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bioRxiv2026

From Big Bang to Biochemistry: Entropy-Oriented Mechanics and Information Force Fields as a Unifying Framework for the Origin of Carbon-Based Life

The emergence of amino acids (AAs) and nucleobases (NBs) across meteorites, interstellar ices, and laboratory shock experiments presents a paradox: why do these specific molecular motifs--a minuscule subset of organic chemistrys combinatorial space--appear repeatedly across diverse environments, in the absence of biological selection? We identify a physical mechanism, prebiotic selection, which biases driven chemical systems toward configurations with high stationary probability p*(x) under sustained entropy flux. The bias is quantified by an information quasi-potential {Phi}I (x) = - ln p*(x), entering the overdamped Langevin dynamics O_FD O_INLINEFIG[Formula 1]C_INLINEFIGM_FD(1)C_FD where {Sigma} is the local entropy production rate (Schnakenberg 1976). {Phi}I is defined self-consistently via the full non-equilibrium stationary density, avoiding the circularity of identifying it with a scalar potential. Two central theorems underlie the framework. Theorem 1 establishes that {nabla}{Sigma} and {nabla}{Phi}I are generically linearly independent off equilibrium, so the dynamics is genuinely two-field. Theorem 2 (structural constraints on single-field gradient dynamics) shows that single-field models on compact manifolds (i) produce yield curves that are at most unimodal under linear driving, and (ii) combine disjoint perturbations additively, giving superlinearity factor S = 1 + O(||{delta} V ||2). The observed superlinear synergy of Ferris et al. (1996) lies far outside this perturbative bound and therefore requires the two-field structure of EOM-IFF; the non-monotonic peak of Blank et al. (2001) is consistent with two-field dynamics and also with single-field dynamics in the unimodal-with-peak case of Theorem 2 part 1, so it does not by itself discriminate. From these results, we: (i) define a formal substrate-minimal criterion for prebiotic selection; (ii) show consistency with the non-monotonic shock-synthesis yield of Blank et al. (2001) (R2 = 0.885, peak at P* = 28.4 {+/-} 1.4 GPa); (iii) show consistency with the superlinear clay-catalysed RNA polymerisation of Ferris et al. (1996) (synergy factor S {approx} 5.75, robust under {+/-}1-nucleotide measurement uncertainty); and (iv) state two further falsifiable predictions awaiting dedicated experimental tests. Every lemma and theorem is accompanied by explicit assumptions, regime of validity, and regime of failure; the frameworks scope is what it claims, not more. Prebiotic selection is identified as a physical process distinct from and prior to biological selection, offering a unified account of chemical convergence in carbon-nitrogen chemistry under sustained entropy flux.

biophysics↗

bioRxiv2026

Microfluidic Devices for Imaging and Biochemistry Analysis of Microbes Under Mechanical Pressure

Growth-induced pressure arises when proliferating cell populations are confined within rigid microenvironments and is increasingly recognized as a determinant of microbial physiology in soils, biofilms, and host tissues. We present two complementary microfluidic devices that confine microbes within polydimethylsiloxane (PDMS) chambers and apply defined, optically read-out growth-induced pressure of up to 1.5 MPa. The first, the self-closing (SC) chip, confines cells in multiple small chambers accommodating hundreds of cells; it provides excellent nutrient supply and rapid medium exchange, supports single-cell imaging, and allows multiplexing of cellular or chemical conditions. The second, the pressure-recovery (PR) chip, confines cells in a single 5 cm-long chamber accommodating hundreds of thousands of cells; a scalpel-cut step recovers live cells from the channel within minutes for bulk biochemical assays. The two devices share a single two-layer soft-lithography fabrication process and a common brightfield wall-displacement pressure readout. Together, they enable single-cell imaging and bulk biochemical analysis under matched, defined pressure conditions. We illustrate the protocol with two representative validations: rapid {beta}-estradiol-induced transcription in the SC chip, in which nascent transcription foci appear within 5 min independently of the applied pressure, and the PR chip, which produces uniform growth-induced pressure along the entire 5 cm channel and recovers between 0.1x10^6 and 1x10^6 cells per device in a pressure-tunable manner.

biophysics↗

bioRxiv2019

Use of polygenic risk scores and other molecular markers to enhance cardiovascular risk prediction: Prospective cohort study and modelling analysis

BackgroundThere is debate about the value of adding information on genetic and other molecular markers to conventional cardiovascular disease (CVD) risk predictors.\n\nMethodsUsing data on 306,654 individuals without a history of CVD from UK Biobank, we calculated measures of risk-discrimination and reclassification upon addition of polygenic risk scores (PRS) and a panel of 27 clinical biochemistry markers to a conventional risk prediction model (i.e., including age, sex, systolic blood pressure, smoking status, history of diabetes, total cholesterol and HDL cholesterol). We then modelled implications of initiating guideline-recommended statin therapy after the assessment of molecular markers for a UK primary-care setting.\n\nFindingsThe C-index was 0.710 (95% CI, 0.703-0.717) for a CVD prediction model containing conventional risk predictors alone. The C-index increased by similar amounts when adding information on PRS or biochemistry markers (0.011 and 0.014, respectively; P<0.001), and it increased still further (0.022; P<0.001) when information on both was combined. Among cases and controls, continuous net reclassification improvements were about 12% and 19%, respectively, when both PRS and biochemistry markers were added. If PRS and biochemistry markers were to be assessed in the entire primary care population aged 40-75, then it could help prevent one additional CVD event for every 893 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5-10%) 10-year CVD risk could help prevent one additional CVD event for every 233 individuals screened. This targeted strategy could help reclassify 16% of the intermediate-risk group to the high-risk (i.e., [≥]10%) category, preventing 11% more CVD events than conventional risk prediction.\n\nInterpretationAdding information on both PRS and selected biochemistry markers moderately enhanced CVD predictive accuracy and could improve primary prevention of CVD. However, our modelling suggested that targeted assessment of molecular markers among individuals at intermediate-risk would be more efficient than blanket approaches.

