蛋白质的错误折叠与疾病

蛋白质是生物体内一切功能的执行者.人体内的任何功能,从催化化学反应到抵御外来侵略都是蛋白质作用的结果.蛋白质折叠是生命活动的最基本过程,近年发现蛋白质的错误折叠可以导致一些疾病.蛋白质的错误折叠与疾病的关系已成为分子生物学新的研究前沿.介绍了细胞内保证蛋白质正常功能的“质量控制”系统,重点讨论了翻译后的质量控制、与蛋白质错误折叠有关的一些疾病和治疗这一类疾病的原则方法.     

关于高中生物课本中蛋白质合成示意图的讨论

现行高中和师范学校生物课本中蛋白质合成示意图我认为有误。产生错误的原因主要是教材的更新落后于学科的发展,在此提出和有关同志共同讨论,以达到统一认识提高教学质量的目的。第一,示意图把mRNA和tRNA的反向平行视为同向平行;第二,由于对密码子和反密码子相互作用的关系缺乏一定的了解,因而对密码子UCU和GCU的反密码子确定是错误的。蛋白质合成的内容十分复杂,现仅就以上两点加以讨论。一、信息RNA(mRNA)和转远RNA(tRNA)是反向平行的。     

氨基酸的反密码子有多少种?

从DNA和RNA分子的序列分析中,又获得了许多关于氨基酸密码子组成的新资料。发现密码子配对关系的变偶规律(“wobblerules”)对这些资料也适用。由于没有在反密码子的第一个核苷酸位置上发现碱基     

伯克利研究者制造含非天然氨基酸的重组蛋白

在重组蛋白质中20种天然氨基酸之间彼此相互替代的情况每天都会发生。所有这些都需改变在基因中编码氨基酸的三碱基密码子,使之变成所需蛋白的氨基酸密码。这是蛋白质工程的基本技术。现在,伯克利加里弗尼亚大学的Peter G. Schultz, Christopher J. Noren及其同事们发明了一种用天然蛋白中从未发现过的氨基酸替代天然氨基酸的新方法(Science 244:182～188)。这极大地增加了对改变蛋白质分子形状和特性的选择。为使非天然氨基酸替代氨基酸,伯克利的研究者们用“空白”错误密码ATG(胸腺嘧啶-腺嘌呤-鸟嘌呤)替换编码天然氨基酸的寡核苷酸密码子,信使RNA(mRNA)一般     

拟南芥基因密码子偏爱性分析

密码子偏爱性对外源基因的表达强度有一定影响,特别是编码蛋白质N端7～8个氨基酸残基的密码子.通过对拟南芥染色体中26 827个蛋白质对应的基因密码子进行分析,得到了编码氨基酸的61种密码子在拟南芥中的使用频率,并与大肠杆菌和哺乳动物进行了比较,结果表明三者间的密码子偏爱性有较大差异.这一分析结果对于动物基因在植物中的表达,及植物基因在微生物中的表达具有一定指导意义.同时提供了一种直接以XML文档为数据源解析巨型XML格式染色体数据的方法.     

线粒体具有不同的遗传密码

最近已陆续有证据表明真核生物（包括人） 的线粒体可能并不完全遵循遗传密码的普遍性 原则。首先令人惊奇的是酵母ATP酶的第9亚 基的基因核普酸顺序和蛋白质的氨基酸顺序并 不一致。Hensgems等发现此蛋白质第46位氨基 酸是苏氨酸,而DNA顺序上相应的密码子却是 CUAo CUA在平时是编码亮氨酸的,顺序测定 的研究证明这种明显的差异既不是顺序排列的 错误;也不是遗传多态性。     

