Physics of biomolecular recognition and conformational dynamics

分子识别 能源景观 分子动力学 折叠(DSP实现) 物理 计算生物学 纳米技术 人工智能 生物 计算机科学 分子 材料科学 量子力学 热力学 电气工程 工程类
作者
Wen‐Ting Chu,Zhiqiang Yan,Xiakun Chu,Xiliang Zheng,Zuojia Liu,Li Xu,Kun Zhang,Jin Wang
出处
期刊:Reports on Progress in Physics [IOP Publishing]
卷期号:84 (12): 126601-126601 被引量:12
标识
DOI:10.1088/1361-6633/ac3800
摘要

Abstract 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.
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