Analysis of protein chameleon sequence characteristics

Conversion of local structural state of a protein from an α-helix to a β-strand is usually associated with a major change in the tertiary structure. Similar changes were observed during the self assembly of amyloidogenic proteins to form fibrils, which are implicated in severe diseases conditions, e.g., Alzheimer disease. Studies have emphasized that certain protein sequence fragments known as chameleon sequences do not have a strong preference for either helical or the extended conformations. Surprisingly, the information on the local sequence neighborhood can be used to predict their secondary at a high accuracy level. Here we report a large scale-analysis of chameleon sequences to estimate their propensities to be associated with different local structural states such as α -helices, β-strands and coils. With the help of the propensity information derived from the amino acid composition, we underline their complexity, as more than one quarter of them prefers coil state over to the regular secondary structures. About half of them show preference for both α-helix and β-sheet conformations and either of these two states is favored by the rest.     

Insilco analysis of functionally important residues in folate receptors

Lack of crystal structure data of folate binding proteins has left so many questions unanswered (for example, important residues in active site, binding domain, important amino acid residues involved in interactions between ligand and receptor). With sequence alignment and PROSITE motif identification, we attempted to answer evolutionarily significant residues that are of functional importance for ligand binding and that form catalytic sites. We have analyzed 46 different FRs and FBP sequences of various organisms obtained from Genbank. Multiple sequence alignment identified 44 highly conserved identical amino acid residues with 10 cysteine residues and 12 motifs including ECSPNLGPW (which might help in the structural stability of FR).     

新型抗菌多肽LC1的1H—NMR谱峰归属和二级结构分析

LC1是从枯草杆菌A0 14的分泌物中分离出的一种新型抗菌多肽 ,具有很强的抗水稻白叶枯致病菌的能力。应用 2D NMR技术研究LC1的溶液构象 ,通过分析其在水和重水中的DQF COSY、TOCSY和NOESY等1H NMR谱 ,识别了LC1全部 4 7个氨基酸残基的自旋体系 ,并通过分析NOESY谱中dαN、dNN、dβN和dαδ的联系完成了序列专一谱峰归属 ,标定了全部主链质子和绝大部分侧链质子的化学位移。谱峰归属结果和NMR数据分析表明LC1的二级结构主要为伸展构象 ,其中肽段Phe2 5～Asp3 1和Tyr3 6～Glu42 构成反平行 β折叠 ,并由Ser3 2 ～Gly3 5所形成的β转角相连接。LC1不含或仅含少量α螺旋。同时 ,通过对LC1的大量疏水氨基酸残基之间NOE联系的分析 ,推测LC1具有一个以Trp2 3 为中心的疏水核心。     

SMS 2.0: an updated database to study the structural plasticity of short peptide fragments in non-redundant proteins

The function of a protein molecule is greatly influenced by its three-dimensional (3D) structure and therefore structure prediction will help identify its biological function. We have updated Sequence, Motif and Structure (SMS), the database of structurally rigid peptide fragments, by combining amino acid sequences and the corre-sponding 3D atomic coordinates of non-redundant (25%) and redundant (90%) protein chains available in the Protein Data Bank (PDB). SMS 2.0 provides information pertaining to the peptide fragments of length 5-14 resi-dues. The entire dataset is divided into three categories, namely, same sequence motifs having similar, intermedi-ate or dissimilar 3D structures. Further, options are provided to facilitate structural superposition using the pro-gram structural alignment of multiple proteins (STAMP) and the popular JAVA plug-in (Jmol) is deployed for visualization. In addition, functionalities are provided to search for the occurrences of the sequence motifs in other structural and sequence databases like PDB, Genome Database (GDB), Protein Information Resource (PIR) and Swiss-Prot. The updated database along with the search engine is available over the World Wide Web through the following URL http://cluster.physics.iisc.ernet.in/sms/.     

Deconvolution of the circular dichroism spectra of proteins: the circular dichroism spectra of the antiparallel beta-sheet in proteins.

A recently developed algorithm, called Convex Constraint Analysis (CCA), was successfully applied to determine the circular dichroism (CD) spectra of the pure beta-pleated sheet in globular proteins. On the basis of X-ray diffraction determined secondary structures, the original data set used (Perczel, A., Hollosi, M., Tusnady, G. Fasman, G.D. Convex constraint analysis: A natural deconvolution of circular dichroism curves of proteins, Prot. Eng., 4:669-679, 1991), was improved by the addition of proteins with high beta-pleated sheet content. The analysis yielded CD curves of the pure components of the main secondary structural elements (alpha-helix, antiparallel beta-pleated sheet, beta-turns, and unordered conformation), as well as a curve attributed to the "aromatic contribution" in the wavelength range of 195-240 nm. Upon deconvolution the curves obtained were assigned to various secondary structures. The calculated weights (percentages determining the contributions of each pure component curve in the measured CD spectra of a given protein) were correlated with the X-ray diffraction determined percentages in an assignment procedure and were evaluated. The Pearson product correlation coefficients (R) are significant for all five components. The new pure component curves, which were obtained through deconvolution of the protein CD spectra alone, are promising candidates for determining the percentages of the secondary structural components in globular proteins without the necessity of adopting an X-ray database. The CD spectrum of the CheY protein was interesting because it has the characteristic shape associated with the alpha-helical structure, but upon analysis yielded a considerable amount of beta-sheet in agreement with the X-ray structure.     

