Biology subjects

Bhowmik, D.

Publications and source records attributed to Bhowmik, D..

18 recordsLinked to original sources

bioRxiv2026

Organelle communication networks rewire to support lipid metabolism during neuronal differentiation

Cell fate transitions require coordinated remodeling of intracellular organelles, but how organelle morphology and interactions rewire during neurogenesis remains unclear. Here we combine multispectral imaging with quantitative organelle signature analysis to simultaneously map eight organelles as human induced pluripotent stem cells differentiate into forebrain-like neurons. We find compartment and time-specific rescaling of organelles and a progressive increase in higher-order membrane contacts, with mitochondria emerging as an early interaction hub. Later, endoplasmic reticulum (ER)-organelle contacts dominate with ER-peroxisome contacts promoting ether lipid biosynthesis, membrane homeostasis and synapse formation. Disrupting this contact impairs plasmalogen production, synaptic organization, and neuronal activity, identifying the ER-peroxisome axis as a key regulator of neuronal maturation.

cell biology↗

bioRxiv2025

HUGMi: Human Uro-Genital Microbiome database and hybrid classifier for improved species level annotation of 16S rRNA amplicon sequences

The human urogenital microbiota encompasses commensal microorganisms harbouring the urinary and reproductive tracts, and has been associated with the pathophysiology of multiple urogenital disorders. Here, the Human Urogenital Microbiome (HUGMi) workflow is introduced, wherein a manually curated, niche-specific, 16S rRNA database is integrated with a novel hybrid classification algorithm to enhance amplicon-based species-level taxonomic resolution in urogenital microbiome investigations. The HUGMi database is curated through rigorous quality assessment of sequences, nomenclature correction of taxonomies and meticulous selection of bacterial species biologically significant to the human urogenital region. Quantitative evaluation of taxonomic assignment with mock communities demonstrates that HUGMi consistently achieved superior accuracy compared to conventional databases, with statistically significant improvement in F1 scores. Comparative analyses across three sets of biological data also reveal a robust and versatile performance, with both QIIME2 BLAST and sklearn-based taxonomic classifiers and with varied 16S regions. Following these outcomes the HUGMi hybrid classifier was developed that combines the specificity of BLAST-based methods with the probabilistic framework of learning-based classifiers. This approach enabled superior species-level identification through varying confidence thresholds. In contrast to the limited number of species identified in real-time studies, the integrated outcome of the HUGMi database and hybrid classifier, not just replicated the original results but simultaneously revealed a spectrum of previously unidentified species. Despite its crucial role in human health, the urogenital microbiome is comparatively understudied with respect to other body sites. The HUGMi pipeline addresses this empirical lacuna by identifying a substantially expanded range of species-level taxa. This enhanced resolution will not only augment downstream functional studies, but also aid in the investigation of specific bacterial influence on urogenital homeostasis and disease pathogenesis.

bioinformatics↗

bioRxiv2024

Exploring Breast Cancer-Related Biochemical Changes in Circulating Extracellular Vesicles Using Raman Spectroscopy

Extracellular vesicles (EVs) are a subgroup of the circulating particles, released by cells in both normal and diseased states, carrying active biomolecules. They have gained significant attention as potential cancer biomarkers, particularly in breast cancer (BC). Previous research showed variations in EVs content and quantity between BC patients and healthy controls (HC). However, studying EVs biochemical profile remains challenging due to their low abundance and complex composition. Additionally, EVs may interact with other plasma components, like lipoproteins (LPs), forming a so called "biomolecular corona" that further complicates their analysis. Here, Raman spectroscopy (RS) is proposed as a fast tool to obtain the biochemical profile of circulating EVs in the context of BC. RS was employed to differentiate various extracellular particles (EPs) in blood, including LPs and EVs. The study also evaluated RSs capability to quantify major classes of biomolecules and compared these results with those obtained by traditional biochemical assays. Finally, compositional differences in large EVs (lEVs) and small EVs (sEVs) were assessed between 30 HC and 34 BC patients. RS revealed the existence of distinct biochemical signatures associated with BC, highlighting increased levels of nucleic acids and lipids in the BC group.

biophysics↗

bioRxiv2024

Chaperone-mediated heterotypic phase separation prevents the amyloid formation of the pathological Y145Stop variant of the prion protein