genomics↗

bioRxiv2020

The ModelSEED Database for the integration of metabolic annotations and the reconstruction, comparison, and analysis of metabolic models for plants, fungi, and microbes

For over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical "Rosetta Stone" to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

systems biology↗

bioRxiv2020

Structural control of fibrin bioactivity by mechanical deformation

Fibrin is a fibrous protein network that entraps blood cells and platelets to form blood clots following vascular injury. As a biomaterial, fibrin acts a biochemical scaffold as well as a viscoelastic patch that resists mechanical insults. The biomechanics and biochemistry of fibrin have been well characterized independently, showing that fibrin is a hierarchical material with numerous binding partners. However, comparatively little is known about how fibrin biomechanics and biochemistry are coupled: how does fibrin deformation influence its biochemistry at the molecular level? In this study, we show how mechanically-induced molecular structural changes in fibrin affect fibrin biochemistry and fibrin-platelet interaction. We found that tensile deformation of fibrin lead to molecular structural transitions of -helices to {beta}-sheets, which reduced binding of tissue plasminogen activator (tPA), an enzyme that initiates fibrinolysis, at the network and single fiber level. Moreover, binding of tPA and Thioflavin T (ThT), a commonly used {beta}-sheet marker, was primarily mutually exclusive such that tPA bound to native (helical) fibrin whereas ThT bound to strained fibrin. Finally, we demonstrate that conformational changes in fibrin suppressed the biological activity of platelets on mechanically strained fibrin due to attenuated IIb{beta}3 integrin binding. Our work shows that mechanical strain regulates fibrin molecular structure and fibrin biological activity in an elegant mechano-chemical feedback loop, which likely influences fibrinolysis and wound healing kinetics.

bioengineering↗

bioRxiv2026

Form I and II Rubiscos Exhibit Temperature Dependent Carbon Kinetic Isotope Effects

Nearly all carbon on Earth today is fixed by the enzyme ribulose-1,5-bisphopshate carboxylase/oxygenase ( rubisco), which converts carbon dioxide (CO2) to sugar phosphates. All rubiscos measured thus far display a kinetic isotope effect (KIE) where 12CO2 is fixed at a faster rate than 13CO2. The relationship between rubiscos KIE and the carbon isotope composition of plants, algae, and organic matter is central to many fields in the Earth sciences, plant biology, and biochemistry. Currently, all applications assume that the KIE does not vary with temperature. Here, we examine this assumption experimentally with in vitro KIE measurements of two rubiscos from phylogenetically distinct host organisms and rubisco protein clades - a Form I rubisco from the plant, Spinacia oleracea (spinach) and a Form II rubisco from the bacterium Rhodosprillium rubrum. We that find that both KIEs decrease linearly by [~]4.5{per thousand} from 10-35{degrees}C with statistically indistinguishable slopes. We place these results into biological and geologic contexts by comparing them to observed variations in the carbon isotope composition of modern terrestrial plants and marine organic carbon, the geologic carbon isotope record, and rubiscos biochemistry. We show that the measured temperature dependencies are sufficiently large to impact our interpretations of the enzymatic processes that drive variations in rubisco KIEs, as well as applications of stable carbon isotopes in the Earth and biological sciences. Significance StatementThe carbon isotope composition of plants, algae, and organic matter are interpreted with models that assume the kinetic isotope effect of the carbon-fixing enzyme rubisco is temperature-independent, even though temperature varies by tens of degrees across the Earth today and in the past. Here, we demonstrate that the kinetic isotope effect of rubisco is temperature-dependent, suggesting that some of this isotopic variation may be due to intrinsic enzyme properties alone. In addition, though the rubiscos we measured are from diverse organisms (plant vs. bacteria), their KIEs show statistically indistinguishable temperature dependencies. This data forms the basis for future thermodynamic models on rubisco biochemistry.

biochemistry↗

bioRxiv2021

Scaling laws in enzyme function reveal a new kind of biochemical universality

All life on Earth is unified by its use of a shared set of component chemical compounds and reactions, providing a detailed model for universal biochemistry. However, this notion of universality is specific to currently observed biochemistry and does not allow quantitative predictions about examples not yet observed. Here we introduce a more generalizable concept of biochemical universality, more akin to the kind of universality discussed in physics. Using annotated genomic datasets including an ensemble of 11955 metagenomes and 1282 archaea, 11759 bacteria and 200 eukaryotic taxa, we show how four of the major enzyme functions - the oxidoreductases, transferases, hydrolases and ligases - form universality classes with common scaling behavior in their relative abundances observed across the datasets. We verify these universal scaling laws are not explained by the presence of compounds, reactions and enzyme functions shared across all known examples of life. We also demonstrate how a consensus model for the last universal common ancestor (LUCA) is consistent with predictions from these scaling laws, with the exception of ligases and transferases. Our results establish the existence of a new kind of biochemical universality, independent of the details of the component chemistry, with implications for guiding our search for missing biochemical diversity on Earth, or other for any biochemistries that might deviate from the exact chemical make-up of life as we know it, such as at the origins of life, in alien environments, or in the design of synthetic life.

