Biology subjects

Mondal, J.

Publications and source records attributed to Mondal, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Conformation of the Catalytic Lysine is a Key Determinant of 2-Deoxyribose-5-phosphate Aldolase (DERA) Stereoselectivity

2-Deoxyribose-5-Phosphate Aldolase (DERA) is a key enzyme in the pentose phosphate pathway. Due to its C-C bond formation and stereoselective capabilities, DERA has been widely used for biocatalytic applications including the synthesis of chiral intermediates for antiviral and anticancer drugs. While protein engineering has expanded its substrate pool, improved yield, and enhanced stereoselectivity, the molecular basis of stereoselectivity remains unclear. Here, we determined the crystal structures of wildtype DERA from Geobacillus sp. and two of its variants with opposite stereoselectivity. Using a combination of structural biology, biochemistry, organic synthesis and molecular dynamic simulations, we show that the catalytic Lysine adopts two conformations and the Lysine conformation is a key determinant of DERA stereoselectivity. We also identified a mechanism of regulating stereoselectivity via a key amino acid. Using DERA from E. coli, we show that these findings are most likely conserved among bacteria.

biochemistry↗

bioRxiv2026

Task-Specialized Protein Language Models Decode the Sequence Grammar of Post-Translational Modification Sites

Post-translational modifications (PTMs) regulate protein signaling, localization, degradation, and cellular decision-making, yet the sequence determinants that distinguish modified from chemically eligible but unmodified residues remain difficult to decode at proteome scale. Here, we examine whether adapting a general protein language model to PTM-site prediction can reveal the biochemical logic underlying residue-level modification. We fine-tune ESM2, a protein language model trained on tens of millions of evolutionarily diverse protein sequences, for phosphorylation, acetylation, and ubiquitination-site prediction. To address the pronounced class imbalance inherent in proteome-wide PTM annotation, we combine parameter-efficient fine-tuning with focal-loss training. The resulting task-specialized models show that PTM recognition depends on model capacity, annotation depth, and modification chemistry: phosphorylation benefits from larger models, whereas acetylation and ubiquitination peak at intermediate scale. More importantly, the fine-tuned phosphorylation model exposes three layers of biological organization: it recovers canonical kinase-recognition motifs without kinase-label supervision, resolves pathway-level functional relationships among proteins from sequence-derived embeddings, and preserves evolutionary signatures of homologous phosphorylation sites across 200 eukaryotic species. These results establish task-specialized protein language models as interpretable instruments for probing PTM-site biochemistry, kinase specificity, functional organization, and evolutionary conservation.

bioinformatics↗

bioRxiv2026

How Generative Models Approach Molecular Conformational Sampling

Characterising equilibrium conformational ensembles with deep generative models requires assessing not only whether a model reproduces the target distribution, but also the mechanism of how it arrives here. Here we examine two distinct routes to generative conformational sampling-- stochastic relaxation and deterministic transport--through a study of denoising diffusion probabilistic models (DDPM) and rectified-flow (RF) models across molecular systems of increasing complexity. Using systems of increasing complexity, including a multimodal two-dimensional potential, the folded mini-protein Trp-cage, and a high-dimensional dihedral subspace of the intrinsically disordered protein -synuclein, we show that the key distinction between these paradigms lies not only in endpoint fidelity but in how distributional error is resolved during sampling. Diffusion models converge via pronounced late-stage stochastic relaxation and exhibits robust recovery of configurational breadth across neural architectures. Rectified flow approaches the target more gradually through deterministic transport and therefore depends much more strongly on architectural expressivity, particularly in heterogeneous high-dimensional landscapes. Analyses of entropy and moment evolution further show that diffusion more reliably restores both ensemble location and fluctuation structure, whereas RF requires Transformer-level feature mixing to represent the transport geometry accurately. These results establish convergence mechanism as a key design principle for generative sampling.

biophysics↗

bioRxiv2026

Extracellular vesicle-bound bacterial toxin pneumolysin triggers membrane engagement and damage beyond canonical pore formation

