Chu Wen-Ting, Yan Zhiqiang, Chu Xiakun, Zheng Xiliang, Liu Zuojia, Xu Li, Zhang Kun, Wang Jin
State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China.
Department of Chemistry & Physics, State University of New York at Stony Brook, Stony Brook, NY 11794, United States of America.
Rep Prog Phys. 2021 Dec 8;84(12). doi: 10.1088/1361-6633/ac3800.
Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.
生物分子识别通常会导致结合复合物的形成，且常常伴随着大规模的构象变化。这一过程是分子和细胞水平上生物功能的基础。揭示生物分子识别的物理机制并量化关键的生物分子相互作用对于理解这些功能至关重要。最近发展起来的能量景观理论已成功地量化了识别过程并揭示了其潜在机制。最近的研究表明，除了亲和力外，特异性对于生物分子识别也至关重要。基于潜在能量景观理论提出的内在特异性这一物理概念提供了一种量化特异性的实用方法。亲和力和特异性的优化可作为指导分子识别进化和设计的原则。这种方法在实践中也可用于药物发现，通过多维筛选来识别先导化合物。分子识别的能量景观形貌对于揭示潜在的柔性结合或结合-折叠机制很重要。在这篇综述中，我们首先介绍分子识别的能量景观理论，然后探讨与生物分子识别和构象动力学相关的四个关键问题：（1）分子识别的特异性量化；（2）分子识别中的进化和设计；（3）柔性分子识别；（4）染色体结构动力学。这里描述的结果以及从能量景观形貌中获得的见解的讨论可为生物分子识别和构象动力学的进一步计算和实验研究提供有价值的指导。
