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bioRxiv2025

CLUSTERING DYNAMICALLY MODULATE THE BIOPHYSICS OF VOLTAGE-GATED SODIUM CHANNELS: HOW NANOSCALE PHENOMENA DETERMINE HEALTH AND DISEASE

Precise regulation of ion channel biophysics is an essential life process that governs electrical signaling in excitable tissues. Many ion channels including voltage-gated Na+ channels (NaVs) exist in the membrane as clusters, which show distinct biophysical behavior not predicted by single-channel measurements. In both heterologous and native systems, we report that single-channel-based predictions significantly overestimated Na+ current (INa) amplitudes from multi-channel clusters. Computational modeling suggested that these observations could reflect interactions between adjacent channels, such as recently reported between NaVs, and identified specific biophysical consequences thereof. This updated model not only accurately predicted behaviors observed from NaV clusters and consequent cellular physiology, but also suggested the possibility that clustered NaVs may respond differently to use-dependent pharmacological agents. Experiments validated the latter prediction and further identified modulation of clustering as a novel approach to correcting macroscopic electrophysiological dysfunction resulting from NaV defects linked to life-threatening arrhythmias and seizures. Thus, our study not only motivates a fundamental revision of how ion channels behave when clustered but also highlights resulting biophysical effects as important considerations for pharmacology and a potential therapeutic target to address human disease.

physiology↗

bioRxiv2025

Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding

Deep neural networks have improved the accuracy of many difficult prediction tasks in biology, but it remains challenging to interpret these networks and learn molecular mechanisms. Here, we address the interpretability challenges associated with predicting transcriptional activation domains from protein sequence. Activation domains, regions within transcription factors that drive gene expression, were traditionally difficult to predict due to their sequence diversity and poor conservation. Multiple deep neural networks can now accurately predict activation domains, but these predictors are difficult to interpret. With the goal of interpretability, we designed simple neural networks that incorporated biophysical models of activation domains. The simplicity of these neural networks allowed us to visualize their parameters and directly interpret what the networks learned. The biophysical neural networks revealed two new ways that arrangement (i.e. the sequence grammar) of activation domain controlled function: 1) hydrophobic residues both increase activation domain strength and decrease protein abundance, and 2) acidic residues control both activation domain strength and protein abundance. Notably, the biophysical neural networks helped us to recognize the same signatures in complex interpreters of the deeper neural networks. We demonstrate how combining biophysical and deep neural networks maximizes both prediction accuracy and interpretability to yield insights into biological mechanisms.

systems biology↗

bioRxiv2026

Multi-omics and biophysical phosphoproteomics upon BRAF inhibition uncover functional networks of BRAFV600E-driven signaling

Dysregulated kinase activity drives oncogenic signaling, disrupts cellular homeostasis, and promotes tumour progression. The BRAFV600E mutation constitutively activates the MAPK pathway and is a key therapeutic target in melanoma and other cancers, but the functional relevance of most downstream phosphorylation events and mechanisms of drug resistance remain unclear. To address this, a global multi-omic model of BRAF inhibition response was established in BRAFV600E-mutant cells by integrating time-resolved and biophysical phosphoproteomics, transcriptomics, and thermal proteome profiling. Ultradeep phosphoproteomics revealed extensive phosphorylation changes upon BRAF inhibitor treatment, while biophysical phosphoproteomics identified phosphorylation events linked to altered protein solubility and subcellular localization, suggesting changes in nucleic acid interactions and nuclear reorganisation. Network-based integration of these datasets prioritized functionally relevant phosphorylation sites and kinases. Experimental validation identified CDK9, CLK3, and TNIK as critical regulators of BRAFV600E signaling and candidate targets for combinatorial inhibition capable of re-sensitising resistant cells. The transcription factor ETV3 emerged as a previously unrecognised effector of BRAF signaling. Biophysical proteomics data confirmed that ETV3 phosphorylation modulates DNA-binding, while functional assays combining knockdown, metabolomics, and drug screening demonstrated its role in coordinating transcriptional and metabolic adaptations to BRAF inhibition. This study provides a systems-level framework linking phosphorylation dynamics to protein function and phenotype, identifies ETV3 as a new node in oncogenic BRAF signaling, and illustrates how integrated, site-resolved models can reveal mechanisms of kinase-driven oncogenesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/704793v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@1df1401org.highwire.dtl.DTLVardef@9a77a5org.highwire.dtl.DTLVardef@124f819org.highwire.dtl.DTLVardef@1c6b57_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITime- and cell type-resolved phosphoproteomics maps BRAF inhibition dynamics C_LIO_LIBiophysical phosphoproteomics, combining quantitative phosphoproteomics with solubility profiling or nuclear fractionation, reveals phosphorylation-driven changes of protein solubility and localization C_LIO_LIIntegration of abundance and biophysical phosphoproteomics data identifies functionally relevant phosphorylation events of BRAFV600E signaling C_LIO_LINetwork integration of multimodal phosphoproteomic, transcriptomic and thermal proteome profiling data links signaling to protein function and cellular phenotypes C_LIO_LIBiophysical evidence improves models and identifies non-canonical kinases driving BRAF signaling as well as novel downstream regulators such as ETV3 C_LIO_LIFollow-up experiments reveal a ETV3-GLUT3-mediated metabolic adaptation in BRAFV600E cells C_LI

