Biology subjects

Dao Duc, K.

Publications and source records attributed to Dao Duc, K..

16 recordsLinked to original sources

bioRxiv2026

Mapping the Functional Landscape and Viral Diversity of 2A Peptides

2A peptides are short (~18 to 22 amino acid) sequences utilized by a subset of RNA viruses to process their polyproteins in an unusual co-translational event known as StopGo or ribosome skipping. This process involves ribosome pausing at a conserved C-terminal D(V/I)ExNPGP motif, followed by peptidyl-tRNA hydrolysis of the nascent peptide-tRNAGly, and continued translation from the downstream proline codon. However, how nascent 2A peptide interacts with the ribosomal exit tunnel to promote these event(s) remains poorly understood and the molecular determinants governing 2A mediated StopGo translation remain undefined. Here, we developed a mammalian fluorescence resonance energy transfer (FRET) based high-throughput reporter system to quantitatively measure 2A activity across large libraries of natural and engineered 2A peptides. Systematic mutational analysis of canonical viral 2A peptides from porcine tescovirus and foot-and-mouth disease virus (P2A, F2A) identified key residues required for activity and revealed critical contributions from previously uncharacterized N-terminal residues. Extending this approach to metagenomic viral datasets uncovered conserved sequences governing StopGo efficiency leading to functional ranking of diverse viral 2A peptides in mammalian cells, including identification of a novel subclass that supports high stop-go activity (>99%). Phylogenetic analysis of viral genomes revealed clustering of functional 2A peptides and specific motifs within specific viral lineages branches, suggesting an evolutionary trajectory underlying 2A diversification and optimization. These findings define the sequence and functional landscape of viral 2A peptides and provide mechanistic and evolutionary insights into how nascent peptide ribosome exit tunnel interactions mediate StopGo translation.

biochemistry↗

bioRxiv2026

Learning Continuous Morphological Trajectories via Latent Principal Curves

Inferring continuous morphological transformations from collections of static biological snapshots is an important, yet challenging problem. In the context of cellular biology, prevailing approaches reduce 3D shape collections to static reconstructions or hand-crafted descriptors, which fail to capture smooth, multi-dimensional transitions. We present MorphCurveVAE, a two-stage pipeline for constructing continuous morphological trajectories from sets of static, segmented 3D microscopy images. Stage 1 learns a smooth, compact latent manifold of volumetric morphologies using a multi-branch convolutional variational auto-encoder (VAE) that can encode multiple correlated substructures into disentangled subspaces. Stage 2 extracts a constrained, topologically-aware principal curve through the augmented latent space to produce directional and correlated trajectories of structural dynamics. To demonstrate our framework, we apply MO_SCPLOWORPHC_SCPLOWCO_SCPLOWURVEC_SCPLOWVAE to a large public dataset (Allen Institute WTC-11) of segmented volumetric cell and nucleus images spanning the mitosis cycle. Our results indicate high-quality reconstructions, low projection errors to the fitted principal curve, and biologically and visually plausible continuous animations. These results suggest MorphCurveVAE as a practical tool for modeling biological morphological trajectories, while remaining broadly applicable to other biological imaging domains where time-resolved observations are unavailable.

biophysics↗

bioRxiv2025

Fast and interpretable quantification of biological shape heterogeneity via stratified Wasserstein kernel

Modern imaging technologies produce vast collections of cellular and subcellular structures, calling for principled methods that enable shape comparison across individuals and populations. We introduce the stratified Wasserstein framework, which treats each shape as an unstructured point cloud and embeds it into Euclidean space via ranked local distance profiles. This embedding yields an isometry-invariant Euclidean distance and a positive-definite kernel for population analysis, with a consistent sample-based estimator that supports large datasets in near-quadratic time. By leveraging kernel methods, the framework enables statistically rigorous tasks such as nonparametric hypothesis testing, providing theoretical guarantees as well as interpretability. We demonstrate our frameworks applicability to large-scale biological datasets. Analyzing 2D cancer cell contours, we quantify population-level discrepancies and identify representative cells contributing most strongly to the observed differences. Using 3D volumes of cell envelope and nucleus, we reveal progression patterns that capture morphological changes across cell populations both at the level of individual shapes. These results establish a simple and principled tool for population-level biological shape analysis, with potential impact across diverse domains of computational imaging and data science.