Biomolecular condensates formed via phase separation of proteins and nucleic acids are crucial for the spatiotemporal regulation of a diverse array of essential cellular functions and the maintenance of cellular homeostasis. However, aberrant liquid-to-solid phase transitions of such condensates are associated with several fatal human diseases. Such dynamic membraneless compartments can contain a range of molecular chaperones that can regulate the phase behavior of proteins involved in the formation of these biological condensates. Here, we show that a heat shock protein 40 (Hsp40), Ydj1, exhibits a holdase activity by potentiating the phase separation of a disease-associated stop codon mutant of the prion protein (Y145Stop) either by recruitment into Y145Stop condensates or via Y145Stop-Ydj1 two-component heterotypic phase separation that prevents the conformational conversion of Y145Stop into amyloid fibrils. Utilizing site-directed mutagenesis, multicolor fluorescence imaging, single-droplet steady-state and picosecond time-resolved fluorescence anisotropy, fluorescence recovery after photobleaching, and fluorescence correlation spectroscopy, we delineate the complex network of interactions that govern the heterotypic phase separation of Y145Stop and Ydj1. We also show that the properties of such heterotypic condensates can further be tuned by RNA that promotes the formation of multicomponent multiphasic protein-RNA condensates. Our vibrational Raman spectroscopy results in conjunction with atomic force microscopy imaging reveal that Ydj1 effectively redirects the self-assembly of Y145Stop towards a dynamically-arrested non-amyloidogenic pathway, preventing the formation of typical amyloid fibrils. Our findings underscore the importance of chaperone-mediated heterotypic phase separation in regulating aberrant phase transitions and amyloid formation associated with a wide range of deadly neurodegenerative diseases.

biophysics↗

bioRxiv2024

Hypertrophic cardiomyopathy mutations Y115H and E497D disrupt the folded-back state of human beta-cardiac myosin allosterically

At the molecular level, clinical hypercontractility associated with many hypertrophic cardiomyopathy (HCM)-causing mutations in {beta}-cardiac myosin appears to be driven by their disruptive effect on the energy-conserving, folded-back, super relaxed (SRX) OFF-state of myosin. A pathological increase in force production results from release of heads from this OFF-state, which results in an increase in the number of heads free to interact with actin and produce force. Pathogenic mutations in myosin can conceivably disrupt the OFF-state by (1) directly affecting the intramolecular interfaces stabilizing the folded-back state, or (2) allosterically destabilizing the folded-back state via disruption of diverse conformational states of the myosin motor along its chemomechanical cycle. However, very little is understood about the mutations that fall in the latter group. Here, using recombinant human {beta}-cardiac myosin, we analysed the biomechanical properties of two such HCM-causing mutations, Y115H (in the transducer) and E497D (in the relay helix), neither of which falls in the regions that interact to stabilize myosins folded-back state. We find these mutations have diverse effects on the contractility parameters of myosin, yet the primary hypercontractile change in both cases is the destabilization of the OFF-state of myosin and increased availability of active myosin heads for actin-binding. Experimental data and molecular dynamics simulations indicate that these mutations likely destabilize the pre-powerstroke state of myosin, the conformation the motor adopts in the inactive folded-back state. We propose that destabilization of the folded-back state of myosin, directly and/or allosterically, is the molecular basis of hypercontractility in HCM in a far greater number of pathogenic mutations than currently thought.

biochemistry↗

bioRxiv2023

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Further MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web-server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

bioinformatics↗

bioRxiv2022

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing

Characterizing the structural ensembles of intrinsically disordered proteins (IDPs) is essential for studying structure-function relationships as conformational dynamics govern proteins biological functions. Due to the notable difference between the neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, the experimental timescale typically results in measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a protein complex formed by two IDP domains, the NCBD/ACTR complex, using data from selective labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. By incorporating structural metrics derived from the SANS experiments as constraints in MD structure classification, we describe a structural ensemble that captures the experimental SANS and, in addition, NMR data better than MD ensembles generated by one single force field. Based on structural similarity, DL reveals three clusters of distinct conformations in the ensemble. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

biophysics↗

bioRxiv2022

Gating interactions steer loop conformational changes in the active site of the L1 metallo-β-lactamase

