Machine learning based models of plant protein mixtures for sustainable food design

Speaker: Maxim Brodmerkel

Abstract:

Plant-based proteins offer sustainable alternatives to animal proteins, but their complex phase behavior makes food design challenging. Such design requires prediction of aggregation and rheological behavior of naturally occurring complex protein mixtures. Physics based prediction is powerful, but complicated by molecular interactions such as charge dissociation and electrostatic forces, and conformational changes. We aim to model and reverse-engineer protein mixtures using machine learning, linking microscopic protein structure and dynamics to macroscopic gelation properties. To do so we develop a transferable ultra coarse-grained (CG) model to predict structural, aggregation and rheological properties of complex mixtures on a larger scale. We use ML (Bayesian optimization) to train effective potentials for CG models to be used in large-scale simulations that can bridge the length-scale gap.

Physical mechanisms of MetaParticle cellular uptake

Speaker: Massimiliano Paesani

Abstract:

Nanoparticles (NPs) are promising drug carriers across diverse biomedical applications, including targeted cancer therapies, diagnostic imaging, and advanced vaccines. However, the clinical translation of nanocarriers remains limited due to complex biological barriers that reduce cellular uptake and therapeutic efficacy. The intrinsic flexibility of NPs, dictated by both the core composition and ligand conformations, significantly influences their interactions with these barriers. Traditional NP models often oversimplify particle flexibility and its dynamic interaction with biological membranes, leaving fundamental uptake mechanisms unclear.

To address this, we introduce a coarse-grained model designed to capture the inherent flexibility of nanoparticles, termed “MetaParticles” (MPs). Employing Brownian dynamics simulations, we investigate how MP size, topology, and internal arrangements govern mechanical properties and diffusivity. Our results confirm that nanocarrier diffusivity correlates strongly with size, while mechanical properties are determined by topology and internal structure. We extend this model to investigate MP interactions with coarse-grained lipid membranes, mimicking cellular barriers. By simulating MP diffusion toward and interactions with a coarse-grained membrane model, we analyze the impact of nanocarrier flexibility on membrane binding, deformation, and potential translocation pathways. We observe that specific MP-membrane interactions induce bending and fast endocytosis, suggesting a mechanism for enhanced cellular entry specific for our MPs.

This framework offers a significant advance in understanding and rationally designing flexible nanocarriers for improved drug delivery. By tuning MP mechanical properties to optimize membrane interactions, we aim to guide the development of NPs that efficiently translocate across cell membranes via flexibility-mediated pathways. This approach promises to inform the design of next-generation drug delivery systems and materials with broad applications in biomedical engineering and soft matter.

This seminar will take place in room D1.112