systems biology↗

bioRxiv2026

Biophysical Constraints Dictate the Stability of Social Traits in Pseudomonas aeruginosa Aggregates

The maintenance of cooperative behaviors within microbial populations remains an evolutionary puzzle, particularly in spatially structured environments like those found in chronic infection. In Pseudomonas aeruginosa, quorum sensing (QS) directs the production of costly public goods that are vulnerable to exploitation by non-producing cheaters. However, the biophysical mechanisms that stabilize this cooperation in complex environments remain poorly understood. Here, we elucidate how the biophysical properties of the bacterial cell surface govern the micron-scale architecture of bacterial aggregates to promote or constrain the stability of cooperation. Using a polymer-structured growth environment, we demonstrate that cell surface hydrophobicity acts as a primary determinant of spatial organization, where hydrophilic wild-type cells assemble into "stacked" aggregates, whereas hydrophobic O-specific antigen-deficient cells form dense "clumps". In mixed populations, distinct surface properties exhibit phase-separation-like immiscibility, segregating the cells at the micron scale. We show that this segregation directly suppresses cheater fitness by physically sequestering cooperative clusters. Specifically, hydrophobic QS-deficient cells were effectively excluded by hydrophilic QS cooperators, limiting their access to public goods. Furthermore, invasion experiments revealed that hydrophilic cells are inherently fitter, capable of unidirectionally invading populations with a hydrophobic cell surface and independent of social dynamics. Conversely, the need to gain access to nutrient resources can fine-tune these barriers, enabling hydrophilic QS-deficient cells to cluster proximally to hydrophobic cooperators. These findings establish that bacterial cell surface traits impact strongly on microbial social interactions and offer new insights into the maintenance and loss of cooperative traits in chronic infections such as those found in chronic lung infections. Significance StatementCooperative behaviors are essential for the survival of pathogenic bacteria like Pseudomonas aeruginosa, yet they are continually threatened by "cheater" cells that exploit shared public good resources. We demonstrate that the physical properties of the cell surface, specifically hydrophobicity, act as a key architect of microbial social structure. In polymer-rich environments, differences in surface hydrophobicity drive a distinct "oil-and-water" segregation between cell populations. This micron-scale phase separation physically sequesters cooperative cells, insulating them from exploitation by cheaters. Consequently, spatial structure serves as a biophysical stabilizer of cooperation, limiting the metabolic advantages typically held by cheaters. Our findings demonstrate that microbial social evolution is governed not only by genetic strategies, but by fundamental biophysical constraints that dictate the spatial limits of exploitation.

microbiology↗

bioRxiv2026

Accurate de novo design of peptides from programming biophysical landscape

Peptides regulate virtually every cellular process and emerge as transformative modalities in bioengineering and therapeutics. However, the de novo design of functional peptides remains challenging, as peptide-protein interactions are governed by delicate and dynamic biophysics that are difficult to model, and experimental datasets are scarce. Here we present NeoPep, a generative deep-learning framework that encodes biophysical principles to accurately design functional peptides de novo. By integrating over 5 million peptide-protein complexes spanning experimentally determined, sequence mimics, and structure ensembles, NeoPep learns the complex biophysical landscape governing peptide function and enables its programmable control. In prospective application across 10 diverse and challenging targets, NeoPep generated potent peptide binders, agonists, and antagonists with hit rates of 12.5-66.7%, even in the absence of defined binding sites or structure information. Beyond de novo co-design, NeoPep supports standalone structure or sequence redesign, readily discriminating subtle context differences. In the structure redesign mode, it generates highly selective peptides with atomic-level conformational accuracy (C RMSD < 2.0 [A] to our solved cryo-EM structure). This structural precision circumvents the need for experimental structure determination, further accelerating iterative sequence redesign to yield a 43.3-fold improvement in potency. These findings establish a general framework for translating biophysical principles into actionable peptide functions, with broad implications for basic research, bioengineering, and medicine.

