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bioRxiv2026

Liquid-Liquid Phase Separation of the m6A RNA ALKBH9B Demethylase: An IDR-dependent Mechanism driving the Alfalfa Mosaic Virus Infection

Biomolecular condensates play crucial role in plant-virus interactions. The plant m6A demethylase ALKBH9B acts as a proviral factor for Alfalfa mosaic virus (AMV), yet the biophysical mechanisms underlying its function remain elusive. Here, we investigate the liquid-liquid phase separation (LLPS) properties of ALKBH9B and their requirements for viral infection. Using in vitro and in vivo assays (Nicotiana benthamiana), we demonstrate that ALKBH9B assembles into highly dynamic, liquid-like cytoplasmic condensates driven primarily by weak hydrophobic interactions. Deletion and mutagenesis analysis identified a C-terminal intrinsically disordered region (IDR3), specifically its arginine-glycine (RG) motifs, as the essential molecular driver of LLPS. Importantly, this domain exhibits dual functionality, mediating both phase separation and direct binding to AMV RNA, which regulates condensate assembly and size. Disruption of the RG motifs completely abolishes ALKBH9B condensation and reduces its proviral capacity, compromising viral accumulation. Collectively, our findings reveal that the spatial compartmentalization of ALKBH9B into dynamic liquid hubs is functionally indispensable for AMV infection. This establishes LLPS as a critical regulatory checkpoint in viral pathogenesis, highlighting how plant viruses exploit the biophysical properties of host epitranscriptomic enzymes to establish specialized, protective microenvironments. HighlightsOur study establishes a direct mechanistic link between the biophysical properties of a plant m6A demethylase and viral pathogenesis, demonstrating that ALKBH9B functions as a key host factor by harnessing LLPS to regulate virus-host interactions.

plant biology↗

bioRxiv2026

Scop3P-Toolkit: executable structure-aware workflows linking PTMs, peptides, and mutations to protein function

Post-translational modifications (PTMs) and genetic variants regulate protein function, signalling, and disease, but their interpretation requires integration of sequence annotations with structural, interaction, and biophysical context. Although resources such as Scop3P, UniProt, the Protein Data Bank, and AlphaFold provide extensive annotations and structural information, integrating these data into reproducible structure-aware analyses still requires custom scripting and manual coordination between multiple independent tools. To address this challenge, we developed Scop3P-Toolkit, an open-source executable analytical environment for interactive analysis of PTMs, mutations, and proteomics-derived peptides in their structural context. The toolkit integrates protein annotation retrieval with structural mapping, residue interaction network analysis, comparative structural analysis, and residue-level biophysical profiling within a unified framework. Experimentally supported phosphosites, phosphopeptides, and phosphoproteomics evidence are provided for human proteins through Scop3P, with optional integration of curated UniProt PTM annotations. UniProt-derived PTMs, sequence features, and genetic variants are available for proteins from any species, extending the framework beyond the human phosphoproteome. Scop3P-Toolkit supports structure-centric analyses including interpretation of PTMs and disease-associated variants, analysis of residue interaction networks and their rewiring across alternative conformations, structural localisation of peptides, and exploration of protein-protein, protein-ligand, and host-pathogen interfaces. Interactive visualisation links sequence annotations, three-dimensional structures, residue interaction networks, and biophysical profiles, enabling coordinated exploration across multiple molecular representations. The toolkit is distributed as Jupyter notebooks, browser-based Voila applications, and a Galaxy interactive tool, providing transparent, accessible, and reproducible workflows for both computational and experimental researchers. By integrating biological annotation resources into executable, structure-aware workflows, Scop3P-Toolkit enables reproducible interpretation of PTMs, mutations, and proteomics data.