systems biology↗

bioRxiv2022

Cinemeducation improves early clinical exposure to Inborn Errors of Metabolism

BackgroundCinemeducation has been shown to be an effective tool to help the students develop humanistic skills. However, there is a dearth of studies to find out if this can also be utilized to improve the interest and satisfaction of students learning about rare diseases such as the Inborn Errors of Metabolism. The aim was to introduce cinemeducation as part of early clinical exposure in teaching the inborn errors of metabolism for first-year MBBS students in the Department of Biochemistry. The Objectives were 1) to develop, validate and assess the feasibility of structured modules on Cinemeducation in teaching the Inborn Errors of Metabolism, 2) to study the impact of Cinemeducation on the satisfaction, interest, and academic performance of first-year MBBS students and 3) to study the perception of students and faculty towards Cinemeducation MethodsThis was a quasi-experimental, cross-over study involving 100 MBBS students in the Department of Biochemistry, AIIMS Kalyani. Movie clips prepared from Lorenzos Oil (for Lipid metabolism) and Extraordinary Measures (for Lysosomal Storage Disease) and corresponding paper-based cases; questionnaires and feedback forms were validated by expert group review. Analysis of quantitative data was performed in GraphPad, MS Excel and MAXQDA2022 software. Findings, Discussions and ConclusionsStudents academic performance was found to be improved in the groups where the Extraordinary Measures movie was used for Cinemeducation. Both learners and facilitators were satisfied with Cinemeducation. Cinemeducation was effectively introduced for teaching Inborn Errors of Metabolism in the Department of Biochemistry.

scientific communication and education↗

bioRxiv2024

Resource allocation in biochemically structured metabolic networks

Microbes tune their metabolism to environmental challenges by changing protein expression levels, metabolite concentrations, and reaction rates simultaneously. Here, we establish an analytical model for microbial resource allocation that integrates enzyme biochemistry and the global architecture of metabolic networks. We describe the production of protein biomass from external nutrients in pathways of Michaelis-Menten enzymes and compute the resource allocation that maximizes growth under constraints of mass conservation and metabolite dilution by cell growth. This model predicts generic patterns of growth-dependent microbial resource allocation to proteome and metabolome. In a nutrient-rich medium, optimal protein expression depends primarily on the biochemistry of individual synthesis steps, while metabolite concentrations and fluxes decrease along successive reactions in a metabolic pathway. Under nutrient limitation, individual protein expression levels change linearly with growth rate, the direction of change depending again on the enzymes biochemistry. Metabolite levels and fluxes show a stronger, nonlinear decline with growth rate. We identify a simple, metabolite-based regulatory logic by which cells can be tuned to near-optimal growth. Finally, our model predicts evolutionary stable states of metabolic networks, including local biochemical parameters and the global metabolite mass fraction, in tune with empirical data.

systems biology↗

bioRxiv2024

Parental kynurenine 3-monooxygenase genotype in mice directs sex-specific behavioral outcomes in offspring

