的实际应用和进一步的发展打下了良好基础.     

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。     

基于序列特征网络的蛋白质结构类型研究

利用复杂网络的方法来探索序列特征因素对蛋白质结构的影响。由于蛋白质的序列对结构具有重要且复杂的影响,因此将蛋白质的结构以及序列特征之间的关系模拟成一个复杂系统,通过利用互相关系数、标准化互信息和传递熵等方法来建立以序列特征为节点的加权网络,进而利用网络中心性的方法来分析不同蛋白质结构类型对应加权网络的中心性分布的差异,探索不同结构类型蛋白质的序列特征差异。发现不同的蛋白质结构类型对应的序列特征网络既有共性又有差异,文章将针对每一种结构类型的网络中心性分布,以及不同结构类型之间的共性与差异进行详细地讨论。研究结果对蛋白质序列与结构之间关系的研究,特别是结构分类研究具有重要的意义。     

蛋白质复合体及蛋白质相互作用研究新策略—化学交联结合质谱分析法

近年来化学交联法结合质谱分析法被广泛用于蛋白质复合体结构及蛋白质相互作用的研究。研究表明这两种方法的有机结合为研究蛋白质复合体结构及蛋白质相互作用提供了一条新的途径。文章对不同类型的化学交联剂、质谱分析中的Bottom-up 与Top-down 两种研究策略,以及化学交联法结合质谱分析法在蛋白质复合体结构、蛋白质相互作用研究中的应用进行综述。这两种方法的不断发展与完善,将会极大促进生物大分子复合体结构及蛋白质相互作用的研究。     

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。     

蛋白质突变体基因库构建方法的研究进展

体外定向进化是蛋白质工程中一个非常有效的设计策略。最近几年,在过去常用的寡核苷酸介导的随机突变、易错PCR和DNA改组等方法的基础上又出现了一些新的定向进化方法。本文对这些方法及其特点加以总结,为解决特定问题选取何种方法提供一定依据。最近研究表明：定向进化和理性设计相结合、定向进化和以结构为基础的计算设计方法相结合正成为蛋白质工程中两个新的发展方向。     

枯草杆菌蛋白酶与蛋白质工程

在以蛋白质三维结构及其与功能关系为基础的蛋白质工程的兴起中,枯草杆菌蛋白酶的定位诱变起了重要作用。以该酶为代表的蛋白质的工程研究,反映了这种生物技术所取得的进展和存在的主要问题。虽然,为了达到工程改造蛋白质的目标仍需要做大量基础性研究,但目前取得的成果足以说明蛋白质工程发展的光明前景。     

蛋白质工程

蛋白质工程张海银（广州市中华英豪学校５１０９６０）叶言山（安徽省庐江矾矿中学２３１５００）蛋白质工程（Ｐｒｏｔｅｉｎｅｎｇｉｎｅｅｒｉｎｇ）是在重组ＤＮＡ方法用于“操纵”蛋白质结构之后发展起来的分子生物学分支。它是以蛋白质分子的结构及其功能为基础,通...     

蛋白质二级结构预测: 基于词条的最大熵马尔科夫方法

提出了一种新的蛋白质二级结构预测方法. 该方法从氨基酸序列中提取出和自然语言中的“词”类似的与物种相关的蛋白质二级结构词条, 这些词条形成了蛋白质二级结构词典, 该词典描述了氨基酸序列和蛋白质二级结构之间的关系. 预测蛋白质二级结构的过程和自然语言中的分词和词性标注一体化的过程类似. 该方法把词条序列看成是马尔科夫链, 通过Viterbi算法搜索每个词条被标注为某种二级结构类型的最大概率, 其中使用词网格描述分词的结果, 使用最大熵马尔科夫模型计算词条的二级结构概率. 蛋白质二级结构预测的结果是最优的分词所对应的二级结构类型. 在4个物种的蛋白质序列上对这种方法进行测试, 并和PHD方法进行比较. 试验结果显示, 这种方法的Q3准确率比PHD方法高3.9%, SOV准确率比PHD方法高4.6%. 结合BLAST搜索的局部相似的序列可以进一步提高预测的准确率. 在50个CASP5目标蛋白质序列上进行测试的结果是: Q3准确率为78.9%, SOV准确率为77.1%. 基于这种方法建立了一个蛋白质二级结构预测的服务器, 可以通过http://www.insun.hit.edu.cn:81/demos/biology/index.html来访问.     