bioinformatics↗

bioRxiv2026

ProtEnrich: Residual Multimodal Enrichment of Protein Sequence Embeddings

Protein language models effectively capture evolutionary and functional signals from sequence data but lack explicit representation of the biophysical properties that govern protein structure and dynamics. Existing multimodal approaches attempt to integrate such physical information through direct fusion, often requiring multimodal inputs at inference time and distorting the geometry of the sequence embedding space, which can disrupt the semantic organization learned from evolutionary information. Consequently, a fundamental challenge of how to incorporate structural and dynamical knowledge into sequence representations without disrupting their semantic organization, enabling sequence-based models to better capture the biophysical properties governing protein structure and function. We introduce ProtEnrich, a representation learning framework based on a residual multimodal enrichment paradigm. ProtEnrich decomposes sequence embeddings into two complementary latent subspaces, an anchor subspace that preserves sequence semantics, and an alignment subspace that encodes biophysical relationships. By converting multimodal information derived from ProstT5 and RocketSHP to a low-energy residual component, our approach injects physical representation while maintaining the original sequence embedding while preserving their original semantic geometry, avoiding the need for multimodal inputs at inference time. Across eight diverse protein foundational models trained on 550,120 SwissProt proteins with AlphaFold structures, enriched embeddings improved zero-shot remote homology retrieval, increasing Precision@10 and MRR by up to 0.13 and 0.11, respectively. Downstream performance also improved on structure-dependent tasks, reducing fluorescence prediction error by up to 16% and increasing metal ion binding AUCROC by up to 2.4 points, while requiring only sequence input at inference. Source code is available at https://github.com/pcdslab/ProtEnrich, pretrained models and datasets are available at https://huggingface.co/collections/SaeedLab/protenrich.

bioinformatics↗

bioRxiv2026

Phosphoregulation of hierarchical protein condensate formation during clathrin-mediated endocytosis

The sequential assembly of over 50 proteins during clathrin-mediated endocytosis (CME) is well documented in yeast. However, the mechanisms governing this ordered recruitment remain poorly understood. Biomolecular condensation is proposed to orchestrate CME, but defining biophysical parameters in vivo remains challenging. Here, we developed a quantitative in vivo titration strategy to systematically determine CME protein assembly profiles. Beyond the previously reported Ede1 (yeast Eps15), seven additional proteins formed enlarged, concentration-dependent assemblies with distinct biophysical properties. Single-cell profiling revealed two biophysical classes among these eight proteins: homeostatic buffers with defined saturation boundaries and continuous assemblers that scale linearly. Acting as scaffolds and clients, these conserved proteins form an interconnected network of kinases, phosphatase recruiters, and substrates. Within this network, reversible phosphorylation modulates assembly stability to drive recruitment transitions. Collectively, this work establishes a two-stage model of protein condensation that drives CME progression from initiation to maturation and offers a general framework for how protein phosphorylation dynamically modulates biomolecular condensation in vivo.

cell biology↗

bioRxiv2026

Sociality shapes context-dependent stress responses to anthropogenic disturbance in African elephants

Social relationships can buffer individuals against environmental challenges, reducing the physiological costs of disturbance. Assessing how social and environmental conditions jointly shape physiological stress is critical for predicting individual responses to disturbance, particularly in managed populations. We evaluated whether social and environmental stressors act additively or whether social context modifies physiological responses to environmental challenges. We combined behavioural data and faecal glucocorticoid metabolite samples from a small, intensively monitored population of African elephants (Loxodonta africana) in a fenced reserve. Using a Bayesian framework, we tested how social factors interacted with variation in the biophysical environment, including natural environmental and anthropogenic disturbances, to predict glucocorticoid concentrations. Natural environmental variables, including weather, water availability, and season, did not predict glucocorticoid concentrations. In contrast, helicopter use, a common management intervention, was associated with higher glucocorticoid concentrations. Social effects were context dependent. Following disturbance, low-ranking individuals showed higher glucocorticoid concentrations than high-ranking individuals, whereas this pattern reversed under low-disturbance conditions. Individuals with more social connections had lower glucocorticoid concentrations, but this effect was evident only under non-disturbance conditions. Measures of betweenness centrality did not predict glucocorticoid levels. These results demonstrate that social factors mediate physiological responses to anthropogenic disturbance and that management actions can impose uneven physiological costs across individuals. Quantifying interactions between social factors and the biophysical environment may improve conservation and management strategies for social species. Integrating physiological responses to interactions between social factors and the biophysical environment can guide conservation strategies and improve management interventions in social species.

animal behavior and cognition↗

bioRxiv2014

Relationship between protein thermodynamic constraints and variation of evolutionary rates among sites

Evolutionary-rate variation among sites within proteins depends on functional and biophysical properties that constrain protein evolution. It is generally accepted that proteins must be able to fold stably in order to function. However, the relationship between stability constraints and among-sites rate variation is not well understood. Here, we present a biophysical model that links the thermodynamic stability changes due to mutations at sites in proteins ({Delta}{Delta}G) to the rate at which mutations accumulate at those sites over evolutionary time. We find that such a "stability model" generally performs well, displaying correlations between predicted and empirically observed rates of up to 0.75 for some proteins. We further find that our model has comparable predictive power as does an alternative, recently proposed "stress model" that explains evolutionary-rate variation among sites in terms of the excess energy needed for mutants to adopt the correct active structure ({Delta}{Delta}G*). The two models make distinct predictions, though, and for some proteins the stability model outperforms the stress model and vice versa. We conclude that both stability and stress constrain site-specific sequence evolution in proteins.

