Biology subjects

Uddin, M. R.

Publications and source records attributed to Uddin, M. R..

10 recordsLinked to original sources

bioRxiv2025

Deep Disentangled Representation Learning Reveals Neuron Subtype-Specific Nuclear Morphologies Across Aging in Mice and Humans

We present a computational pipeline that links nuclear morphology to mRNA expression-based cell phenotypes under diverse biological conditions, including aging, disease progression, and drug response, using RNAscope imaging. The pipeline consists of three components: nuclear segmentation from RNAscope images, nuclear morphology identification, and downstream statistical analysis. Central to our approach is a novel unsupervised method, based on deep disentangled representation learning, which effectively captures diverse nuclear morphologies in large-scale datasets, as validated on synthetic benchmarks. We applied the full pipeline to RNAscope data targeting dopaminergic and glutamatergic neuron populations in the midbrains of mice and humans. Our analyses uncovered distinct nuclear morphology differences between dopaminergic and non-dopaminergic, as well as glutamatergic and non-glutamatergic neurons, in both species. Moreover, we identified a significant interaction between neurotransmitter identity and healthy aging in mice, reflected in systematic changes in nuclear morphology. These findings position nuclear morphology as a scalable and informative imaging-based readout of cell identity and physiological state.

bioinformatics↗

bioRxiv2025

Unsupervised Multi-scale Segmentation of Cellular Cryo-electron Tomograms with Stable Diffusion Foundation Model

We introduce an unsupervised approach for segmenting multiscale subcellular objects in 3D volumetric cryo-electron tomography (cryo-ET) images, addressing key challenges such as large data volumes, low signal-to-noise ratios, and the heterogeneity of subcellular shapes and sizes. The method requires users to select a small number of slabs from a few representative tomograms in the dataset. It leverages features extracted from all layers of a Stable Diffusion foundation model, followed by a novel heuristic-based feature aggregation strategy. Segmentation masks are generated using adaptive thresholding, refined with CellPose to split composite regions, and then utilized as pseudo-ground truth for training deep learning models. We validated our pipeline on publicly available cryo-ET datasets of S. Pombe and C. Eleganscell sections, demonstrating performance that closely approximates expert human annotations. This fully automated, data-driven framework enables the mining of multi-scale subcellular patterns, paving the way for accelerated biological discoveries from large-scale cellular cryo-ET datasets.

bioinformatics↗

bioRxiv2025

Molecular basis for multidrug efflux by an anaerobic RND transporter

Bacteria can resist antibiotics and toxic substances within demanding ecological settings, such as low oxygen, extreme acid, and during nutrient starvation. MdtEF, a proton motive force-driven efflux pump from the resistance-nodulation-cell division (RND) superfamily, is upregulated in these conditions but its molecular mechanism is unknown. Here, we report cryo-electron microscopy structures of Escherichia coli multidrug transporter MdtF within native-lipid nanodiscs, including a single-point mutant with an altered multidrug phenotype and associated substrate-bound form. We reveal that drug binding domain and channel conformational plasticity likely governs promiscuous substrate specificity, analogous to its closely related, constitutively expressed counterpart, AcrB. Whereas we discover distinct transmembrane state transitions within MdtF, which create a more engaged proton relay network, altered drug transport allostery and an acid-responsive increase in efflux efficiency. Physiologically, this provides means of xenobiotic and metabolite disposal within remodelled cell membranes that presage encounters with acid stresses, as endured in the gastrointestinal tract.