bioinformatics↗

bioRxiv2025

The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge

Despite the rise of single particle cryo-electron microscopy (cryo-EM) as a premier method for resolving macromolecular structures at atomic resolution, methods to address molecular heterogeneity in vitrified samples have yet to reach maturity. With an increasing number of new methods to analyze the multitude of heterogeneous states captured in single particle images, a systematic approach to validation in this field is needed. With this motivation, we issued a challenge to the community to analyze two cryo-EM particle image sets of thyroglobulin that exhibit continuous conformational heterogeneity. The first dataset was experimental and the second was generated with a simulator, allowing control over the distribution of molecular structures and enabled direct comparison between participants submissions and the ground truth molecular structures and distributions. Participants were asked to submit 80 volumes representing the heterogeneous ensemble and estimate their respective populations in the image sets provided. Participation of the research community in the challenge was strong, with submissions from nearly all developers of heterogeneity methods, resulting in 41 submissions across both datasets. Submissions qualitatively exceeded expectations, with the molecular motions identified by methods resembling both each other and the ground truth motion. However, quantitatively assessing these similarities was a challenge in and of itself. In the process of assessing the submissions, we developed several validation metrics, most of which require reference to the underlying ground truth volumes. However, we have also explored the use of metrics that do not necessarily reference ground truth. This is particularly apt for experimental datasets where ground truth is inaccessible. These approaches allowed us to assess the similarity and accuracy in volume quality, molecular motions, and conformational distribution of di!erent submissions. These metrics and the e!orts of all participants help chart a path forward for the improvements of heterogeneity methods for cryo-EM and for future challenges to validate these new methods as they continue to be developed by the community.

biophysics↗

bioRxiv2025

Quantifying the organization and dynamics of M. smegmatis morphology from Long-Term Time-Lapse Atomic Force Microscopy

The rod shaped Mycobacterium smegmatis displays complex cell surface morphology, characterized by wave-form cell surface features and driven by asymmetric growth dynamics. To systematically analyze these morphological variations, we developed a comprehensive computational pipeline for automated processing of Long-Term Time-Lapse Atomic Force Microscopy (LTTL-AFM) images of M. smegmatis cells cultured in axenic conditions of growth and stress. Upon running the pipeline to produce large enough datasets of single cell height profiles, we identify and statistically study key features that govern cell surface morphology: We confirm that M. smegmatis cells undergo bi-phasic, asymmetric pole growth with constant elongation rate at the old pole and a shift in the rate of elongation after a lag phase at the new pole. Stable wave-form cell surface peaks and troughs propagate along the long axis of the cell, which emerge as a result of polar elongation. Backtracking in time from cell division, we detect that division-site selection occurs at the wave-trough nearest mid-cell. To reproduce the fundamental cell features observed, we introduced a reaction-diffusion mathematical model on an evolving one-dimensional surface. Our simulations indicate that the dynamic manifestation of wave-form cell surface morphology in M. smegmatis can be explained by the interaction of as few as two "chemical species", providing a plausible theoretical basis for how molecular determinants may functionally control wave-form morphology in pole-growing bacteria.

cell biology↗

bioRxiv2023

Using a Riemannian elastic metric for statistical analysis of tumor cell shape heterogeneity

We examine how a specific instance of the elastic metric, the Square Root Velocity (SRV) metric, can be used to study and compare cellular morphologies from the contours they form on planar surfaces. We process a dataset of images from osteocarcoma (bone cancer) cells that includes different treatments known to affect the cell morphology, and perform a comparative statistical analysis between the linear and SRV metrics. Our study indicates superior performance of the SRV at capturing the cell shape heterogeneity, with a better separation between different cell groups when comparing their distance to their mean shape, as well as a better low dimensional representation when comparing stress statistics. Therefore, our study suggests the use of a Riemannian metric, such as the SRV as a potential tool to enhance morphological discrimination for large datasets of cancer cell images.

cell biology↗

bioRxiv2023

Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets

Conventional dimensionality reduction methods like Multidimensional Scaling (MDS) are sensitive to the presence of orthogonal outliers, leading to significant defects in the embedding. We introduce a robust MDS method, called DeCOr-MDS (Detection and Correction of Orthogonal outliers using MDS), based on the geometry and statistics of simplices formed by data points, that allows to detect orthogonal outliers and subsequently reduce dimensionality. We validate our methods using synthetic datasets, and further show how it can be applied to a variety of large real biological datasets, including cancer image cell data and human microbiome project data.

bioinformatics↗

bioRxiv2023

CAT PETR: A Graphical User Interface for Differential Analysis of Phosphorylation Data

Antibody microarray data provides a powerful and high-throughput tool to monitor global changes in cellular response to perturbation or genetic manipulation. However, while collecting such data has become increasingly accessible, a lack of specific computational tools has made their analysis limited. Here we present CAT PETR, a user friendly web application for the differential analysis of expression and phosphorylation data collected via antibody microarrays. Our application addresses the limitations of other GUI based tools by providing various data input options and visualizations. To illustrate its capabilities on real data, we show that CAT PETR both replicates previous findings, and reveals additional insights, using its advanced visualization and statistical options.