用碱洗脱法检测DNA-蛋白质交联和DNA链间交联

本方法以DNA单链断裂的检测为基础,在背景γ射线照射下进行DNA交联检测。所建方法与Kohn氏原法相比,洗脱时间大为缩短,实验所用主要材料都能立足国内。本文引入“交联度”这个参数,能同时相对定量地表示DNA总交联、DNA-蛋白质交联和DNA链间交联。此外还从DNA、蛋白质两方面确证了DNA-蛋白质交联的存在。     

研发动态

<正>美国发现DNA暗藏第二套编码美国华盛顿大学近日发现了DNA暗藏的第二套编码,所包含的信息将改变科学家对DNA指令的解读以及对健康和疾病有重要意义的突变的解释,相关论文发表在最新的《科学》杂志上。遗传编码包含64种密码子,John Stamatoyannopoulos博士领导的研究团队发现,部分密码子——他们称之为duons——含有双层意义,一层与蛋白质序列有关,一层与基因控制有关。它们演化的方式彼此互相影响。其中基因控制指令能帮助保持某些蛋白质     

翻译速率与蛋白质二级结构的关系

统计分析了人的 119种蛋白质和大肠杆菌的 92种蛋白质密码子翻译速率和蛋白质二级结构的关系。据m 密码子片段在不同二级结构中的频数分布 ,我们发现人和大肠杆菌中翻译速率与蛋白质二级结构之间有一定关系 :高翻译速率时倾向编码α螺旋、不倾向编码线团 (coil) ;低翻译速率时倾向编码线团、不倾向编码α螺旋 ;β折叠结构则随翻译速率表现出明显的振荡。同时 ,密码子的使用在不同片段内一般也是不均匀的 :在α螺旋片段内 ,结构尾部偏向使用高翻译速率密码子 ;中部倾向使用中翻译速率密码子 ;而头部使用的密码子翻译速率偏低。这样的倾向性不大可能归结为随机起伏的影响。     

“垃圾”基因，垃圾还是宝物？

何谓“垃圾”基因 “垃圾”基因是相对基因而言的。基因是具有遗传效应的特定DNA序列,通俗地讲,基因就是编码某种蛋白质的一段基因。它们就像散落于天幕的星星一样,分散在我们的基因组中。而在这些基因间存在的大片大片的DNA片段是不能编码蛋白质的,即“非编码序列”。由于功能不清,美国加州理工学院的大野·乾于1972年提出用“垃圾基因”（junk DNA）来形容它们。     

A lower bound on the reversal and transposition diameter.

One possible model to study genome evolution is to represent genomes as permutations of genes and compute distances based on the minimum number of certain operations (rearrangements) needed to transform one permutation into another. Under this model, the shorter the distance, the closer the genomes are. Two operations that have been extensively studied are the reversal and the transposition. A reversal is an operation that reverses the order of the genes on a certain portion of the permutation. A transposition is an operation that "cuts" a certain portion of the permutation and "pastes" it elsewhere in the same permutation. In this note, we show that the reversal and transposition distance of the signed permutation pi(n) = (-1 -2.-(n - 1)-n) with respect to the identity is left floor n/2 right floor + 2 for all n>or=3. We conjecture that this value is the diameter of the permutation group under these operations.     

The chirality of ground DNA knots and links.

The chirality of ground DNA knots and links is described and characterized in terms of color symmetry groups (CSG), i.e. color symmetry groups I and II, which correspond to topochirality (topological chirality) and topoachirality (topological achirality) which bear an uncanny resemblance to point groups I (proper) and point groups II (improper) used for testing geochirality (geometrical chirality) and geoachirality (geometrical achirality), respectively. By regarding these two crossing modes in mirror images as white and black vertices, DNA knots and links with minimal crossings can be mapped to vertex-bicolored graphs under a working hypothesis that DNA knots and links exist in ground states with minimal energy m0. The color symmetry group of a vertex-bicolored graph G is defined as the set of all permutations and permutation asymmetrizations of the vertices of G that preserve its topology (connectivity), where asymmetrization, denoted as (a), is the operation of changing vertices' colors, and a permutation followed by an (a) is a permutation asymmetrization. The color symmetry groups I contains only permutations, whereas color symmetry groups II comprise permutation asymmetrizations as well as permutations. Four DNA knots and links in nature are analyzed and tabulated consisely. In addition, the well-known figure-of-eight knot and Borromean rings are discussed in much the same way.     