同义密码子的反常蛋白质二级结构偏好性

统计分析了 119种人蛋白质和 92种大肠杆菌蛋白质的mRNA序列和蛋白质二级结构的关系 .从二肽频数出发 ,研究了同义密码子使用对蛋白质二级结构的影响 ,证明其影响在 10 %到 2 0 %的量级 .对于人和大肠杆菌 ,在 90 %置信水平上 ,4 0 0对二肽中分别有 79对和 6 0对 ,在 95 %置信水平上 ,分别有 4 5对和 36对二肽的相应密码子二联体具有不同于氨基酸的反常二级结构偏好性 ,并且这种反常不能归因于随机涨落     

蛋白质结构型的定义和识别

提出紧结构域的概念,由二级结构序列中一段或几段连续的α螺旋和β折叠构成的空间紧密堆集的最大折叠体称为紧结构域.利用3种紧结构域（α域,β域和α/β域）定义球蛋白的5种结构型：α型蛋白,β型蛋白,α/β型蛋白,多域蛋白和ζ型蛋白.将1 261个代表性的蛋白质(1 022家族)进行分类,并和SCOP库的分类做了比较.进行了删去序列冗余的分析.在此基础上提出结构型的预测方案,成功率在82%～85%.     

The C-Terminal Domain of RNA Polymerase II Is a Multivalent Targeting Sequence that Supports Drosophila Development with Only Consensus Heptads

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蛋白质结构型的识别方法

给出了α型、β型、α/β型、多域型蛋白质二级结构主序列六联体的分布规律.提出了根据蛋白质二级结构主序列对蛋白质结构型进行识别(分类)的方法.以蛋白质二级结构主序列三联体为参数,利用Mahalanobis距离方法对上述4种结构型的蛋白质进行识别,分类的总体准确率为81%;以二级结构主序列中六联体的频数构成蛋白质结构的多样性源,利用多样性增量极小化对上述4种结构型进行识别,分类的总体准确率为83%. 同时也给出了对紧结构域的识别途径.     

Substitution rates in α-helical transmembrane proteins

It has been shown previously that some membrane proteins have a conserved core of amino acid residues. This idea not only serves to orient helices during model building exercises but may also provide insight into the structural role of residues mediating helix-helix interactions. Using experimentally determined high-resolution structures of alpha-helical transmembrane proteins we show that, of the residues within the hydrophobic transmembrane spans, the residues at lipid and subunit interfaces are more evolutionarily variable than those within the lipid-inaccessible core of a polypeptide's transmembrane domain. This supports the idea that helix-helix interactions within the same polypeptide chain and those at the interface between different polypeptide chains may arise in distinct ways. To show this, we use a new method to estimate the substitution rate of an amino acid residue given an alignment and phylogenetic tree of closely related proteins. This method gives better sensitivity in the otherwise-conserved transmembrane domains than a conventional similarity analysis and is relatively insensitive to the sequences used.     

Partitioning protein structures into domains: why is it so difficult?

This analysis takes an in-depth look into the difficulties encountered by automatic methods for domain decomposition from three-dimensional structure. The analysis involves a multi-faceted set of criteria including the integrity of secondary structure elements, the tendency toward fragmentation of domains, domain boundary consistency and topology. The strength of the analysis comes from the use of a new comprehensive benchmark dataset, which is based on consensus among experts (CATH, SCOP and AUTHORS of the 3D structures) and covers 30 distinct architectures and 211 distinct topologies as defined by CATH. Furthermore, over 66% of the structures are multi-domain proteins; each domain combination occurring once per dataset. The performance of four automatic domain assignment methods, DomainParser, NCBI, PDP and PUU, is carefully analyzed using this broad spectrum of topology combinations and knowledge of rules and assumptions built into each algorithm. We conclude that it is practically impossible for an automatic method to achieve the level of performance of human experts. However, we propose specific improvements to automatic methods as well as broadening the concept of a structural domain. Such work is prerequisite for establishing improved approaches to domain recognition. (The benchmark dataset is available from http://pdomains.sdsc.edu).