bioinformatics↗

bioRxiv2022

RiboXYZ: A comprehensive database for visualizing and analyzing ribosomestructures

Recent advances in Cryo-EM led to a surge of ribosome structures deposited over the past years, including structures from different species, conformational states, or bound with different ligands. Yet, multiple conflicts of nomenclature make the identification and comparison of structures and ortholog components challenging. We present RiboXYZ (available at https://ribosome.xyz), a database that provides organized access to ribosome structures, with several tools for visualisation and study. The database is up-to-date with the Protein Data Bank (PDB) but provides a standardized nomenclature that allows for searching and comparing ribosomal components (proteins, RNA, ligands) across all the available structures. In addition to structured and simplified access to the data, the application has several specialized visualization tools, including the identification and prediction of ligand binding sites, and 3D superimposition of ribosomal components. Overall, RiboXYZ provides a useful toolkit that complements the PDB database, by implementing the current conventions and providing a set of auxiliary tools that have been developed explicitly for analyzing ribosome structures. This toolkit can be easily accessible by both experts and non-experts in structural biology so that they can search, visualize and compare structures, with various potential applications in molecular biology, evolution, and biochemistry.

molecular biology↗

bioRxiv2022

Geometric differences in the ribosome exit tunnel impact the escape of small nascent proteins

The exit tunnel is the sub-compartment of the ribosome that contains the nascent polypeptide chain and as such, is involved in various vital functions, including regulation of translation and protein folding. As the geometry of the tunnel shows important differences across species, we focus on key geometrical features of eukaryote and prokaryote tunnels. We used a simple coarse-grained molecular dynamics model to study the role of the tunnel geometry in the post-translational escape of short proteins (sORFs), with lengths ranging from 6 to 56 amino acids. We found that the probability of escape for prokaryotes is one for all but the 12-mer chains. Moreover, proteins of this length have an extremely low escape probability in eukaryotes. A detailed examination of the associated single trajectories and energy profiles showed that these variations can be explained by the interplay between the protein configurational space and the confinement effects introduced by the constriction sites of the ribosome exit tunnel. For certain lengths, either one or both of the constriction sites can lead to the trapping of the protein in the "pocket" regions preceding these sites. As the distribution of existing sORFs indicate some bias in length that is consistent with our findings, we finally suggest that the constraints imposed by the tunnel geometry have impacted the evolution of sORFs.

biophysics↗

bioRxiv2020

MorphOT: Transport-based interpolation between EM maps with UCSF ChimeraX

MotivationCryogenic Electron-Microscopy offers the unique potential to capture conformational heterogeneity, by solving multiple 3D classes that co-exist within a single cryo-EM image dataset. To investigate the extent and implications of such heterogeneity, we propose to use an optimal-transport based metric to interpolate barycenters between EM maps and produce morphing trajectories. While standard linear interpolation mostly fails to produce realistic transitions, our method yields continuous trajectories that displace densities to morph one map into the other, instead of blending them. ImplementationOur method is implemented as a plug-in for ChimeraX called MorphOT, which allows the use of both CPU or GPU resources. The code is publicly available on GitHub (https://github.com/kdd-ubc/MorphOT.git), with documentation containing tutorial and datasets. Contactkdd@math.ubc.ca

bioinformatics↗

bioRxiv2018

The key parameters that govern translation efficiency

Translation of mRNA into protein is a fundamental yet complex biological process with multiple factors that can potentially affect its efficiency. In particular, different genes can have quite different initiation rates, while site-specific elongation rates can vary substantially along a given transcript. Here, we analyze a stochastic model of translation dynamics to identify the key parameters that govern the overall rate of protein synthesis and the efficiency of ribosome usage. The mathematical model we study is an interacting particle system that generalizes the Totally Asymmetric Simple Exclusion Process (TASEP), where particles correspond to ribosomes. While the TASEP and its variants have been studied for the past several decades through simulations and mean field approximations, a general analytic solution has remained challenging to obtain. By analyzing the so-called hydrodynamic limit, we here obtain exact closed-form expressions for stationary currents and particle densities that agree well with Monte Carlo simulations. In addition, we provide a complete characterization of phase transitions in the system. Surprisingly, phase boundaries depend on only four parameters: the particle size, and the first, last and minimum particle jump rates. Relating these theoretical results to translation, we formulate four design principles that detail how to tune these parameters to optimize translation efficiency in terms of protein production rate and resource usage. We then analyze ribosome profiling data of S. cerevisiae and demonstrate that its translation system is generally efficient, consistent with the design principles we found. We discuss implications of our findings on evolutionary constraints and codon usage bias.

