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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Evaluating and optimizing computational protein design force fields using fixed composition-based negative design
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Evaluating and optimizing computational protein design force fields using fixed composition-based negative design

机译：使用基于固定成分的阴性设计评估和优化计算蛋白质设计力场

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摘要

An accurate force field is essential to computational protein design and protein fold prediction studies. Proper force field tuning is problematic, however, due in part to the incomplete modeling of the unfolded state. Here, we evaluate and optimize a protein design force field by constraining the amino acid composition of the designed sequences to that of a well behaved model protein. According to the random energy model, unfolded state energies are dependent only on amino acid composition and not the specific arrangement of amino acids. Therefore, energy discrepancies between computational predictions and experimental results, for sequences of identical composition, can be directly attributed to flaws in the force field's ability to properly account for folded state sequence energies. This aspect of fixed composition design allows for force field optimization by focusing solely on the interactions in the folded state. Several rounds of fixed composition optimization of the 56-residue β1 domain of protein G yielded force field parameters with significantly greater predictive power: Optimized sequences exhibited higher wild-type sequence identity in critical regions of the structure, and the wild-type sequence showed an improved Z-score. Experimental studies revealed a designed 24-fold mutant to be stably folded with a melting temperature similar to that of the wild-type protein. Sequence designs using engrailed homeodomain as a scaffold produced similar results, suggesting the tuned force field parameters were not specific to protein G.
机译：精确的力场对于计算蛋白质设计和蛋白质折叠预测研究至关重要。但是，适当地调整力场是有问题的，部分原因是展开状态的建模不完整。在这里，我们通过将设计序列的氨基酸组成限制为行为良好的模型蛋白来评估和优化蛋白质设计力场。根据随机能量模型，未折叠状态能量仅取决于氨基酸组成，而不取决于氨基酸的特定排列。因此，对于相同组成的序列，计算预测和实验结果之间的能量差异可以直接归因于力场正确考虑折叠状态序列能量的能力中的缺陷。固定构图设计的这一方面可以通过仅关注折叠状态下的相互作用来实现力场优化。蛋白G的56个残基β1结构域的几轮固定成分优化产生了具有明显更高预测力的力场参数：优化的序列在结构的关键区域显示出更高的野生型序列同一性，而野生型序列显示出改进的Z得分。实验研究表明，设计的24倍突变体可以在类似于野生型蛋白的融解温度下稳定折叠。序列设计使用衔接同源域作为支架产生类似的结果，表明调整的力场参数不是特定于蛋白G。

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