Sequence variations within protein families are linearly related to structural variations

It is commonly believed that similarities between the sequences of two proteins infer similarities between their structures. Sequence alignments reliably recognize pairs of protein of similar structures provided that the percentage sequence identity between their two sequences is sufficiently high. This distinction, however, is statistically less reliable when the percentage sequence identity is lower than 30% and little is known then about the detailed relationship between the two measures of similarity. Here, we investigate the inverse correlation between structural similarity and sequence similarity on 12 protein structure families. We define the structure similarity between two proteins as the cRMS distance between their structures. The sequence similarity for a pair of proteins is measured as the mean distance between the sequences in the subsets of sequence space compatible with their structures. We obtain an approximation of the sequence space compatible with a protein by designing a collection of protein sequences both stable and specific to the structure of that protein. Using these measures of sequence and structure similarities, we find that structural changes within a protein family are linearly related to changes in sequence similarity.     

对称蛋白质序列与结构关系研究

进化的观点认为,蛋白质结构的对称性是基因复制和融合的结果,但是由于在长期进化过程中的氨基酸突变,绝大多数现有的蛋白质序列都失去了这种直观的重复性特征。该文简要地回顾了国际上发展的寻找蛋白质序列中重复片段的方法,重点介绍了作者自己提出的分析蛋白质序列和结构对称性的方法以及在蛋白质对称结构形成机理方面的初步工作,并系统分析了各类对称折叠子的序列与结构关系,发现它们的序列都具有隐含的与结构相同的对称性,或者说序列的对称性决定结构的对称性。     

多功能生物材料蛛丝蛋白的一级结构特征和生物合成进展

蜘蛛丝是已知复合性能最强的天然纤维,兼具极高的抗拉伸强度与韧性,有着“生物钢”之美誉,且具有优良的生物相容性和形状记忆性能,在生物医药、组织工程等多个领域有着巨大的应用潜力。蜘蛛丝由一类富有结构多样性的高分子蛛丝蛋白所组成,天然蛛丝蛋白基因GC含量高、重复核心区氨基酸序列高度重复、特定氨基酸含量高以及分子量大等特点给其异源表达带来了较大的困难。本文重点阐述了蛛丝蛋白重复单元中重复核心区特征基序与其结构、纺丝性能和异源表达之间的相关性。对重组蛛丝蛋白的序列进行优化设计,结合异源表达策略,极大地促进了多功能蛛丝蛋白生物合成的发展。本综述可为重组蛛丝蛋白的理性设计与高效合成提供思路。     

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%.     

Molecular Comparison of Dengue Type 1 Mochizuki Strain Virus and Other Selected Viruses Concerning Nucleotide and Amino Acid Sequences of Genomic RNA: A Consideration of Viral Epidemiology and Variation

Dengue-1 (D1) Mochizuki strain was examined for its nucleotide and amino acid sequences of genomic RNA and the data obtained were compared with those of other selected virus strains reported previously. Genomic regions corresponding to C, preM and M proteins were the major subjects of study. Parts of E protein were additionally examined. Among the D1 viruses investigated, the Mochizuki virus which was isolated in 1943 in Japan was shown to be close to Philippine 836-1 strain isolated in 1984 and Nauru Island strain isolated in 1974 at the respective places, in contrast with Thai AHF 82-80 strain isolated in 1980 and Caribbean CV1636/77 strain isolated in 1977. At the same time, a difference was noted between the Mochizuki and Philippine/Nauru strains at the cleavage site of preM/M junction: Mochizuki possessed RRGKR/S sequence whereas the Philippine/Nauru had RRDKR/S. The glycosylation site in preM and hydrophobic regions at the carboxyl termini of M and E were well conserved. Significances of the data are discussed in connection with viral epidemiology and variation.     

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.     

Knowledge-based model building of proteins: concepts and examples.

We describe how to build protein models from structural templates. Methods to identify structural similarities between proteins in cases of significant, moderate to low, or virtually absent sequence similarity are discussed. The detection and evaluation of structural relationships is emphasized as a central aspect of protein modeling, distinct from the more technical aspects of model building. Computational techniques to generate and complement comparative protein models are also reviewed. Two examples, P-selectin and gp39, are presented to illustrate the derivation of protein model structures and their use in experimental studies.     

Will my protein crystallize? A sequence‐based predictor

We propose a machine-learning approach to sequence-based prediction of protein crystallizability in which we exploit subtle differences between proteins whose structures were solved by X-ray analysis [or by both X-ray and nuclear magnetic resonance (NMR) spectroscopy] and those proteins whose structures were solved by NMR spectroscopy alone. Because the NMR technique is usually applied on relatively small proteins, sequence length distributions of the X-ray and NMR datasets were adjusted to avoid predictions biased by protein size. As feature space for classification, we used frequencies of mono-, di-, and tripeptides represented by the original 20-letter amino acid alphabet as well as by several reduced alphabets in which amino acids were grouped by their physicochemical and structural properties. The classification algorithm was constructed as a two-layered structure in which the output of primary support vector machine classifiers operating on peptide frequencies was combined by a second-level Naive Bayes classifier. Due to the application of metamethods for cost sensitivity, our method is able to handle real datasets with unbalanced class representation. An overall prediction accuracy of 67% [65% on the positive (crystallizable) and 69% on the negative (noncrystallizable) class] was achieved in a 10-fold cross-validation experiment, indicating that the proposed algorithm may be a valuable tool for more efficient target selection in structural genomics. A Web server for protein crystallizability prediction called SECRET is available at http://webclu.bio.wzw.tum.de:8080/secret.     

Protein family clustering for structural genomics

A major goal of structural genomics is the provision of a structural template for a large fraction of protein domains. The magnitude of this task depends on the number and nature of protein sequence families. With a large number of bacterial genomes now fully sequenced, it is possible to obtain improved estimates of the number and diversity of families in that kingdom. We have used an automated clustering procedure to group all sequences in a set of genomes into protein families. Bench-marking shows the clustering method is sensitive at detecting remote family members, and has a low level of false positives. This comprehensive protein family set has been used to address the following questions. (1) What is the structure coverage for currently known families? (2) How will the number of known apparent families grow as more genomes are sequenced? (3) What is a practical strategy for maximizing structure coverage in future? Our study indicates that approximately 20% of known families with three or more members currently have a representative structure. The study indicates also that the number of apparent protein families will be considerably larger than previously thought: We estimate that, by the criteria of this work, there will be about 250,000 protein families when 1000 microbial genomes have been sequenced. However, the vast majority of these families will be small, and it will be possible to obtain structural templates for 70-80% of protein domains with an achievable number of representative structures, by systematically sampling the larger families.