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bioRxiv2021

Visualizing the Role of Lipid Dynamics during Infrared Neural Stimulation with Hyperspectral Stimulated Raman Scattering Microscopy

Infrared neural stimulation, or INS, is a method of using pulsed infrared light to yield label-free neural stimulation with broad experimental and translational utility. Despite its robust demonstration, the mechanistic and biophysical underpinnings of INS have been the subject of debate for more than a decade. The role of lipid membrane thermodynamics appears to play an important role in how fast IR-mediated heating nonspecifically drives action potential generation. Direct observation of lipid membrane dynamics during INS remains to be shown in a live neural model system. To directly test the involvement of lipid dynamics in INS, we used hyperspectral stimulated Raman scattering (hsSRS) microscopy to study biochemical signatures of high-speed vibrational dynamics underlying INS in a live neural cell culture model. Findings suggest that lipid bilayer structural changes are occurring during INS in vitro in NG108-15 neuroglioma cells. Lipid-specific signatures of cell SRS spectra were found to vary with stimulation energy and radiant exposure. Spectroscopic observations were verified against high-speed ratiometric fluorescence imaging of a conventional lipophilic membrane structure reporter, di-4-ANNEPS. Overall, the presented data supports the hypothesis that INS causes changes in the lipid membrane of neural cells by changing lipid membrane packing order - which coincides with likelihood of cell stimulation. Furthermore, this work highlights the potential of hsSRS as a method to study biophysical and biochemical dynamics safely in live cells.

biophysics↗

bioRxiv2021

STED super-resolution imaging of membrane packing and dynamics by exchangeable polarity-sensitive dyes

Understanding the plasma membrane nano-scale organisation and dynamics in living cells requires microscopy techniques with high spatial and temporal resolution, permitting for long acquisition times, and that allow for the quantification of membrane biophysical properties such as lipid ordering. Among the most popular super-resolution techniques, stimulated emission depletion (STED) microscopy offers one of the highest temporal resolutions, ultimately defined by the scanning speed. However, monitoring live processes using STED microscopy is significantly limited by photobleaching, which recently has been circumvented by exchangeable membrane dyes that only temporarily reside in the membrane. Here, we show that NR4A, a polarity-sensitive exchangeable plasma membrane probe based on Nile Red, permits the super-resolved quantification of membrane biophysical parameters in real time with high temporal and spatial resolution as well as long acquisition times. The potential of this polarity-sensitive exchangeable dye is showcased by live-cell real-time 3D-STED recordings of bleb formation and lipid exchange during membrane fusion, as well as by STED-fluorescence correlation spectroscopy (STED-FCS) experiments for the simultaneous quantification of membrane dynamics and lipid packing, which correlate in model and live-cell membranes.

biophysics↗

bioRxiv2021

The dynamic properties of a nuclear coactivator binding domain are evolutionarily conserved

Evolution of proteins is constrained by their structure and function. While there is a consensus that the plasticity of intrinsically disordered proteins relaxes the structural constraints on evolution there is a paucity of data on the molecular details of these processes. The Nuclear co-activator binding domain (NCBD) from CREB-binding protein is a protein-protein interaction domain, which contains a hydrophobic core but is not behaving as a typical globular domain, and has been desribed as molten-globule like. The highly dynamic properties of NCBD makes it an interesting model system for evolutionary structure-function investigation of intrinsically disordered proteins. We have here compared the structure and biophysical properties of an ancient version of NCBD present in a bilaterian animal ancestor living around 600 million years ago with extant human NCBD. Using a combination of NMR spectroscopy, circular dichroism and kinetic methods we show that although NCBD has increased its thermodynamic stability, it has retained its dynamic biophysical properties in the ligand-free state in the evolutionary lineage leading from the last common bilaterian ancestor to humans. Our findings suggest that the dynamic properties of NCBD have been maintained by purifying selection and thus are important for its function, which includes mediating several distinct protein-protein interactions.

biophysics↗

bioRxiv2021

Lipid nanodiscs facilitate the identification of a fragment compound inhibiting the enzymatic activity of the bacterial membrane protein MsbA.

