Biology subjects

Saurabh, A.

Publications and source records attributed to Saurabh, A..

5 recordsLinked to original sources

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↗