BackgroundDisruptions in brain development can impact behavioral traits and increase the risk of neurodevelopmental conditions such as autism spectrum disorder, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder, often in sex-specific ways. Dysregulation of the kynurenine pathway (KP) of tryptophan metabolism has been implicated in cognitive and neurodevelopmental disorders. Increased brain kynurenic acid (KYNA), a neuroactive metabolite implicated in cognition and sleep homeostasis, and variations in the Kmo gene, which encodes kynurenine 3-monooxygenase (KMO), have been identified in these patients. We hypothesize that parental Kmo genetics influence KP biochemistry, sleep behavior and brain energy demands, contributing to impairments in cognition and sleep in offspring through the influence of parental genotype and genetic nurture mechanisms. MethodsA mouse model of partial Kmo deficiency, Kmo heterozygous (HET-Kmo+/-), was used to examine brain KMO activity, KYNA levels, and sleep behavior in HET-Kmo+/- compared to wild-type control (WT-Control) mice. Brain mitochondrial respiration was assessed, and KP metabolites and corticosterone levels were measured in breast milk. Behavioral assessments were conducted on wild-type offspring from two parental groups: i) WT-Control from WT-Control parents, ii) wild-type Kmo (WT-Kmo+/+) from Kmo heterozygous parents (HET-Kmo+/-). All mice were C57Bl/6J background strain. Adult female and male offspring underwent behavioral testing for learning, memory, anxiety-like behavior and sleep-wake patterns. ResultsHET-Kmo+/- mice exhibited reduced brain KMO activity, increased KYNA levels, and disrupted sleep architecture and electroencephalogram (EEG) power spectra. Mitochondrial respiration (Complex I and complex II activity) and electron transport chain protein levels were impaired in the hippocampus of HET-Kmo+/- females. Breast milk from HET-Kmo+/- mothers increased kynurenine exposure during lactation but corticosterone levels were unchanged. Compared to WT-Control offspring, WT-Kmo+/+ females showed impaired spatial learning, heightened anxiety, reduced sleep and abnormal EEG spectral power. WT-Kmo+/+ males had deficits in reversal learning but no sleep disturbances or anxiety-like behaviors. ConclusionsThese findings suggest that Kmo deficiency impacts KP biochemistry, sleep behavior, and brain mitochondrial function. Even though WT-Kmo+/+ inherit identical genetic material as WT-Control, their development might be shaped by the parents physiology, behavior, or metabolic state influenced by their Kmo genotype, leading to phenotypic sex-specific differences in offspring. Plain English summaryInteractions between genetic and environmental factors are carefully regulated during the intricate process of brain development. While genetic information is directly inherited from parents, emerging evidence suggests that parental genetic factors can also shape the environment influencing childrens development in a sex-specific ways. Disruptions in brain development can impact cognitive and behavioral traits and increase the risk of neurodevelopmental conditions such as autism spectrum disorder, attention-deficit/hyperactivity disorder, schizophrenia, and bipolar disorder. This study explored how kynurenine 3-monooxygenase (Kmo) genotype affects female and male mice, focusing on potential sex-specific behavioral changes in offspring born to parents with a genetic disruption in Kmo. We found that female and male mice with partial Kmo deficiency experienced reduced sleep and increased sleep pressure. In female mice, Kmo deficiency impaired mitochondrial energy production in the brain. We also observed alterations in tryptophan metabolism and nutrient composition in the breast milk of Kmo-deficient females. In adult offspring born to Kmo-deficient parents, females exhibited learning difficulties, heightened anxiety-like behaviors, and sleep disturbances. In contrast, male offspring showed mild cognitive impairments but no major sleep issues. These findings highlight that parental Kmo genotype can influence sex differences in cognitive and sleep-related behaviors in offspring. This underscores the importance of considering parental genetic factors when studying neurodevelopmental disorders and associated behavioral outcomes. HighlightsO_LIMice with a partial Kmo deficiency, Kmo heterozygous (HET-Kmo+/-), were used to evaluate the impact of parental Kmo genetics on kynurenine pathway biochemistry, brain mitochondrial function, sleep patterns, and cognitive performance. C_LIO_LIFemale and male HET-Kmo+/- mice exhibited elevated brain KYNA levels and had reduced sleep, prolonged wakefulness, and altered spectra power during sleep. In HET-Kmo+/- females, hippocampal mitochondrial respiration and electron transport chain protein levels were significantly altered. Breast milk from HET-Kmo+/- females increased kynurenine exposure during lactation to offspring, including their wild-type (WT-Kmo+/+) offspring. C_LIO_LISex differences were observed in learning and memory, anxiety-like behavior, and sleep-wake patterns between WT-Control, from wild-type parents, and WT-Kmo+/+ offspring from HET-Kmo+/- parents. Female WT-Kmo+/+ offspring had impaired spatial learning, increased anxiety-like behavior, reduced sleep, but elevated delta power during sleep. Male WT-Kmo+/+ offspring exhibited deficits in reversal learning only and not sleep impairments. C_LI

neuroscience↗

bioRxiv2020

On the diversity of F420-dependent oxidoreductases: a sequence- and structure-based classification

The F420 deazaflavin cofactor is an intriguing molecule as it structurally resembles the canonical flavin cofactor, although biochemically behaves as a nicotinamide cofactor. Since its discovery, numerous enzymes relying on it have been described. The known deazaflavoproteins are taxonomically restricted to Archaea and Bacteria. The biochemistry of the deazaflavoenzymes is diverse and they exhibit some degree of structural variability as well. In this study a thorough sequence and structural homology evolutionary analysis was performed in order to generate an overarching classification of all known F420-dependent oxidoreductases. Five different superfamilies are described: Superfamily I, TIM-barrel F420-dependent enzymes; Superfamily II, Rossmann fold F420-dependent enzymes; Superfamily III, {beta}-roll F420-dependent enzymes; Superfamily IV, SH3 barrel F420-dependent enzymes and Superfamily V, 3 layer {beta}{beta} sandwich F420-dependent enzymes. This classification aims to be the framework for the identification, the description and the understanding the biochemistry of novel deazaflavoenzymes.

biochemistry↗

bioRxiv2021

The mechanism of MinD stability modulation by MinE in Min protein dynamics

The patterns formed both in vivo and in vitro by the Min protein system have attracted much interest because of the complexity of their dynamic interactions given the apparent simplicity of the component parts. Despite both the experimental and theoretical attention paid to this system, the details of the biochemical interactions of MinD and MinE, the proteins responsible for the patterning, are still unclear. For example, no model consistent with the known biochemistry has yet accounted for the observed dual role of MinE in the membrane stability of MinD. Until now, a statistical comparison of models to the time course of Min protein concentrations on the membrane has not been carried out. Such an approach is a powerful way to test existing and novel models that are difficult to test using a purely experimental approach. Here, we extract time series from previously published fluorescence microscopy time lapse images of in vitro experiments and fit two previously described and one novel mathematical model to the data. We find that the novel model, which we call the Asymmetric Activation with Bridged Stability Model, fits the time-course data best. It is also consistent with known biochemistry and explains the dual MinE role via MinE-dependent membrane stability that transitions under the influence of rising MinE to membrane instability with positive feedback. Our results reveal a more complex network of interactions between MinD and MinE underlying Min-system dynamics than previously considered.

biochemistry↗

bioRxiv2021

REVOLVER: a low-cost automated protein purifier based on parallel preparative gravity column workflows

