Biology subjects

Saurabh, A.

Publications and source records attributed to Saurabh, A..

10 recordsLinked to original sources

bioRxiv2026

Biased stochastic motor dynamics drive bidirectional, processive translocation by the AAA+ disaggregase Hsp104.

Protein aggregation disrupts proteostasis and drives neurodegeneration. Hsp104 is a hexameric, ring-shaped AAA+ ATPase that dissolves protein aggregates, yet how hexamers translocate and extract polypeptides trapped in mechanically resistant aggregates remains unclear. Using substrates that recapitulate the physical constraints of aggregates, we establish that Hsp104 is a processive, bidirectional translocase that can dynamically switch direction while threading a single polypeptide. On mechanically restrained substrates and prions, Hsp104 hexamers execute biased stochastic transitions among three conformational states at individual interprotomer interfaces: closed, extended, and a previously unobserved hyperextended form. These transitions follow kinetically favored paths rather than a rigid rotary sequence. The resulting biased stochastic stepping, enabled by the conformational plasticity of Hsp104 hexamers, underpins operational adaptability and redefines the functional logic of AAA+ motors.

biochemistry↗

bioRxiv2025

Highly multiplexed spectral FLIM via physics informed data analysis

Spectral fluorescence lifetime imaging (S-FLIM) allows for the simultaneous deconvolution of signal from multiple fluorophore species by leveraging both spectral and lifetime information. However, existing analyses still face multiple difficulties in decoding information collected from typical S-FLIM experiments. These include: using information from pre-calibrated spectra in environments that may differ from the cellular context in which S-FLIM experiments are performed; limitations in the ability to deconvolute species due to overlapping spectra; high photon budget requirements, typically about a hundred photons per pixel per species. Yet information on the spectra themselves are already encoded in the data and do not require pre-calibration. What is more, efficient photon-by-photon analyses are possible reducing both the required photon budget and making it possible to use larger budgets in order to discriminate small differences in spectra to resolve spatially co-localized fluorophore species. To achieve this, we propose a Bayesian S-FLIM framework capable of simultaneously learning spectra and lifetimes photon-by-photon ultimately using limited photon counts and being highly data efficient. We demonstrate the proposed framework using a range of synthetic and experimental data and show that it can deconvolve up to 9 species with heavily overlapped spectra.

biophysics↗

bioRxiv2025

Bayesian Nonparametrics for FRET using Realistic Integrative Detectors

Biomolecular dynamics are often strikingly heterogeneous, with individual molecules sampling different states and kinetics--violating the "average molecule" assumption. Yet FRET analyses cannot resolve such variability or distinguish states differing mainly in kinetics, rather than FRET efficiency, as molecular configurations are projected onto 1D FRET signals. Here we introduce BNP-FRET-Bin, inferring state numbers and their kinetics directly from FRET data. In doing so, we eliminate user-specified parameters and expose molecule-to-molecule heterogeneity revealing new biologically relevant Holliday junction states with near identical FRET efficiencies.

biophysics↗

bioRxiv2025

DNA origami-based platform for multi-axial single-molecule force spectroscopy reveals hidden dynamics in Holliday junctions

Biomolecules in living cells experience complex multi-directional mechanical forces that regulate their structure, dynamics, and function. However, most single-molecule techniques primarily exert force along a single axis, thereby failing to emulate the mechanical environment of cells. Here, we demonstrate that single-axis force application fundamentally restricts conformational dynamics by kinetically trapping molecules within distinct dynamic classes, preventing interconversion and exploration of the full conformational landscape. We developed MAESTRO (Multi-Axial Entropic Spring Tweezer along a Rigid Origami), a molecular platform that applies up to 9 pN forces from up to 4 directions simultaneously using programmable ssDNA entropic springs anchored to a DNA origami scaffold. We applied MAESTRO to Holliday junctions (HJs), 4-way DNA intermediates that experience multi-directional tension during homologous recombination. Counterintuitively, we discovered >5x slower kinetics of the HJ conformations under multi-axial tension than under tension-free conditions, enabling direct observations of previously hidden HJ conformational intermediates. Most remarkably, we discovered that multi-axial forces unlock conformational dynamics, enabling interconversion between 5 distinct kinetic classes that remain kinetically inaccessible under zero force or single-axial tension. Furthermore, we demonstrated that tension regulates T7 endonuclease I cleavage site selection, directly linking mechanical environments and molecular mechanics to enzymatic function. By overcoming single-axis limitations, MAESTRO opens new frontiers in molecular mechanobiology, revealing how multi-directional cellular force environments are essential for unlocking the full conformational landscape of biomolecules, and that these complex force patterns serve as master regulators of biological function through mechanisms hidden from conventional approaches.