Pneumolysin (PLY) is a cholesterol-dependent pore-forming toxin and a key virulence factor of Streptococcus pneumoniae, the leading cause of pneumonia worldwide in children aged below 5 years. At sublytic toxin doses, host cells shed PLY-laden extracellular vesicles (EVs) during membrane repair response. However, it remains unclear how these toxin-bearing EVs engage and damage target cell membranes. Here, we combine molecular dynamics simulations, liposome fusion assays, and cell-based experiments to elucidate the membrane interaction potential of vesicle-bound PLY. Simulations indicate that EV-embedded PLY uses an exposed helix to bind target cell membranes, inducing pronounced curvature, bilayer thinning, and water influx. Liposome fusion assays demonstrate that both PLY and membrane cholesterol promote vesicle-membrane interactions. Characterization of vesicle subpopulations released from PLY-challenged monocytes by western blotting and immunogold electron microscopy demonstrated that plasma membrane-derived microvesicles are preferentially enriched in membrane-bound PLY compared to small extracellular vesicles. Consistent with these findings, MVs purified from wild-type PLY-challenged monocytes, but not the toxoid mutant PLYW433F fuse with human immune cells, delivering toxin and causing membrane damage. Together our results reveal a noncanonical, pore-independent mode of toxin dissemination, in which, vesicle-bound pneumolysin fuses and destabilize target cell membranes, representing a new proposal for EV-mediated toxin activity and a potential target for therapeutic intervention.

microbiology↗

bioRxiv2026

From Prefix to Path: Learning Temporally Consistent Biomolecular Dynamics from Limited Initial Data

Molecular dynamics (MD) simulations provide detailed insights into biomolecular motion but are often limited by the prohibitive cost of sampling long-timescale behavior. Here, we present a Transformer-based framework that reconstructs temporally continuous dynamical trajectories from only a small fraction of the initial data, directly targeting time-ordered evolution rather than independent ensemble snapshots. Using three systems spanning distinct dynamical regimes (intrinsically disordered -Synuclein, Cytochrome P450 ligand-binding motion, and a synthetic three-well potential), we show that the model learns both local fluctuations and long-range temporal structure. At inference time, the model generates full trajectories autoregressively from an initial prefix as prompt, capturing metastable transitions, basin-to-basin movements, and system-specific dynamical signatures. Free-energy surfaces computed from generated trajectories closely match ground-truth landscapes and, in several cases, we observe enhanced sampling in generated trajectories relative to the trained trajectories--while preserving kinetically meaningful transition patterns. These results demon-strate that Transformer architectures can serve as efficient, system-agnostic tools for time-continuous molecular trajectory prediction, offering a data-driven complement to long MD simulations and enabling accelerated exploration of conformational space.

biophysics↗

bioRxiv2026

A Comprehensive Atlas and Machine-Learning Framework for Predicting IDR-Protein Binding Affinity

Intrinsically disordered regions (IDRs) enable pervasive, regulatable protein interactions, yet predicting their binding affinities remains challenging because disorder permits heterogeneous interfaces and context-dependent recognition. Here we introduce IBPC-Kd, a curated dataset of 1,785 IDR-ordered protein complexes with experimentally measured dissociation constants (Kd), spanning more than six orders of magnitude in affinity and covering diverse IDR lengths and partner classes. Across this resource, we find that global interfacial shape complementarity is a consistent correlate of affinity, followed by the structural order of the partner surface and a systematic electrostatic asymmetry between IDRs and their binders. Local physicochemical patterning modulates binding in a partner-dependent manner. Guided by these observations, we develop IDRBindNet, a graph-transformer model that integrates protein language model embeddings with interface geometry and residue-level chemical context to predict dissociation constants from IDR-partner complexes. IDRBindNet achieves state-of-the-art accuracy on held-out complexes (R2 up to 0.911) while implicitly learning biologically relevant attention patterns corresponding to partner disorder and geometric fit. Notably, the framework generalizes through external validation on a set of recently reported de novo designed binders targeting the human surfaceome, showing encouraging concordance across nanomolar-to-micromolar affinities. Together, IBPC-Kdand the accompanying predictor IDRBindNet provide a quantitative framework for interrogating determinants of IDR recognition and for prioritizing candidate interactions for mechanistic and design studies.

biophysics↗

bioRxiv2026

Fast Adversarial Generation of Molecular Dynamics Trajectories with Kinetic Fidelity

Molecular dynamics (MD) simulations yield atomic-level insights into molecular motion but struggle to reach the long timescales needed for rare events due to prohibitive computational costs. Generative machine-learning models (e.g., diffusion models and normalizing flows) offer a promising route to accelerate sampling, yet they generate independent equilibrium snapshots without temporal correlation or kinetic information. Autoregressive sequence models can learn time evolution by producing one frame at a time, but this stepwise generation often accumulates errors and drifts from true dynamics. Here, we propose a complementary approach inspired by advances in image and video generation: we treat finite MD trajectory segments as high-dimensional objects and learn their joint distribution using Generative Adversarial Networks (GANs). Using a Wasserstein GAN with gradient penalty, we directly generate entire time-series trajectories in one shot, that remain physically coherent over time without explicitly integrating the equations of motion. We demonstrate the generality of this method on molecular systems of increasing complexity: a 2D triple-well potential energy landscape, a protein-ligand binding process (cytochrome P450), the dynamics of an intrinsically disordered protein (-synuclein) in a latent coordinate space, and even the conditional generation of folding trajectories for the Trp-cage mini-protein. In all cases, the GAN-generated trajectories closely reproduce the true free-energy landscapes and kinetic signatures of the systems, while enabling efficient sampling of rare events that would ordinarily require months of conventional MD simulation.

