Biology subjects

Ray, K. K.

Publications and source records attributed to Ray, K. K..

10 recordsLinked to original sources

bioRxiv2026

Individual mouse mitotic chromosomes exhibit cell type-specific differences in biomechanical properties

Cyclic episodes of chromosome compaction and de-condensation are features of eukaryotic cell division that aid mitotic segregation and help prevent aneuploidy. While biophysical data on mitotic chromosome structure has been previously obtained, heterogeneity within samples can confound analyses and precludes direct like-for-like comparisons. To circumvent this, we employed advanced flow cytometry to purify specific metaphase chromosomes with biotinylated telomeres from stably engineered mouse cells. We show that ESC-derived metaphase chromosomes 3 and 19 display distinct properties but share a conserved force-dependent mechanical response. In contrast, chromosome equivalents isolated from NSCs and preB cells show markedly different force-dependent responses, reflecting progressive differentiation stages. Covalent crosslinking of ESC-derived chromosomes alters biomechanical properties to mimic equivalents from more differentiated cells. Collectively, these results highlight the need to isolate specific, homogeneous metaphase chromosome samples to accurately decipher their complex behaviours.

biophysics↗

bioRxiv2026

DeepDiffusion: a Physics-Informed Neural Network for Heterogeneous Facilitated 1D Diffusion

Single-molecule fluorescence microscopy combined with optical tweezers has enabled the direct observation of diffusing proteins on tethered DNA. These and complementary techniques reveal facilitated one-dimensional diffusion as a common functional mechanism for numerous DNA-binding proteins, with a wide range of heterogeneous diffusive behaviours arising from different DNA-binding modes. However, detailed investigations have been limited by the lack of methods to detect such heterogeneous diffusion. We have developed DeepDiffusion, a physics-informed neural network model for estimating the instantaneous diffusion at each point along a single-molecule trajectory. We show, using synthetic trajectories, that DeepDiffusion can accurately detect subtle changes in diffusion even when challenged with large underlying errors. DeepDiffusion can recapitulate previously characterised heterogeneity in experimental data and reveal mechanistic details of facilitated diffusion that are inaccessible to current methods. We expect DeepDiffusion to become a powerful tool for single-molecule researchers, allowing them to investigate the diffusion of their proteins of interest in unprecedented detail.

biophysics↗

bioRxiv2025

The ultrafine-bridge-associated endonuclease ANKLE1 is stimulated by tension in DNA to process branch-points

Covalent linkages between chromosomes are naturally formed as a result of recombination or DNA replication. These can persist until late mitosis, resulting in interchromosomal ultrafine bridges, preventing cell division and causing genome instability. The endonuclease ANKLE1, localised at the cell midbody during cytokinesis and selective for DNA branchpoints, is ideally poised to act as an enzyme of last resort to process interchromosomal bridges, enabling cell division to proceed. However, how ANKLE1 cleaves DNA under the tension existing in interchromosomal bridges during mitosis remains unexplored. Using optical tweezers, we show that ANKLE1 is a tension-stimulated endonuclease. High tension in DNA increases the ANKLE1 junction cleavage rate, with a twenty-fold increase at 60 pN. This indicates that ANKLE1 has evolved to respond to tension-induced DNA structural changes, thereby facilitating nucleolytic activity. This novel mechano-enzymological response of ANKLE1 reveals how it is well-suited to process ultrafine-bridges during late mitosis.

biophysics↗

bioRxiv2025

A cascade of structural rearrangements positions peptide release factor II for polypeptide hydrolysis on the ribosome

Peptide release factor II (RF2) catalyzes the release of the nascent polypeptide from the bacterial ribosomal complex during translation termination and a subset of ribosome rescue pathways. Despite its critical role, the mechanisms that govern RF2 function and regulation remain elusive. Here, using single-molecule fluorescence energy transfer (smFRET), we characterize the conformational landscape that RF2 explores on the ribosomal complex and show that RF2 binding and dissociation from the ribosome follows a series of conformational rearrangements which depend on its ribosomal binding platform. We also show how further interactions with the ribosomal complex are necessary to properly position RF2 for polypeptide release. This work investigates not only the dynamics RF2 undergoes while in complex with the ribosome, but also identifies a potential mechanism by which the regulation of these dynamics may be disrupted, which may be exploited for future development of broad-spectrum antibiotics.

biophysics↗

bioRxiv2024

RNA adapts its flexibility to efficiently fold and resist unfolding

Recent studies have demonstrated that the mechanisms through which biopolymers like RNA interconvert between multiple folded structures are critical for their cellular functions. A major obstacle to elucidating these mechanisms is the lack of experimental approaches that can resolve these interconversions between functionally relevant biomolecular structures. Here, we dissect the complete set of structural rearrangements executed by an ultra-stable RNA, the UUCG stem-loop, at the single-molecule level using a nano-electronic device with microsecond time resolution. We show that the stem-loop samples at least four conformations along two folding pathways leading to two distinct folded structures, only one of which has been previously observed. By modulating its flexibility, the stem-loop can adaptively select between these pathways, enabling it to both fold rapidly and resist unfolding. This paradigm of stabilization through compensatory changes in flexibility broadens our understanding of stable RNA structures and is expected to serve as a general strategy employed by all biopolymers.

