Biology subjects

Noe, F.

Publications and source records attributed to Noe, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Rewired mRNA homeostasis enforces cell-size associated senescence

Cellular senescence refers to a permanent state of cell cycle exit and plays a key role in blocking the proliferation of compromised cells. Despite diverse drivers, senescent cells often share common features, one of which is their increased size. Though previously viewed as a passive outcome of cell growth during senescence, recent work shows that excess cell size can actively promote senescence via the p53-p21 pathway, making it both a consequence and a cause of cell cycle arrest. How cells sense their own size and communicate this information to the cell cycle control apparatus remains unresolved. Using genome-wide proximity mapping, we show that excess cell size globally redistributes chromatin between nuclear speckles and the nuclear lamina. These changes correspond with altered gene expression profiles, including the de-repression of typically silenced lamina-associated genes, some of which are involved in the senescence-associated secretory phenotype (SASP). Consistent with altered nuclear speckle function, we find that excess cell size perturbs normal splice isoform usage. In particular, MDM4, a negative regulator of p53 signaling, is differentially spliced when cells become enlarged to yield a non-functional variant, MDM4-S. Re-introducing correctly spliced MDM4 transcripts restores G1 cell cycle progression, thereby reverting senescent phenotypes in enlarged cells. Together, these data indicate that excess cell size is sensed via changes in mRNA homeostasis associated with a reorganized genome, driving both the secretory phenotype and permanent cell cycle exit that are characteristic of cellular senescence.

cell biology↗

bioRxiv2024

Scalable emulation of protein equilibrium ensembles with generative deep learning

Following the sequence and structure revolutions, predicting the dynamical mechanisms of proteins that implement biological function remains an outstanding scientific challenge. Several experimental techniques and molecular dynamics (MD) simulations can, in principle, determine conformational states, binding configurations and their probabilities, but suffer from low throughput. Here we develop a Biomolecular Emulator (BioEmu), a generative deep learning system that can generate thousands of statistically independent samples from the protein structure ensemble per hour on a single graphical processing unit. By leveraging novel training methods and vast data of protein structures, over 200 milliseconds of MD simulation, and experimental protein stabilities, BioEmus protein ensembles represent equilibrium in a range of challenging and practically relevant metrics. Qualitatively, BioEmu samples many functionally relevant conformational changes, ranging from formation of cryptic pockets, over unfolding of specific protein regions, to large-scale domain rearrangements. Quantitatively, BioEmu samples protein conformations with relative free energy errors around 1 kcal/mol, as validated against millisecond-timescale MD simulation and experimentally-measured protein stabilities. By simultaneously emulating structural ensembles and thermodynamic properties, BioEmu reveals mechanistic insights, such as the causes for fold destabilization of mutants, and can efficiently provide experimentally-testable hypotheses.

molecular biology↗

bioRxiv2024

Conformational changes, excess area, and elasticity of the Piezo protein-membrane nanodome from coarse-grained and atomistic simulations

The mechanosensitive ion channels Piezo 1 and 2 induce a curved protein-membrane nanodome that flattens with increasing membrane tension{gamma} . The tension-induced flattening of the nanodome is associated with Piezo activation and driven by the energy{gamma}{Delta} A where {Delta}A is the excess area of the curved nanodome relative to its planar projected area. Based on extensive coarse-grained and atomistic simulations of membrane-embedded Piezo 1 and 2 proteins, we report here an excess area {Delta}A for the Piezo protein-membrane nanodome of about 40 nm2 in tensionless membranes, and a half-maximal reduction of {Delta}A at tension values of about 3 to 4 mN/m, which is within the range of experimentally determined values for the half-maximal activation of Piezo 1. In line with recent experimental investigations of Piezo proteins in cell membranes and membrane vesicles, the membrane-embedded Piezo proteins adopt conformations in our simulations that are significantly less curved than the protein conformation in the detergent micelles of cryo-EM structures. An elasticity analysis of the nanodome shapes and protein conformations obtained from our simulations leads to an elastic model for Piezo activation that distinguishes the different energy components of the protein and the membrane in the tension-induced flattening of the nanodome.

molecular biology↗

bioRxiv2024

Sex-specific role of epigenetic modification of a leptin upstream enhancer in the adipose tissue