biophysics↗

bioRxiv2025

Selective targeting of oncogenic KRAS G12D using peptide nucleic acid oligomers attached to cell-penetrating peptides

KRAS is a proto-oncogene that contains activating mutations in up to 30% of tumors. Many conventional therapies inhibit both cancerous and normal cells, which may cause toxicity. Thus, programmable mutant-selective targeted inhibitors are needed. Peptide nucleic acids (PNAs) incorporate base sequences analogous to DNA, with modified peptide backbones instead of ribose-phosphate backbones, allowing PNAs to hybridize with DNA with high avidity to suppress transcription. Here, we developed KRAS G12D-selective PNA oligomers with novel cell-penetrating flanking regions. Fluorescein-labeled PNA oligomers displayed high uptake rates in cells and nuclei. Exposure to PNA-delivery peptide conjugates resulted in repression of KRAS G12D mRNA and protein expression within 2 hours and lasting up to 48 hours. Varying cell-penetrating peptide (CPP) compositions and lengths of complementary KRAS sequences were tested using dose-response cell viability assays. These experiments identified configurations that were effective at selectively preventing growth of on-target KRAS G12D cells, while relatively sparing off-target KRAS G12C cells. Electrophoretic mobility shift assays demonstrated in vitro binding and selectivity for KRAS G12D DNA sequences. CPP-PNA-G12D-1 was effective against a panel of pancreatic ductal adenocarcinoma cell lines and patient-derived xenografts in vivo. These results show promise for an enhanced PNA-delivery peptide conjugate strategy as both a tool for studying tumors driven by oncogenic point mutations and as a potential therapeutic strategy to selectively target mutant cancer cells.

cancer biology↗

bioRxiv2025

I helix Mediates the Allosteric Regulation in Cytochrome P450cam

Cytochrome P450cam, a key monooxygenase in the P450 superfamily, is pivotal in metabolic and industrial processes. Despite extensive studies, a unified mechanism governing its conformational heterogeneity, substrate-dependent allostery, and multi-substrate binding remains elusive. Here, integrating molecular dynamics simulations, NMR pseudocontact shift (PCS) analysis, and crystallographic data, we identify the I-helix (I) as the central regulator of P450cams allostery. Its intrinsic flexibility, dictated by glycine residues (G248 and G249), orchestrates enzyme conformational dynamics. Specifically, I transitions between straight and kinked conformations, modulating the opening and closing of substrate access channels (channel-1 and channel-2) and mediating allosteric communication between active and allosteric sites. Substrate binding stabilizes the straight conformation, promoting channel closure and enhancing allosteric regulation. This I-helix-based mechanism reconciles 125 crystallographic poses, spanning straight-to-kinked I conformations. Notably, the kink-inducing glycine G249 is evolutionarily conserved across species, including humans, underscoring Is fundamental role in enzyme function and broader significance within the P450 superfamily. NMR PCS measurements align with the kinked and straight conformations in the substrate-free and substrate-bound states, with Q-scores of 0.108 and 0.061, respectively. Leveraging this mechanistic insight, we designed proof-of-concept P450cam mutants locked in either constitutively open or closed conformations for the first time. By shifting the focus from the traditional FG-helix-centric view to an I-helix-centric framework, this study provides a comprehensive blueprint for conformational and allosteric regulation, paving the way for engineering tailored P450 variants.

biochemistry↗

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↗

bioRxiv2025

Elucidating Protein Dynamics through the Optimal Annealing of Variational Autoencoders

