Biology subjects

Bera, P.

Publications and source records attributed to Bera, P..

10 recordsLinked to original sources

bioRxiv2025

Assessing Generative Diffusion Models for Enhanced Sampling of Folded and Disordered Protein States Across Scales and in All-atom Resolution

Molecular dynamics (MD) simulations are fundamental for probing the structural dynamics of biomolecules, yet their efficiency is limited by the high computational cost of exploring long-timescale events. Generative machine learning (ML) models, particularly the Denoising Diffusion Probabilistic Model (DDPM), offer an emerging strategy to enhance conformational sampling. In this study, we evaluate the capabilities and limitations of DDPM in generating atomistically accurate conformational ensembles across proteins of varying size and structural order, ranging from the 20-residue folded Trp-cage and 58-residue BPTI to the 83-residue intrinsically disordered region Ash1 and the 140-residue intrinsically disordered protein -Synuclein. Training DDPM on relatively short MD trajectories using both torsion angle and all-atom coordinate data, we demonstrate that it can reproduce key structural features such as secondary structure, radius of gyration, and contact maps, while effectively sampling sparsely populated regions of the conformational landscape. Notably, DDPM can also generate novel conformations, including transitions not explicitly observed in the training data. However, the model occasionally overlooks low-probability regions and may produce conformers with unclear physical relevance, warranting independent validation. These limitations are particularly evident in flexible systems such as IDPs. Overall, this work benchmarks DDPM as a viable tool for augmenting MD simulations, offering enhanced sampling with significant computational savings, while noting its limitations in capturing low-populated conformers. At the same time, it highlights the importance of rigorous validation and thoughtful interpretation when deploying generative models in computational biophysics.

biophysics↗

bioRxiv2024

Predicting the Future States of Physicochemical Systems Using Transformer-Based generative Machine Learning Model

Capturing the time evolution and predicting future kinetic states of physicochemical systems present significant challenges due to the precision and computational effort required. In this study, we demonstrate that the transformer, a machine learning model renowned for machine translation and natural language processing, can be effectively adapted to predict the dynamical state-to-state transition kinetics of biologically relevant physicochemical systems. Specifically, by using sequences of time-discretized states from Molecular Dynamics (MD) simulation trajectories as input, we show that a transformer can learn the complex syntactic and semantic relationships within the trajectory. This enables this generative pre-trained transformer (GPT) to predict kinetically accurate sequences of future states for a diverse set of models and biomolecules of varying complexity. Remarkably, the GPT can predict future states much faster than traditional MD simulations. We show that it is particularly adept at forecasting the time evolution of an out-of-equilibrium active system that do not maintain detailed balance. An analysis of self-attention mechanism inherent in transformers is found to hold crucial role for capturing the long-range correlations necessary for accurate state-to-state transition predictions. Together, our results highlight the ability of transformer based machine learning model in generating future states of physicochemical systems with statistical precision.

biophysics↗

bioRxiv2024

Protein Translation Can Fluidize Bacterial Cytoplasm

The cytoplasm of bacterial cells is densely packed with highly polydisperse macromolecules that exhibit glassy dynamics. Research has revealed that metabolic activities in living cells can counteract the glassy nature of these macromolecules, allowing the cell to maintain critical fluidity for its growth and function. While it has been proposed that the crowded cytoplasm is responsible for this glassy behavior, a detailed explanation for how cellular activity induces fluidization remains elusive. In this study, we introduce and validate a novel hypothesis through computer simulations: protein synthesis in living cells contributes to the metabolism-dependent fluidization of the cytoplasm. The main protein synthesis machinery, ribosomes, frequently shift between fast and slow diffusive states. These states correspond to the independent movement of ribosomal subunits and the actively translating ribosome chains called polysomes, respectively. Our simulations demonstrate that the frequent transitions of the numerous ribosomes, which constitute a significant portion of the cell proteome, greatly enhance the mobility of other macromolecules within the bacterial cytoplasm. Considering that ribosomal protein synthesis is the largest consumer of ATP in growing bacterial cells, the translation process likely serves as the primary mechanism for fluidizing the cytoplasm in metabolically active cells.

biophysics↗

bioRxiv2023

Machine Learning Unravels Inherent Structural Patterns in Escherichia Coli Hi-C Matrices and Predicts DNA Dynamics