Biophysics↗

bioRxiv2017

Quantitative hybrid modeling reveals predictable mechanical stress response of growing tumor spheroids

Model simulations indicate that the response of growing cell populations on mechanical stress follows the same functional relationship and is predictable over different cell lines and growth conditions despite the response curves look largely different. We develop a hybrid model strategy in which cells are represented by coarse-grained individual units calibrated with a high resolution cell model and parameterized measurable biophysical and cell-biological parameters. Cell cycle progression in our model is controlled by volumetric strain, the latter being derived from a bio-mechanical relation between applied pressure and cell compressibility. After parameter calibration from experiments with mouse colon carcinoma cells growing against the resistance of an elastic alginate capsule, the model adequately predicts the growth curve in i) soft and rigid capsules, ii) in different experimental conditions where the mechanical stress is generated by osmosis via a high molecular weight dextran solution, and iii) for other cell types with varying doubling times. Our model simulation results suggest that the growth response of cell population upon externally applied mechanical stress is the same, as it can be quantitatively predicted using the same growth progression function.\n\nAuthor summaryThe effect of mechanical resistance on the growth of tumor cells remains today largely unquantified. We studied data from two different experimental setups that monitor the growth of tumor cells under mechanical compression. The existing data in the first experiment examined growing CT26 cells in an elastic permeable capsule. In the second experiment, growth of tumor cells under osmotic stress of the same cell line as well as other cell lines were studied. We have developed and agent-based model with measurable biophysical and cell-biological parameters that can simulate both experiments. Cell cycle progression in our model is a Hill function of cell volumetric strain, derived from a bio-mechanical relation between applied pressure and cell compressibility. After calibration of the model parameters within the data of the first experiment, we are able predict the growth rates in the second experiment. We show that that the growth response of cell populations upon externally applied mechanical stress in the two different experiments and over different cell lines can be predicted using the same growth progression function once the growth kinetics of the cell lines in abscence of mechanical stress is known.

biophysics↗

bioRxiv2017

Structural Propensity Database Of Proteins

An overwhelming amount of experimental evidence suggests that elucidations of protein function, interactions, and pathology are incomplete without inclusion of intrinsic protein disorder and structural dynamics. Thus, to expand our understanding of intrinsic protein disorder, we have created a database of secondary structure (SS) propensities for proteins (dSPP) as a reference resource for experimental research and computational biophysics. The dSPP comprises SS propensities of 7,094 unrelated proteins, as gauged from NMR chemical shift measurements in solution and solid state. Here, we explain the concept of SS propensity and analyze dSPP entries of therapeutic relevance, -synuclein, MOAG-4, and the ZIKA NS2B-NS3 complex to show: (1) how propensity mapping generates novel structural insights into intrinsically disordered regions of pathologically relevant proteins, (2) how computational biophysics tools can benefit from propensity mapping, and (3) how the residual disorder estimation based on NMR chemical shifts compares with sequence-based disorder predictors. This work demonstrates the benefit of propensity estimation as a method that reports both on protein structure, lability, and disorder.

biophysics↗

bioRxiv2017

Thermodynamic Integration in 3n Dimensions without Biases or Alchemy for Protein Interactions

Thermodynamic integration (TI), a powerful formalism for computing the Gibbs free energy, has been implemented for many biophysical processes characterized by one-dimensional order parameters with alchemical schemes that require delicate human efforts to choose/design biasing potentials for sampling the desired biophysical events and to remove their artifactitious consequences afterwards. Theoretically, an alchemical scheme is exact but practically, it causes error amplification. Small relative errors in the interaction parameters can be amplified many times in their propagation into the computed free energy [due to subtraction of similar numbers such as (105 {+/-} 5) - (100 {+/-} 5) = 5 {+/-} 7], which would render the results significantly less accurate than the input interaction parameters. In this paper, we present an unsophisticated implementation of TI in 3n dimensions (3nD) (n=1,2,3...) without alchemy or biasing potentials. In TI3nD, the errors in the interaction parameters will not be amplified and human efforts are not required to design biasing potentials that generate unphysical consequences. Using TI3nD, we computed the standard free energies of three protein complexes: trometamol in Salmonella effector SpvD (n=1), biotin in avidin (n=2), and Colicin E9 endonuclease with cognate immunity protein Im9 (n=3) and the hydration energies of ten biologically relevant compounds (n=1 for water, acetamide, urea, glycerol, trometamol, ammonium and n=2 for erythritol, 1,3-propanediol, xylitol, biotin). The computed results all agree with available experimental data. Each of the 13 computations is accomplishable within two (for a hydration problem) to ten (for the protein-recognition problem) days on an inexpensive workstation (two Xeon E5-2665 2.4GHz CPUs and one nVidia P5000 GPU).