Proteins traverse intricate conformational landscapes, with transitions and long-lived states that hold the key to their biological function. Yet, unraveling these dynamics remains a formidable challenge. An emerging approach has been to train the conformational ensemble via deep Variational autoencoders (VAEs) in a bid to machine learn the underlying reduced dimensional representation. However, training VAEs typically involves a fixed {beta} value of 1, where {beta} acts as the crucial weighing factor between the reconstruction and regularization terms. This static setup can often lead to poste-rior collapse, which significantly hinders the models ability to capture complex protein dynamics accurately. To mitigate this issue, annealing the {beta} parameter offers a potential alternative. However, this approach frequently falls short in fully addressing the problem, majorly due to arbitrary choice of upper bound of {beta} and annealing schedule. In this work, we introduce an innovative approach for selecting the {beta} parameter by utilizing the Fraction of Variance Explained (FVE) score to identify its optimal value. We demonstrate that training annealed VAEs at their optimum {beta} in a single cycle consistently outperformed their non-annealed counterparts, as evident from their higher Variational Approach for Markov Processes-2 and Generalized Matrix Rayleigh Quotient scores and distinct free energy surface minima on both folded and intrinsically disordered proteins. The improved latent space representations significantly improve state space discretization, thereby refining Markov State Models and providing more accurate insights into conformational landscapes as reflected in distinct contact maps. These findings not only underscore the potential of annealed VAEs in resolving complex conformational spaces but also highlight the critical interplay between annealing schedules and latent space structures.

biophysics↗

bioRxiv2024

Unbiased learning of protein conformational representation via unsupervised random forest

Accurate data representation is paramount in biophysics to capture the functionally relevant motions of biomolecules. Traditional feature selection methods, while effective, often rely on labeled data based on prior knowledge and user-supervision, limiting their applicability to novel systems. Here, we present unsupervised random forest (URF), a self-supervised adaptation of traditional random forests that identifies functionally critical features of biomolecules without requiring prior labels. By devising a memory-efficient implementation, we first demonstrate URFs capability to learn important sets of inter-residue features of a protein and subsequently to resolve its complex conformational landscape, performing at par or surpassing its traditional supervised counterpart and 15 other leading baseline methods. Crucially, URF is supplemented by an internal metric, the learning coefficient, which automates the process of hyper-parameter optimization, making the method robust and user-friendly. URFs remarkable ability to learn important protein features in an unbiased fashion was validated against 10 independent protein systems including both both folded and intrinsically disordered states. In particular, benchmarking investigations showed that the protein representations identified by URF are functionally meaningful in comparison to current state-of-the-art deep learning methods. As an application, we show that URF can be seamlessly integrated with downstream analyses pipeline such as Markov state models to attain better resolved outputs. The investigation presented here establishes URF as a leading tool for unsupervised representation learning in protein biophysics.

biophysics↗

bioRxiv2024

Dynamic Insights into Hsp90 Inhibitor Binding: Uncovering Intermediate States and Conformational Plasticity

Heat shock protein 90 (Hsp90) is a vital molecular chaperone involved in protein folding, stabilization, and activation, making it a key target in cancer therapy. Tar-geting the N-terminal domain of Hsp90 (N-Hsp90) has emerged as a potential strategy for developing anticancer therapies due to its involvement in the folding and function of oncoproteins. However, the precise molecular details of how inhibitors bind to N-Hsp90 remain elusive due to inherent conformational plasticity involving loop-in and loop-out states of loop-4 (L4). In this work, we utilized unbiased molecular dynamics (MD) simulations coupled with Markov state modeling (MSM) and machine learning techniques to investigate the binding dynamics of the inhibitor Geldanamycin (GDM). Our findings reveal a complex two-stage binding mechanism involving the formation of a non-native intermediate state prior to the final bound state. We demonstrate that GDM binding predominantly stabilizes the loop-out conformation of N-Hsp90, chal-lenging prior beliefs and suggesting that both conformational selection and induced fit mechanisms are involved. Through a detailed residue-level analysis using Random For-est based ML classifiers, we quantify the relative contributions of these mechanisms, highlighting the dynamic interplay between Hsp90s conformational states during in-hibitor binding. These findings enhance our understanding of Hsp90s conformational plasticity and could inform the design of more effective Hsp90 inhibitors.

biochemistry↗

bioRxiv2024

Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories

With the remarkable stride in computing power and advances in Molecular Dynamics (MD) simulation programs, the crucial challenge of storing and sharing large biomolecular simulation datasets has emerged. By leveraging AutoEncoders, a type of artificial neural network, we developed a method to compress MD trajectories into significantly smaller latent spaces. Our method can save up to 98% in disk space compared to xtc, a highly compressed trajectory format from the widely used MD program package GROMACS, thus facilitating storage and sharing of simulation trajectories. Atom coordinates are very accurately reconstructed from compressed data. The method was tested across a diverse sets of biomolecular systems, including folded proteins, intrinsically disordered proteins, phospholipid bilayers, proteinligand complexes, large protein complexes and membrane-bound protein systems. The reconstructed trajectories demonstrated consistent accuracy in recovering key biophysically relevant properties for proteins, lipids and composite systems. The compression efficiency was particularly beneficial for larger systems. This approach enables the scientific community to efficiently store and share large-scale biomolecular simulation data, potentially enhancing collaborative research efforts. The workflow, termed "compresstraj", is implemented in PyTorch and is publicly available at https://github.com/SerpentByte/compresstraj, offering a practical solution for handling the increasing volumes of data generated in biomolecular simulation studies.

