Toward Rational Design of Ion-Exchange Nanofiber Membranes: Meso-Scale Computational Approaches

TL;DR

This paper reviews how computational methods can help design better ion-exchange nanofiber membranes for protein purification.

Contribution

The paper introduces meso-scale computational approaches as a novel framework for predicting and optimizing ion-exchange nanofiber membrane performance.

Findings

Meso-scale simulations can model protein–fibre and protein–protein interactions under varying conditions.

Computational modeling can identify key design parameters like binding accessibility and surface charge density.

Combining simulations with experiments can reduce costs and accelerate membrane optimization.

Abstract

This review highlights the growing relevance of ion-exchange nanofibrous membranes (IEX-NFMs) in membrane chromatography (MC) for protein purification, emphasising their structural advantages such as high porosity, tunable surface functionality, and low-pressure drops. While the adsorption of IEX-NFMs in MC is expanding due to their potential for high throughput and rapid mass transfer, a critical limitation remains: the precise binding capacity of these membranes is not well understood. Traditional experimental methods to evaluate protein–membrane interactions and optimise binding capacities are labour-intensive, time-consuming, and costly. Therefore, this review underscores the importance of computational modelling as a viable predictive approach to guide membrane design and performance prediction. Yet major obstacles persist, including the challenge of accurate representation of the complex and often irregular pore structures, as well as limited and/or oversimplified adsorption models. Along with molecular-scale simulations such as molecular dynamics (MD) simulations and quantum simulations, meso-scale simulations can provide insight into protein–fibre and protein–protein interactions under varying physicochemical conditions for larger time scales and lower computational burden. These tools can help identify key parameters such as binding accessibility, ionic strength effects, and surface charge density, which are essential for the rational design and performance prediction of IEX-NFMs. Moreover, integrating simulations with experimental validation can accelerate optimisation process while reducing cost. This technical review sets the foundation for a computationally driven design framework for multifunctional IEX-NFMs, supporting their use in next-generation chromatographic separations and broadening their applications in bioprocessing and analytical biotechnology.

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