biophysics↗

bioRxiv2023

Scale-dependent hierarchical resolution: applications to atomic resolution and model validation in cryoEM

Resolution is a poorly defined, ad hoc concept in structural biology, and multiple, seemingly conflicting definitions exist. In part, this ambiguity is because the structural information describing a biomolecule manifests in different ways depending upon the length scale being considered, while typical approaches to resolution just consider a single length scale. To address this shortcoming, here we develop a definition of resolution as a measure of how effectively those different manifestations of biomolecular structural information are embedded in experimental data across different length scales. We show how biomolecular structures can be decomposed into a hierarchy of structural elements, and combinations of those structural elements yield representations of the biomolecule with different degrees of spatial detail. A probabilistic measure, which we call "hierarchical resolution," can then be calculated to determine the statistically optimal representation of a biomolecule in an experiment. Hierarchical resolution is a local, detection-sensitive resolution measure capable of quantifying the biomolecular structural content of an experiment. Altogether, these advances comprise a technique-independent definition of resolution, and clarify the term "atomic resolution" for high-resolution techniques.

biophysics↗

bioRxiv2023

Increasing the accuracy of single-molecule data analysis using tMAVEN

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physico-chemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule dataset and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from pre-processing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule dataset with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule datasets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physico-chemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule datasets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses. Statement of SignificanceThe power of time-dependent single-molecule biophysical experiments lies in their ability to uncover the molecular mechanisms governing experimental systems by computationally applying kinetic models to the data. While many software solutions have been developed to estimate the optimal parameters of such models, the results reported here show that the models themselves are often inherently mismatched with the molecular mechanisms they are being used to analyze. To investigate these mismatches and demonstrate how to best model the kinetics of a molecular mechanism, we have used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), an open-source software platform we have developed that, among other features, enables the analysis of single-molecule datasets using different kinetic models within a single, extensible, and customizable pipeline.

biophysics↗

bioRxiv2023

Cryo-EM studies of the four E. coli paralogs establish ABCF proteins as master plumbers of the peptidyl-transferase center of the ribosome

The genomes of most mesophilic organisms encode multiple ATP-Binding Cassette F (ABCF) proteins. EttA, one of four E. coli paralogs, regulates synthesis of the first peptide bond on the ribosome dependent on ATP/ADP ratio, while Antibiotic Resistance factors (AREs), paralogs in other organisms, both regulate and directly mediate resistance to ribosome-targeted antibiotics. However, the physiological functions remain unclear for most paralogs, and the mechanism-of-action has yet to be rigorously established for any paralog. We herein present single particle cryogenic electron microscopy structures of ribosome complexes of all four E. coli ABCF paralogs (EttA, Uup, YbiT, and YheS), which, together with previously determined ARE structures, show that ABCFs control the binding geometry of the tRNA in the peptidyl-tRNA-binding (P) site on the ribosome. They modulate the position of its acceptor stem relative to the peptidyl transferase center (PTC) in a manner that can either promote (EttA and Uup) or disrupt (YbiT, YheS, and the AREs) proper catalytic geometry. The YbiT/70S reconstructions include a conformation with no density for ribosomal protein bL33, and structural analyses support the exchange of this sub-stoichiometric ribosomal protein being functionally related to conformational changes in YbiT controlled by sequence variations in the strongly non-canonical Signature Sequence in its first ABC domain. Our studies establish general structural/enzymological principles by which the ATPase activity of ABCF proteins controls translation elongation coupled to modulation of conformation and stereochemistry in the catalytic core of the ribosome.

biophysics↗

bioRxiv2022

Entropic control of the free energy landscape of an archetypal biomolecular machine

Biomolecular machines are complex macromolecular assemblies that utilize thermal and chemical energy to perform essential, multistep, cellular processes. Despite possessing different architectures and functions, an essential feature of the mechanisms-of-action of all such machines is that they require dynamic rearrangements of structural components. Surprisingly, biomolecular machines generally possess only a limited set of such motions, suggesting that these dynamics must be repurposed to drive different mechanistic steps. Although ligands that interact with these machines are known to drive such repurposing, the physical and structural mechanisms through which ligands achieve this remain unknown. Using temperature-dependent, single-molecule measurements analyzed with a time-resolution-enhancing algorithm, here we dissect the free energy landscape of an archetypal biomolecular machine, the bacterial ribosome, to reveal how its dynamics are repurposed to drive distinct steps during ribosome-catalyzed protein synthesis. Specifically, we show that the free energy landscape of the ribosome encompasses a network of allosterically coupled structural elements that coordinates the motions of these elements. Moreover, we reveal that ribosomal ligands which participate in disparate steps of the protein synthesis pathway repurpose this network by differentially modulating the structural flexibility of the ribosomal complex (i.e., the entropic component of the free energy landscape). We propose that such ligand-dependent entropic control of free energy landscapes has evolved as a general strategy through which ligands may regulate the functions of all biomolecular machines. Such entropic control is therefore an important driver in the evolution of naturally occurring biomolecular machines and a critical consideration for the design of synthetic molecular machines.

biophysics↗

bioRxiv2020

Bayesian inference: The comprehensive approach to analyzing single-molecule experiments

Biophysics experiments performed at single-molecule resolution contain exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. Here, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous manner to incorporate information from multiple experiments into a single analysis and to find the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.

biophysics↗