Identifying functionally informative evolutionary sequence profiles
Identifying functionally informative evolutionary sequence profiles
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DOI：
10.1093/bioinformatics/btx779
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发表时间：
2018-04-15
期刊：
影响因子：
5.8
通讯作者：
Fiser，Andras
中科院分区：
文献类型：
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作者：
Gil，Nelson;Fiser，Andras
多序列比对（MSA）可以为许多生物信息学应用提供必要的输入，包括蛋白质结构预测和功能注释。然而，最佳选择的序列，以获得生物信息的MSAs，用于这样的目的是很差的探索，并已在传统上进行manual.ResultsWe目前选择的最大互信息（SAMMI），一个自动化的，基于序列的方法，客观地选择一个最佳的MSA从一个大的一组抽样的替代品从一般的序列数据库搜索的对齐。这种方法的假设是，MSA列之间的互信息将是最大的那些MSA，包含最多样化的一组可能的最结构和功能上的同质蛋白质序列。SAMMI进行了测试，以选择MSA的功能位点残基预测的保守模式的分析，从蛋白质配体（肽，核酸和小底物）和蛋白质-蛋白质相互作用databases.Availability和implementationA免费访问的程序，包括源代码，实现SAMMI可在https://github.com/nelsongil92/SAMMI.git.Supplementary信息补充数据可在Bioinformaticsonline。
MotivationMultiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually.ResultsWe present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein–ligand (peptides, nucleic acids and small substrates) and protein–protein interaction databases.Availability and implementationA freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git.Supplementary informationSupplementary data are available atBioinformaticsonline.