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Progress in the development and application of computational methods for probabilistic protein design

机译:概率蛋白设计计算方法的开发和应用进展

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摘要

Proteins exhibit a wide range of physical and chemical properties,including highly selective molecular recognition and catalysis,and are also key components in biological metabolic,catabolic,and signaling pathways.Given that proteins are well-structured and can be rapidly synthesized,they are excellent targets for engineering both molecular structure and biological function.Computational analysis of the protein design problem allows scientists to explore sequence space and systematically discover novel protein molecules.Nonetheless,the complexity of proteins,the subtlety of the determinants of folding,and the exponentially large number of possible sequences impede the search for peptide sequences compatible with a desired structure and function.Directed search algorithms,which identify directly a small number of sequences,have achieved some success in identifying sequences with desired structures and functions.Alternatively,one can adopt a probabilistic approach.Instead of a finite number of sequences,such calculations result in a probabilistic description of the sequence ensemble.In particular,by casting the formalism in the language of statistical mechanics,the site-specific amino acid probabilities of sequences compatible with a target structure may be readily estimated.These computed probabilities are well suited for both de novo protein design of particular sequences as well as combinatorial,library-based protein engineering.The computed site-specific amino acid profile may be converted to a nucleotide base distribution to allow assembly of a partially randomized gene library.The ability to synthesize readily such degenerate oligonucleotide sequences according to the prescribed distribution is key to constructing a biased peptide library genuinely reflective of the computational design.Herein we illustrate how a standard DNA synthesizer can be used with only a slight modification to the synthesis protocol to generate a pool of degenerate DNA sequences,which encodes a predetermined amino acid distribution with high fidelity.
机译:蛋白质具有广泛的物理和化学特性,包括高度选择性的分子识别和催化作用,也是生物代谢,分解代谢和信号传导途径中的关键成分。鉴于蛋白质结构良好且可以快速合成,因此它们非常出色蛋白质设计问题的计算分析使科学家能够探索序列空间并系统地发现新的蛋白质分子。尽管如此,蛋白质的复杂性,折叠决定因素的微妙性和指数级的数量可能的序列选择阻碍了与所需结构和功能兼容的肽序列的搜索。直接识别少量序列的定向搜索算法在鉴定具有所需结构和功能的序列方面取得了一些成功。或者,可以采用概率而不是有限的数序列中的这种计算会导致对序列集合的概率描述。特别是,通过用统计力学的语言来表达形式主义,可以容易地估计与目标结构兼容的位点特定氨基酸的概率。计算的概率非常适合特定序列的从头蛋白质设计以及基于组合库的蛋白质工程。计算的位点特异性氨基酸图谱可以转换为核苷酸碱基分布,从而可以组装部分随机的基因库能够根据规定的分布轻松合成此类简并寡核苷酸序列的能力是构建真正反映计算设计的有偏肽库的关键。在此,我们说明了如何使用标准DNA合成仪,而只需对合成方案进行一些修改即可生成简并的DNA序列库具有高保真度的预定氨基酸分布。

著录项

  • 来源
    《Computers & Chemical Engineering》 |2005年第3期|p.407-421|共15页
  • 作者单位

    Department of Chemistry,Makineni Theoretical Laboratories,University of Pennsylvania,231 South 34th Street,Philadelphia,PA 19104,USA;

    Department of Chemistry,Makineni Theoretical Laboratories,University of Pennsylvania,231 South 34th Street,Philadelphia,PA 19104,USA;

    Department of Chemistry,Makineni Theoretical Laboratories,University of Pennsylvania,231 South 34th Street,Philadelphia,PA 19104,USA;

    Neutron Research Center and Center for Promotion of Computational Science and Engineering,Japan Atomic Energy Research Institute,8-1 Umemidai,Kizu-cho,Souraku-gun,Kyoto 619-0215,Japan;

    Department of Chemical and Biomolecular Engineering,University of Pennsylvania,220 South 33rd Street,Philadelphia,PA 19104,USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化工计算;
  • 关键词

    Computational protein design; Combinatorial library; Protein engineering; Biased codon library;

    机译:计算蛋白设计;组合文库;蛋白质工程;双密码子文库;

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