The large dimension of the Hi-C-derived chromosomal contact map, even for a bacterial cell, presents challenges in extracting meaningful information related to its complex organization. Here we first demonstrate that a machine-learnt (ML) low-dimensional embedding of a recently reported Hi-C interaction map of archetypal bacteria E. Coli can decode crucial underlying structural pattern. In particular, a three-dimensional latent space representation of (928x928) dimensional Hi-C map, derived from an unsupervised artificial neural network, automatically detects a set of spatially distinct domains that show close correspondences with six macro-domains (MDs) that were earlier proposed across E. Coli genome via recombination assay-based experiments. Subsequently, we develop a supervised random-forest regression model by machine-learning intricate relationship between large array of Hi-C-derived chromosomal contact probabilities and diffusive dynamics of each individual chromosomal gene. The resultant ML model dictates that a minimal subset of important chromosomal contact pairs (only 30 %) out of full Hi-C map is sufficient for optimal reconstruction of the heterogenous, coordinate-dependent sub-diffusive motions of chromosomal loci. Specifically the Ori MD was predicted to exhibit most substantial contribution in chromosomal dynamics among all MDs. Finally, the ML models, trained on wild-type E. Coli was tested for its predictive capabilities on mutant bacterial strains, shedding light on the structural and dynamic nuances of {Delta}MatP30MM and {Delta}MukBEF22MM chromosomes. Overall our results illuminate the power of ML techniques in unraveling the complex relationship between structure and dynamics of bacterial chromosomal loci, promising meaningful connections between our ML-derived insights and real-world biological phenomena.

biophysics↗

bioRxiv2023

Spatiotemporal dynamics and emergent ordering in a mixture of morphologically distinct bacteria having different cell motility

Microbial communities exhibit complex behaviors driven by species interactions and individual characteristics. In this study, we delve into the dynamics of a mixed bacterial population comprising two distinct species with different morphology and motility aspects. Employing agent-based modeling and computer simulations, we analyze the impacts of size ratios and packing fractions on dispersal patterns, aggregate formation, clustering, and spatial ordering. Notably, we find that motility and anisotropy of elongated bacteria significantly influence the distribution and spatial organization of nonmotile spherical species. Passive spherical cells display superdiffusive behavior, particularly at smaller size ratios, while active rod-like cells exhibit normal diffusive behavior in the diffusion regime. As the size ratio increases, clustering of passive cells is observed, accompanied by enhanced alignment and closer packing of active cells in the presence of higher passive cell area fractions. As the size ratio increases, clustering of passive cells is observed, accompanied by enhanced alignment and closer packing of active cells in the presence of higher passive cell area fractions. Additionally, we identify the pivotal role of passive cell area fraction in influencing the response of active cells toward nematicity, with its dependence on size ratio. These findings shed light on the significance of morphology and motility in shaping the collective behavior of microbial communities, providing valuable insights into complex microbial behaviors with implications for ecology, biotechnology, and bioengineering.

biophysics↗

bioRxiv2023

On the Spatial Positioning of Ribosomes around chromosome in E. coli Cytoplasm

The spatial organization of ribosomes in the cytoplasm of Escherichia coli (E. coli) bacteria has been a subject of longstanding intrigue. Previous investigations suggested that ribosomes remain completely excluded from the chromosome-rich nucleoid region in a "poor solvent" like cytoplasm. Here, we present an integrated model of the bacterial cytoplasm, informed by experimental data, which updates this prevailing narrative. We demonstrate that ribosomes maintain a delicate balance of both attractive and repulsive interactions with the chromosome, contrary to the conventional notion of it acting as an inert crowder in cytoplasm. The multi-dimensional spatial distribution of free ribosomes (30S and 50S) and bound ribosomes (70S polysome) inside the cytoplasm reveals that the extent of mutual ribosome-chromosome segregation is relatively less pronounced due to the presence of non-negligible amount of ribosomes within the inner core of the cytoplasm. In particular, we identify a central void within the inner-most core of the nucleoid that lacks chromosomal DNA but can accommodate finite proportion of both free (11 %) and bound (18 %) ribosomes. Furthermore, our analysis of the chromosome mesh size and the conformation of bound ribosomes suggests that bound ribosomes remain elongated and would be able to navigate past the chromosome mesh to access the central void. Together by highlighting the dynamic nature of ribosome localization in E. coli, this investigation proposes that this segregation is crucial for maximizing the utilization of synthesized mRNA and facilitating efficient translation into proteins, which are essential for bacterial survival.

biophysics↗

bioRxiv2023

Development of a Data-driven Integrative Model of Bacterial Chromosome

The chromosome of archetypal bacteria E. coli is known for a complex topology with 4.6 x 106 base pairs (bp) long sequence of nucleotide packed within a micrometer-sized celllular confinement. The inherent organization underlying this chromosome eludes general consensus due to the lack of a high-resolution picture of its conformation. Here we present our development of an integrative model of E. coli at a 500 bp resolution (https://github.com/JMLab-tifrh/ecoli_finer), which optimally combines a set of multi-resolution genome-wide experimentally measured data within a framework of polymer based architecture. In particular the model is informed with intra-genome contact probability map at 5000 bp resolution derived via Hi-C experiment and RNA-sequencing data at 500 bp resolution. Via dynamical simulations, this data-driven polymer based model generates appropriate conformational ensemble commensurate with chromosome architectures that E. coli adopts. As a key hallmark, the model chromosome spontaneously self-organizes into a set of non-overlapping macrodomains and suitably locates plectonemic loops near the cell membrane. As novel extensions, it predicts a contact probability map simulated at a higher resolution than precedent experiments and can demonstrate segregation of chromosomes in a partially replicating cell. Finally, the modular nature of the model helps us to devise control simulations to quantify the individual role of key features in hierarchical organization of the bacterial chromosome.