Membrane proteins are critical elements of numerous therapeutic approaches ranging from cancer to bacterial infections. MsbA is a bacterial membrane protein that has received increasing attention as an antibacterial target for its role in the processing of Lipid A, a key precursor of lipopolysaccharide that is essential for bacterial growth. When employing nanodiscs it is possible to stabilize MsbA by providing a membrane-like environment that enhances its enzymatic activity. Taking advantage of this property we have carried out a fragment screening using the biophysical method of surface plasmon resonance. This approach identified several compounds that bind specifically to MsbA. In particular, one of these fragment molecules not only binds to the target, but also inhibits the ATPase activity of the MsbA protein. The similarity of this fragment to the adenine moiety of ATP points at a route to generate stronger and more potent inhibitors for MsbA and even other proteins of its family of ABC transporters. Collectively, our study reveals biophysical approaches that facilitate the identification of fragment candidates inhibiting the activity of membrane proteins.

biophysics↗

bioRxiv2021

Brain fatty acid binding protein exhibits non-preferential and mutation-resistant binding towards fatty acids

Members of the fatty acid binding protein (FABP) family function as intracellular transporters of long chain fatty acids and other hydrophobic molecules to different cellular compartments. Brain fatty acid binding protein (FABP7) exhibits ligand-directed differences in cellular transport behavior. For example, when FABP7 binds to docosahexaenoic acid (DHA), the complex relocates to the nucleus and influences transcriptional activity, whereas FABP7 bound with monosaturated fatty acids remain in the cytosol. We used a variety of biophysical techniques to enhance understanding of ligand-directed transport. Specifically, we examine how FABP7 binds to fatty acids, including saturated stearic acid (SA), monounsaturated oleic acid (OA), and polyunsaturated DHA. We find that at 37{degrees}C FABP7 has near equivalent binding affinities for the fatty acids, while at lower temperatures, FABP7 exhibits a preference for the unsaturated fatty acids. Therefore, nuclear localization of the FABP7-DHA complex cannot be explained by binding preferences. Using NMR spectroscopy and molecular dynamics simulations, we observe that DHA uniquely affects the portal region of FABP7, which could enhance the complexs nuclear localization. Mutations to purported critical binding residues (R126L and Y128F) have little effect on fatty acid binding, with molecular dynamics simulations revealing that the bound fatty acid can adopt binding poses that can accommodate the mutations. SignificanceThis work studies FABP7 at physiological temperature and shows that nuclear localization of FABP7 cannot be initiated by tighter ligand interactions. Through biophysical experiments and simulations, we show ligand-dependent conformational changes, instead of binding affinities, are associated with certain biological outcomes. Extensive simulations reveal redundancy in available ligand binding conformations, which permits mutant-resistant binding. This suggests that these mutations do not affect ligand binding affinities, but changes in protein conformation and dynamics may result in disease associated cellular outcomes.

biophysics↗

bioRxiv2021

Atomic-Resolution Prediction of Degrader-mediated Ternary Complex Structures by Combining Molecular Simulations with Hydrogen Deuterium Exchange