ObjectiveMaternal hormonal status can have long-term effects on offspring metabolic health, likely regulated via epigenetic mechanisms. We elucidated the effect of maternal thyroid hormones on epigenetic regulation of Leptin (Lep) transcription in adipose tissue (AT) and investigated the role of DNA methylation at a Lep upstream enhancer (UE) in adipocyte biology. MethodsPregnant mice were treated with Triiodothyronine (T3). Offspring body composition and adipose tissue were analysed at 6 months (treatment: N = 8, control: N = 12). DNA methylation at the Lep UE and Lep mRNA levels in gonadal white adipose tissue (gWAT) and leptin serum levels were measured. The Lep UE was hypomethylated in murine preadipocytes using a dCas9-SunTag-TET1 system, followed by differentiation. In human visceral AT samples (N = 52) from the Leipzig Obesity BioBank (LOBB) LEP UE methylation and LEP mRNA levels were assessed alongside leptin serum levels and correlated with body fat percentage. Causal relationships were explored using mediation analysis. ResultsHigh maternal T3 levels reduced offspring body weight, total fat, and gWAT mass. Female offspring of T3-treated mothers showed lower Lep mRNA levels and higher Lep UE methylation compared to controls. CRISPR/dCas9 editing reduced Lep UE methylation by [~]20% in preadipocytes without altering Lep expression or lipid accumulation after differentiation. In humans, a mediation of sex-specific difference in body fat percentage via Lep UE methylation was found. ConclusionMaternal thyroid hormones might affect offspring gWAT Lep expression in a sex-specific manner, potentially driven by DNA methylation at the Lep UE, influencing body fat percentage.

genetics↗

bioRxiv2023

Structure prediction of protein-ligand complexes from sequence information with Umol

Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly, given a multiple sequence alignment representation of the protein and a SMILES string representing the ligand. At a high accuracy threshold, unseen protein-ligand complexes can be predicted more accurately than for RoseTTAFold-AA, and at medium accuracy even classical docking methods that use known protein structures as input are surpassed. The high accuracy presented here suggests that the goal of AI-based drug discovery is one step closer, but there is still a way to go to fully grasp the complexity of protein-ligand interactions. Umol is available at: https://github.com/patrickbryant1/Umol

bioinformatics↗

bioRxiv2023

Sub-membrane actin rings compartmentalize the plasma membrane

The compartmentalization of the plasma membrane is a fundamental feature of cells. The diffusivity of membrane proteins in the plane of the membrane is significantly lower in cells than observed in artificial membranes. This seems due to the sub-membranous actin cortex, but tractable assays to prove a direct dependence of membrane protein motion on actin filaments have been elusive. We recently showed that a periodic array of sub-membrane actin filaments that surround neuronal axons in ring-like structures in the axonal initial segment (AIS) confines membrane protein motion between them, but the local enrichment of ion channels may offer an alternative explanation for compartmentalization. Here we show using computational modeling that in contrast to actin rings, the dense array of ion channels in the AIS cannot mediate membrane protein confinement. We go on to show that indeed the actin rings are closely apposed to the plasma membrane, and that they confine membrane protein motion between them in a [~] 200 nm periodic pattern in progenitor-derived neuronal cells. We find that this compartmentalization is also detectable for inner-leaflet membrane proteins and multi-spanning receptors. Strikingly, several cell types with actin rings in their protrusions, like progenitor-derived astrocytes and oligodendrocytes, also exhibit lateral confinement of membrane proteins in a periodic pattern consistent with actin ring spacing. Actin ring-mediated membrane compartmentalization is thus not unique to neurons. Finally, we show that acute actin disruption in live progenitor-derived neuronal cells leads to a loss of membrane compartmentalization in areas that were compartmentalized before treatment. Taken together, we here develop a much-needed tractable experimental system for the investigation of membrane compartmentalization and show that actin rings compartmentalize the plasma membrane of cells.

biophysics↗

bioRxiv2023

Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile

Structure prediction of protein complexes has improved significantly with AlphaFold2 and AlphaFold-multimer (AFM), but only 60% of dimers are accurately predicted. A way to improve the predictions is to inject noise to generate more diverse predictions. However, thousands of predictions are needed to obtain a few that are accurate in difficult cases. Here, we learn a bias to the MSA representation that improves the predictions by performing gradient descent through the AFM network. We effectively denoise the MSA profile, similar to how a blurry image would be sharpened. We demonstrate the performance on seven difficult targets from CASP15 and increase the average MMscore to 0.76 compared to 0.63 with AFM. We evaluate the procedure on 334 protein complexes where AFM fails and demonstrate an increased success rate (MMscore>0.75) of 8% on these hard targets. Our protocol, AFProfile, provides a way to direct predictions towards a defined target function guided by the MSA. We expect gradient descent over the MSA to be useful for different tasks, such as generating alternative conformations. AFProfile is freely available at: https://github.com/patrickbryant1/AFProfile

bioinformatics↗

bioRxiv2023

Rapid protein-protein interaction network creation from multiple sequence alignments with Deep Learning

AlphaFold2 (AF) can evaluate protein-protein interactions (PPIs) with high accuracy by finding evolutionary signals between proteins but comes with a high computational cost. Here, we speed up the prediction with AF for PPI network prediction 40x and reduce the disk space requirements 4000x for a set of 1000 proteins. Our protocol is easy to install and freely available from: https://github.com/patrickbryant1/SpeedPPI.

