The genetic code is one in a million
The genetic code is one in a million
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DOI：
10.1007/pl00006381
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发表时间：
1998-09-01
影响因子：
3.9
通讯作者：
Hurst, LD
中科院分区：
文献类型：
--
作者：
Freeland, SJ;Hurst, LD
对遗传密码的统计学和生物化学研究发现了密码子分配的非随机分布模式的证据。例如，已经表明，密码子可以最大限度地减少点突变或误译的影响：错误的密码子要么是同义的，要么是化学性质与没有发生错误时存在的氨基酸非常相似的氨基酸的密码子。这项工作表明，密码子的第二个碱基在这方面的效率比第一个和第三个碱基低约三个数量级。这些结果是基于这样的假设，即所有碱基的所有形式的错误都是同样可能的。我们将这项工作扩展到研究（1）不同的权重转换错误的影响，从颠换错误和（2）不同的权重每个基地的影响，这取决于报告的误译偏见。我们发现，如果偏见影响所有的密码子位置一样，可能是预期的代码适应突变环境与过渡/颠换偏置，然后任何合理的过渡/颠换偏置增加了第二个碱基的相对效率的数量级。此外，如果我们使用权重来允许翻译中的偏差，那么每百万个随机替代代码中只有一个比自然代码更有效。因此，我们得出的结论不仅是，自然遗传密码在最大限度地减少错误的影响方面非常有效，而且它的结构反映了这些错误的偏差，正如我们所预期的那样，密码是选择的产物。
Statistical and biochemical studies of the genetic code have found evidence of nonrandom patterns in the distribution of codon assignments. It has, for example, been shown that the code minimizes the effects of point mutation or mistranslation: erroneous codons an either synonymous or code for an amino acid with chemical properties very similar to those of the one that would have been present had the error not occurred. This work has suggested that the second base of codons is less efficient in this respect, by about three orders of magnitude, than the first and third bases. These results are based on the assumption that all forms of error at all bases are equally likely. We extend this work to investigate (1) the effect of weighting transition errors differently from transversion errors and (2) the effect of weighting each base differently, depending on reported mistranslation biases. We find that if the bias affects all codon positions equally, as might be expected were the code adapted to a mutational environment with transition/transversion bias, then any reasonable transition/transversion bias increases the relative efficiency of the second base by an order of magnitude. In addition, if we employ weightings to allow for biases in translation, then only 1 in every million random alternative codes generated is more efficient than the natural code. We thus conclude not only that the natural genetic code is extremely efficient at minimizing the effects of errors, but also that its structure reflects biases in these errors, as might be expected were the code the product of selection.