Targeted protein degradation (TPD) has emerged as a powerful approach in drug discovery for removing (rather than inhibiting) proteins implicated in diseases. A key step in this approach is the formation of an induced proximity complex, where a degrader molecule recruits an E3 ligase to the protein of interest (POI), facilitating the transfer of ubiquitin to the POI and initiating the proteasomal degradation process. Here, we address three critical aspects of the TPD process: 1) formation of the ternary complex induced by a degrader molecule, 2) conformational heterogeneity of the ternary complex, and 3) assessment of ubiquitination propensity via the full Cullin Ring Ligase (CRL) macromolecular assembly. The novel approach presented here combines experimental biophysical data--in this case hydrogen-deuterium exchange mass spectrometry (HDX-MS, which measures the solvent exposure of protein residues)--with all-atom explicit solvent molecular dynamics (MD) simulations aided by enhanced sampling techniques to predict structural ensembles of ternary complexes at atomic resolution. We present results demonstrating the efficiency, accuracy, and reliability of our approach to predict ternary structure ensembles using the bromodomain of SMARCA2 (SMARCA2BD) with the E3 ligase VHL as the system of interest. The simulations reproduce X-ray crystal structures - including prospective simulations validated on a new structure that we determined in this work (PDB ID: 7S4E) - with root mean square deviations (RMSD) of 1.1 to 1.6 [A]. The simulations also reveal a structural ensemble of low-energy conformations of the ternary complex within a broad energy basin. To further characterize the structural ensemble, we used snapshots from the aforementioned simulations as seeds for Hamiltonian replica exchange molecular dynamics (HREMD) simulations, and then perform 7.1 milliseconds of aggregate simulation time using Folding@home. The resulting free energy surface identifies the crystal structure conformation within a broad low-energy basin and the dynamic ensemble is consistent with solution-phase biophysical experimental data (HDX-MS and small-angle x-ray scattering, SAXS). Finally, we graft structures from the ternary complexes onto the full CRL and perform enhanced sampling simulations, where we find that differences in degradation efficiency can be explained by the proximity distribution of lysine residues on the POI relative to the E2-loaded ubiquitin. Several of the top predicted ubiquitinated lysine residues are validated prospectively through a ubiquitin mapping proteomics experiment.

biophysics↗

bioRxiv2021

Dynamic Allostery Highlights the Evolutionary Differences between the CoV-1 and CoV-2 Main Proteases

The SARS-CoV-2 coronavirus has become one of the most immediate and widely-studied systems since its identification and subsequent global outbreak from 2019-2020. In an effort to understand the biophysical changes as a result of mutations, the mechanics of multiple different proteins within the SARS-CoV-2 virus have been studied and compared with SARS-CoV-1. Focusing on the main protease (mPro), we first explored the long range dynamic-relationship, particularly in cross-chain dynamics, using the Dynamic Coupling Index (DCI) to investigate the dynamic coupling between the catalytic site residues and the rest of the protein, both inter and intra chain for the CoV-1 and CoV-2 mPro. We found that there is significant cross-chain coupling between these active sites and distal residues in the CoV-2 mPro but it was missing in CoV-1. The enhanced long distance interactions, particularly between the two chains, suggest subsequently enhanced cooperativity for CoV-2. A further comparative analysis of the dynamic flexibility using the Dynamic Flexibility Index (DFI) between the CoV-1 and CoV-2 mPros shows that the inhibitor binding near active sites induces change in flexibility to a distal region of the protein, opposite in behavior between the two systems; this region becomes more flexible upon inhibitor binding in CoV-1 while it becomes less flexible in the CoV-2 mPro. Upon inspection, we show that, on average, the dynamic flexibility of the sites substituted from CoV-1 to CoV-2 changes significantly less than the average calculated across all residues within the structure, indicating that the differences in behaviors between the two systems is likely the result of allosteric influence, where the new substitutions in COV-2 induce flexibility and dynamical changes elsewhere in the structure. SIGNIFICANCEHere we have conducted a comparative analysis between the SARS-CoV-1 and SARS-CoV-2 mPro systems to shed mechanistic insight on the biophysical changes associated with the mutations between these two enzymes. Our work shows that the CoV-2 mPro system exhibits enhanced cross-chain communication between catalytic site residues and the rest of the structure. Further, both dynamic coupling and dynamic flexibility analyses indicates that, largely, the dynamic changes as evaluated by DCI and DFI occur at sites other than the mutation sites themselves, indicating that the functional differences between these two proteins are a result of dynamic allostery