molecular biology↗

bioRxiv2026

Magnetic Levitation of Intact Tumor Biopsies Reveals Multivariate Biophysical Signatures of Breast Cancer Aggressiveness

Diagnostic assessment of breast cancer biopsies remains reliant on resource-intensive histopathology and molecular profiling, which often lack real-time physiological readouts. Magnetic levitation (MagLev) enables label-free density profiling of single cells, yet its application to intact tissue biopsies has been precluded by size-dependent geometric artifacts and the absence of analytical frameworks for biopsy-scale samples. Here, we report the first application of MagLev to intact invasive breast carcinoma biopsies (200-600 m) for biophysical profiling, generating multivariate biophysical signatures from 203 samples across 17 patients. We developed a physics-based size-correction algorithm (xmc) that isolates biological density from geometric artifact, and demonstrate that tissue viability is predicted not by average levitation height, but by spatial heterogeneity across replicate samples, reflecting the microenvironmental complexity of metabolically active tumors. Multivariate integration using Partial Least Squares (PLS) regression and Factor Analysis of Mixed Data (FAMD) identified nodal status (N) as the strongest biophysical predictor, suggesting that lymphatic dissemination capacity leaves a measurable signature in the primary tumor density profile. Unsupervised patient clustering in PLS-derived latent space recovered three clinically coherent subgroups aligned with molecular subtypes. This 30-minute, low-cost assay provides exploratory biophysical stratification complementary to existing diagnostics, particularly in resource-limited settings.

bioengineering↗

bioRxiv2026

BindCORE: Biophysical Ensemble Learning for Predicting Interaction Sites in Intrinsically Disordered Regions

Intrinsically disordered proteins and regions (IDPs/IDRs) mediate diverse cellular functions through binding segments whose functional properties are encoded in dynamic conformational ensembles rather than a single static state. Existing predictors of linear interacting peptides (LIPs) and molecular recognition features (MoRFs) rely primarily on sequence-derived features, leaving ensemble-level biophysical properties largely unexplored. Here, we introduce BindCORE, an ensemble-aware deep learning framework that integrates global, local, and pairwise biophysical descriptors to predict interaction sites within IDRs. These features are processed through a multi-scale architecture that enables information exchange between sequence- and ensemble-based global, local, and pairwise information. Across established LIP and MoRF benchmarks, BindCORE consistently improves performance over sequence-based baselines, demonstrating the predictive signals of ensemble-derived properties beyond sequence-based representations alone. Feature-attribution analyses reveal that pairwise descriptors are the dominant contributors to prediction, while solvent accessibility, backbone dihedral entropy, and global geometric properties provide complementary information. Feature-importance rankings vary substantially across ensemble flavours, indicating that different conformational generators encode distinct biophysical signatures of interaction-site propensity. Together, our results show that conformational ensembles contain interpretable determinants of LIP and MoRF binding residues and establish BindCORE as a general framework for incorporating biophysical information into the prediction of functional regions in intrinsically disordered proteins. BindCORE is freely available as a ready-to-use Google Colab notebook at https://gitlab.inria.fr/delta/bindcore.

bioinformatics↗

bioRxiv2016

Biophysical models of protein evolution: Understanding the patterns of evolutionary sequence divergence

For decades, rates of protein evolution have been interpreted in terms of the vague concept of \"functional importance\". Slowly evolving proteins or sites within proteins were assumed to be more functionally important and thus subject to stronger selection pressure. More recently, biophysical models of protein evolution, which combine evolutionary theory with protein biophysics, have completely revolutionized our view of the forces that shape sequence divergence. Slowly evolving proteins have been found to evolve slowly because of selection against toxic mis-folding and misinteractions, linking their rate of evolution primarily to their abundance. Similarly, most slowly evolving sites in proteins are not directly involved in function, but mutating them has large impacts on protein structure and stability. Here, we review the studies of the emergent field of biophysical protein evolution that have shaped our current understanding of sequence divergence patterns. We also propose future research directions to develop this nascent field.

