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Energy functions in de novo protein design: Current challenges and future prospects

机译:从头蛋白质设计中的能量功能:当前的挑战和未来的前景

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摘要

In the past decade, a concerted effort to successfully capture specific tertiary packing interactions produced specific three-dimensional structures for many de novo designed proteins that are validated by nuclear magnetic resonance and/or X-ray crystallographic techniques. However, the success rate of computational design remains low. In this review, we provide an overview of experimentally validated, de novo designed proteins and compare four available programs, RosettaDesign, EGAD, Liang-Grishin, and RosettaDesign-SR, by assessing designed sequences computationally. Computational assessment includes the recovery of native sequences, the calculation of sizes of hydrophobic patches and total solvent-accessible surface area, and the prediction of structural properties such as intrinsic disorder, secondary structures, and three-dimensional structures. This computational assessment, together with a recent community-wide experiment in assessing scoring functions for interface design, suggests that the next-generation protein-design scoring function will come from the right balance of complementary interaction terms. Such balance may be found when more negative experimental data become available as part of a training set.
机译:在过去的十年中,为成功捕获特定的第三级包装相互作用而做出的共同努力为许多从头设计的蛋白质产生了特定的三维结构,这些结构已通过核磁共振和/或X射线晶体学技术验证。但是,计算设计的成功率仍然很低。在这篇综述中,我们提供了经过实验验证的从头设计的蛋白质的概述,并通过计算评估设计的序列,比较了四个可用程序,即RosettaDesign,EGAD,Liang-Grishin和RosettaDesign-SR。计算评估包括恢复天然序列,计算疏水性斑块的大小和溶剂可及的总表面积,以及预测结构特性,例如内在无序,二级结构和三维结构。这项计算评估,再加上最近在社区中评估界面设计评分功能的实验,表明下一代蛋白质设计评分功能将来自互补相互作用项的正确平衡。当更多的负面实验数据可以作为训练集的一部分时,可以找到这种平衡。

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