Sorting by short swaps.

A short swap is an operation on a permutation that switches two elements that have at most one element between them. This paper investigates the problem of finding a minimum-length sorting sequence of short swaps for a given permutation. A polynomial-time 2-approximation algorithm for this problem is presented, and bounds for the short-swap diameter (the length of the longest minimum sorting sequence among all permutations of a given length) are also obtained.     

New bounds and tractable instances for the transposition distance

The problem of sorting by transpositions asks for a sequence of adjacent interval exchanges that sorts a permutation and is of the shortest possible length. The distance of the permutation is defined as the length of such a sequence. Despite the apparently intuitive nature of this problem, introduced in 1995 by Bafna and Pevzner, the complexity of both finding an optimal sequence and computing the distance remains open today. In this paper, we establish connections between two different graph representations of permutations, which allows us to compute the distance of a few nontrivial classes of permutations in linear time and space, bypassing the use of any graph structure. By showing that every permutation can be obtained from one of these classes, we prove a new tight upper bound on the transposition distance. Finally, we give improved bounds on some other families of permutations and prove formulas for computing the exact distance of other classes of permutations, again in polynomial time     

Comparing genomes with duplications: a computational complexity point of view

In this paper, we are interested in the computational complexity of computing (dis)similarity measures between two genomes when they contain duplicated genes or genomic markers, a problem that happens frequently when comparing whole nuclear genomes. Recently, several methods ( [1], [2]) have been proposed that are based on two steps to compute a given (dis)similarity measure M between two genomes G_1 and G_2: first, one establishes a oneto- one correspondence between genes of G_1 and genes of G_2 ; second, once this correspondence is established, it defines explicitly a permutation and it is then possible to quantify their similarity using classical measures defined for permutations, like the number of breakpoints. Hence these methods rely on two elements: a way to establish a one-to-one correspondence between genes of a pair of genomes, and a (dis)similarity measure for permutations. The problem is then, given a (dis)similarity measure for permutations, to compute a correspondence that defines an optimal permutation for this measure. We are interested here in two models to compute a one-to-one correspondence: the exemplar model, where all but one copy are deleted in both genomes for each gene family, and the matching model, that computes a maximal correspondence for each gene family. We show that for these two models, and for three (dis)similarity measures on permutations, namely the number of common intervals, the maximum adjacency disruption (MAD) number and the summed adjacency disruption (SAD) number, the problem of computing an optimal correspondence is NP-complete, and even APXhard for the MAD number and SAD number.     

A Reminiscence

Leslie Orgel and Francis Crick with Gobind Khorana in Madison, Wisconsin (December 1965). I first met Leslie at the Endicott House (MIT) in February 1964. Leslie was then spending a period of time at MIT and the occasion was a party for him. During our conversation, Leslie talked about starting some experimental work. He seemed to be particularly interested in polyphosphates and the chemical activation of small molecules (building blocks).Shortly after his move to the Salk Institute in the Fall of 1964 I visited him in January 1965. He already had a lab going. I remember meeting Jim Ferris, in particular, and John Sulston sometime later. That particular time was exciting for my research as well. We had the first results on the Genetic Code using the chemical-biochemical approach that my lab had developed. Francis Crick was also at the Salk Institute during the time of my visit. Both Leslie and Francis were very excited by my results and they began to ask a lot of questions and gave me a whole lot of suggestions about further experiments. In fact, my thinking and planning of things that we were doing were so scrutinized and clarified during these discussions that, it seemed to me, my own group had only to turn out all the experiments that were needed. These interactions with Francis and Leslie continued intensively throughout that year and later. In fact, both Leslie and Francis accepted my invitation to Madison in December 1965 for more discussions.Since those early days of the Salk Institute, I have made numerous visits over the years to Leslie and his research group. It has always been very exciting to learn about the many discoveries bearing on chemical evolution that have unfolded from Leslie's research group. In addition, I have always benefitted from the insightful comments that Leslie invariably provided on my own research. I look forward to our continued interactions and friendship in the future.Leslie, A Happy Birthday!     