biophysics↗

bioRxiv2017

Preparation of H-Ras GTPase conjugated to lipid nanodiscs for NMR spectroscopy

Ras GTPase is a peripheral membrane protein central to cellular signaling of growth and proliferation. Membrane attachment is critical for a range of Ras activities, therefore, ability to make faithful in-vitro samples of a mem-brane-bound Ras for detailed biophysical studies is a highly desirable goal. In this manuscript, we are describing preparation of a large-scale sample of isotopically labeled H-Ras conjugated to lipid nanodiscs. We demonstrate that the Ras-nanodisc sample is fairly stable to allow for a range of Nuclear Magnetic Resonance (NMR) and other biophysical measurements. The need to achieve a homogeneous protein-nanodisc ratio is also emphasized.

biophysics↗

bioRxiv2017

Shaping Epigenetic Memory via Genomic Bookmarking

Reconciling the stability of epigenetic patterns with the rapid turnover of histone modifications and their adaptability to external stimuli is an outstanding challenge. Here, we propose a new biophysical mechanism that can establish and maintain robust yet plastic epigenetic domains via genomic bookmarking (GBM). We model chromatin as a recolourable polymer whose segments bear non-permanent histone marks (or colours) which can be modified by \"writer\" proteins. The three-dimensional chromatin organisation is mediated by protein bridges, or \"readers\", such as Polycomb Repressive Complexes and Transcription Factors. The coupling between readers and writers drives spreading of biochemical marks and sustains the memory of local chromatin states across replication and mitosis. In contrast, GBM-targeted perturbations destabilise the epigenetic patterns. Strikingly, we demonstrate that GBM alone can explain the full distribution of Polycomb marks in a whole Drosophila chromosome. We finally suggest that our model provides a starting point for an understanding of the biophysics of cellular differentiation and reprogramming.

biophysics↗

bioRxiv2019

Physiological Adaptability and Parametric Versatility in a Simple Genetic Circuit

The intimate relationship between the environment and cellular growth rate has remained a major topic of inquiry in bacterial physiology for over a century. Now, as it becomes possible to understand how the growth rate dictates the wholesale reorganization of the intracellular molecular composition, we can interrogate the biophysical principles underlying this adaptive response. Regulation of gene expression drives this adaptation, with changes in growth rate tied to the activation or repression of genes covering enormous swaths of the genome. Here, we dissect how physiological perturbations alter the expression of a circuit which has been extensively characterized in a single physiological state. Given a complete thermodynamic model, we map changes in physiology directly to the biophysical parameters which define the expression. Controlling the growth rate via modulating the available carbon source or growth temperature, we measure the level of gene expression from a LacI-regulated promoter where the LacI copy number is directly measured in each condition, permitting parameter-free prediction of the expression level. The transcriptional output of this circuit is remarkably robust, with expression of the repressor being largely insensitive to the growth rate. The predicted gene expression quantitatively captures the observations under different carbon conditions, indicating that the bio-physical parameters are indifferent to the physiology. Interestingly, temperature controls the expression level in ways that are inconsistent with the prediction, revealing temperature-dependent effects that challenge current models. This work exposes the strengths and weaknesses of thermodynamic models in fluctuating environments, posing novel challenges and utility in studying physiological adaptation. SignificanceCells adapt to changing environmental conditions by repressing or activating gene expression from enormous fractions of their genome, drastically changing the molecular composition of the cell. This requires the concerted adaptation of transcription factors to the environmental signals, leading to binding or releasing of their cognate sequences. Here, we dissect a well characterized genetic circuit in a number of physiological states, make predictions of the response, and measure how the copy number of a regulator and its gene target are affected. We find the parameters defining the regulators behavior are remarkably robust to changes in the nutrient availability, but are susceptible to temperature changes. We quantitatively explore these two effects and discuss how they challenge current models of transcriptional regulation.

