{"title":"Interface-packing analysis of F1-ATPase using integral equation theory and manifold learning","authors":"Takashi Yoshidome, Shota Arai","doi":"10.1016/j.physa.2024.130201","DOIUrl":null,"url":null,"abstract":"<div><div>It has been shown that the translational entropy of water plays a key role in biological processes such as protein folding and ligand binding. Under the physiological condition, tightly packed protein conformations like native structures are achieved so that the translational entropy of water is maximized. In this study, we investigate the rotation mechanism of a rotary protein motor, F<sub>1</sub>-ATPase, by analyzing the packing at the interfaces between the subunits. The packing at the interface between a subunit pair is analyzed using the change in the solvent entropy upon forming subunit pair, <span><math><mrow><mo>∆</mo><mi>S</mi></mrow></math></span>. It is found that as the γ subunit rotates, the <span><math><mrow><mo>∆</mo><mi>S</mi></mrow></math></span> value of a α-β subunit pair decrease because the interface packing becomes loose. However, because the interface packing of another α-β subunit pair becomes tighter upon the rotation, <span><math><mrow><mo>∆</mo><mi>S</mi></mrow></math></span> of this α-β subunit pair increases, leading to a compensation of the decrease in <span><math><mrow><mo>∆</mo><mi>S</mi></mrow></math></span>. Such compensation would be necessary to maximize the solvent entropy of F<sub>1</sub>-ATPase. In this study, packing at the interfaces between the subunits is also analyzed using a manifold-learning technique, and it is suggested a possibility that a qualitative estimation of the <span><math><mrow><mo>∆</mo><mi>S</mi></mrow></math></span> values of some α-β subunit pairs can be predicted using a manifold-learning technique.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":"655 ","pages":"Article 130201"},"PeriodicalIF":2.8000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437124007106","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
It has been shown that the translational entropy of water plays a key role in biological processes such as protein folding and ligand binding. Under the physiological condition, tightly packed protein conformations like native structures are achieved so that the translational entropy of water is maximized. In this study, we investigate the rotation mechanism of a rotary protein motor, F1-ATPase, by analyzing the packing at the interfaces between the subunits. The packing at the interface between a subunit pair is analyzed using the change in the solvent entropy upon forming subunit pair, . It is found that as the γ subunit rotates, the value of a α-β subunit pair decrease because the interface packing becomes loose. However, because the interface packing of another α-β subunit pair becomes tighter upon the rotation, of this α-β subunit pair increases, leading to a compensation of the decrease in . Such compensation would be necessary to maximize the solvent entropy of F1-ATPase. In this study, packing at the interfaces between the subunits is also analyzed using a manifold-learning technique, and it is suggested a possibility that a qualitative estimation of the values of some α-β subunit pairs can be predicted using a manifold-learning technique.
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.