bioinformatics↗

bioRxiv2022

Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, such as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations.

cell biology↗

bioRxiv2022

3D-surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semi-supervised deep learning

Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nm range and strong contrast for membranous structures without requirement for labeling or chemical fixation. The short acquisition time and the relatively large volumes acquired allow for fast acquisition of large amounts of tomographic image data. Segmentation of these data into accessible features is a necessary step in gaining biologically relevant information from cryo-soft X-ray tomograms. However, manual image segmentation still requires several orders of magnitude more time than data acquisition. To address this challenge, we have here developed an end-to-end automated 3D-segmentation pipeline based on semi-supervised deep learning. Our approach is suitable for high-throughput analysis of large amounts of tomographic data, while being robust when faced with limited manual annotations and variations in the tomographic conditions. We validate our approach by extracting three-dimensional information on cellular ultrastructure and by quantifying nanoscopic morphological parameters of filopodia in mammalian cells.

bioinformatics↗

bioRxiv2022

Deep learning to decompose macromolecules into independent Markovian domains

The increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient statistics of state probabilities or state-to-state transitions because for large molecular systems the number of metastable states grows exponentially with size. In this manuscript, we approach this challenge by introducing a method that combines our recent progress on independent Markov decomposition (IMD) with VAMPnets, a deep learning approach to Markov modeling. We establish a training objective that quantifies how well a given decomposition of the molecular system into independent subdomains with Markovian dynamics approximates the overall dynamics. By constructing an end-to-end learning framework, the decomposition into such subdomains and their individual Markov state models are simultaneously learned, providing a data-efficient and easily interpretable summary of the complex system dynamics. While learning the dynamical coupling between Markovian subdomains is still an open issue, the present results are a significant step towards learning "Ising models" of large molecular complexes from simulation data.

biophysics↗

bioRxiv2021

Quantitative analysis of membrane-mediated interactions and aggregation of flexible peripheral proteins orchestrating large-scale membrane remodeling

Biomembrane remodeling is essential for cellular trafficking, with membrane-binding peripheral proteins playing a key role in it. Significant membrane remodeling as in endo- and exocytosis is often due to aggregates of many proteins with direct or membrane-mediated interactions. Understanding this process via computer simulations is extremely challenging: protein-membrane systems involve time- and lengthscales that make atomistic simulations impractical, while most coarse-grained models fall short in resolving dynamics and physical effects of protein and membrane flexibility. Here, we develop a coarse-grained model of the bilayer membrane bestrewed with rotationally-symmetric flexible proteins, parametrized to reflect local curvatures and lateral dynamics of proteins. We investigate the kinetics, equilibrium distributions, and the free energy landscape governing the formation and break-up of protein clusters on the surface of the membrane. We demonstrate how the flexibility of the proteins as well as their surface concentration play deciding roles in highly selective macroscopic aggregation behavior.

biophysics↗

bioRxiv2021

Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes

In order to advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increase exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits. Significance StatementMolecular simulations of proteins are often interpreted using Markov state models (MSMs), in which each protein configuration is assigned to a global state. As we explore larger and more complex biological systems, the size of this global state space will face a combinatorial explosion, rendering it impossible to gather sufficient sampling data. In this work, we introduce an approach to decompose a system of interest into separable subsystems. We show that MSMs built for each subsystem can be later coupled to reproduce the behaviors of the global system. To aid in the choice of decomposition we also describe a score to quantify its goodness. This decomposition strategy has the promise to enable robust modeling of complex biomolecular systems.

biophysics↗

bioRxiv2020

What Markov state models can and cannot do: Correlation versus path-based observables in protein folding models

Markov state models (MSMs) have been widely applied to study the kinetics and pathways of protein conformational dynamics based on statistical analysis of molecular dynamics (MD) simulations. These MSMs coarse-grain both configuration space and time in ways that limit what kinds of observables they can reproduce with high fidelity over different spatial and temporal resolutions. Despite their popularity, there is still limited understanding of which biophysical observables can be computed from these MSMs in a robust and unbiased manner, and which suffer from the space-time coarse-graining intrinsic in the MSM model. Most theoretical arguments and practical validity tests for MSMs rely on long-time equilibrium kinetics, such as the slowest relaxation timescales and experimentally observable time-correlation functions. Here, we perform an extensive assessment of the ability of well-validated protein folding MSMs to accuractely reproduce path-based observable such as mean first-passage times (MFPTs) and transition path mechanisms compared to a direct trajectory analysis. We also assess a recently proposed class of history-augmented MSMs (haMSMs) that exploit additional information not accounted for in standard MSMs. We conclude with some practical guidance on the use of MSMs to study various problems in conformational dynamics of biomolecules. In brief, MSMs can accurately reproduce correlation functions slower than the lag time, but path-based observables can only be reliably reproduced if the lifetimes of states exceed the lag time, which is a much stricter requirement. Even in the presence of short-lived states, we find that haMSMs reproduce path-based observables more reliably.

