Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions

被引:27
|
作者
Loshbaugh, Amanda L. [1 ,2 ]
Kortemme, Tanja [1 ,2 ,3 ,4 ]
机构
[1] Univ Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA 94143 USA
[2] Univ Calif San Francisco, Biophys Grad Program, San Francisco, CA 94143 USA
[3] Univ Calif San Francisco, Quantitat Biosci Inst, San Francisco, CA 94143 USA
[4] Chan Zuckerberg Biohub, San Francisco, CA USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
algorithms; amino acid sequence; binding sites; computational biology; *methods; molecular models; SIDE-CHAIN; SEQUENCE DIVERSITY; ENERGY FUNCTIONS; FLEXIBILITY; PREDICTION; SPACE; OPTIMIZATION; SIMULATION; TEMPLATES; ALGORITHM;
D O I
10.1002/prot.25790
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.
引用
收藏
页码:206 / 226
页数:21
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