biophysics↗

bioRxiv2021

The chromatin regulator HMGA1a undergoes phase separation in the nucleus

The protein high mobility group A1 (HMGA1) is an important regulator of chromatin organization and function. However, the mechanisms by which it exerts its biological function are not fully understood. Here, we report that the HMGA isoform, HMGA1a, nucleates into foci that display liquid-like properties in the nucleus, and that the protein readily undergoes phase separation to form liquid condensates in vitro. By bringing together machine-leaning modelling, cellular and biophysical experiments and multiscale simulations, we demonstrate that phase separation of HMGA1a is critically promoted by protein-DNA interactions, and has the potential to be modulated by post-transcriptional effects such as phosphorylation. We further show that the intrinsically disordered C-terminal tail of HMGA1a significantly contributes to its phase separation through cation-{pi} and electrostatic interactions. Our work sheds light on HMGA1 phase separation as an emergent biophysical factor in regulating chromatin structure.

biophysics↗

bioRxiv2021

Rigid DNA nanotube tethers suppress high frequency noise in dual-trap optical tweezers systems

Dual trap optical tweezers are a powerful tool to trap and characterize the biophysical properties of single biomolecules such as the folding pathways of proteins and nucleic acids, and the chemomechanical activity of molecular motors. Despite its vastly successful application, noise from drift and fluctuation of the optics, and Brownian motion of the trapped beads still hinder the techniques ability to directly visualize folding of small biomolecules or the single nucleotide stepping of polymerases, especially at low forces (<10 pN) and sub-millisecond timescales. Rigid DNA nanotubes have been used to replace the conventional dsDNA linker to reduce optical tweezers noise in the low force range. However, optical tweezers are used to study a wide range of biophysical events, with timescales ranging from microseconds to seconds, and length changes ranging from sub nanometers to tens of nanometers. In this study, we systematically evaluate how noise is distributed across different frequencies in dual trap optical tweezers systems-and show that rigid DNA nanotube tethers suppress only high frequency noise (kHz), while the low frequency noise remains the same when compared to that of dsDNA tethers.

biophysics↗

bioRxiv2022

Structure-function relationships in mitochondrial transcriptional condensates

Phase separation organizes many membraneless structures in cells. The functional consequences of concentrating cellular machinery into biomolecular condensates, however, are largely unclear. One fundamental cellular function that has been linked to condensate formation is transcription. Here, we have reconstituted mitochondrial transcription in condensates from purified components. We find that the core components of the mttranscriptional machinery form multi-phasic, viscoelastic condensates in vitro. Strikingly, the rates of condensate-mediated transcription are substantially lower than equivalent reactions in bulk solution. These condensate-mediated decreases in transcriptional rates are associated with the formation of dynamically arrested vesicular structures that are driven by the production and accumulation of RNA during transcription. Using coarse-grained, equilibrium simulations, we show that the generation of RNA alters the phase behavior and the organization of transcriptional components within condensates and that the in vitro mtcondensates are non-equilibrium structures. Together, our in vitro and in silico approaches shed light on how proteins and (ribo)nucleic acids biophysically self-assemble within mitochondria in vivo. Our results highlight the complex morphologies of transcribing, multicomponent condensates and they illustrate the interdependent structure-function relationships in condensates. Significance StatementMitochondria condense their genome into transcriptionally active mt-nucleoids. These structures fit the definition of biomolecular condensates that form via macromolecular phase separation. We take advantage of the ability to reconstitute mitochondrial transcriptional condensates in vitro from minimal components. We find that the production and accumulation of RNA alters the phase behavior of transcriptional condensates. The altered phase behavior is linked to the formation of arrested, non-equilibrium vesicular structures. Similar changes to phase behavior of proteins and (ribo)nucleic acids can be recapitulated in live mitochondria through knockdown of mt-nucleoid core components. Computer simulations help identify biophysical mechanisms that are needed to maintain the steady-state structures of transcriptional condensates.