{beta}-lactam antibiotics are the most important and widely used antibacterial agents across the world. However, the widespread dissemination of {beta}-lactamases among pathogenic bacteria limits the efficacy of {beta}-lactam antibiotics. This has created a major public health crisis. The use of {beta}-lactamase inhibitors has proven useful in restoring the activity of {beta}-lactam antibiotics, yet, effective clinically approved inhibitors against class B metallo-{beta}-lactamases (MBLs) are not available. L1, a class B3 enzyme expressed by Stenotrophomonas maltophilia, is a significant contributor to the {beta}-lactam resistance displayed by this opportunistic pathogen. Structurally, L1 is a tetramer with two elongated loops, 3-{beta}7 and {beta}12-5, present around the active site of each monomer. Residues in these two loops influence substrate/inhibitor binding. To study how the conformational changes of the elongated loops affect the active site in each monomer, enhanced sampling molecular dynamics (MD) simulations were performed, Markov State Models (MSM) were built, and convolutional variational autoencoder (CVAE)-based deep learning was applied. The key identified residues (D150a, H151, P225, Y227, R236) were mutated and the activity of the generated L1 variants was evaluated in cell-based experiments. The results demonstrate that there are extremely significant gating interactions between 3-{beta}7 and {beta}12-5 loops. Taken together, the gating interactions with the conformational changes of the key residues play an important role in the structural remodeling of the active site. These observations offer insights into the potential for novel drug development exploiting these gating interactions.

biophysics↗

bioRxiv2022

Gut microbiota modulation in response to combination of Escherichia coli Nissle 1917 and sugars: Lessons from comparative analysis of fecal microbiota of two healthy donors from 2019-2021

The Escherichia coli Nissle 1917 strain (EcN) has shown its probiotic efficacy against many enteric pathogenic bacteria infecting human, including Vibrio cholerae, either alone or in combination with prebiotics. Understanding of these mechanisms of infection control requires the basic knowledge of probiotic mediated gut microbial community alterations especially in presence of different prebiotics. The present study has used the ex-vivo microbiota model and Next Generation Sequencing techniques to demonstrate the effect of EcN along with different sugars, namely glucose, galactose and starch, on the human gut microbiome community composition. The microbiome compositional changes have been observed at two different time-points, set one and a half years apart, in fecal slurries obtained from two donors. The study has indicated that the extent of microbiome alterations varies with different carbohydrate prebiotics and EcN probiotic and most of the alterations are broadly dependent upon the existing gut microbial community structure of the donors. The major distinct compositional changes have been found in the conditions where glucose and starch were administered, both with and without EcN, in spite of the inter-donor microbial community variation. Several of these microbiome component variations also remain consistent for both the time-points, including genus like Bacteroides, Prevotella and Lactobacillus. Altogether, the present study has shown the effectiveness of EcN along with glucose and starch towards specific changes of microbial community alterations independent of initial microbial composition. This type of model study can be implemented for hypothesis testing in case of therapeutic and prophylactic use of probiotic and prebiotic combinations.

microbiology↗

bioRxiv2021

Language Models for the Prediction of SARS-CoV-2 Inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on [~]9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

bioinformatics↗

bioRxiv2021

Hypertrophic cardiomyopathy mutations in the pliant and light chain-binding regions of the lever arm of human β-cardiac myosin have divergent effects on myosin function

Mutations in the lever arm of {beta}-cardiac myosin are a frequent cause of hypertrophic cardiomyopathy (HCM), a disease characterized by hypercontractility and eventual hypertrophy of the left ventricle. Here, we studied five such mutations: three in the pliant region of the lever arm (D778V, L781P, and S782N) and two in the light chain-binding region (A797T and F834L). We investigated their effects on both motor function and myosin S2 tail-based autoinhibition. The pliant region mutations had varying effects on the motor function of a myosin construct lacking the S2 tail: overall, D778V increased power output, L781P reduced power output, and S782N had little effect on power output, while all three reduced the external force sensitivity of the actin detachment rate. With a myosin containing the motor domain and the proximal S2 tail, the pliant region mutations also attenuated autoinhibition in the presence of filamentous actin but had no impact in the absence of actin. By contrast, the light chain-binding region mutations had little effect on motor activity but produced marked reductions in autoinhibition in both the presence and absence of actin. Thus, mutations in the lever arm of {beta}-cardiac myosin have divergent allosteric effects on myosin function, depending on whether they are in the pliant or light chain-binding regions.

biochemistry↗

bioRxiv2021

Genomic surveillance and phylodynamic analyses reveal emergence of novel mutation and co-mutation patterns within SARS-CoV2 variants prevalent in India

Emergence of distinct viral clades has been observed in SARS-CoV2 variants across the world and India. Identification of the genomic diversity and the phylodynamic profiles of the prevalent strains of the country are critical to understand the evolution and spread of the variants. We performed whole-genome sequencing of 54 SARS-CoV2 strains collected from COVID-19 patients in Kolkata, West Bengal during August to October 2020. Phylogeographic and phylodynamic analyses were performed using these 54 and other sequences from India and abroad available in GISAID database. Spatio-temporal evolutionary dynamics of the pathogen across various regions and states of India over three different time periods in the year 2020 were analyzed. We estimated the clade dynamics of the Indian strains and compared the clade specific mutations and the co-mutation patterns across states and union territories of India over the time course. We observed that GR, GH and G (GISAID) or 20B and 20A (Nextstrain) clades were the prevalent clades in India during middle and later half of the year 2020. However, frequent mutations and co-mutations observed within the major clades across time periods do not show much overlap, indicating emergence of newer mutations in the viral population prevailing in the country. Further, we explored the possible association of specific mutations and co-mutations with the infection outcomes manifested within the Indian patients.

