Biology subjects

Rubenstein, B. M.

Publications and source records attributed to Rubenstein, B. M..

9 recordsLinked to original sources

bioRxiv2026

Exploring the GM-CSF Histidine Triad as a Modulator of Structure, Molecular Motion, and Ligand Binding

Granulocyte macrophage-colony stimulating factor (GM-CSF) is a cytokine that plays a role in immune modulation. Its expression is associated with a multitude of different effects ranging from harmful, as in diseases such as rheumatoid arthritis and multiple sclerosis, to beneficial, as in the case of mitigation of diabetes type I and neutropenia. However, there is a large gap in knowledge explaining how GM-CSF toggles its structure for such physiological and pathological interactions. Our work describes an ongoing attempt to address this gap by focusing on a clustered histidine triad within -helices near the N-terminus, which prior studies have suggested play a role in binding ligands at an acidic pH. While GM-CSF is known to be highly flexible at a more acidic pH, several properties of its histidine triad remain unclear at the physiological pH at which GM-CSF would encounter its binding partners. We describe an effort to characterize the role of the GM-CSF histidines under physiological pH, specifically to determine if these histidines are key to GM-CSF structural integrity, and whether individual histidine residues modulate binding as they do at a lower pH. Our findings reveal that, while the histidine residues have an impact on GM-CSF structure, flexibility, and stability, they alone do not modulate the affinity for ligands at neutral pH. These data provide an initial explanation for the pleiotropic functions and interactions of GM-CSF within a biophysical context. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/700583v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@a6fffcorg.highwire.dtl.DTLVardef@1f00c30org.highwire.dtl.DTLVardef@b04c50org.highwire.dtl.DTLVardef@6224d9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

FastConformation: A Standalone ML-Based Toolkit for Modeling and Analyzing Protein Conformational Ensembles at Scale

Deep learning approaches like AlphaFold 2 (AF2) have revolutionized structural biology by accurately predicting the ground state structures of proteins. Recently, clustering and subsampling techniques that manipulate multiple sequence alignment (MSA) inputs into AlphaFold to generate conformational ensembles of proteins have also been proposed. Although many of these techniques have been made open source, they often require integrating multiple packages and can be challenging for researchers who have a limited programming background to employ. This is especially true when researchers are interested in subsampling to produce predictions of protein conformational ensembles, which require multiple computational steps. This manuscript introduces FastConformation, a Python-based application that integrates MSA generation, structure prediction via AF2, and interactive analysis of protein conformations and their distributions, all in one place. FastConformation is accessible through a user-friendly GUI suitable for non-programmers, allowing users to iteratively refine subsampling parameters based on their analyses to achieve diverse conformational ensembles. Starting from an amino acid sequence, users can make protein conformation predictions and analyze results in just a few hours on their local machines, which is significantly faster than traditional molecular dynamics (MD) simulations. Uniquely, by leveraging the subsampling of MSAs, our tool enables the generation of alternative protein conformations. We demonstrate the utility of FastConformation on proteins including the Abl1 kinase, LAT1 transporter, and CCR5 receptor, showcasing its ability to predict and analyze the protein conformational ensembles and effects of mutations on a variety of proteins. This tool enables a wide range of high-throughput applications in protein biochemistry, drug discovery, and protein engineering.

biophysics↗

bioRxiv2024

Mobility of Four Structural Regions Drives Isoform-Specific Properties of Plant LPOR

Light-dependent protochlorophyllide oxidoreductase (LPOR) is a photocatalytic enzyme in the chlorophyll (Chl) biosynthetic pathway that underwent duplications in angiosperms, resulting in the emergence of multiple isoforms across various plant species. The physiological roles of these LPOR homologs remained unclear, so we selected six plant species with different number of isoforms of the enzyme and characterized their properties in vitro. Our findings revealed that these isoforms vary in their affinity for the reaction product, chlorophyllide (Chlide), as well as for NADPH under lipid-free conditions and in reaction mixtures supplemented with plant lipids. Additionally, we observed differences in their oligomerization behavior. Our experimental approach generated a dataset comprising several hundred pairs of spectra, recorded before and after reaction-triggering illumination. This data was used to analyze the correlation between fluorescence emission maxima before and after photoconversion. The analysis showed that some isoforms rapidly release Chlide after the reaction, while others retain the pigment in the binding pocket, especially at high NADPH concentrations. These results suggest that LPOR isoforms differ in their rates of Chlide release and complex disassembly, potentially influencing the overall rate of the Chl biosynthetic pathway, even in mature leaves. We further analyzed the flexibility of these isoforms using AlphaFold2 predictions, identifying four regions of the enzyme that are particularly mobile. Two of these regions are involved in pigment binding, while the other two play a role in oligomerization. Based on these findings, we propose a model of conformational changes that drive the formation of LPOR oligomers.