biophysics↗

bioRxiv2020

Impact of nanoscale hindrances on the relationship between lipid packing and diffusion in model membranes

Membrane models have allowed for precise study of the plasma membranes biophysical properties, helping to unravel both structural and dynamic motifs within cell biology. Free standing and supported bilayer systems are popular models to reconstitute the membrane related processes. Although it is well-known that each have their advantages and limitations, comprehensive comparison of their biophysical properties is still lacking. Here, we compare the diffusion and lipid packing in giant unilamellar vesicles, planar and spherical supported membranes and cell-derived giant plasma membrane vesicles. We apply florescence correlation spectroscopy, spectral imaging and super-resolution STED-FCS to study the diffusivity, lipid packing and nanoscale architecture of these membrane systems, respectively. Our data show that lipid packing and diffusivity is tightly correlated in free-standing bilayers. However, nanoscale interactions in the supported bilayers cause deviation from this correlation. This data is essential to develop accurate theoretical models of the plasma membrane and will serve as a guideline for suitable model selection in future studies to reconstitute biological processes.

biophysics↗

bioRxiv2020

Effect of Erufosine on Membrane Lipid Order in Breast Cancer Cell Models

Alkylphospholipids are a novel class of antineoplastic drugs showing remarkable therapeutic potential. Among them, Erufosine (EPC3) is a promising drug for the treatment of several types of tumors. While EPC3 is supposed to exert its function by interacting with lipid membranes, the exact molecular mechanisms involved are not known yet. In this work, we applied a combination of several fluorescence microscopy and analytical chemistry approaches (i.e., scanning fluorescence correlation spectroscopy, line-scan fluorescence correlation spectroscopy, generalized polarization imaging, as well as thin layer and gas chromatography) to quantify the effect of EPC3 in biophysical models of the plasma membrane, as well as in cancer cell lines. Our results indicate that EPC3 affects lipid-lipid interactions in cellular membranes by decreasing lipid packing and increasing membrane disorder and fluidity. As a consequence of these alterations in the lateral organization of lipid bilayers, the diffusive dynamics of membrane proteins are also significantly increased. Taken together, these findings suggest that the mechanism of action of EPC3 might be linked to its effects on fundamental biophysical properties of lipid membranes, as well as lipid metabolism in cancer cells.

biophysics↗

bioRxiv2020

Melatonin alters fluid phase co-existence in POPC/DPPC/cholesterol membranes

The structure and biophysical properties of lipid biomembranes are important for normal function of plasma and organelle membranes, which is essential for proper functioning of living cells. In Alzheimers disease (AD) the structure of neuronal membranes becomes compromised by the toxic effect of amyloid-{beta} (A{beta}) protein which accumulates at neuron synapses, resulting in membrane perforation and dysfunction, oxidative stress and cell death. Melatonin is an important pineal gland hormone that has been shown to be protective against A{beta} toxicity in cellular and animal studies, but the molecular mechanism of this protection is not well understood. It has been shown that melatonin can interact with model lipid membranes and alter the membrane biophysical properties, such as membrane molecular order and dynamics. This effect of melatonin has been previously studied in simple model bilayers with one or two lipid components, we consider a more complex ternary lipid mixture as our membrane model. In this study, we used 2H-NMR to investigate the effect of melatonin on lipid phase behaviour of a three-component model lipid membranes composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and cholesterol. We used deuterium labelled palmitoyl-d31 in POPC-d31 and DPPC-d62 separately, to probe the changes in hydrocarbon chain order as a function of temperature and varying concentrations of melatonin. We found that melatonin concentration influences phase separation in these ternary mixtures somewhat differently depending on whether POPC-d31 or DPPC-d62was used. At 5 mol% melatonin we observed phase separation in samples with POPC-d31, but not with DPPC-d62. However, at 10 mol% melatonin phase separation was observed in both samples with either POPC-d31 or DPPC-d62. These results indicate that melatonin can have a strong effect on membrane structure and physical properties, which may provide some clues to understanding how melatonin protects against A{beta}. SIGNIFICANCEMelatonin has been shown to be protective against A{beta} pathology in animal and cellular studies. Although the mechanism of this protection is not well-understood, melatonins membrane-active properties may be important in this regard. In this work solid-state deuterium nuclear magnetic resonance was used to study the effect of melatonin on the POPC/DPPC/cholesterol model membranes. Specifically, we showed that melatonin modifies lipid hydrocarbon chain order to promote phase separation. This knowledge helps to explain the role of melatonin in lipid domain formation and may provide a deeper understanding of the mechanism of melatonin neuroprotection in AD.