Protein purification is a ubiquitous operation in biochemistry and life sciences and represents a key step to producing purified proteins for research (understanding how proteins work) and various applications. The need for scalable and parallel protein purification systems keeps growing due to the increase in throughput in the production of recombinant proteins and in the ever-growing scale of biochemistry research. Therefore, automating the process to handle multiple samples in parallel with minimal human intervention is highly desirable; yet only a handful of such tools have been developed, all of which are closed source and expensive. To address this challenge, we present REVOLVER, a 3D-printed programmable and automatic protein purification system based on gravity-column workflows and controlled by Arduino boards that can be built for under $130 USD. REVOLVER completes a full protein purification process with almost no human intervention and yields results equivalent to those obtained by an experienced biochemist when purifying a real-world protein sample. We further present and describe MULTI-VOLVER, a scalable version of the REVOLVER that allows for parallel purification of up to six samples and can be built for under $250 USD. Both systems will be useful to accelerate protein purification and ultimately link them to bio-foundries for protein characterization and engineering. Specifications Table O_TBL View this table: org.highwire.dtl.DTLVardef@13fff66org.highwire.dtl.DTLVardef@98aa2forg.highwire.dtl.DTLVardef@cd2e70org.highwire.dtl.DTLVardef@a3bd86org.highwire.dtl.DTLVardef@1efc67d_HPS_FORMAT_FIGEXP M_TBL C_TBL

biochemistry↗

bioRxiv2022

Structural and functional analysis of the human Cone-rod homeobox transcription factor

The cone-rod homeobox (CRX) protein is a critical K50 homeodomain transcription factor responsible for the differentiation and maintenance of photoreceptor neurons in the vertebrate retina. Mutant alleles in the human gene encoding CRX result in a variety of distinct blinding retinopathies, including retinitis pigmentosa, cone-rod dystrophy, and Leber congenital amaurosis. Despite the success of using in vitro biochemistry, animal models, and genomics approaches to study this clinically relevant transcription factor over the past 24 years since its initial characterization, there are no high-resolution structures in the published literature for the CRX protein. In this study, we use bioinformatic approaches and small-angle x-ray scattering (SAXS) structural analysis to further understand the biochemical complexity of the human CRX homeodomain (CRX-HD). We find that the CRX-HD is a compact, globular monomer in solution that can specifically bind functional cis-regulatory elements encoded upstream of retina -specific genes. This study presents the first structural analysis of CRX, paving the way for a new approach to studying the biochemistry of this protein and its disease-causing mutant protein variants.

biochemistry↗

bioRxiv2024

Accurate in silico predictions of modified RNA interactions to a prototypical RNA-binding protein with {lambda}-dynamics

RNA-binding proteins shape biology through their widespread functions in RNA biochemistry. Their function requires the recognition of specific RNA motifs for targeted binding. These RNA binding elements can be composed of both unmodified and chemically modified RNAs, of which over 170 chemical modifications have been identified in biology. Unmodified RNA sequence preferences for RNA-binding proteins have been widely studied, with numerous methods available to identify their preferred sequence motifs. However, only a few techniques can detect preferred RNA modifications, and no current method can comprehensively screen the vast array of hundreds of natural RNA modifications. Prior work demonstrated that {lambda}-dynamics is an accurate in silico method to predict RNA base binding preferences of an RNA-binding antibody. This work extends that effort by using {lambda}-dynamics to predict unmodified and modified RNA binding preferences of human Pumilio, a prototypical RNA binding protein. A library of RNA modifications was screened at eight nucleotide positions along the RNA to identify modifications predicted to affect Pumilio binding. Computed binding affinities were compared with experimental data to reveal high predictive accuracy. In silico force field accuracies were also evaluated between CHARMM and Amber RNA force fields to determine the best parameter set to use in binding calculations. This work demonstrates that {lambda}-dynamics can predict RNA interactions to a bona fide RNA-binding protein without the requirements of chemical reagents or new methods to experimentally test binding at the bench. Advancing in silico methods like {lambda}-dynamics will unlock new frontiers in understanding how RNA modifications shape RNA biochemistry.

biochemistry↗

bioRxiv2018

Quantitative variations of ADF/cofilin’s multiple actions on actin filaments with pH