Biophysics↗

bioRxiv2020

Asymmetric phospholipids impart novel biophysical properties to lipid bilayers allowing environmental adaptation

Phospholipids are a diverse group of biomolecules consisting of a hydrophilic head group and two hydrophobic acyl tails. The nature of the head and length and saturation of the acyl tails are important for defining the biophysical properties of lipid bilayers. It has recently been shown that the membranes of certain yeast species contain high levels of unusual asymmetric phospholipids, consisting of one long and one medium chain acyl moiety - a configuration not common in mammalian cells or other well studied model yeast species. This raises the possibility that structurally asymmetric phospholipids impart novel biophysical properties to the yeast membranes. Here, we use atomistic molecular dynamics simulations (MD) and environmentally-sensitive fluorescent membrane probes to characterize key biophysical parameters of membranes formed from asymmetric lipids for the first time. Interestingly, we show that saturated, but asymmetric phospholipids maintain membrane lipid order across a wider range of temperatures and do not require acyl tail unsaturation or sterols to maintain their properties. This may allow cells to maintain membrane fluidity even in environments which lack the oxygen required for the synthesis of unsaturated lipids and sterols.

biophysics↗

bioRxiv2020

A biophysical limit for quorum sensing in biofilms

Bacteria grow on surfaces in complex immobile communities known as biofilms, which are composed of cells embedded in an extracellular matrix. Within biofilms, bacteria often interact with members of their own species, and cooperate or compete with members of other species via quorum sensing (QS). QS is a process by which microbes produce, secrete, and subsequently detect small molecules called autoinducers (AIs) to assess their local population density. We explore the competitive advantage of QS through agent-based simulations of a spatial model in which colony expansion via extracellular matrix production provides greater access to a limiting diffusible nutrient. We note a significant difference in results based on whether AI production is constitutive or limited by nutrient availability: If AI production is constitutive, simple QS-based matrix-production strategies can be far superior to any fixed strategy. However, if AI production is limited by nutrient availability, QS-based strategies fail to provide a significant advantage over fixed strategies. To explain this dichotomy, we derive a novel biophysical limit for the dynamic range of nutrient-limited AI concentrations in biofilms. This range is remarkably small (less than 10-fold) for the realistic case in which a growth-limiting diffusible nutrient is taken up within a narrow active growth layer. This biophysical limit implies that for QS to be most effective in biofilms, AI production should be a protected function not directly tied to metabolism. Significance StatementBiofilms are a ubiquitous form of bacterial community. Within biofilms, bacteria communicate via chemical signals in a process called quorum sensing (QS). However, if signal production is nutrient-limited, then the nutrient-deficient interior of a biofilm cannot contribute to QS, which limits the ability of bacteria to assess their own population and behave accordingly. Numerical simulations of competitions among biofilm bacteria led us to discover a novel biophysical limit on the efficacy of nutrient-limited QS. In view of this limit, we conclude that to be most effective, QS signal production should be a prized function that is not metabolically slaved.

biophysics↗

bioRxiv2020

Online biophysical predictions for SARS-CoV-2 proteins

The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. We here present a website (http://sars2.bio2byte.be/) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, {beta}-sheet aggregation and protein-protein interaction propensities. These predictions attempt to capture the emergent properties of the proteins, so the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide an indication of the biophysical variation that is observed in homologous proteins, which give an indication of the limits of the functionally relevant biophysical behaviour of these proteins. With this website, we therefore hope to provide researchers with further clues on the behaviour of SARS-CoV-2 proteins.

biophysics↗

bioRxiv2020

The biophysical basis of protein domain compatibility in ion channels

Understanding the biophysical mechanisms that govern the combination of protein domains into viable proteins is essential for advancing synthetic biology and biomedical engineering. Here, we use massively parallel genotype/phenotype assays to determine cell surface expression of over 300,000 variants of the inward rectifier K+ channel Kir2.1 recombined with hundreds of protein motifs. We use machine learning to derive a quantitative biophysical model and practical rules for domain recombination. Insertional fitness depends on nonlinear interactions between the biophysical properties of inserted motifs and the recipient protein, which adds a new dimension to the rational design of fusion proteins. Insertion maps reveal a generalizable hierarchical organization of Kir2.1 and several other ion channels that balances stability needed for folding and dynamics required for function. SummaryMassively parallel assays reveal interactions between donor domains and recipient proteins govern domain compatibility

biophysics↗

bioRxiv2021

Graph Signal Processing on protein residue networks helps in studying its biophysical properties