biophysics↗

bioRxiv2022

Lifetime of actin-dependent protein nanoclusters

Protein nanoclusters (PNCs) are dynamic collections of a few proteins that spatially organize in nanometer length clusters. PNCs are one of the principal forms of spatial organization of membrane proteins and they have been shown or hypothesized to be important in various cellular processes, including cell signaling. PNCs show remarkable diversity in size, shape, and lifetime. In particular, the lifetime of PNCs can vary over a wide range of timescales. The diversity in size and shape can be explained by the interaction of the clustering proteins with the actin cytoskeleton or the lipid membrane, but very little is known about the processes that determine the lifetime of the nanoclusters. In this paper, using mathematical modelling of the cluster dynamics, we model the biophysical processes that determine the lifetime of actin-dependent PNCs. In particular, we investigated the role of actin aster fragmentation, which had been suggested to be a key determinant of the PNC lifetime, and found that it is important only for a small class of PNCs. A simple extension of our model allowed us to investigate the kinetics of protein-ligand interaction near PNCs. We found an anomalous increase in the lifetime of ligands near PNCs, which agrees remarkably well with experimental data on RAS-RAF kinetics. In particular, analysis of the RAS-RAF data through our model provides falsifiable predictions and novel hypotheses that will not only shed light on the role of RAS-RAF kinetics in various cancers, but also will be useful in studying membrane protein clustering in general. SignificanceSpatial organization of biomolecules shapes the behavior of a cell. It is particularly important during cell-signaling, where transient, dynamic organization of the biomolecules helps cells process signals and respond to them. Nanoclusters of peripheral membrane proteins, such as KRAS, a specific form of dynamic organization of biomolecules, play a critical part in the modulation of cell signals that control various cellular behaviors including cell growth, proliferation, and differentiation. Although we have made significant progress in understanding the structure, size, and origin of the nanoclusters, very little is known about the biophysical processes that control their lifetime. In this paper, we present a mathematical framework that provides quantitative insights into these processes and explains how oncogenic mutations in KRAS may lead to cancers.

biophysics↗

bioRxiv2022

Transbilayer Coupling of Lipids in Cells Investigated by Imaging Fluorescence Correlation Spectroscopy

Plasma membrane hosts numerous receptors, sensors, and ion channels involved in cellular signaling. Phase separation of the plasma membrane is emerging as a key biophysical regulator of signaling reactions in multiple physiological and pathological contexts. There is much evidence that plasma membrane composition supports the co-existence liquid-ordered (Lo) and liquid-disordered (Ld) phases or domains at physiological conditions. However, this phase/domain separation is nanoscopic and transient in live cells. It is recently proposed that transbilayer coupling between the inner and outer leaflets of the plasma membrane is driven by their asymmetric lipid distribution and by dynamic cytoskeleton-lipid composites that contribute to the formation and transience of Lo/Ld phase separation in live cells. In this Perspective, we highlight new approaches to investigate how transbilayer coupling may influence phase separation. For quantitative evaluation of the impact of these interactions, we introduce an experimental strategy centered around Imaging Fluorescence Correlation Spectroscopy (ImFCS), which measures membrane diffusion with very high precision. To demonstrate this strategy we choose two well-established model systems for transbilayer interactions: crosslinking by multivalent antigen of immunoglobulin E bound to receptor Fc{varepsilon}RI, and crosslinking by cholera toxin B of GM1 gangliosides. We discuss emerging methods to systematically perturb membrane lipid composition, particularly exchange of outer leaflet lipids with exogenous lipids using methyl alpha cyclodextrin. These selective perturbations may be quantitatively evaluated with ImFCS and other high-resolution biophysical tools to discover novel principles of lipid-mediated phase separation in live cells in the context of their pathophysiological relevance.

biophysics↗

bioRxiv2022

Particle flow modulates growth dynamics and nanoscale-arrested growth of transcription factor condensates in living cells