Actin Depolymerizing Factor (ADF)/cofilin is the main protein family promoting the disassembly of actin filaments, which is essential for numerous cellular functions. ADF/cofilin proteins disassemble actin filaments through different reactions, as they bind to their sides, sever them, and promote the depolymerization of the resulting ADF/cofilin-saturated filaments. Moreover, the efficiency of ADF/cofilin is known to be very sensitive to pH. ADF/cofilin thus illustrates two challenges in actin biochemistry: separating the different regulatory actions of a single protein, and characterizing them as a function of specific biochemical conditions. Here, we investigate the different reactions of ADF/cofilin on actin filaments, over four different values of pH ranging from pH 6.6 to pH 7.8, using single filament microfluidics techniques. We show that lowering pH reduces the effective filament severing rate by increasing the rate at which filaments become saturated by ADF/cofilin, thereby reducing the number of ADF/cofilin domain boundaries, where severing can occur. The severing rate per domain boundary, however, remains unchanged at different pH values. The ADF/cofilin-decorated filaments (refered to as \"cofilactin\" filaments) depolymerize from both ends. We show here that, at physiological pH (pH 7.0 to 7.4), the pointed end depolymerization of cofilactin filaments is barely faster than that of bare filaments. In contrast, cofilactin barbed ends undergo an \"unstoppable\" depolymerization (depolymerizing for minutes despite the presence of free actin monomers and capping protein in solution), throughout our range of pH. We thus show that, at physiological pH, the main contribution of ADF/cofilin to filament depolymerization is at the barbed end.\n\nA number of key cellular processes rely on the proper assembly and disassembly of actin filament networks 1. The central regulator of actin disassembly is the ADF/cofilin protein family 2, 3, which comprises three isoforms in mammals: cofilin-1 (cof1, found in nearly all cell types), cofilin-2 (cof2, found primarily in muscles) and Actin Depolymerization Factor (ADF, found mostly in neurons and epithelial cells). We refer to them collectively as \"ADF/cofilin\".\n\nOver the years, the combined efforts of several labs have led to the following understanding of actin filament disassembly by ADF/cofilin. Molecules of ADF/cofilin bind stoechiometrically 4, 5 to the sides of actin filaments, with a strong preference for ADP-actin subunits 6-10. Though ADF/cofilin molecules do not contact each other 11, they bind in a cooperative manner, leading to the formation of ADF/cofilin domains on the filaments 5, 7, 9, 12, 13. Compared to bare F-actin, the filament portions decorated by ADF/cofilin (refered to as \"cofilactin\") are more flexible 14, 15 and exhibit a shorter right-handed helical pitch, with a different subunit conformation 11, 16-19. Thermal fluctuations are then enough to sever actin filaments at (or near) domain boundaries8, 9, 13, 20, 21. Cofilactin filaments do not sever, but depolymerize from both ends 13 thereby renewing the actin monomer pool.\n\nADF/cofilin thus disassembles actin filaments through the combination of different actions. As such, it vividly illustrates a current challenge in actin biochemistry: identifying and quantifying the multiple reactions involving a single protein. This is a very difficult task for bulk solution assays, where a large number of reactions take place simultaneously, and single-filament techniques have played a key role in deciphering ADF/cofilins actions 9, 13, 20, 22-24. In particular, the microfluidics-based method that we have developed over the past years, is a powerful tool for such investigations 25. It has recently allowed us to quantify the kinetics of the aforementioned reactions, and to discover that ADF/cofilin-saturated filament (cofilactin) barbed ends can hardly stop depolymerizing, even when ATP-G-actin and capping protein are present in solution 13.\n\nIn addition, ADF/cofilin is very sensitive to pH 4, 5, 26-29. In cells, pH can be a key regulatory factor 30. It can vary between compartments, between cell types, and be specifically modulated. We can consider that a typical cytoplasmic pH would be comprised between 7.0 and 7.4. Recently, we have quantified the different reactions involving ADF/cofilin at pH 7.8 13, leaving open the question of how these reaction rates are indivdually affected by pH variations. For instance, it has been reported that ADF/cofilin is a more potent filament disassembler at higher pH values 4, 5, 26-29 but the actual impact of pH on the rate constants of individual reactions has yet to be characterized. Moreover, whether the unstoppable barbed end depolymerization that we have recently discovered for ADF/cofilin-saturated filaments at pH 7.8 13 remains significant at lower, more physiological pH values is an open question.\n\nHere, we investigate how the different contributions of ADF/cofilin (using unlabeled ADF, unlabeled cof1 and eGFP-cof1) to actin filament disassembly depend on pH, which we varied from 6.6 to 7.8. We first present the methods which we have used to do so, based on the observation of individual filaments, using microfluidics (Fig. 1). We measured cofilins abitility to decorate actin filament by binding to its sides (Fig. 2), and the rate at which individual cofilin domains severed actin filaments (Fig. 3). We next quantified the kinetic parameters of filament ends, for bare and ADF/cofilin-saturated (cofilactin) filaments (Fig. 4), and we specifically quantified the extent to which the barbed ends of cofilactin filaments are in a state which can hardly stop depolymerizing (Fig. 5). We finally summarize our results (Fig. 6).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC=\"FIGDIR/small/422824_fig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@5a6fdorg.highwire.dtl.DTLVardef@1165d4borg.highwire.dtl.DTLVardef@146f0b7org.highwire.dtl.DTLVardef@658f72_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Using microfluidics to monitor individual actin filaments and the binding of cofilin. (A) Experiments are performed in microfluidic chambers, sketched from above. The main channel is connected through three inlets to different protein solutions. Controlling the pressure in each inlet allows one to rapidly change the solution in the field of view.\n\n(B) Sketch of a typical experiment (side view). Filaments are elongated from coverslip-anchored spectrin-actin seeds, by flowing in with ATP-G-actin. Filaments are then aged by flowing in a solution of ATP-G-actin at the critical concentration, for at least 15 min. This results in >99% of the monomers in the ADP-state. Finally, filaments are exposed to ADF/cofilin.\n\n(C) Example of a field of view, imaged with TIRFm. ADP-F-actin labelled with Alexa-488 is exposed to mCherry-cofilin-1, which forms observable domains on the filaments.