Understanding the physical and chemical properties of proteins is vital, and many efforts have been made to study the emergent properties of the macro-molecules as a combination of long chains of amino acids. Here, we present a graph signal processing based approach to model the biophysical property of proteins. For each protein inter-residue proximity-based network is used as basis graph and the respective amino acid properties are used as node-signals. Signals on nodes are decomposed on networks Laplacian eigenbasis using graph Fourier transformations. We found that the intensity in low-frequency components of graph signals of residue features could be used to model few biophysical properties of proteins. Specifically, using our approach, we could model protein folding-rate, globularity and fraction of alpha-helices and beta-sheets. Our approach also allows amalgamation of different types of chemical and graph theoretic properties of residue to be used together in a multi-variable regression model to predict biophysical properties.

biophysics↗

bioRxiv2022

Biophysical characterisation of the structure of a SARS-CoV-2 self-amplifying - RNA (saRNA) vaccine

The current SARS-Covid-2 pandemic has led to an acceleration of messenger - ribonucleic acid (mRNA) vaccine technology. The development of production processes for these large mRNA molecules, especially self-amplifying mRNA (saRNA) has required concomitant development of analytical characterisation techniques. Characterising the purity, shape and structure of these biomolecules is key to their successful performance as drug products. This paper describes the biophysical characterisation of the Imperial College London Self-amplifying viral RNA vaccine (IMP-1) developed for SARS-CoV-2. A variety of analytical techniques have been used to characterise the IMP-1 RNA molecule. In this paper we use UV spectroscopy, dynamic light scattering (DLS), size-exclusion chromatography small angle scattering (SEC-SAXS) and circular dichroism (CD) to determine key biophysical attributes of IMP-1. Each technique provides important information about the concentration, size, shape, structure and purity of the molecule. Statement of significanceThis paper is highly significant as it provides a prescient biophysical characterisation of an efficacious Sars-Cov-2 vaccine self-amplifying (sa)RNA molecule. RNA vaccines have been a major scientific breakthrough of the Covid-19 pandemic. saRNA is a further development of conventional mRNA vaccines, amplifying the RNA of interest in the cell, allowing the vaccine to be administered at lower dosages. These new biologics are distinct from previous biologics and have required distinct analytical characterisation. The analytics described herein provide detailed information on the size, shape, and structure of the RNA molecule. This paper is therefore an important step in characterising large saRNA biological relevant molecules.

biophysics↗

bioRxiv2023

Modulation of Biophysical Properties of Nucleocapsid Protein in the Mutant Spectrum of SARS-CoV-2

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also exhibiting functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

biophysics↗

bioRxiv2024

Lipid loss and compositional change during preparation of liposomes by common biophysical methods

Liposomes are widely used as model lipid membrane platforms in many fields, ranging from basic biophysical studies to drug delivery and biotechnology applications. Various methods exist to prepare liposomes, but common procedures include thin-film hydration followed by extrusion, freeze-thaw, and/or sonication. These procedures have the potential to produce liposomes at specific concentrations and membrane compositions, and researchers often assume that the concentration and composition of their liposomes are similar to, if not identical, to what would be expected if no lipid loss occurred during preparation. However, lipid loss and concomitant biasing of lipid composition can in principle occur at any preparation step due to nonideal mixing, lipid-surface interactions, etc. Here, we report a straightforward method using HPLC-ELSD to quantify the lipid concentration and membrane composition of liposomes, and apply that method to study the preparation of simple POPC/cholesterol liposomes. We examine many common steps in liposome formation, including vortexing during re-suspension, hydration of the lipid film, extrusion, freeze-thaw, sonication, and the percentage of cholesterol in the starting mixture. We found that the resuspension step can play an outsized role in determining the overall lipid loss (up to [~]50% under seemingly rigorous procedures). The extrusion step yielded smaller lipid losses ([~]10-20%). Freeze-thaw and sonication could both be employed to improve lipid yields. Hydration times up to 60 minutes and increasing cholesterol concentrations up to 50 mole% had little influence on lipid recovery. Fortunately, even conditions with large lipid loss did not substantially influence the target membrane composition more than [~]5% under the conditions we tested. From our results, we identify best practices for producing maximum levels of lipid recovery and minimal changes to lipid composition during liposome preparation protocols. We expect our results can be leveraged for improved preparation of model membranes by researchers in many fields. Statement of SignificanceLiposomes are spherical lipid membranes that can be prepared by a variety of biophysical techniques. Researchers use liposomes in a variety of ways, including fundamental biophysical studies of lipid membranes, in drug delivery, drug formulation, and other biotechnology applications. In this report, we study the process to prepare liposomes by several common techniques and validate how reliable each technique is at producing consistent liposome concentrations and lipid compositions. We identify best practices for researchers to produce reliable liposome preparations.

