摘要翻译：
讨论了一种新的蛋白质多序列比对方法：该方法与基于取代矩阵的方法（如基于Blosum或PAM的方法）基本一致，并暗示了一种更确定的氨基酸化学/物理亚分组方法。氨基酸(aa)通过连续的衍生物被分成亚群，从而根据所考虑的性质形成聚类。这些属性可以是用户定义的，也可以在默认方案之间进行选择，就像在这里描述的分析中使用的那些。从最初的20个天然氨基酸开始，根据它们的极性/疏水性指数依次进行划分，分辨率增加到4个细分级别。其他细分方案也是可能的：在本论文的工作中，也采用了基于物理/结构性质（溶剂暴露、侧链迁移率和二级结构倾向）的方案，并将其与化学方案进行了比较。在本章所述的方法中，对齐中每个位置的总得分反映了氨基酸之间不同程度的相似性。评分值的结果来自每一个被考虑的个体属性的每一个选择性水平的贡献。简单地说，该方法（称为M_Al）分析每个位点的n序列比对位置，并给出一个由aa恒等式加上n位点氨基酸之间的化学或结构亲和力的综合评价所贡献的分数。该方法已在用python语言编写的一系列程序中实现；这些程序已经在一些生物案例中进行了测试，具有基准目的。
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英文标题：
《A successive sub-grouping method for multiple sequence alignments
  analysis》
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作者：
Stefano Marino
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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英文摘要：
  A novel approach to protein multiple sequence alignment is discussed: substantially this method counterparts with substitution matrix based methods (like Blosum or PAM based methods), and implies a more deterministic approach to chemical/physical sub-grouping of amino acids . Amino acids (aa) are divided into sub-groups with successive derivations, that result in a clustering based on the considered property. The properties can be user defined or chosen between default schemes, like those used in the analysis described here. Starting from an initial set of the 20 naturally occurring amino acids, they are successively divided on the basis of their polarity/hydrophobic index, with increasing resolution up to four level of subdivision. Other schemes of subdivision are possible: in this thesis work it was employed also a scheme based on physical/structural properties (solvent exposure, lateral chain mobility and secondary structure tendency), that have been compared to the chemical scheme with testing purposes. In the method described in this chapter, the total score for each position in the alignment accounts for different degree of similarity between amino acids. The scoring value result form the contribution of each level of selectivity for every individual property considered. Simply the method (called M_Al) analyse the n sequence alignment position per position and assigns a score which have contributes by aa identity plus a composed valuation of the chemical or of the structural affinity between the n aligned amino acids. This method has been implemented in a series of programs written in python language; these programs have been tested in some biological cases, with benchmark purposes.
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PDF链接：
https://arxiv.org/pdf/0705.4429