Levy Yaakov, Onuchic José N
Center for Theoretical Biological Physics, Department of Physics, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
Acc Chem Res. 2006 Feb;39(2):135-42. doi: 10.1021/ar040204a.
Many cellular functions rely on interactions among proteins and between proteins and nucleic acids. The limited success of binding predictions may suggest that the physical and chemical principles of protein binding have to be revisited to correctly capture the essence of protein recognition. In this Account, we discuss the power of reduced models to study the physics of protein assembly. Since energetic frustration is sufficiently small, native topology-based models, which correspond to perfectly unfrustrated energy landscapes, have shown that binding mechanisms are robust and governed primarily by the protein's native topology. These models impressively capture many of the binding characteristics found in experiments and highlight the fundamental role of flexibility in binding. The essential role of solvent molecules and electrostatic interactions in binding is also discussed. Despite the success of the minimally frustrated models to describe the dynamics and mechanisms of binding, the actual degree of frustration has to be explored to quantify the capacity of a protein to bind specifically to other proteins. We have found that introducing mutations can significantly reduce specificity by introducing an additional binding mode. Deciphering and quantifying the key ingredients for biological self-assembly is invaluable to reading out genomic sequences and understanding cellular interaction networks.
许多细胞功能依赖于蛋白质之间以及蛋白质与核酸之间的相互作用。结合预测的有限成功可能表明，必须重新审视蛋白质结合的物理和化学原理，以正确把握蛋白质识别的本质。在本综述中，我们讨论了简化模型在研究蛋白质组装物理学方面的作用。由于能量受挫足够小，基于天然拓扑结构的模型，即对应于完全无受挫能量景观的模型，已表明结合机制是稳健的，并且主要由蛋白质的天然拓扑结构决定。这些模型令人印象深刻地捕捉到了实验中发现的许多结合特征，并突出了灵活性在结合中的基本作用。还讨论了溶剂分子和静电相互作用在结合中的重要作用。尽管最小受挫模型在描述结合的动力学和机制方面取得了成功，但必须探索实际的受挫程度，以量化蛋白质与其他蛋白质特异性结合的能力。我们发现，引入突变可以通过引入额外的结合模式显著降低特异性。解读和量化生物自组装的关键要素对于解读基因组序列和理解细胞相互作用网络具有重要价值。