biochemistry↗

bioRxiv2024

TomoPicker: Annotation-Efficient Particle Picking in cryo-electron Tomograms

MotivationLocalizing macromolecules in crowded cellular cryo-electron tomograms (cryo-ET) is crucial for determining their in situ structures. Traditional template matching-based approaches for this task suffer from template-specific biases and have low throughput. Given these problems, learning-based solutions are necessary. However, the paucity of annotated data for training poses substantial challenges for such learning-based methods. Moreover, preparing extensively annotated cellular cryo-ET tomograms for training macromolecule localization methods is extremely time-consuming and burdensome due to the large volume and low signal-to-noise ratio of the tomograms. ResultsIn this work, we developed TomoPicker, an annotation-efficient macromolecule localization method for cryo-ET tomograms. To achieve such annotation-efficiency, TomoPicker regards macromolecule localization as a voxel classification problem and solves it with two different positive-unlabeled learning approaches. We evaluated TomoPicker on two experimental cryo-electron tomography (cryo-ET) datasets of crowded eukaryotic cells and one experimental dataset of relatively less crowded prokaryotic cell. We observed that, with only 10 annotated macromolecule locations, TomoPicker with positive unlabeled learning achieved a performance comparable to that of state-of-the-art supervised methods trained with several hundred annotations. In other words, TomoPicker achieved plausible segmentation with up to 98% less data compared to supervised learning-based methods. Furthermore, it demonstrated substantial improvements over existing learning-based macromolecule localization methods under sparse annotation scenarios. CodeThe code to train and use TomoPicker is available on https://github.com/DuranRafid/TomoPicker.

bioinformatics↗

bioRxiv2024

Finding the E-Channel Proton Loading Sites by Calculating the Ensemble of Protonation Microstates

The aerobic electron transfer chain builds a proton gradient by proton coupled electron transfer reactions through multiple proteins. Complex I is the first enzyme in this chain. On transfer of two electrons from NADH to quinone four protons are pumped from the N- (negative, higher pH) to the P- (positive, lower) side. Protons move pthrough three linear antiporter paths, with a few amino acids and waters providing the route and the E-channel, which is a complex of competing paths, with clusters of interconnected protonatable residues. Proton loading sites (PLS) are residues that transiently bind protons as they are transported from N- to P-compartments. PLS can be individual residues or extended clusters. The program MCCE uses Monte Carlos sampling to analyze the E-channel proton binding in equilibrium with individual Molecular Dynamics snapshots from trajectories of Thrmus thermuphillus Complex I in the apo, quinone or quinol bound states. At pH 7, the five E-channel subunits (Nqo4, Nqo7, Nqo8, Nqo10, and Nqo11) have accepted >25,000 protonation microstates, each with different residues protonated. The microstate explosion is tamed by analyzing interconnected clusters of residues along the proton transfer paths. A proton is bound and released to five coupled residues in a cluster on the protein N-side and to six coupled residues in the protein center. Loaded microstates bind protons to sites closer to the P-side in the forward pumping direction. MCCE microstate analysis shows the strong coupling of proton binding amongst individual residues in the two PLS clusters.

biophysics↗

bioRxiv2024

Mg2+-dependent mechanism of environmental versatility in a multidrug efflux pump

Tripartite resistance nodulation and cell division multidrug efflux pumps span the periplasm and are a major driver of multidrug resistance among Gram-negative bacteria. The periplasm provides a distinct environment between the inner and outer membranes of Gram-negative bacteria. Cations, such as Mg2+, become concentrated within the periplasm and, in contrast to the cytoplasm, its pH is sensitive to conditions outside the cell. Here, we reveal an interplay between Mg2+ and pH in modulating the dynamics of the periplasmic adaptor protein, AcrA, and its function within the prototypical AcrAB-TolC multidrug efflux pump from Escherichia coli. In the absence of Mg2+, AcrA becomes increasingly plastic within acidic conditions, but when Mg2+ is bound this is ameliorated, resulting in domain specific organisation in neutral to weakly acidic regimes. We establish a unique histidine residue directs these structural dynamics and is essential for sustaining pump efflux activity across acidic, neutral, and alkaline conditions. Overall, we propose Mg2+ conserves the structural mobility of AcrA to ensure optimal AcrAB-TolC function within rapid changing environments commonly faced by the periplasm during bacterial infection and colonization. This work highlights that Mg2+ is an important mechanistic component in this pump class and possibly across other periplasmic lipoproteins.