Liquid-liquid phase separation (LLPS) is emerging as key physical principle for biological organization inside living cells, forming condensates that play important roles in the regulation of multiple functions. Inside living nuclei, transcription factor (TF) condensates regulate transcriptional initiation and amplify transcriptional output of expressed genes. Yet, the biophysical parameters controlling TF condensation are still poorly understood. Here we applied a battery of single molecule imaging tools, theory and simulations to investigate the physical properties of TF condensates of the Progesterone Receptor (PR) in vivo. Analysis of individual PR trajectories at different ligand concentrations showed marked signatures of a ligand-tunable and regulated LLPS process. Using a machine learning architecture, we uncovered that diffusion within condensates follows fractional Brownian motion, reflecting viscoelastic interactions between PR and chromatin within condensates. High density single molecule localization maps further revealed that condensate growth dynamics is dominated by Brownian motion coalescence (BMC) at shorter times, but deviate at longer timescales reaching a growth plateau with nanoscale condensate sizes. To understand our observations we developed an extension of the BMC model by including stochastic unbinding of particles within condensates. The model reproduced the BMC behavior together with finite condensate sizes a steady-state, fully recapitulating our experimental data. Our results are thus consistent with droplet growth dynamics being regulated by the escaping probability of TFs molecules from condensates. The interplay between condensation assembly and molecular escaping maintains an optimum physical condensate size. Such phenomena must have implications for the biophysical regulation of other TF condensates and could also operate in multiple biological scenarios.

biophysics↗

bioRxiv2022

Unveiling molecular interactions that stabilize bacterial adhesion pili

Adhesion pili assembled by the chaperone-usher pathway are superelastic helical filaments on the surface of bacteria, optimized for attachment to target cells. Here, we investigate the biophysical function and structural interactions that stabilize P pili from uropathogenic bacteria. Using optical tweezers we measure P pilus subunit-subunit interaction dynamics and show that pilus compliance is contour-length dependent. Atomic details of subunit-subunit interactions of pili under tension are shown using steered molecular dynamics (sMD) simulations. sMD results also indicate that the N-terminal "staple" region of P pili significantly stabilizes the helical filament structure, consistent with previous structural data, suggesting more layer-to-layer interactions could compensate for the lack of a staple in Type 1 pili. This study informs our understanding of essential structural and dynamic features of adhesion pili, supporting the hypothesis that the biophysical function of pili is niche-adapted rather than a direct consequence of genetic similarity or diversity.

biophysics↗

bioRxiv2022

Force sensing on cells and tissues by atomic force microscopy

Biosensors are aimed to detect tiny physical and chemical stimuli in biological systems. Physical forces are ubiquitous, being implied in all cellular processes, including cell adhesion, migration, and differentiation. Given the strong interplay between cells and their microenvironment, the extracellular matrix (ECM), the structural and mechanical properties of the ECM play an important role in the transmission of external stimuli to single cells within the tissue. Vice versa, also cells themselves use self-generated forces to probe the biophysical properties of the ECM. ECM mechanics influences cell fate, regulates tissue development and show peculiar features in health and disease conditions of living organisms. Force sensing in biological systems is therefore crucial to dissect and understand complex biological processes, such as mechanotransduction. Atomic Force Microscopy (AFM), which can both sense and apply forces at the nanoscale, with sub-nanoNewton sensitivity, represents an enabling technology and a crucial experimental tool in biophysics and mechanobiology. In this work, we report on the application of AFM to study of biomechanical fingerprints of different components of biological systems, such as the ECM, the whole cell, and cellular components, like the nucleus and the glycocalyx. We show that physical observables like the (spatially resolved) Youngs modulus of elasticity of ECMs or cells, and the effective thickness and stiffness of the glycocalyx, can be quantitatively characterised by AFM. Their modification can be correlated to changes of the microenvironment, physio-pathological conditions, or gene regulation.