biophysics↗

bioRxiv2020

Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity

Antiviral therapy is urgently needed to combat the coronavirus disease 2019 (COVID-19) pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The protease inhibitor camostat mesylate inhibits SARS-CoV-2 infection of lung cells by blocking the virus-activating host cell protease TMPRSS2. Camostat mesylate has been approved for treatment of pancreatitis in Japan and is currently being repurposed for COVID-19 treatment. However, potential mechanisms of viral resistance as well as camostat mesylate metabolization and antiviral activity of metabolites are unclear. Here, we show that SARS-CoV-2 can employ TMPRSS2-related host cell proteases for activation and that several of them are expressed in viral target cells. However, entry mediated by these proteases was blocked by camostat mesylate. The camostat metabolite GBPA inhibited the activity of recombinant TMPRSS2 with reduced efficiency as compared to camostat mesylate and was rapidly generated in the presence of serum. Importantly, the infection experiments in which camostat mesylate was identified as a SARS-CoV-2 inhibitor involved preincubation of target cells with camostat mesylate in the presence of serum for 2 h and thus allowed conversion of camostat mesylate into GBPA. Indeed, when the antiviral activities of GBPA and camostat mesylate were compared in this setting, no major differences were identified. Our results indicate that use of TMPRSS2-related proteases for entry into target cells will not render SARS-CoV-2 camostat mesylate resistant. Moreover, the present and previous findings suggest that the peak concentrations of GBPA established after the clinically approved camostat mesylate dose (600 mg/day) will result in antiviral activity.

molecular biology↗

bioRxiv2020

Molecular mechanism of SARS-CoV-2 cell entry inhibition via TMPRSS2 by Camostat and Nafamostat mesylate

The entry of the coronavirus SARS-CoV-2 into human cells can be inhibited by the approved drugs camostat and nafamostat. Here we elucidate the molecular mechanism of these drugs by combining experiments and simulations. In vitro assays confirm the hypothesis that both drugs act by inhibiting the human protein TMPRSS2. As no experimental structure is available, we provide a model of the TMPRSS2 equilibrium structure and its fluctuations by relaxing an initial homology structure with extensive 280 microseconds of all-atom molecular dynamics (MD) and Markov modeling. We describe the binding mode of both drugs with TMPRSS2 in a Michaelis complex (MC) state preceding the formation of a long-lived covalent inhibitory state. We find that nafamostat to has a higher MC population, which in turn leads to the more frequent formation of the covalent complex and thus higher inhibition efficacy, as confirmed in vitro and consistent with previous virus cell entry assays. Our TMPRSS2-drug structures are made public to guide the design of more potent and specific inhibitors.

biophysics↗

bioRxiv2020

Discovery of a hidden transient state in all bromodomain families

Bromodomains (BDs) are small protein modules that interact with acetylated marks in histones. These post-translational modifications are pivotal to regulate gene expression, making BDs promising targets to treat several diseases. While the general structure of BDs is well known, their dynamical features and their interplay with other macromolecules are poorly understood, hampering the rational design of potent and selective inhibitors. Here we combine extensive molecular dynamics simulations, Markov state modeling and structural data to reveal a novel and transiently formed state that is conserved across all BD families. It involves the breaking of two backbone hydrogen bonds that anchor the ZA-loop with the A helix, opening a cryptic pocket that partially occludes the one associated with histone binding. Our results suggest that this novel state is an allosteric regulatory switch for BDs, potentially related to a recently unveiled BD-DNA binding mode.

biophysics↗

bioRxiv2020

Coupling of conformational switches in calcium sensor unraveled with local Markov models and transfer entropy

Proteins often have multiple switching domains that are coupled to each other and to binding of ligands in order to realize signaling functions. Here we investigate the C2A domain of Synaptotagmin-1 (Syt-1), a calcium sensor in the neurotransmitter release machinery and a model system for the large family of C2 membrane binding domains. We combine extensive molecular dynamics (MD) simulations and Markov modeling in order to model conformational switching domains, their states and their dependence on bound calcium ions. Using transfer entropy, we characterize how the switching domains are coupled via directed or allosteric mechanisms and give rise to the calcium sensing function of the protein. Our switching mechanism contributes to the understanding of the neurotransmitter release machinery and the proposed methodological approach serves as a template to analyze conformational switching domains and study their coupling in macromolecular machines in general.

biophysics↗