microbiology↗

bioRxiv2022

A mechanistic understanding of biofilm morphogenesis: Coexistence of mobile and sessile aggregates and phase-separated patterns

Most bacteria in the natural environment self-organize into collective phases such as cell clusters, swarms, patterned colonies, or biofilms. The occurrence of different phases and their coexistence is governed by several intrinsic and extrinsic factors such as the growth, motion, and physicochemical interactions. Hence, it is crucial to predict the conditions under which a collective phase emerges due to the individual-level interactions. Here we develop a particle-based biophyiscal model of bacterial cells and self-secreted extracellular polymeric substances (EPS) to decipher the interplay of growth, motility-mediated dispersal, and mechanical interactions during microcolony morphogenesis. We show that depending upon the heterogeneous production and physicochemical properties of EPS, whether sticky or nonadsorbing in nature, the microcolony dynamics and architecture significantly varies. In particular, in sticky EPS, microcolony shows the coexistence of both motile and sessile aggregates rendering a transition towards biofilm formation. Wherein for the nonad-sorbing EPS, which behaves as depletant in the media, a variety of phase-segregated patterned colonies either localizing the matrix component or cells at the colony periphery may emerge. We identified that the interplay of differential dispersion and the mechanical interactions among the components of the colony determines the fate of the colony morphology. Our results provide a significant understanding of the mechano-self-regulation during biofilm morphogenesis and open up possibilities of designing experiments to test the predictions.

biophysics↗

bioRxiv2021

Hi-C Contacts Encode Heterogeneity in Sub-diffusive Motion of E. coli Chromosomal Loci

Underneath its apparently simple architecture, the circular chromosome of E. coli is known for displaying complex dynamics in its cytoplasm. Recent experiments have hinted at an inherently heterogeneous dynamics of chromosomal loci, the origin of which has largely been elusive. In this regard, here we investigate the loci dynamics of E. coli chromosome in a minimally growing condition at 30{degrees}C by integrating the experimentally derived Hi-C interaction matrix within a computer model. Our quantitative analysis demonstrates that, while the dynamics of the chromosome is sub-diffusive in a viscoelastic media in general, the diffusion constants and the diffusive exponents are strongly dependent on the spatial coordinates of chromosomal loci. In particular, the loci in Ter Macro-domain display slower mobility compared to the others. The result is found to be robust even in the presence of active noise. Interestingly, a series of control investigations reveal that the absence of Hi-C interactions in the model would have abolished the heterogeneity in loci diffusion, indicating that the observed coordinate-dependent chromosome dynamics is heavily dictated via Hi-C-guided longrange inter-loci communications. Overall, the study underscores the key role of Hi-C interactions in guiding the inter-loci encounter and in modulating the underlying heterogeneity of the loci diffusion.

biophysics↗

bioRxiv2021

Mechanistic underpinning of nonuniform collective motions in swarming bacteria

Self-propelled bacteria can exhibit a large variety of non-equilibrium self-organized phenomena. Swarming is one such fascinating dynamical scenario where a number of motile individuals grouped into clusters and move in synchronized flows and vortices. While precedent investigations in rod-like particles confirm that increased aspect-ratio promotes alignment and order, recent experimental studies in bacteria Bacillus subtilis show a non-monotonic dependence of cell-aspect ratio on their swarming motion. Here, by computer simulations of an agent-based model of selfpropelled, mechanically interacting, rod-shaped bacteria in overdamped condition, we explore the collective dynamics of bacterial swarm subjected to a variation of cell-aspect ratio. When modeled with an identical self-propulsion speed across a diverse range of cell aspect ratio, simulations demonstrate that both shorter and longer bacteria exhibit slow dynamics whereas the fastest speed is obtained at an intermediate aspect ratio. Our investigation highlights that the origin of this observed non-monotonic trend of bacterial speed and vorticity with cell-aspect ratio is rooted in the cell-size dependence of motility force. The swarming features remain robust for a wide range of surface density of the cells, whereas asymmetry in friction attributes a distinct effect. Our analysis identifies that at an intermediate aspect ratio, an optimum cell size and motility force promote alignment, which reinforces the mechanical interactions among neighboring cells leading to the overall fastest motion. Mechanistic underpinning of the collective motions reveals that it is a joint venture of the short-range repulsive and the size-dependent motility forces, which determines the characteristics of swarming.

biophysics↗