An algorithm to enumerate sorting reversals for signed permutations.

The rearrangement distance between single-chromosome genomes can be estimated as the minimum number of inversions required to transform the gene ordering observed in one into that observed in the other. This measure, known as "inversion distance," can be computed as the reversal distance between signed permutations. During the past decade, much progress has been made both on the problem of computing reversal distance and on the related problem of finding a minimum-length sequence of reversals, which is known as "sorting by reversals." For most problem instances, however, many minimum-length sequences of reversals exist, and in the absence of auxiliary information, no one is of greater value than the others. The problem of finding all minimum-length sequences of reversals is thus a natural generalization of sorting by reversals, yet it has received little attention. This problem reduces easily to the problem of finding all "sorting reversals" of one permutation with respect to another - that is, all reversals rho such that, if rho is applied to one permutation, then the reversal distance of that permutation from the other is decreased. In this paper, an efficient algorithm is derived to solve the problem of finding all sorting reversals, and experimental results are presented indicating that, while the new algorithm does not represent a significant improvement in asymptotic terms (it takes O(n(3)) time, for permutations of size n; the problem can now be solved by brute force in Theta(n(3)) time), it performs dramatically better in practice than the best known alternative. An implementation of the algorithm is available at www.cse.ucsc.edu/~acs.     

The ‘Butterfly effect’ in Cayley graphs with applications to genomics

Suppose a finite set X is repeatedly transformed by a sequence of permutations of a certain type acting on an initial element x to produce a final state y. For example, in genomics applications, X could be a set of genomes and the permutations certain genome ‘rearrangements’ or, in group theory, X could be the set of configurations of a Rubik’s cube and the permutations certain specified moves. We investigate how ‘different’ the resulting state y′ to y can be if a slight change is made to the sequence, either by deleting one permutation, or replacing it with another. Here the ‘difference’ between y and y′ might be measured by the minimum number of permutations of the permitted type required to transform y to y′, or by some other metric. We discuss this first in the general setting of sensitivity to perturbation of walks in Cayley graphs of groups with a specified set of generators. We then investigate some permutation groups and generators arising in computational genomics, and the statistical implications of the findings.     

Naturally occurring circular permutations in proteins

A pair of proteins is defined to be related by a circular permutation if the N-terminal region of one protein has significant sequence similarity to the C-terminal of the other and vice versa. To detect pairs of proteins that might be related by circular permutation, we implemented a procedure based on a combination of a fast screening algorithm that we had designed and manual verification of candidate pairs. The screening algorithm is a variation of a dynamic programming string matching algorithm, in which one of the sequences is doubled. This algorithm, although not guaranteed to identify all cases of circular permutation, is a good first indicator of protein pairs related by permutation events. The candidate pairs were further validated first by application of an exhaustive string matching algorithm and then by manual inspection using the dotplot visual tool. Screening the whole Swissprot database, a total of 25 independent protein pairs were identified. These cases are presented here, divided into three categories depending on the level of functional similarity of the related proteins. To validate our approach and to confirm further the small number of circularly permuted protein pairs, a systematic search for cases of circular permutation was carried out in the Pfam database of protein domains. Even with this more inclusive definition of a circular permutation, only seven additional candidates were found. None of these fitted our original definition of circular permutations. The small number of cases of circular permutation suggests that there is no mechanism of local genetic manipulation that can induce circular permutations; most examples observed seem to result from fusion of functional units.