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=180 SRC=\"FIGDIR/small/422824_fig2.gif\" ALT=\"Figure 2\">\nView larger version (47K):\norg.highwire.dtl.DTLVardef@1aca24borg.highwire.dtl.DTLVardef@d2f404org.highwire.dtl.DTLVardef@1924892org.highwire.dtl.DTLVardef@daaaac_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Cofilin binds more slowly to filaments at higher pH values. (A). Experimental configuration. Actin filaments are grown from spectrin-actin seeds with a long middle segment of unlabelled ADP-actin.\n\n(B). Time-lapse showing an unlabeled ADP-actin filament become saturated by eGFP-cof1 over time.\n\n(C-D). Mean normalized eGFP-cofilin-1 fluorescence signal, binding onto unlabelled ADP-F-actin. 150 nM (C) and 400 nM (D) eGFP-cofilin-1 was injected in the chamber from time t=0 onwards. The fluorescence signal was averaged along 20 to 35 pixels (3.5 to 6 {micro}m) for each filment. Number of filaments (C) N = 10, 10, 18, 20, for pH 6.6 Hepes, 7.0 Hepes, 7.0 Tris and 7.4 Tris, respectively, and\n\n(D) N = 10 in all conditions.\n\n(E). Number of cofilin subunits in individual domains, increasing over time. For clarity, the time origin has been shifted for each curve. Lines: linear fit. Condition: 400 nM eGFP-cofilin-1, pH 7.0 Hepes.\n\n(F). Growth rate of individual cofilin domains at different eGFP-cofilin-1 concentrations and pH. Value: median, error bars: interquartile range. N = 10 domains, except N = 9 for pH 6.6 Hepes 150 nM cof1, and for pH 7.8 Tris 400 nM cof1.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=41 SRC=\"FIGDIR/small/422824_fig3.gif\" ALT=\"Figure 3\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@1fb31d4org.highwire.dtl.DTLVardef@846198org.highwire.dtl.DTLVardef@12379d0org.highwire.dtl.DTLVardef@127b96_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3.C_FLOATNO The severing rate per cofilin domain is unaffected by pH. (A). Experimental setup. Alexa-568-labelled actin filaments are polymerized from actin-spectrin seeds, and aged before being exposed to eGFP-cof1.\n\n(B). Typical kymograph. 200 nM eGFP-cof1 (green) is constantly injected, binds F-actin (red) and induces severing (lightning symbols).\n\n(C). Fraction of cofilin domains with no severing event detected near their edges, over time. Time t=0 is defined for each domain as the last frame before they become visible. The survival fraction curves are calculated using the Kaplan-Meier method over 22 to 43 filaments, 78 to 90 cofilin domains and 30 to 33 severing events, for each data set.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC=\"FIGDIR/small/422824_fig4.gif\" ALT=\"Figure 4\">\nView larger version (32K):\norg.highwire.dtl.DTLVardef@16fdad8org.highwire.dtl.DTLVardef@889caborg.highwire.dtl.DTLVardef@e5b14aorg.highwire.dtl.DTLVardef@1da73c9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 4.C_FLOATNO Higher pH slows down polymerization and depolymerization of bare F-actin but accelerates depolymerization of ADF/cofilin-saturated filaments at both ends. (A-C) Polymerization from the barbed-end.\n\n(A) Sketch of the experimental configuration, where filaments were grown from actin-spectrin seeds with G-ATP-actin and profilin.\n\n(B) Kymograph of a typical elongating filament.\n\n(C) Polymerization rate at different pH. N = 20 filaments for each condition.\n\n(D-E) Depolymerization from the barbed-end.\n\n(D) Sketch of the experimental configuration. ADP-F-actin is exposed either to buffer only, or to 1-2{micro}M unlabelled ADF or cofilin-1 in order to fully saturate the filament in less than a minute.\n\n(E) Depolymerization rate for different pH values. Right: zoom into the 0-5 sub/s range. From left to right, N = 20, 32, 22, 32, 31 (buffer only); N=9, 14, 23, 33, 34 (ADF-saturated); N=17, 18, 16 (cofilin-1-saturated).\n\n(F-H) Depolymerization from the pointed-end.\n\n(F) Sketch of the experimental configuration. ADP-F-actin was bound to the surface by gelsolin. Filaments were exposed to buffer only (supplemented with 0.4 mM CaCl2 to ensure gelsolin-actin tight binding), containing 1 to 2 {micro}M unlabelled ADF or cofilin-1 to rapidly saturate filaments.\n\n(G) Typical kymograph of a depolymerizing filament saturated with ADF.\n\n(H) Pointed-end depolymerization rate at different pH. N = 14, 20, 15, 20, 20 (buffer); N=20, 20, 16, 20, 20 (ADF-saturated); N= 20, 20, 20 (cofilin-1-saturated).\n\n(C, E, H) Symbol: median, error bars: interquartile range.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC=\"FIGDIR/small/422824_fig5.gif\" ALT=\"Figure 5\">\nView larger version (22K):\norg.highwire.dtl.DTLVardef@e79318org.highwire.dtl.DTLVardef@16a4bb1org.highwire.dtl.DTLVardef@18f8fbdorg.highwire.dtl.DTLVardef@25dab6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 5.C_FLOATNO The \"unstoppable\" depolymerization of cofilactin barbed ends is observed throughout our pH range. (A-C) Synergy of CP and ADF/cofilin to saturate filaments and initiate barbed end depolymerization.\n\n(A) Sketch of experimental configuration and events. Filaments grow until they are capped with CP. ADF/cofilin can then saturate the filaments, up to their BE which thus uncaps and depolymerizes.\n\n(B) Kymograph of a filament continuously exposed to the same solution containing 0.8 {micro}M G-ATP-actin, 1 {micro}M ADF and 2 nM CP. The filament polymerizes, pauses as it is capped by CP, and eventually depolymerizes.\n\n(C) Fraction of barbed ends that transitioned from a pause to depolymerization. Time t = 0 corresponds to the beginning of the pause (as shown on B). N = 24, 32, 32 filaments for pH 7.0 Hepes, pH 7.0 Tris, pH 7.4 Tris, respectively.\n\n(D-F) Cofilactin barbed ends sustain depolymerization in the presence of ATP-G-actin.\n\n(D) Sketch of the experimental configuration and events. Filaments are polymerized from spectrin-actin seeds and saturated with ADF. Depolymerizing cofilactin filaments are then constantly exposed to a solution of ATP-G-actin.\n\n(E) Fraction of barbed ends that transitioned from depolymerization to polymerization over time, when exposed to 1 {micro}M ATP-G-actin and 0.5 {micro}M ADF (to keep filaments saturated). N = 25, 16, 25, 24, 31 for pH 6.6 Hepes, 7.0 Hepes, 7.0 Tris, 7.4 Tris, 7.8 Tris, respectively.\n\n(F) Same as (E), with 1 {micro}M profilin added to the solution. N= 21, 27, 30 for pH 6.6 Hepes, 7.4 Tris, 7.8 Tris, respectively.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC=\"FIGDIR/small/422824_fig6.gif\" ALT=\"Figure 6\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@13a07bborg.highwire.dtl.DTLVardef@d19d2forg.highwire.dtl.DTLVardef@1a697daorg.highwire.dtl.DTLVardef@3b9d59_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 6.C_FLOATNO Summary of results: barbed end depolymerization is an important contribution of cofilin disassembly at physiological pH. Within the range of pH that we have explored (pH 6.6 to 7.8) we have made the following observations (from top to bottom, on this sketch). A lower pH favors the rapid decoration of filaments by ADF/cofilin, but the severing rate per cofilin domain does not vary with pH. As a consequence, at a higher pH, domain boundaries persist longer (before domains merge) and severing is more efficient. The acceleration of pointed end depolymerization for cofilactin filaments is mostly observed at high pH. The \"unstoppable\" depolymerization of cofilactin barbed ends is observed at all pH, and is more pronounced at lower pH values.\n\nC_FIG