biochemistry↗

bioRxiv2023

Structural characterization of a novel luciferase-like-monooxygenase from Pseudomonas meliae an in silico approach

BackgroundLuciferase is a well-known oxidative enzyme that produces bioluminescence. The Pseudomonas meliae is a plant pathogen that causes wood rot on nectarine and peach and possesses a luciferase-like monooxygenase. After activation, it produces bioluminescence, and the pathogens bioluminescence is a visual indicator of diseased plants. MethodsThe present study aims to model and characterize the luciferase-like monooxygenase protein in P. meliae for its similarity to well-established luciferase. In this study, the luciferase-like monooxygenase from P. meliae infects chinaberry plants has been modeled first and then studied by comparing it with existing known luciferase. Also, the similarities between uncharacterized luciferase from P. meliae and template from Geobacillus thermodenitrificans were analyzed to find the novelty of P. meliae. ResultsThe results suggest that the absence of bioluminescence in P. meliae could be due to the evolutionary mutation in positions 138 and 311. The active site remains identical except for two amino acids; P. meliae Tyr138 instead of His138 and Leu311 instead of His311. Therefore, the P. meliae will have a potential future application, and mutation of the residues 138 and 311 can be restored luciferase light-emitting ability. ConclusionsThis study will help further improve, activate, and repurpose the luciferase from P. meliae as a reporter for gene expression.

bioinformatics↗

bioRxiv2022

Conformational restriction shapes inhibition of a multidrug efflux adaptor protein

Membrane efflux pumps play a major role in bacterial multidrug resistance. The tripartite multidrug efflux pump system from Escherichia coli, AcrAB-TolC, is a target for inhibition to lessen resistance development and restore antibiotic efficacy, with homologs in other ESKAPE pathogens. Here, we rationalize a mechanism of inhibition against the periplasmic adaptor protein, AcrA, using a combination of hydrogen/deuterium exchange mass spectrometry, cellular efflux assays, and molecular dynamics simulations. We define the structural dynamics of AcrA and find that an inhibitor can inflict long-range stabilisation across all four of its domains, whereas an interacting efflux substrate has minimal effect. Our results support a model where an inhibitor forms a molecular wedge within a cleft between the lipoyl and {beta} domains of AcrA, diminishing its conformational transmission of drug-evoked signals from AcrB to TolC. This work provides molecular insights into multidrug adaptor protein function which could be valuable for developing antimicrobial therapeutics.

biochemistry↗

bioRxiv2019

eMED-DNA: An in silico operating system for clinical medical data storage within the human genome

The propitious developments in molecular biology and next generation sequencing have enabled the possibility for DNA storage technologies. However, the full application and power of our genomic revolution have not been fully utilized in clinical medicine given a lack of transition from research to real world clinical practice. This has identified an increasing need for an operating system which allows for the transition from research to clinical use. We present eMED-DNA, an in silico operating system for archiving and managing all forms of electronic health records (EHRs) within ones own copy of the sequenced genome to aid in the application and integration of genomic medicine within real world clinical practice. We incorporated an efficient and sophisticated in-DNA file management system for the lossless management of EHRs within a genome. This represents the first in silico integrative system which would bring closer the utopian ideal for integrating genotypic data with phenotypic clinical data for future medical practice.

bioinformatics↗

bioRxiv2019

SAINT: Self-Attention AugmentedInception-Inside-Inception Network Improves ProteinSecondary Structure Prediction

MotivationProtein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g., X-ray crystallography, nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the secondary structure of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of secondary structure contains more useful information and is much more challenging than the Q3 prediction. ResultsWe present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception (Deep3I) network in order to effectively capture both the short-range and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step towards the accurate and reliable prediction of secondary structures of proteins. AvailabilitySAINT is freely available as an open source project at https://github.com/SAINTProtein/SAINT.

bioinformatics↗