biophysics↗

bioRxiv2022

Plasticity in structure and assembly of SARS-CoV-2 nucleocapsid protein

Worldwide SARS-CoV-2 sequencing efforts track emerging mutations in its spike protein, as well as characteristic mutations in other viral proteins. Besides their epidemiological importance, the observed SARS-CoV-2 sequences present an ensemble of viable protein variants, and thereby a source of information on viral protein structure and function. Charting the mutational landscape of the nucleocapsid (N) protein that facilitates viral assembly, we observe variability exceeding that of the spike protein, with more than 86% of residues that can be substituted, on average by 3-4 different amino acids. However, mutations exhibit an uneven distribution that tracks known structural features but also reveals highly protected stretches of unknown function. One of these conserved regions is in the central disordered linker proximal to the N-G215C mutation that has become dominant in the Delta variant, outcompeting G215 variants without further spike or N-protein substitutions. Structural models suggest that the G215C mutation stabilizes conserved transient helices in the disordered linker serving as protein-protein interaction interfaces. Comparing Delta variant N-protein to its ancestral version in biophysical experiments, we find a significantly more compact and less disordered structure. N-G215C exhibits substantially stronger self-association, shifting the unliganded protein from a dimeric to a tetrameric oligomeric state, which leads to enhanced co-assembly with nucleic acids. This suggests that the sequence variability of N-protein is mirrored by high plasticity of N-protein biophysical properties, which we hypothesize can be exploited by SARS-CoV-2 to achieve greater efficiency of viral assembly, and thereby enhanced infectivity.

biophysics↗

bioRxiv2022

Model-free analysis in the spectral domain of postmortem mouse brain EPSI reveals inconsistencies with model-based analyses of the free induction decay

PurposeDysmyelinating disorders lead to abnormalities in myelin structure that produce detectable effects in an echo-planar spectroscopic imaging (EPSI) signal. To estimate the voxel-wise proportion of myelin, data are typically fit to compartmental models in the time domain. This work characterizes limitations in these models by comparing high-resolution water spectra measured in postmortem fixed mouse brains to spectra predicted from time-domain models fit to the same data, specifically by comparing spectra from control and shiverer mice, a model for dysmyelination. MethodsPerfusion-fixed, resected control (n = 5) and shiverer (n = 4) mouse brains were imaged using 3D EPSI with 100 {micro}m isotropic resolution. The free induction decay (FID) was sampled every 2.74 ms over 192 echoes and Fourier transformed to produce water spectra with 1.9 Hz resolution. FIDs were also fit to two biophysical models and the resulting fits were converted to spectra with a Fourier transform. Spectral asymmetry was computed and compared before and after fitting the data to models. ResultsSpectra derived from both models did not show the magnitude of asymmetric broadening observed in the raw data. Correlations between data- and model-derived asymmetries and estimated frequency shifts are weak, leading to a reduction in spectral sensitivity to changes in white-matter structure after fitting the data to models. ConclusionThe results demonstrate spectral inconsistencies between biophysical model predictions and measured data, promoting the further incorporation of spectral analysis methods to develop and benchmark new model-based approaches.

biophysics↗

bioRxiv2022

State-of-the-Art Estimation of Protein Model Accuracy using AlphaFold

The problem of predicting a proteins 3D structure from its primary amino acid sequence is a longstanding challenge in structural biology. Recently, approaches like AlphaFold have achieved remarkable performance on this task by combining deep learning techniques with coevolutionary data from multiple sequence alignments of related protein sequences. The use of coevolutionary information is critical to these models accuracy, and without it their predictive performance drops considerably. In living cells, however, the 3D structure of a protein is fully determined by its primary sequence and the biophysical laws that cause it to fold into a low-energy configuration. Thus, it should be possible to predict a proteins structure from only its primary sequence by learning a highly-accurate biophysical energy function. We provide evidence that AlphaFold has learned such an energy function, and uses coevolution data to solve the global search problem of finding a low-energy conformation. We demonstrate that AlphaFolds learned potential function can be used to rank the quality of candidate protein structures with state-of-the-art accuracy, without using any coevolution data. Finally, we explore several applications of this potential function, including the prediction of protein structures without MSAs.

biophysics↗