Biology subjects

Schneebeli, S. T.

Publications and source records attributed to Schneebeli, S. T..

4 recordsLinked to original sources

bioRxiv2026

A multiscale modeling framework for transport of PEGylated lipid nanoparticle through the extracellular matrix

Lipid nanoparticles (LNPs) are one of the leading platforms for delivering nucleic acid therapeutics, yet their efficacy is limited by physicochemical interactions with the extracellular matrix (ECM) that trap the particles before they reach target cells. PEGylated nanoparticles mitigate these interactions by forming a protective steric layer on their surfaces. However, there is a lack of a predictive tool that gives mechanistic insights about how PEG surface density governs the underlying interaction and results in enhanced diffusive transport of LNPs through the ECM. Here, we present a multiscale hierarchical computational framework that couples all-atom constant pH molecular dynamics (CpHMD) with a highly coarse-grained model of the complete LNP within a crosslinked hyaluronic acid (HA) network. These atomistic simulations resolve the free energy of interaction between the LNP surface and HA chains across varying PEG lipid compositions, and integrate these free energy profiles to inform the coarse-grained simulations of LNP transport through the matrix. This work highlights that even a slightly PEGylated surface depletes the near-contact shell between the LNP and HA chains, which disrupts their adhesive interactions. These protective PEG layers produce a sharp, non-linear enhancement in LNP diffusivities, with just 1% PEG increasing the diffusivity nearly eight-fold relative to bare LNPs, which remain trapped in the matrix structures. This work provides a quantitative estimate of how PEG surface density governs LNP transport through the ECM, which offers predictive guidance for engineering LNP surface properties in target-specific drug delivery.

biophysics↗

bioRxiv2026

Progressive Backmapping of Highly Coarse-Grained Protein Models

Reconstructing all-atom (AA) structures from highly coarse-grained (HCG) models remains a significant challenge in multiscale molecular dynamics (MD) simulations, particularly for mesoscale biomolecular assemblies that are beyond the reach of conventional MD methods. Building upon ProNet Backmapping, a neural-network-based thermodynamically consistent approach, we introduce a progressive backmapping framework that reconstructs AA models in a stepwise manner across neighboring resolutions, for example, from a 3-residue-per-site HCG model to a 1-residue-per-site model, then to an AA model. This progressive backmapping method achieves high accuracy across a wide range of proteins and effectively reconstructs flexible linkers in multidomain architectures. Moreover, it supports hierarchical reconstruction of complex protein assemblies, including multiple virus-like particles spanning tens of nanometers and containing hundreds of subunits. Using this framework, we demonstrate--for the first time--the ability to hierarchically backmap entire viral assemblies from HCG to full AA resolution, covering at least three different resolutions. Overall, our method provides a scalable framework for incorporating atomistic detail into mesoscale simulations of complex systems across many applications in chemistry and biology. Table of contents figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/709104v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@4af423org.highwire.dtl.DTLVardef@e2669borg.highwire.dtl.DTLVardef@1be80eforg.highwire.dtl.DTLVardef@2e679_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

Molecular Basis of PAC1R Allosteric Modulation with Lipids in Membranes

The pituitary adenylate cyclase-activating polypeptide receptor I (PAC1R) represents a highly sought-after therapeutic target for chronic pain, migraine, and post-traumatic stress. As a class B G protein-coupled receptor (GPCR), the PAC1R is highly expressed throughout the neuronal and central nervous system membranes, with the receptor subject to hormone activation and subsequent signal transduction. Despite its desirable indication, small molecule agonists for PAC1R have been notoriously difficult to develop due to competition with PACAP. For this reason, allosteric activation of the receptor has emerged as a promising pathway. To probe potential allosteric sites, herein, we present a study of the receptor in biomimetic concentrations of lipid membranes. Our results reveal that cholesterol recognizes two canonical and two non-canonical binding sites at PAC1R, which may influence critical residues in the transmembrane domain and PAC1R activation. Also, our simulations suggest the glycolipid GM3 interacts with PAC1R in both the extracellular and transmembrane domains. These lipid binding hotspots may hold high potential for advancing our understanding of class B GPCR signaling and the discovery of new molecules targeting PAC1R. Statement of SignificanceThe PAC1R is a highly sought after therapeutic target in the GPCR family. Understanding its natural process signaling is highly interesting in identifying new modes of control and is currently not well established. This work uses coarse-grained molecular dynamics simulations to examine diverse PAC1R models interactions with their endogenous lipid bilayer in compositions that match the regions in the brain where the receptor is expressed. Two lipids, cholesterol and GM3, have been previously identified in similar receptors as allosteric modulators and were specifically examined in this study. This work also showcases multiple new lipid binding sites at transmembrane and extracellular sites highly implicated in PAC1R signaling.

biophysics↗

bioRxiv2020

A Generative Approach toward Precision Antimicrobial Peptide Design

As the emergence of bacterial resistance is outpacing the development of new antibiotics, we must find cost-effective and innovative approaches to discover new antibacterial therapeutics. Antimicrobial peptides (AMPs) represent one promising solution to fill this void, since they generally undergo faster development, display rapid onsets of killing, and most importantly, show lower risks of induced resistance. Despite prior success in AMP design with physics- and/or knowledge-based approaches, an efficient approach to precisely design peptides with high activity and selectivity is still lacking. Toward this goal, we have invented a novel approach which utilizes a generative model to predict AMP-like sequences, followed by molecular modeling to rank the candidates. Thus, we can identify peptides with desirable sequences, structures, and potential specific interactions with bacterial membranes. For the proof of concept, we curated a dataset that comprises 500,000 non-AMP peptide sequences and nearly 8,000 labeled AMP sequences to train the generative model. For 12 generated peptides that are cationic and likely helical, we assessed the membrane binding propensity via extensive all-atom molecular simulations. The top six peptides were promoted for synthesis, chemical characterizations, and antibacterial assays, showing various inhibition to bacterial growth. Three peptides were validated with broad-spectrum antibacterial activity. In aggregate, the combination of AMP generator and sophisticated molecular modeling affords enhanced speed and accuracy in AMP design. Our approach and results demonstrate the viability of a generative approach to develop novel AMPs and to help contain the rise of antibiotic resistant microbes.

biophysics↗