{"title":"Towards a free energy-based elastic network model and its application to the SARS-COV2 binding to ACE2.","authors":"Hyuntae Na, Guang Song","doi":"10.1088/1478-3975/acd6cd","DOIUrl":null,"url":null,"abstract":"<p><p>Classical normal mode analysis (cNMA) is a standard method for studying the equilibrium vibrations of macromolecules. A major limitation of cNMA is that it requires a cumbersome step of energy minimization that also alters the input structure significantly. Variants of normal mode analysis (NMA) exist that perform NMA directly on PDB structures without energy minimization, while maintaining most of the accuracy of cNMA. Spring-based NMA (sbNMA) is such a model. sbNMA uses an all-atom force field as cNMA does, which includes bonded terms such as bond stretching, bond angle bending, torsional, improper, and non-bonded terms such as van der Waals interactions. Electrostatics was not included in sbNMA because it introduced negative spring constants. In this work, we present a way to incorporate most of the electrostatic contributions in normal mode computations, which marks another significant step toward a free-energy-based elastic network model (ENM) for NMA. The vast majority of ENMs are entropy models. One significance of having a free energy-based model for NMA is that it allows one to study the contributions of both entropy and enthalpy. As an application, we apply this model to study the binding stability between SARS-COV2 and angiotensin converting enzyme 2 (or ACE2). Our results show that the stability at the binding interface is contributed nearly equally by hydrophobic interactions and hydrogen bonds.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1088/1478-3975/acd6cd","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Classical normal mode analysis (cNMA) is a standard method for studying the equilibrium vibrations of macromolecules. A major limitation of cNMA is that it requires a cumbersome step of energy minimization that also alters the input structure significantly. Variants of normal mode analysis (NMA) exist that perform NMA directly on PDB structures without energy minimization, while maintaining most of the accuracy of cNMA. Spring-based NMA (sbNMA) is such a model. sbNMA uses an all-atom force field as cNMA does, which includes bonded terms such as bond stretching, bond angle bending, torsional, improper, and non-bonded terms such as van der Waals interactions. Electrostatics was not included in sbNMA because it introduced negative spring constants. In this work, we present a way to incorporate most of the electrostatic contributions in normal mode computations, which marks another significant step toward a free-energy-based elastic network model (ENM) for NMA. The vast majority of ENMs are entropy models. One significance of having a free energy-based model for NMA is that it allows one to study the contributions of both entropy and enthalpy. As an application, we apply this model to study the binding stability between SARS-COV2 and angiotensin converting enzyme 2 (or ACE2). Our results show that the stability at the binding interface is contributed nearly equally by hydrophobic interactions and hydrogen bonds.
期刊介绍:
Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity.
Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as:
molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions
subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure
intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division
systems biology, e.g. signaling, gene regulation and metabolic networks
cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms
cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis
cell-cell interactions, cell aggregates, organoids, tissues and organs
developmental dynamics, including pattern formation and morphogenesis
physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation
neuronal systems, including information processing by networks, memory and learning
population dynamics, ecology, and evolution
collective action and emergence of collective phenomena.