biophysics↗

bioRxiv2025

Simultaneous particle tracking, phase retrieval and point spread function reconstruction

3D tracking and localization of particles, typically fluorescently labeled biomolecules, provides a direct means of monitoring cellular transport and communication. However, sample-induced wavefront distortions of emitted fluorescent light as it passes through the sample and onto the detector often yield point spread function (PSF) aberrations, presenting an important challenge to 3D particle tracking using pre-calibrated PSFs. PSF calibration is typically performed outside cellular samples, ignoring sample-induced aberrations, which can result in localization errors on the order of tens to hundreds of nanometers, ultimately compromising sub-diffraction limited tracking. In practice, correcting sample-induced aberrations currently requires sample-specific hardware adjustments, such as adaptive optics. Yet, information on sample-induced aberrations and PSF shape can be directly decoded from data collected using a 3D imaging setup (e.g., bi-focal). To this end, we propose a framework for simultaneous particle tracking, phase retrieval, and PSF reconstruction (SPT-PR) directly from the input data themselves. We apply it to sub-diffraction tracking of lytic granules released at the immunological synapse of T cells revealing slower motions in proximity of the plasma cell membrane, consistent with assembly of the fusion machinery and, ultimately, degranulation and release of toxic payloads. To accomplish this, we operate within a Bayesian paradigm, placing continuous priors on all possible pupil phase and amplitudes warranted by the data without limiting ourselves to a finite Zernike set-thereby allowing capture of intricate pupil phase details. We benchmark our framework using a wide range of synthetic and experimental data from static to diffusing particles, and generalize to multiple diffusing particles with overlapping PSFs. Further, as a result of simultaneous particle tracking, phase retrieval, and PSF reconstruction, we retrieve the pupil phase with errors smaller than 10% under a range of realistic scenarios while demonstrating that for tracking lytic granules under an idealized Gaussian PSF assumption, we recover discrepancies as large as hundreds of nanometers.

biophysics↗

bioRxiv2023

Substrate-interacting pore loops of two ATPase subunits determine the degradation efficiency of the 26S proteasome

The 26S proteasome is the major eukaryotic protease responsible for protein quality control, proteostasis, and the modulation of numerous vital processes through the degradation of regulatory proteins. Commitment to degradation occurs when conserved pore loops in the proteasomal heterohexameric ATPase motor engage the flexible initiation region of a polyubiquitinated protein substrate for subsequent mechanical unfolding and translocation into a proteolytic chamber. Here, we used in vitro biochemical and single-molecule FRET-based assays with mutant reconstituted 26S proteasomes from yeast to characterize how the pore-1 loops of individual ATPase subunits in the AAA+ motor contribute to the different steps of substrate degradation and affect the proteasome conformational dynamics. We found that the pore-1 loop of the Rpt6 ATPase subunit plays particularly important roles in substrate capture, engagement, and unfolding, while the pore-1 loop of the Rpt4 ATPase is critical for providing sufficient grip for substrate unraveling and maintaining a processing-competent state of the proteasome. Interestingly, these pore-1-loop contributions correlate with their positions in the spiral-staircase arrangements of ATPase subunits in the substrate-free and substrate-degrading proteasome, providing new insights into the mechanisms of substrate processing by the 26S proteasome and related hexameric ATPase motors.