biophysics↗

bioRxiv2024

Examining the Biophysical Properties of the Inner Membrane of Gram-Negative ESKAPE Pathogens

The World Health Organization has identified multidrug-resistant bacteria as a serious global health threat. Gram-negative bacteria are particularly prone to antibiotic resistance, and their high rate of antibiotic resistance has been suggested to be related to the complex structure of their cell membrane. The outer membrane of Gram-negative bacteria contains lipopolysaccharides that protect the bacteria against threats such as antibiotics, while the inner membrane houses 20-30% of the bacterial cellular proteins. Given the cell membranes critical role in bacterial survival, antibiotics targeting the cell membrane have been proposed to combat bacterial infections. However, a deeper understanding of the biophysical properties of the bacterial cell membrane is crucial for developing effective and specific antibiotics. In this study, Martini coarse-grain molecular dynamics simulations were used to investigate the interplay between membrane composition and biophysical properties of the inner membrane across four pathogenic bacterial species: Klebsiella pneumoniae, Pseudomonas aeruginosa, Enterobacter cloacae, and Escherichia coli. The simulations indicate the impact of species-specific membrane composition on dictating the overall membrane properties. Specifically, cardiolipin concentration in the inner membrane is a key factor influencing the membrane features. Model membranes with varying concentrations of bacterial lipids (phosphatidylglycerol, phosphatidylethanolamine, and cardiolipin) further support the significant role of cardiolipin in determining the membrane biophysical properties. The bacterial inner membrane models developed in this work pave the way for future simulations of bacterial membrane proteins and for simulations investigating novel strategies aimed at disrupting the bacterial membrane to treat antibiotic-resistant infections.

biophysics↗

bioRxiv2024

Biophysical and biochemical evidence for the role of acetate kinases (AckAs) in an acetogenic pathway in pathogenic spirochetes

Unraveling the metabolism of Treponema pallidum is a key component to understanding the pathogenesis of the human disease that it causes, syphilis. For decades, it was assumed that glucose was the sole carbon/energy source for this parasitic spirochete. But the lack of citric-acid-cycle enzymes suggested that alternative sources could be utilized, especially in microaerophilic host environments where glycolysis should not be robust. Recent bioinformatic, biophysical, and biochemical evidence supports the existence of an acetogenic energy-conservation pathway in T. pallidum and related treponemal species. In this hypothetical pathway, exogenous D-lactate can be utilized by the bacterium as an alternative energy source. Herein, we examined the final enzyme in this pathway, acetate kinase (named TP0476), which ostensibly catalyzes the generation of ATP from ADP and acetyl-phosphate. We found that TP0476 was able to carry out this reaction, but the protein was not suitable for biophysical and structural characterization. We thus performed additional studies on the homologous enzyme (75% amino-acid sequence identity) from the oral pathogen Treponema vincentii, TV0924. This protein also exhibited acetate kinase activity, and it was amenable to structural and biophysical studies. We established that the enzyme exists as a dimer in solution, and then determined its crystal structure at a resolution of 1.36 [A], showing that the protein has a similar fold to other known acetate kinases. Mutation of residues in the putative active site drastically altered its enzyme activity. A second crystal structure of TV0924 in the presence of AMP (at 1.3 [A] resolution) provided insight into the binding of one of the enzymes substrates. On balance, this evidence strongly supported the roles of TP0476 and TV0924 as acetate kinases, buttressing the existence of the acetogenic pathway in pathogenic treponemes.

biophysics↗