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Aerosol-OT (AOT) is a very versatile surfactant that exhibits a plethora of self-assembly behaviors. In particular, due to its double-tail structure, it is capable of forming vesicles in water. However, the size of these structures, and the time scales over which they form, make them difficult to study using traditional all-atomistic molecular dynamics simulations. Here, three coarse-grained models are developed for AOT with different levels of detail. The models take advantage of the Martini 3 force field, which enables 2:1 mappings to be employed for the tail groups. It is shown that these models are able to reproduce the self-assembly behavior of AOT in water at three concentrations: below the critical vesicle concentration (CVC), above the CVC, and in the lamellar phase. The results also demonstrate the formation of vesicles from bicelles above the critical vesicle concentration, which is an important milestone for the continued study of vesicle behavior.
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http://dx.doi.org/10.1021/acs.jpcb.5c00472 | DOI Listing |
Proc Natl Acad Sci U S A
September 2025
Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands.
Multivalent binding and the resulting dynamical clustering of receptors and ligands are known to be key features in biological interactions. For optimizing biomaterials capable of similar dynamical features, it is essential to understand the first step of these interactions, namely the multivalent molecular recognition between ligands and cell receptors. Here, we present the reciprocal cooperation between dynamic ligands in supramolecular polymers and dynamic receptors in model cell membranes, determining molecular recognition and multivalent binding via receptor clustering.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, 5735 S. Ellis Ave., SCL 123, Chicago, Illinois 60637, USA.
Molecular dynamics simulations are essential for studying complex molecular systems, but their high computational cost limits scalability. Coarse-grained (CG) models reduce this cost by simplifying the system, yet traditional approaches often fail to maintain dynamic consistency, compromising their reliability in kinetics-driven processes. Here, we introduce an adversarial training framework that aligns CG trajectory ensembles with all-atom (AA) reference dynamics, ensuring both thermodynamic and kinetic fidelity.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
View Article and Find Full Text PDFFront Syst Biol
August 2025
Systems Biotechnology, School of Engineering and Design, Technical University of Munich, Munich, Germany.
Mathematical models for cellular systems have become more and more important for understanding the complex interplay between metabolism, signalling, and gene expression.In this manuscript, starting from the well-known flux balance analysis, tools and methods are summarised and illustrated by various examples that describe the way to models with kinetics for individual reactions steps that are finally self-contained. While flux analysis requires known (measured) input fluxes, self-contained (or self-sustained) models only get information on concentrations of environmental components.
View Article and Find Full Text PDFJ Thermoplast Compos Mater
August 2025
Institute for Applied Materials - Microstructure Modeling and Simulation, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
In this paper, we introduce a coarse-grained model of polymer crystallization using a multiphase-field approach. The model combines a multiphase-field method, Nakamura's kinetic equation, and the equation of heat conduction for studying microstructural evolution of crystallization under isothermal and non-isothermal conditions. The multiphase-field method provides flexibility in adding any number of phases with different properties making the model effective in studying blends or composite materials.
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