Biology subjects

Prell, J. S.

Publications and source records attributed to Prell, J. S..

5 recordsLinked to original sources

bioRxiv2026

Ion Mobility Mass Spectrometry Guided Modeling with AlphaFold and Rosetta Improves Protein Complex Structure Prediction

Ion mobility mass spectrometry (IM-MS) provides valuable structural information about protein shape and size through collision cross section (CCS). However, it lacks atomic level structural detail. While AlphaFold has been successful in predicting monomeric protein structure, it can struggle with modeling protein complexes. To address these limitations, we developed a method that integrates IM-MS data with AlphaFold and Rosetta to improve complex structure prediction. Our approach uses experimental CCS data to guide the assembly of AlphaFold predicted subunits using a Rosetta docking pipeline and evaluating the resulting complexes with a newly developed score. Using this strategy, we were able to improve root mean square deviation (RMSD) values for 26 of 38 (68%) complexes compared to AlphaFold-Multimer. Furthermore, 16 of these systems improved significantly from greater than 4 [A] RMSD to less than 4 [A]. This method demonstrates a robust approach to overcome limitations in complex assembly modeling. Table of Contents Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/706193v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@cf71c3org.highwire.dtl.DTLVardef@135e09aorg.highwire.dtl.DTLVardef@2cc2fcorg.highwire.dtl.DTLVardef@b53feb_HPS_FORMAT_FIGEXP M_FIG In this integrative modeling work, protein complex structures were modeled by combining AlphaFold predicted subunits with Rosetta docking. Collision cross section data from ion-mobility mass spectrometry were used as evaluation constraints and docked models were scored using the IM-complex score. The best scoring models generally represent accurate protein complex structures. C_FIG

biophysics↗

bioRxiv2024

Changing expression system alters oligomerization and proinflammatory activity of recombinant human S100A9

S100A9 is a Damage Associated Molecular Pattern (DAMP) that activates the innate immune system via Toll-like receptor 4 (TLR4). Despite many years of study, the mechanism of activation remains unknown. To date, much of the biochemical characterization of S100A9 has been performed using recombinant S100A9 expressed in E. coli (S100A9ec). TLR4 is the canonical receptor for LPS, a molecule found in the outer membrane of E. coli, raising the possibility of artifacts due to LPS contamination. Here we report characterization of LPS-free recombinant S100A9 expressed in insect cells (S100A9in). We show that S100A9in does not activate TLR4. This difference does not appear to be due to LPS contamination, protein misfolding, purification artifacts, or differences in phosphorylation. We show instead that S100A9in adopts an altered oligomeric state compared to S100A9ec. Disrupting oligomer formation with the E. coli disaggregase SlyD restores activity to S100A9in. Our results also indicate that the oligomeric state of S100A9 is a major factor in its ability to activate TLR4 and that this can be altered in unexpected ways by the recombinant expression system used to produce the protein.

biochemistry↗

bioRxiv2023

Label-Free Composition Analysis of Supramolecular Polymer - Nanoparticle Hydrogels by Reversed-Phase Liquid Chromatography Coupled with a Charged Aerosol Detector

Supramolecular hydrogels formed through polymer-nanoparticle interactions are promising biocompatible materials for translational medicines. This class of hydrogels exhibits shear-thinning behavior and rapid recovery of mechanical properties following applied stresses, providing desirable attributes for formulating sprayable and injectable therapeutics. Characterization of hydrogel composition and loading of encapsulated drugs is critical to achieving desired rheological behavior as well as tunable in vitro and in vivo payload release kinetics. However, quantitation of hydrogel compositions is challenging due to material complexity, heterogeneity, high molecular weight, and the lack of chromophores. Here, we present a label-free approach to simultaneously determine hydrogel polymeric components and encapsulated payloads by coupling a reversed phase liquid chromatographic method with a charged aerosol detector (RPLC-CAD). The hydrogel studied consists of modified hydroxypropylmethylcellulose, self-assembled PEG-b-PLA nanoparticles, and a therapeutic compound, Bimatoprost. The three components were resolved and quantitated using the RPLC-CAD method with a C4 stationary phase. The method demonstrated robust performance, applicability to alternative cargos (i.e. proteins), and was suitable for composition analysis as well as for evaluating in vitro release of cargos from the hydrogel. Moreover, this method can be used to monitor polymer degradation and material stability, which can be further elucidated by coupling the RPLC method with high resolution mass spectrometry and a Fourier-transform based deconvolution algorithm. To our knowledge, this is the first RPLC-CAD method for characterizing the critical quality attributes of supramolecular hydrogels. We envision this analytical strategy could be generalized to characterize other classes of supramolecular hydrogels, establish structure-property relationships, and provide rational design guidance in hydrogel drug product development.

biochemistry↗

bioRxiv2021

Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

Among a wide variety of mass spectrometry (MS) methodologies available for structural characterizations of proteins, ion mobility (IM) provides structural information about protein shape and size in the form of an orientationally averaged collision cross-section (CCS). While IM data have been predominantly employed for the structural assessment of protein complexes, CCS data from IM experiments have not yet been used to predict tertiary structure from sequence. Here, we are showing that IM data can significantly improve protein structure determination using the modeling suite Rosetta. The Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm was developed that allows for fast and accurate prediction of CCS from structure. Following successful rigorous testing for accuracy, speed, and convergence of PARCS, an integrative modelling approach was developed in Rosetta to use CCS data from IM experiments. Using this method, we predicted protein structures from sequence for a benchmark set of 23 proteins. When using IM data, the predicted structure improved or remained unchanged for all 23 proteins, compared to the predicted models in the absence of CCS data. For 15/23 proteins, the RMSD (root-mean-square deviation) of the predicted model was less than 5.50 [A], compared to only 10/23 without IM data. We also developed a confidence metric that successfully identified near-native models in the absence of a native structure. These results demonstrate the ability of IM data in de novo structure determination.

biophysics↗

bioRxiv2021

Influenza A M2 Channel Oligomerization is Sensitive to its Chemical Environment

Viroporins are small viral ion channels that play important roles in the viral infection cycle and are proven antiviral drug targets. Matrix protein 2 from influenza A (AM2) is the best characterized viroporin, and the current paradigm is that AM2 forms monodisperse tetramers. Here, we used native mass spectrometry and other techniques to characterize the oligomeric state of both the full-length and transmembrane domain (TM) of AM2 in a variety of different pH and detergent conditions. Unexpectedly, we discovered that AM2 formed a range of different oligomeric complexes that were strongly influenced by the local chemical environment. Native mass spectrometry of AM2 in nanodiscs with different lipids showed that lipids also affected the oligomeric states of AM2. Finally, nanodiscs uniquely enabled measurement of amantadine binding stoichiometries to AM2 in the intact lipid bilayer. These unexpected results reveal that AM2 can form a wider range of oligomeric states than previously thought possible, which may provide new potential mechanisms of influenza pathology and pharmacology. Significance StatementMany viruses contain small ion channels called viroporins that play diverse roles in viral infections. Influenza A M2 (AM2) is the best characterized viroporin and the target of the antivirals amantadine and rimantadine. Although past structural studies showed AM2 was a monodisperse tetramer, we discovered that AM2 can form polydisperse and dynamic oligomers that are sensitive to their local chemical environment. Our findings provide a new perspective on the structure and mechanisms of AM2 that may extend to other viroporins.

biophysics↗