biophysics↗

bioRxiv2020

Multimodal Nonlinear Optical and Thermal Imaging Platform for Label-Free Characterization of Biological Tissue

The ability to characterize the combined structural, functional, and thermal properties of biophysically dynamic samples is needed to address critical questions related to tissue structure, physiological dynamics, and disease progression. Towards this, we have developed an imaging platform that enables multiple nonlinear imaging modalities to be combined with thermal imaging on a common sample. Here we demonstrate label-free multimodal imaging of live cells, excised tissues, and live rodent brain models. While potential applications of this technology are wide-ranging, we expect it to be especially useful in addressing biomedical research questions aimed at the biomolecular and biophysical properties of tissue and their physiology.

biophysics↗

bioRxiv2020

Nonallosteric Sirtuin Enzyme Activation: Characterization of Hit Compounds

Mammalian sirtuins (SIRT1-SIRT7) are a family of nicotinamide adenine dinucleotide (NAD+)-dependent protein deacylases that play critical roles in lifespan and age-related diseases. The physiological importance of sirtuins has stimulated intense interest in designing sirtuin-activating compounds. However, except for allosteric activators of SIRT1-catalyzed reactions that are limited to particular substrates, a general framework for the design of sirtuin-activating compounds has been lacking. Recently, we introduced a general mode of sirtuin activation that is distinct from the known modes of enzyme activation, establishing biophysical properties of small molecule modulators that can, in principle, result in enzyme activation for various sirtuins and substrates. Here, we characterize small molecules reported in the literature to activate the SIRT3 enzyme using a variety of computational, biochemical and biophysical techniques including protein-ligand docking, molecular dynamics simulation, nonlinear reaction dynamics simulation, kinetic assays and thermodynamic assays with multiple substrates and protocols. In particular, we identify the mechanism of action of the compound honokiol on the human SIRT3 enzyme, modeling its effect on active site conformational degrees of freedom and demonstrating how it nonallosterically activates the human SIRT3 enzyme under physiologically relevant conditions. We show that honokiol constitutes a hit compound for the design of a new generation of nonallosteric activators that can activate SIRT3 through the proposed mechanism-based mode of activation.

biophysics↗

bioRxiv2020

High resolution ensemble description of metamorphic and intrinsically disordered proteins using an efficient hybrid parallel tempering scheme

Determining the conformational ensemble for proteins with multi-funneled complex free-energy landscapes is often not possible with classical structure-biology methods that produce time and ensemble averaged data. With vastly improved force fields and advances in rare-event sampling methods, molecular dynamics (MD) simulations offer a complementary approach towards determining the collection of 3-dimensional structures that proteins can adopt. However, in general, MD simulations need to either impose restraints or reweigh the generated data to match experiments. The limitations extend beyond systems with high free-energy barriers as is the case with metamorphic proteins such as RFA-H. The predicted structures in even weakly-funneled intrinsically disordered proteins (IDPs) such as Histatin-5 (His-5) are too compact relative to experiments. Here, we employ a new computationally-efficient parallel-tempering based advanced-sampling method applicable across proteins with extremely diverse free-energy landscapes. And we show that the calculated ensemble averages match reasonably well with the NMR, SAXS and other biophysical experiments without the need to reweigh. We benchmark our method against standard model systems such as alanine di-peptide, TRP-cage and {beta}-hairpin and demonstrate significant enhancement in the sampling efficiency. The method successfully scales to large metamorphic proteins such as RFA-H and to highly disordered IDPs such as His-5 and produces experimentally-consistent ensemble. By allowing accurate sampling across diverse landscapes, the method enables for ensemble conformational sampling of deep multi-funneled metamorphic proteins as well as highly flexible IDPs with shallow multi-funneled free-energy landscape. Significance/Authors SummaryGenerating high-resolution ensemble of intrinsically disordered proteins, particularly the highly flexible ones with high-charge and low-hydrophobicity and with shallow multi-funneled free-energy landscape, is a daunting task and often not possible since information from biophysical experiments provide time and ensemble average data at low resolutions. At the other end of the spectrum are the metamorphic proteins with multiple deep funnels and elucidating the structures of the transition intermediates between the fold topologies is a non-trivial exercise. In this work, we propose a new parallel-tempering based advanced-sampling method where the Hamiltonian is designed to allow faster decay of water orientation dynamics, which in turn facilitates accurate and efficient sampling across a wide variety of free-energy landscapes.

biophysics↗