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氨基酸残基归类及用简化后的字符识别蛋白质结构保守区域 被引量:1

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摘要 序列比对是寻找蛋白质结构保守性区域的常用方法,然而当序列相似小于30%时比对准确度却不高,这是因为在这些序列中具有相似结构功能的不同残基在序列比对中往往被错误配对.基于相似的物理化学性质,某些残基可以被归类为一组,而应用这些简化后的残基字符可以有效地简化蛋白质序列的复杂性并保持序列的主要信息.因此,如果20种天然氨基酸残基能够正确的归类,可以有效地提高序列比对的准确度.本文基于蛋白质结构比对数据库DAPS,提出了一种新的氨基酸残基归类方法,并可以同时得到不同简化程度下的替代矩阵用于序列比对.归类的合理性由相互熵方法确认,并且应用简化后的字符表于序列比对来识别蛋白质的结构保守区域.结果表明,当氨基酸残基字符简化到9个左右时能够有效地提高序列比对的准确度.
作者 李菁 王炜
出处 《中国科学(C辑)》 CSCD 北大核心 2006年第6期552-562,共11页 Science in China(Series C)
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参考文献31

  • 1Bowie J U, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science, 1991, 253:164--170
  • 2Jones D T, Taylor W R, Thornton J M. A new approach to protein fold recognition. Nature, 1992, 358:86---89
  • 3Regan L, Degrado W F. Characterization of a helical protein designed from first principles. Science, 1988, 241:976--978
  • 4Kamtekar S. Protein design by binary patterning of polar and nopolar amino acids. Science, 1993, 262:1680--1685
  • 5Plaxco K W. Simplified proteins: minimalist solutions to the "protein folding problem". Curr Opin Struct Biol, 1998, 8:80---85
  • 6Wang J, Wang W. A computational approach to simplifying the protein folding alphabet. Nature Struct Biol, 1999, 6:1033--1038
  • 7Henikoff S, Henikoff J G. Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci USA, 1992, 89:10915--10919
  • 8Ogata K, Ohya M, Umeyama H. Amino acid similarity matrix for homology derived from structural alignment and optimized by the Monte Carlo method. J Mol Graph Model, 1998, 16:178--189
  • 9Zhou H, Zhou Y. Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins, 2005, 58:321--328
  • 10Friedberg I, Kaplan T, Margalit H. Evaluation of PSI-BLAST alignment accuracy in comparison to structural alignments. Protein Sci, 2000, 9:2278--2284

同被引文献19

  • 1施建宇,潘泉,张绍武,梁彦.基于支持向量机融合网络的蛋白质折叠子识别研究[J].生物化学与生物物理进展,2006,33(2):155-162. 被引量:19
  • 2Eisenberg D. Into the black of night. Nature Structural Biology, 1997, 4:95-97
  • 3Shortle D. Structure Prediction: Folding proteins by pattern recognition. Current Biology, 1997, 7(3): 151 - 154
  • 4Chothia C. One thousand families for the molecular biologist. Nature, 1992, 357(6379): 543-544
  • 5David B. A surprising simplicity to protein folding. Nature, 2000, 405(6782): 39-42
  • 6Novotny M, Madsen D, Kleywegt G J. Evaluation of protein fold comparison servers. Proteins, 2004, 54(2): 260-270
  • 7Matsuda K, Nishioka T, Kinoshita K. Finding evolutionary relations beyond superfamilies: Fold-based superfamilies. Protein Science, 2003, 12(10): 2239-2251
  • 8刘晓辉 李晓琴.全α类蛋白质核心结构的折叠分类研究[J].生物物理学报,2006,22:370-371.
  • 9张炜 李晓琴.基于二级结构片段的β类蛋白质折叠类型分类研究[J].生物物理学报,2006,22:387-388.
  • 10Ding C H Q, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 2001, 17(4): 349-358

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