genomics↗

bioRxiv2018

Differences in the path to exit the ribosome across the three domains of life

Recent advances in biological imaging have led to a surge of fine-resolution structures of the ribosome from diverse organisms. Comparing these structures, especially the exit tunnel, to characterize the key similarities and differences across species is essential for various important applications, such as designing antibiotic drugs and understanding the intricate details of translation dynamics. Here, we compile and compare 20 fine-resolution cryo-EM and X-ray crystallography structures of the ribosome recently obtained from all three domains of life (bacteria, archaea and eukarya). We first show that a hierarchical clustering of tunnel shapes closely reflects the species phylogeny. Then, by analyzing the ribosomal RNAs and proteins localized near the tunnel, we explain the observed geometric variations and show direct association between the conservations of the geometry, structure, and sequence. We find that the tunnel is more conserved in its upper part, from the polypeptide transferase center to the constriction site. In the lower part, tunnels are significantly narrower in eukaryotes than in bacteria, and we provide evidence for the existence of a second constriction site in eukaryotic tunnels. We also show that ribosomal RNA and protein sequences are more likely to be conserved closer to the tunnel, as is the presence of positively charged amino acids. Overall, our comparative analysis shows how the geometric and biophysical properties of the exit tunnel play an important role in ensuring proper transit of the nascent polypeptide chain, and may explain the differences observed in several co-translational processes across species.

biophysics↗

bioRxiv2018

Dynamics of ion fluxes between neurons, astrocytes and the extracellular space during neurotransmission

Ionic homeostasis in the brain involves redistribution of ionic fluxes in several cell types and compartments, including neurons, astrocytes and the extracellular space. How the major ionic activity-dependent fluxes of potassium and sodium are individually regulated remains difficult to dissociate and to track experimentally. We here review recent progress in modeling the ionic fluxes evoked by neuronal activity based on mass conservation. Excitability of neurons indeed relies on inward sodium and outward potassium fluxes during action potential firing. Recently, we have developed a tri-compartment model based on mass-action kinetics equations that account for potassium dynamics between neurons, astrocytes and the extracellular space. This review describes how such type of model can be used to spatially and temporally predict potassium fluxes during various regimes of neuronal activity. In particular, the model initially showed that it takes several seconds for astrocytes to buffer the majority of the potassium rapidly released by neurons in both basal and high regime of activity. Such model can also probe the selective contribution of ionic channels, and revealed for instance that disruption of the main astroglial potassium Kir4.1 channels not only favors the emergence of epileptiform activity, but also dysregulates neuronal excitability specifically during slow rhythmic activities. We here also extend the predictions of the model by assessing the selective contribution of the astroglial and neuronal Na/K ATPase, or volume of the extracellular space on potassium dynamics. We discuss these findings and their implications for neuronal information processing in the healthy and diseased brain.

neuroscience↗

bioRxiv2017

Theoretical quantification of interference in the TASEP: Application to mRNA translation shows near-optimality of termination rates

The Totally Asymmetric Exclusion Process (TASEP) is a classical stochastic model for describing the transport of interacting particles, such as ribosomes moving along the mRNA during translation. Although this model has been widely studied in the past, the extent of collision between particles and the average distance between a particle to its nearest neighbor have not been quantified explicitly. We provide here a theoretical analysis of such quantities via the distribution of isolated particles. In the classical form of the model in which each particle occupies only a single site, we obtain an exact analytic solution using the Matrix Ansatz. We then employ a refined mean field approach to extend the analysis to a generalized TASEP with particles of an arbitrary size. Our theoretical study has direct applications in mRNA translation and the interpretation of experimental ribosome profiling data. In particular, our analysis of data from S. cerevisiae suggests a potential bias against the detection of nearby ribosomes with gap distance less than ~ 3 codons, which leads to some ambiguity in estimating the initiation rate and protein production flux for a substantial fraction of genes. Despite such ambiguity, however, we demonstrate theoretically that the interference rate associated with collisions can be robustly estimated, and show that approximately 1% of the translating ribosomes get obstructed.

systems biology↗

bioRxiv2016

Identification and quantitative analysis of the major determinants of translation elongation rate variation

Previous studies have shown that translation elongation is regulated by multiple factors, but the observed heterogeneity remains only partially explained. To dissect quantitatively the different determinants of elongation speed, we use probabilistic modeling to estimate initiation and local elongation rates from ribosome profiling data. This model-based approach allows us to quantify the extent of interference between ribosomes on the same transcript. We show that neither interference nor the distribution of slow codons is sufficient to explain the observed heterogeneity. Instead, we find that electrostatic interactions between the ribosomal exit tunnel and specific parts of the nascent polypeptide govern the elongation rate variation as the polypeptide makes its initial pass through the tunnel. Once the N-terminus has escaped the tunnel, the hydropathy of the nascent polypeptide within the ribosome plays a major role in modulating the speed. We show that our results are consistent with the biophysical properties of the tunnel.

genomics↗