New amino acid substitution matrix brings sequence alignments into agreement with structure matches.

New amino acid substitution matrix brings sequence alignments into agreement with structure matches.
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新的氨基酸取代矩阵使序列比对与结构匹配一致。

DOI:
10.1002/prot.26050
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发表时间:
2021-06
期刊:
影响因子:
2.9
通讯作者:
Jernigan RL
Jernigan RL
中科院分区:
生物学4区
文献类型:
--
作者:
Jia K;Jernigan RL

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蛋白质序列匹配目前无法识别许多高度相似的结构,即使它们具有相同的功能。球状蛋白的高堆积密度导致了相互依赖的取代,这在以前并没有被认为是氨基酸相似的。目前,序列匹配只基于单个氨基酸的相似性来比较序列,忽略了在密集包装的蛋白质中,存在代表两个相互作用的氨基酸之间交换的额外的保守替换,例如将小-大对改变为大-小对替换,而这些替换个体并不那么保守。在这里,我们表明,包括这种取代对的信息会产生更好的序列匹配,并且这些信息会在同一蛋白质对的序列比对和结构匹配之间的一致性中产生显著的收益。结果表明,序列片段与结构片段比对的地方匹配。在所有2002年收集的病例中,存在先前与结构不一致的序列比对匹配的收益。我们的结果还表明,在检测“暮光之区”蛋白质序列的同源性方面取得了显著的进展。推导出的氨基酸替代度量值还有许多其他潜在的应用,如注释、蛋白质设计、突变设计和经验电位推导。
Protein sequence matching presently fails to identify many structures that are highly similar, even when they are known to have the same function. The high packing densities in globular proteins lead to interdependent substitutions, which have not previously been considered for amino acid similarities. At present, sequence matching compares sequences based only upon the similarities of single amino acids, ignoring the fact that in densely packed protein, there are additional conservative substitutions representing exchanges between two interacting amino acids, such as a small-large pair changing to a large-small pair substitutions that are not individually so conservative. Here we show that including information for such pairs of substitutions yields improved sequence matches, and that these yield significant gains in the agreements between sequence alignments and structure matches of the same protein pair. The result shows sequence segments matched where structure segments are aligned. There are gains for all 2002 collected cases where the sequence alignments that were not previously congruent with the structure matches. Our results also demonstrate a significant gain in detecting homology for “twilight zone” protein sequences. The amino acid substitution metrics derived have many other potential applications, for annotations, protein design, mutagenesis design, and empirical potential derivation.
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