蛋白质与抗体微阵列及其在生物医学研究中的应用

随着人类基因组测序的顺利完成及其他相关领域如机械制造、微电子加工技术及生物信息学方面所取得的进展,以蛋白质为研究对象的蛋白质组学愈显重要,高通量的蛋白质与抗体阵列芯片分析技术正日益为人们关注.对蛋白质分析策略及以阵列为基础的蛋白质芯片分析原理、相关的制备方法与检测技术及其在生物学研究、医学与实验诊断应用方面进行了阐述,并对现阶段该技术存在的不足与发展前景进行了讨论.     

Structural analysis, identification, and design of calcium-binding sites in proteins

Assigning proteins with functions based on the 3-D structure requires high-speed techniques to make a systematic survey of protein structures. Calcium regulates many biological systems by binding numerous proteins in different biological environments. Despite the great diversity in the composition of ligand residues and bond angles and lengths of calcium-binding sites, our structural analysis of 11 calcium-binding sites in different classes of proteins has shown that common local structural parameters can be used to identify and design calcium-binding proteins. Natural calcium-binding sites in both EF-hand proteins and non-EF-hand proteins can be described with the smallest deviation from the geometry of an ideal pentagonal bipyramid. Further, two different magnesium-binding sites in parvalbumin and calbindin(D9K) can also be identified using an octahedral geometry. Using the established method, we have designed de novo calcium-binding sites into the scaffold of non-calcium-binding proteins CD2 and Rop. Our results suggest that it is possible to identify calcium- and magnesium-binding sites in proteins and design de novo metal-binding sites.     

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.     

How good is prediction of protein structural class by the component-coupled method?

Proteins of known structures are usually classified into four structural classes: all-alpha, all-beta, alpha+beta, and alpha/beta type of proteins. A number of methods to predicting the structural class of a protein based on its amino acid composition have been developed during the past few years. Recently, a component-coupled method was developed for predicting protein structural class according to amino acid composition. This method is based on the least Mahalanobis distance principle, and yields much better predicted results in comparison with the previous methods. However, the success rates reported for structural class prediction by different investigators are contradictory. The highest reported accuracies by this method are near 100%, but the lowest one is only about 60%. The goal of this study is to resolve this paradox and to determine the possible upper limit of prediction rate for structural classes. In this paper, based on the normality assumption and the Bayes decision rule for minimum error, a new method is proposed for predicting the structural class of a protein according to its amino acid composition. The detailed theoretical analysis indicates that if the four protein folding classes are governed by the normal distributions, the present method will yield the optimum predictive result in a statistical sense. A non-redundant data set of 1,189 protein domains is used to evaluate the performance of the new method. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein. The apparent relatively high accuracy level (more than 90%) attained in the previous studies was due to the preselection of test sets, which may not be adequately representative of all unrelated proteins.     

Protein structural class identification directly from NMR spectra using averaged chemical shifts

Knowledge of the three-dimensional structure of proteins is integral to understanding their functions, and a necessity in the era of proteomics. A wide range of computational methods is employed to estimate the secondary, tertiary, and quaternary structures of proteins. Comprehensive experimental methods, on the other hand, are limited to nuclear magnetic resonance (NMR) and X-ray crystallography. The full characterization of individual structures, using either of these techniques, is extremely time intensive. The demands of high throughput proteomics necessitate the development of new, faster experimental methods for providing structural information. As a first step toward such a method, we explore the possibility of determining the structural classes of proteins directly from their NMR spectra, prior to resonance assignment, using averaged chemical shifts. This is achieved by correlating NMR-based information with empirical structure-based information available in widely used electronic databases. The results are analyzed statistically for their significance. The robustness of the method as a structure predictor is probed by applying it to a set of proteins of unknown structure. Our results show that this NMR-based method can be used as a low-resolution tool for protein structural class identification.     

