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相关论文: Poisson-Boltzmann model for protein-surface electr…

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The continuum theory applied to bimolecular electrostatics leads to an implicit-solvent model governed by the Poisson-Boltzmann equation. Solvers relying on a boundary integral representation typically do not consider features like…

计算物理 · 物理学 2013-11-08 Christopher D. Cooper , Jaydeep P. Bardhan , L. A. Barba

Under the most common experimental conditions, the adsorption of proteins to solid surfaces is an spontaneous process that leads to a rather compact layer of randomly oriented molecules. Due to the importance of this process for the…

软凝聚态物质 · 物理学 2022-06-29 Sergio A. Urzúa , Perla Y. Sauceda-Oloño , Carlos D. García , Christopher D. Cooper

This work uses the long-wavelength limit to compute LSPR response of biosensors, expanding the open-source PyGBe code to compute the extinction cross-section of metallic nanoparticles in the presence of any target for sensing. The target…

计算物理 · 物理学 2020-07-28 Natalia C. Clementi , Christopher D. Cooper , Lorena A. Barba

Protein-surface interactions are ubiquitous in biological processes and bioengineering, yet are not fully understood. In biosensors, a key factor determining the sensitivity and thus the performance of the device is the orientation of the…

生物物理 · 物理学 2016-04-12 Christopher D. Cooper , Natalia C. Clementi , Lorena A. Barba

Electrostatic interactions play crucial roles in biophysical processes such as protein folding and molecular recognition. Poisson-Boltzmann equation (PBE)-based models have emerged as widely used in modeling these important processes.…

计算物理 · 物理学 2017-04-11 Ruxi Qi , Wesley M. Botello-Smith , Ray Luo

In this paper, we present a parallel higher-order boundary integral method to solve the linear Poisson-Boltzmann (PB) equation. In our method, a well-posed boundary integral formulation is used to ensure the fast convergence of Krylov…

数值分析 · 数学 2015-06-12 Weihua Geng

The Poisson-Boltzmann equation (PBE) is a relevant partial differential equation commonly used in biophysical applications to estimate the electrostatic energy of biomolecular systems immersed in electrolytic solutions. A conventional mean…

The nonlinear Poisson-Boltzmann equation (NPBE) is an elliptic partial differential equation used in applications such as protein interactions and biophysical chemistry (among many others). It describes the nonlinear electrostatic potential…

数值分析 · 数学 2023-09-29 Trevor Norton , Jie Xu , Brian Choi , Mark Kon , Julio Enrique Castrillón-Candás

In computational biochemistry and biophysics, understanding the role of electrostatic interactions is crucial for elucidating the structure, dynamics, and function of biomolecules. The Poisson-Boltzmann (PB) equation is a foundational tool…

生物大分子 · 定量生物学 2024-10-08 Yongxian Wu , Qiang Zhu , Ray Luo

The Poisson--Boltzmann equation is widely used to model electrostatics in molecular systems. Available software packages solve it using finite difference, finite element, and boundary element methods, where the latter is attractive due to…

计算物理 · 物理学 2025-12-24 Michal Bosy , Matthew W. Scroggs , Timo Betcke , Erik Burman , Christopher D. Cooper

The Poisson-Boltzmann (PB) model governs the electrostatics of solvated biomolecules, i.e., potential, field, energy, and force. These quantities can provide useful information about protein properties, functions, and dynamics. By…

生物大分子 · 定量生物学 2023-12-29 Xin Yang , Elyssa Sliheet , Reece Iriye , Daniel Reynolds , Weihua Geng

Poisson-Boltzmann (PB) model is one of the most popular implicit solvent models in biophysical modeling and computation. The ability of providing accurate and reliable PB estimation of electrostatic solvation free energy, $\Delta…

生物大分子 · 定量生物学 2016-06-10 Duc D. Nguyen , Bao Wang , Guo-wei Wei

Recent surveys have shown that the number of nanoparticle-based formulations actually used at the clinical level is significantly lower than expected a decade ago. One reason for this is that the nanoparticle physicochemical properties fall…

Physics-informed neural networks (PINN) is a machine learning (ML)-based method to solve partial differential equations that has gained great popularity due to the fast development of ML libraries in the last few years. The…

化学物理 · 物理学 2024-12-31 Martin A. Achondo , Jehanzeb H. Chaudhry , Christopher D. Cooper

A numerical method using implicit surface representations is proposed to solve the linearized Poisson-Boltzmann equations that arise in mathematical models for the electrostatics of molecules in solvent. The proposed method used an implicit…

数值分析 · 数学 2018-04-04 Yimin Zhong , Kui Ren , Richard Tsai

Protein-protein interactions (PPIs) are governed by surface complementarity and hydrophobic interactions at protein interfaces. However, designing diverse and physically realistic protein structure and surfaces that precisely complement…

机器学习 · 计算机科学 2025-11-24 Guanlue Li , Xufeng Zhao , Fang Wu , Sören Laue

Within a dipolar Poisson-Boltzmann theory including electrostatic correlations, we consider the effect of explicit solvent structure on solvent and ion partition confined to charged nanopores. We develop a relaxation scheme for the solution…

软凝聚态物质 · 物理学 2015-10-28 Sahin Buyukdagli

The effective electrostatic interaction between a pair of colloids, both of them located close to each other at an electrolyte interface, is studied by employing the full, nonlinear Poisson-Boltzmann (PB) theory within classical density…

软凝聚态物质 · 物理学 2016-08-17 Arghya Majee , Markus Bier , S. Dietrich

Implicit-solvent models are widely used to study the electrostatics in dissolved biomolecules, which are parameterized using force fields. Standard force fields treat the charge distribution with point charges, however, other force fields…

计算物理 · 物理学 2018-10-10 Christopher D. Cooper

Protein adsorption/desorption upon nanoparticle surfaces is an important process to understand for developing new nanotechnology involving biomaterials, while atomistic picture of the process and its coupling with protein conformational…

生物物理 · 物理学 2017-11-10 Huifang Xiao , Bin Huang , Ge Yao , Wenbin Kang , Sheng Gong , Hai Pan , Yi Cao , Jun Wang , Jian Zhang , Wei Wang
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