biochemistry↗

bioRxiv2024

Compound Mutations in the Abl1 Kinase Cause Inhibitor Resistance by Shifting DFG Flip Mechanisms and Relative State Populations

The intrinsic dynamics of most proteins are central to their function. Protein tyrosine kinases such as Abl1 undergo significant conformational changes that modulate their activity in response to different stimuli. These conformational changes constitute a conserved mechanism for self-regulation that dramatically impacts kinases affinities for inhibitors. Few studies have attempted to extensively sample the pathways and elucidate the mechanisms that underlie kinase inactivation. In large part, this is a consequence of the steep energy barriers associated with many kinase conformational changes, which present a significant obstacle for computational studies using traditional simulation methods. Seeking to bridge this knowledge gap, we present a thorough analysis of the "DFG flip" inactivation pathway in Abl1 kinase. By leveraging the power of the Weighted Ensemble methodology, which accelerates sampling without the use of biasing forces, we have comprehensively simulated DFG flip events in Abl1 and its inhibitor-resistant variants, revealing a rugged landscape punctuated by potentially druggable intermediate states. Through our strategy, we successfully simulated dozens of uncorrelated DFG flip events distributed along two principal pathways, identified the molecular mechanisms that govern them, and measured their relative probabilities. Further, we show that the compound Glu255Lys/Val Thr315Ile Abl1 variants owe their inhibitor resistance phenotype to an increase in the free energy barrier associated with completing the DFG flip. This barrier stabilizes Abl1 variants in conformations that can lead to loss of binding for Type-II inhibitors such as Imatinib or Ponatinib. Finally, we contrast our Abl1 observations with the relative state distributions and propensity for undergoing a DFG flip of evolutionarily-related protein tyrosine kinases with diverging Type-II inhibitor binding affinities. Altogether, we expect that our work will be of significant importance for protein tyrosine kinase inhibitor discovery, while also furthering our understanding of how enzymes self-regulate through highly-conserved molecular switches.

biophysics↗

bioRxiv2023

Predicting Relative Populations of ProteinConformations without a Physics Engine UsingAlphaFold2

This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' single ground state conformations and is limited in its ability to predict fold switching and the effects of mutations on conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different conformations of proteins and even accurately predict changes in those populations induced by mutations by subsampling multiple sequence alignments. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted their relative state populations with accuracies in excess of 80%. Our method offers a fast and cost-effective way to predict protein conformations and their relative populations at even single point mutation resolution, making it a useful tool for pharmacology, analyzing NMR data, and studying the effects of evolution.

bioinformatics↗

bioRxiv2022

Defining the HIV Capsid Binding Site of Nucleoporin 153

The interaction between the HIV-1 capsid (CA) and human nucleoporin 153 (NUP153) is vital for delivering the HIV-1 preintegration complex into the nucleus via the nuclear pore complex. The interaction with CA requires a phenylalanine/glycine-containing motif in the C-terminus of NUP153. This study used molecular modeling and biochemical assays to determine the amino acids of NUP153 that are essential for its interactions with CA. Molecular dynamics, FoldX, and PyRosetta simulations delineated the minimal CA binding motif of NUP153 based on the known structure of NUP153 bound to the HIV-1 CA hexamer. Computational predictions were experimentally validated by testing the interaction of NUP153 with CA using an in vitro binding assay and a cell-based TRIM-NUP153C restriction assay. This multidisciplinary approach identified eight amino acids from P1411 to G1418 that stably engage with CA, with significant correlations between molecular models and empirical experiments. Specifically, P1411, V1414, F1415, T1416, F1417, and G1418 were confirmed as critical amino acids required to interact NUP153 with CA. IMPORTANCEHuman immunodeficiency virus (HIV) can infect non-dividing cells by interacting with host nuclear pores. The host nuclear pore protein NUP153 directly interacts with the HIV capsid to promote viral nuclear entry. This study used a multidisciplinary approach combining computational and experimental techniques to map the essential amino acids of NUP153 required for HIV capsid interaction. This approach revealed that the HIV capsid interacts specifically with only six amino acids of NUP153, suggesting other FG-containing motifs could also interact with the capsid. Based on molecular modeling, naturally occurring polymorphisms in human and non-human primates would be predicted to prevent NUP153 interaction with capsid, potentially protecting from HIV infection.