A hybrid genetic-neural model for predicting protein structural classes

A genetic algorithm (GA) for feature selection in conjunction with neural network was applied to predict protein structural classes based on single amino acid and all dipeptide composition frequencies. These sequence parameters were encoded as input features for a GA in feature selection procedure and classified with a three-layered neural network to predict protein structural classes. The system was established through optimization of the classification performance of neural network which was used as evaluation function. In this study, self-consistency and jackknife tests on a database containing 498 proteins were used to verify the performance of this hybrid method, and were compared with some of prior works. The adoption of a hybrid model, which encompasses genetic and neural technologies, demonstrated to be a promising approach in the task of protein structural class prediction.     

A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model

In this paper, a step-by-step classification algorithm based on double-layer SVM model is constructed to predict the secondary structure of proteins. The most important feature of this algorithm is to improve the prediction accuracy of α+β and α/β classes through transforming the prediction of two classes of proteins, α+β and α/β classes, with low accuracy in the past, into the prediction of all-α and all-β classes with high accuracy. A widely-used dataset, 25PDB dataset with sequence similarity lower than 40%, is used to evaluate this method. The results show that this method has good performance, and on the basis of ensuring the accuracy of other three structural classes of proteins, the accuracy of α+β class proteins is improved significantly.     

Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network

It is a critical challenge to develop automated methods for fast and accurately determining the structures of proteins because of the increasingly widening gap between the number of sequence-known proteins and that of structure-known proteins in the post-genomic age. The knowledge of protein structural class can provide useful information towards the determination of protein structure. Thus, it is highly desirable to develop computational methods for identifying the structural classes of newly found proteins based on their primary sequence. In this study, according to the concept of Chou's pseudo amino acid composition (PseAA), eight PseAA vectors are used to represent protein samples. Each of the PseAA vectors is a 40-D (dimensional) vector, which is constructed by the conventional amino acid composition (AA) and a series of sequence-order correlation factors as original introduced by Chou. The difference among the eight PseAA representations is that different physicochemical properties are used to incorporate the sequence-order effects for the protein samples. Based on such a framework, a dual-layer fuzzy support vector machine (FSVM) network is proposed to predict protein structural classes. In the first layer of the FSVM network, eight FSVM classifiers trained by different PseAA vectors are established. The 2nd layer FSVM classifier is applied to reclassify the outputs of the first layer. The results thus obtained are quite promising, indicating that the new method may become a useful tool for predicting not only the structural classification of proteins but also their other attributes.     

氨基酸组成聚类、蛋白质结构型和结构型的预测

用信息聚类方法对蛋白质的氨基酸组成进行聚类,发现存在梯级成团（大集团分解成小集团）现象,６４５个蛋白质可分成１５个小集团,每一个小集团与蛋白质二级结构含量决定的结构型有一定相关性,但与蛋白质五大结构型相关性不明显。指出了由氨基酸成分和二级结构含量预测结构型的方案中存在的问题。提出了由蛋白质二级结构序列预测蛋白质结构型的新方法,并给出了预测蛋白质结构型的简明预测规则     

Prediction of protein structural class from the amino acid sequence

The multidimensional statistical technique of discriminant analysis is used to allocate amino acid sequences to one of four secondary structural classes: high α content, high β content, mixed α and β, low content of ordered structure. Discrimination is based on four attributes: estimates of percentages of α and β structures, and regular variations in the hydrophobic values of residues along the sequence, occurring with periods of 2 and 3.6 residues. The reliability of the method, estimated by classifying 138 sequences from the Brookhaven Protein Data Bank, is 80%, with no misallocations between α-rich and β-rich classes. The reliability can be increased to 84% by making no allocation for proteins classified with odds close to 1. Classification using previously developed secondary structural prediction methods is considerably less reliable, the best result being 64% obtained using predictions based on the Delphi method.