Computational protein design (CPD) can address the inverse folding problem, exploring a large space of sequences and selecting ones predicted to fold. CPD was used previously to redesign several proteins, employing a knowledge-based energy function for both the folded and unfolded states. We show that a PDZ domain can be entirely redesigned using a “physics-based” energy for the folded state and a knowledge-based energy for the unfolded state. Thousands of sequences were generated by Monte Carlo simulation. Three were chosen for experimental testing, based on their low energies and several empirical criteria. All three could be overexpressed and had native-like circular dichroism spectra and 1D-NMR spectra typical of folded structures. Two had upshifted thermal denaturation curves when a peptide ligand was present, indicating binding and suggesting folding to a correct, PDZ structure. Evidently, the physical principles that govern folded proteins, with a dash of empirical post-filtering, can allow successful whole-protein redesign.

计算蛋白质设计（CPD）可以解决逆折叠问题，探索大空间的序列并选择预测折叠的序列。CPD 之前曾被用来重新设计几种蛋白质，针对折叠和展开状态采用基于知识的能量函数。我们证明，可以使用折叠状态的“基于物理”的能量和展开状态的基于知识的能量来完全重新设计 PDZ 域。蒙特卡洛模拟生成了数千个序列。基于它们的低能量和一些经验标准，选择了三个进行实验测试。所有这三种都可以过度表达，并具有类似天然的圆二色光谱和典型的折叠结构的 1D-NMR 光谱。当存在肽配体时，其中两个热变性曲线上移，表明结合并提示折叠成正确的 PDZ 结构。显然，控制折叠蛋白质的物理原理，加上少量的经验后过滤，可以成功地重新设计全蛋白质。