molecular biology↗

bioRxiv2022

Covalent Docking and Molecular Dynamics Simulations Reveal the Specificity-Shifting Mutations Ala237Arg and Ala237Lys in TEM Beta-Lactamase

The rate of modern drug discovery using experimental screening methods still lags behind the rate at which pathogens mutate, underscoring the need for fast and accurate predictive simulations of protein evolution. Multidrug-resistant bacteria evade our defenses by expressing a series of proteins, the most famous of which is the 29-kilodalton enzyme, TEM {beta}-lactamase. Considering these challenges, we applied a covalent docking heuristic to measure the effects of all possible alanine 237 substitutions in TEM due to this codons importance for catalysis and effects on the binding affinities of commercially-available {beta}-lactam compounds. In addition to the usual mutations that reduce substrate binding due to steric hindrance, we identified two distinctive specificity-shifting TEM mutations, Ala237Arg and Ala237Lys, and their respective modes of action. Notably, we discovered and verified through minimum inhibitory concentration assays that, while these mutations and their bulkier side chains lead to steric clashes that curtail ampicillin binding, these same groups foster salt bridges with the negatively-charged side-chain of the cephalosporin cefixime, widely used in the clinic to treat multi-resistant bacterial infections. To measure the stability of these unexpected interactions, we used molecular dynamics simulations and found the binding modes to be stable despite the application of biasing forces. Finally, we found that both TEM mutants also bind strongly to other drugs containing negatively-charged R-groups, such as carumonam and ceftibuten. As with cefixime, this increased binding affinity stems from a salt bridge between the compounds negative moieties and the positively-charged side chain of the arginine or lysine, suggesting a shared mechanism. In addition to reaffirming the power of using simulations as molecular microscopes, our results can guide the rational design of next-generation {beta}-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrugresistant pathogens. Author SummaryResistance to antibiotics is a major public health threat. Microorganisms are able to resist commonly used drugs by evolving and expressing enzymes capable of neutralizing antibiotics. Understanding the relationships between structural elements in these enzymes and their drug-clearing functions can lead to crucial insights for the discovery of next-generation antibiotics. In this study, we have used cutting-edge computational modeling methods to predict the effects of all naturally-occurring variations of an important region of the binding site of TEM {beta}-lactamase, one of the major resistance-granting enzymes in bacteria. In an effort to identify patterns that could be useful for drug discovery, our simulations sought to understand how chemical changes in the tested region can affect resistance against a collection of over 90 widely used antibiotics. Crucially, through our simulations, we have identified a pathway for bacterial resistance against {beta}-lactam antibiotics containing a negatively-charged moiety. We have also elucidated the mechanism behind the gain of resistance, which involves strong interactions between the drugs negative moieties and the positively-charged chemical shifts resulting from the mutation. Finally, we validated our predictions against fitness experiments for two commonly used antibiotics, which qualitatively corroborated our most important findings.

bioinformatics↗

bioRxiv2020

Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organisms fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 {beta}-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of {beta}-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon {beta}-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 {beta}-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software such as FoldX and PyRosetta designed to compute folding free energies can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in {beta}-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of {beta}-lactamases fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

biophysics↗

bioRxiv2019

Encoding information in synthetic metabolomes

Biomolecular information systems offer numerous potential advantages over conventional semiconductor technologies. Downstream from DNA, the metabolome is an information-rich molecular system with diverse chemical dimensions which could be harnessed for information storage and processing. As a proof of principle of postgenomic data storage, here we demonstrate a workflow for representing abstract data in synthetic metabolomes. Our approach leverages robotic liquid handling for writing digital information into chemical mixtures, and mass spectrometry for extracting the data. We present several kilobyte-scale image datasets stored in synthetic metabolomes, which are decoded with accuracy exceeding 98-99% using multi-mass logistic regression. Cumulatively, >100,000 bits of digital image data was written into metabolomes. These early demonstrations provide insight into the benefits and limitations of postgenomic chemical information systems.

synthetic biology↗