genomics↗

bioRxiv2020

Emergence of multiple variants of SARS-CoV-2 with signature structural changes

This study explores the divergence pattern of SARS-CoV-2 using whole genome sequences of the isolates from various COVID-19 affected countries. The phylogenomic analysis indicates the presence of at least four distinct groups of the SARS-CoV-2 genomes. The emergent groups have been found to be associated with signature structural changes in specific proteins. Also, this study reveals the differential levels of divergence patterns for the protein coding regions. Moreover, we have predicted the impact of structural changes on a couple of important viral proteins via structural modelling techniques. This study further advocates for more viral genetic studies with associated clinical outcomes and hosts response for better understanding of SARS-CoV-2 pathogenesis enabling better mitigation of this pandemic situation.

evolutionary biology↗

bioRxiv2020

Distinct Structural Flexibility within SARS-CoV-2 Spike Protein Reveals Potential Therapeutic Targets

The emergence and rapid worldwide spread of the novel coronavirus disease, COVID-19, has prompted concerted efforts to find successful treatments. The causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), uses its spike (S) protein to gain entry into host cells. Therefore, the S protein presents a viable target to develop a directed therapy. Here, we deployed an integrated artificial intelligence with molecular dynamics simulation approach to provide new details of the S protein structure. Based on a comprehensive structural analysis of S proteins from SARS-CoV-2 and previous human coronaviruses, we found that the protomer state of S proteins is structurally flexible. Without the presence of a stabilizing beta sheet from another protomer chain, two regions in the S2 domain and the hinge connecting the S1 and S2 subunits lose their secondary structures. Interestingly, the region in the S2 domain was previously identified as an immunodominant site in the SARS-CoV-1 S protein. We anticipate that the molecular details elucidated here will assist in effective therapeutic development for COVID-19.

biophysics↗

bioRxiv2019

Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

bioinformatics↗

bioRxiv2019

Biochemical and structural analysis reveals the Neurofibromin (NF1) protein forms a high-affinity dimer

Neurofibromin is the protein product of the NF1 gene which is mutated in the Rasopathy disease Neurofibromatosis Type I. Defects in NF1 lead to aberrant signaling through the RAS-MAPK pathway due to disruption of the Neurofibromin GTPase-activating function on RAS family small GTPases. Very little is known about the function of the majority of Neurofibromin--to date, biochemical and structural data exist only for the GAP domain and the region containing a Sec-PH motif. To better understand the role of this large protein, we carried out a series of biochemical and biophysical studies which demonstrate that full length Neurofibromin forms a high-affinity dimer. Neurofibromin dimerization also occurs in cells, and likely has biological and clinical implications. Analysis of purified full-length and truncated variants of Neurofibromin by negative stain electron microscopy reveals the overall architecture of the dimer and predicts the potential interactions which contribute to the dimer interface. Structures resembling high-affinity full-length dimers could be reconstituted by mixing N- and C-terminal protein domains in vitro. Taken together these data suggest how Neurofibromin dimers might form and be stabilized within the cell.

biochemistry↗

bioRxiv2018

Deep clustering of protein folding simulations

We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. In this paper, we use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely the Fs-peptide (14 s aggregate sampling), villin head piece (single trajectory of 125 s) and the mixed {beta}-{beta}- (BBA) protein (223 + 102 s sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

biophysics↗

bioRxiv2017

A functioning model of human time perception

Despite being a fundamental dimension of experience, how the human brain generates the perception of time remains unknown. Here, we provide a novel explanation for how human time perception might be accomplished, based on non-temporal perceptual clas-sification processes. To demonstrate this proposal, we built an artificial neural system centred on a feed-forward image classification network, functionally similar to human visual processing. In this system, input videos of natural scenes drive changes in network activation, and accumulation of salient changes in activation are used to estimate duration. Estimates produced by this system match human reports made about the same videos, replicating key qualitative biases, including differentiating between scenes of walking around a busy city or sitting in a cafe or office. Our approach provides a working model of duration perception from stimulus to estimation and presents a new direction for examining the foundations of this central aspect of human experience.

neuroscience↗