biochemistry↗

bioRxiv2019

Structural Lipidomics and Molecular Imaging of C=C Positional Isomers Enabled by mCPBA Epoxidation with Tandem Mass Spectrometry

Cellular lipidome is highly regulated through lipogenesis, rendering diverse double-bond positional isomers (C=C isomer) of a given unsaturated lipid species. In recent years, there are increasing reports indicating the physiological roles of C=C isomer compositions associated with diseases, while the biochemistry has not been fully understood due to the challenge in characterizing lipid isomers inherent to conventional mass spectrometry-based lipidomics. To address this challenge, we reported a universal, user-friendly, derivatization-based strategy, MELDI (mCPBA Epoxidation for Lipid Double-bond Identification), which enables both large-scale identification and spatial mapping of biological C=C isomers using commercial mass spectrometers without any instrument modification. With the developed liquid-chromatography mass spectrometry (LC-MS) lipidomics workflow, we elucidated more than 100 isomers among mono- and poly-unsaturated fatty acids and glycerophospholipids in both human serum, where novel isomers of low abundance were unambiguously quantified for the first time. The capability of MELDI-LC-MS in lipidome analysis was further demonstrated using the differentiated 3T3-L1 adipocytes, providing an insight into the cellular lipid reprogramming upon stearoyl-coenzyme A desaturase 1 (SCD1) inhibition. Finally, we highlighted the versatility of MELDI coupled with mass spectrometry imaging to spatially resolve cancer-associated alteration of lipid isomers in a metastatic mouse tissue section. Our results suggested that MELDI will contribute to current lipidomics pipelines with a deeper level of structural information, allowing us to investigate underlying lipid biochemistry.

biochemistry↗

bioRxiv2019

Peppy: A Virtual Reality Environment For Exploring The Principles Of Polypeptide Structure

A key learning outcome for undergraduate biochemistry classes is a thorough understanding of the principles of protein structure. Traditional approaches to teaching this material, which include two-dimensional (2D) images on paper, physical molecular modelling kits, and projections of 3D structures into 2D, are unable to fully capture the dynamic, 3D nature of proteins. We have built a virtual reality application, Peppy, aimed at facilitating teaching of the principles of protein secondary structure. Rather than attempt to model molecules with the same fidelity to the underlying physical chemistry as existing, research-oriented molecular modelling approaches, we took the more straightforward approach of harnessing the Unity video game physics engine. Indeed, the simplicity and limitations of our model are a strength in a teaching context, provoking questions and thus deeper understanding. Peppy allows exploration of the relative effects of hydrogen bonding (and electrostatic interactions more generally), backbone {phi}/{psi} angles, basic chemical structure and steric effects on polypeptide structure in an accessible format that is novel, dynamic and fun to use. As well as describing the implementation and use of Peppy, we discuss the outcomes of deploying Peppy in undergraduate biochemistry courses.\n\nSTATEMENTProtein structure is inherently dynamic and three-dimensional, but traditional teaching tools are static and/or two-dimensional. We have developed a virtual reality teaching tool, Peppy, that facilitates undergraduate teaching of the principles of protein structure. We outline how Peppy works in terms of how it is used and what goes on under the hood. We then illustrate its use in undergraduate teaching, where its playful nature stimulated exploration and, thus, deeper understanding.

biochemistry↗