蛋白质折叠
折叠(DSP实现)
计算生物学
化学
蛋白质稳定性
生物物理学
生物
生物化学
工程类
电气工程
作者
Juan Zeng,Zunnan Huang
标识
DOI:10.2174/0929867325666181017160857
摘要
Background: The rapidly increasing number of known protein sequences calls for more efficient methods to predict the Three-Dimensional (3D) structures of proteins, thus providing basic knowledge for rational drug design. Understanding the folding mechanism of proteins is valuable for predicting their 3D structures and for designing proteins with new functions and medicinal applications. Levinthal’s paradox is that although the astronomical number of conformations possible even for proteins as small as 100 residues cannot be fully sampled, proteins in nature normally fold into the native state within timescales ranging from microseconds to hours. These conflicting results reveal that there are factors in organisms that can assist in protein folding. Methods: In this paper, we selected a crowded cell-like environment and temperature, and the top three Posttranslational Modifications (PTMs) as examples to show that Levinthal’s paradox does not reflect the folding mechanism of proteins. We then revealed the effects of these factors on protein folding. Results: The results summarized in this review indicate that a crowded cell-like environment, temperature, and the top three PTMs reshape the Free Energy Landscapes (FELs) of proteins, thereby regulating the folding process. The balance between entropy and enthalpy is the key to understanding the effect of the crowded cell-like environment and PTMs on protein folding. In addition, the stability/flexibility of proteins is regulated by temperature. Conclusion: This paper concludes that the cellular environment could directly intervene in protein folding. The long-term interactions of the cellular environment and sequence evolution may enable proteins to fold efficiently. Therefore, to correctly understand the folding mechanism of proteins, the effect of the cellular environment on protein folding should be considered.
科研通智能强力驱动
Strongly Powered by AbleSci AI