biophysics↗

bioRxiv2024

Enantiomeric histidine-rich peptide coacervates enhance antigen delivery to T cells

Peptides and peptidomimetics that self-assemble via LLPS have recently emerged as building blocks for fabricating functional biomaterials due to their unique physicochemical properties and dynamic nature. One of lifes most distinctive signatures is its selectivity for chiral molecules and, to date, coacervates comprised of D-amino acids have not been reported. Here, we demonstrate that histidine-rich repeats of (GHGXY)4 (X=L/V/P) and their enantiomers undergo LLPS opening new avenues for enhancing coacervate stability. Through a series of biophysical studies, we find that LLPS kinetics, droplet size, fusion, and encapsulation efficiency are dictated by the primary sequence. Further, these coacervates can encapsulate therapeutic cargo which are then internalized via endocytic mechanisms. Finally, we show that the coacervates enhance antigen presentation to CD4+ and CD8+ T cells resulting in robust proliferation and production of functional cytokines. Collectively, our study describes the development and characterization of enantiomeric peptide coacervates as attractive vaccine delivery vehicles with tunable physicochemical properties. HIGHLIGHTSO_LID amino acid-peptides were used for the first time to construct phase separating coacervates C_LIO_LIChirality does not restrict LLPS or modulate other coacervate properties C_LIO_LIAntigen delivery using chiral coacervates enhances and prolongs presentation to T cells C_LI PROGRESS AND POTENTIALPeptides can undergo self-assembly via liquid-liquid phase separation (LLPS) to result in solute-rich coacervates that can serve as biomaterials. Using histidine-rich peptide repeats, this work demonstrates that peptides composed of entirely D-amino acids can form functional coaceravtes. The kinetics of LLPS and bulk properties of the droplets can be controlled through simple amino acid substitutions. The coacervates, while immunologically inert, exert an adjuvanting effect and enhance antigen presentation to T cells leading to proliferation and functional cytokine production. The materials showcased here possess high translational potential for combined delivery of immunomodulators and antigens for vaccine delivery against infectious diseases or cancer. The deliverables from this study will also inspire the development of chiral systems that will contribute to the knowledge of cellular processes associated with phase changes integral to both physiology and pathology.

bioengineering↗

bioRxiv2024

Crowder induced conformational fluctuations modulate the phase separation of yeast SUP35 NM domain

Intrinsically disordered proteins (IDPs) like Sup35NM can undergo liquid-liquid phase separation (LLPS) to form biomolecular condensates, a process influenced by their conformational flexibility and the crowded intracellular environment. This study investigates how molecular crowding, specifically the size and shape of crowders like Dextran and Ficoll, modulates the conformational states and phase separation behavior of Sup35NM. Using fluorescence correlation spectroscopy (FCS) and molecular dynamics simulations, we observed that Dextran, depending on its molecular weight, induces both compaction and expansion of Sup35NM, driving phase separation at certain thresholds. Notably, rod-like Dextran crowders promote phase separation, while spherical Ficoll does not, highlighting the impact of crowder geometry on IDP behavior. Computational modelling further revealed that the crowder shape influences Sup35NMs conformational ensemble by modulating intra- and inter-domain interactions. These findings elucidate the role of crowding agents in IDP phase behavior, suggesting that cellular crowding may regulate IDP functionality through conformational control.

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

A reaction-diffusion model captures the essence of liquid-liquid phase separation

In this work we propose the formulation of a continuum model for liquid-liquid phase separation (LLPS) using reaction diffusion framework. We consider a well mixed liquid consisting of three phases, the dense droplet phase, the dilute phase and the remaining part to be solvent phase. As a key feature, the model includes both the spatial and temporal aspects and a free energy functional between dense and dilute phase that is physically motivated by reference molecular simulation. The exhaustive numerical simulations of model captures the dynamical formation of droplets and existence of LLPS. As the time progresses, simulation reveal that smaller droplets gradually vanish, and a single droplet undergoes continuous growth until it reaches a stable size. The model predicts that that extent of diffusivity of dense and dilute phase as well as their mutual interaction would modulate the kinetics of droplet formation. Finally we show that introduction of fluctuation in the model accelerate the phase separation process.

biophysics↗