biochemistry↗

bioRxiv2023

A Structured Illumination Microscopy Framework with Spatial-Domain Noise Propagation.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled, manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

biophysics↗

bioRxiv2022

Single Photon smFRET. I. Theory and Conceptual Basis

We present a unified conceptual framework and the associated software package for single molecule Forster Resonance Energy Transfer (smFRET) analysis from single photon arrivals leveraging Bayesian nonparametrics, BNP-FRET. This unified framework addresses the following key physical complexities of a single photon smFRET experiment, including: 1) fluorophore photophysics; 2) continuous time kinetics of the labeled system with large timescale separations between photophysical phenomena such as excited photophysical state lifetimes and events such as transition between system states; 3) unavoidable detector artefacts; 4) background emissions; 5) unknown number of system states; and 6) both continuous and pulsed illumination. These physical features necessarily demand a novel framework that extends beyond existing tools. In particular, the theory naturally brings us to a hidden Markov model (HMM) with a second order structure and Bayesian nonparametrics (BNP) on account of items 1, 2 and 5 on the list. In the second and third companion manuscripts, we discuss the direct effects of these key complexities on the inference of parameters for continuous and pulsed illumination, respectively. Why It MatterssmFRET is a widely used technique for studying kinetics of molecular complexes. However, until now, smFRET data analysis methods required specifying a priori the dimensionality of the underlying physical model (the exact number of kinetic parameters). Such approaches are inherently limiting given the typically unknown number of physical configurations a molecular complex may assume. The methods presented here eliminate this requirement and allow estimating the physical model itself along with kinetic parameters, while incorporating all sources of noise in the data.

biophysics↗

bioRxiv2022

Single Photon smFRET. II. Application to Continuous Illumination

Here we adapt the Bayesian nonparametrics (BNP) framework presented in the first companion manuscript to analyze kinetics from single photon, single molecule Forster Resonance Energy Transfer (smFRET) traces generated under continuous illumination. Using our sampler, BNP-FRET, we learn the escape rates and the number of system states given a photon trace. We benchmark our method by analyzing a range of synthetic and experimental data. Particularly, we apply our method to simultaneously learn the number of system states and the corresponding kinetics for intrinsically disordered proteins (IDPs) using two-color FRET under varying chemical conditions. Moreover, using synthetic data, we show that our method can deduce the number of system states even when kinetics occur at timescales of interphoton intervals. Why It MattersIn the first companion manuscript of this series, we developed new methods to analyze noisy smFRET data. These methods eliminate the requirement of a priori specifying the dimensionality of the physical model describing a molecular complexs kinetics. Here, we apply these methods to experimentally obtained datasets with samples illuminated by time-invariant laser intensities. In particular, we study interactions of IDPs.

biophysics↗

bioRxiv2022

Single Photon smFRET. III. Application to Pulsed Illumination

Forster resonance energy transfer (FRET) using pulsed illumination has been pivotal in leveraging lifetime information in FRET analysis. However, there remain major challenges in quantitative single photon, single molecule FRET (smFRET) data analysis under pulsed illumination including: 1) simultaneously deducing kinetics and number of system states; 2) providing uncertainties over estimates, particularly uncertainty over the number of system states; 3) taking into account detector noise sources such as crosstalk, and the instrument response function contributing to uncertainty; in addition to 4) other experimental noise sources such as background. Here, we implement the Bayesian nonparametric framework described in the first companion manuscript that addresses all aforementioned issues in smFRET data analysis specialized for the case of pulsed illumination. Furthermore, we apply our method to both synthetic as well as experimental data acquired using Holliday junctions. Why It MattersIn the first companion manuscript of this series, we developed new methods to analyze noisy smFRET data. These methods eliminate the requirement of a priori specifying the dimensionality of the physical model describing a molecular complexs kinetics. Here, we apply these methods to experimentally obtained datasets with samples illuminated by laser pulses at regular time intervals. In particular, we study conformational dynamics of Holliday junctions.

biophysics↗