Biology subjects

Chattaraj, A.

Publications and source records attributed to Chattaraj, A..

11 recordsLinked to original sources

bioRxiv2026

Topological Complementarity Predicts Synergistic Drug Pairs in Branched Metabolic Networks.

Drug discovery typically ranks targets by individual sensitivity. Branched metabolic networks, in contrast, have redundant flux routes that buffer single-enzyme inhibition possibly deviating the best target from the most sensitive node. We developed a topology-driven framework for identifying optimal dual-target pairs and applied it to the E. coli folate pathway. A kinetic model of the folate pathway and its extended one-carbon pool showed that single-node inhibition is compensated by flux redistribution. Pairwise synergy scoring, a metric to measure drug-pair potency as compared to single drug, identified DHPS (folP) and DHFR (folA) as a top-ranked complementary pair. Experimental validation in E. coli MG1655 using sulfamethoxazole and trimethoprim confirmed strong dose-dependent synergy at intermediate inhibition and attenuation at saturation. Our results support topological complementarity as a key predictor of combination efficacy and provide a rationale for drug-pair design in branched biosynthetic pathways.

systems biology↗

bioRxiv2026

Linker stretching optimizes bonding entropy in sticker-spacer condensates

Phase separating biopolymers are often described by the sticker-spacer model, which accounts for non-attractive linkers that connect discrete attractive moieties. "Associative polymer" theories describe these systems as two coexisting fluids of stickers and spacers, respectively. In such theories the stickers and spacers are decoupled in the sense that sticker binding does not affect spacer conformations or vice versa. Informed by coarse-grained simulations, we develop a phenomenological mean-field theory to determine the microscopic coupling between the sticker and spacer degrees of freedom. Accounting for linkers connecting adjacent spacers introduces free energy contributions due to conformational entropy of the spacer and the binding combinatorics of the stickers. These contributions have opposite dependencies on spacer length because longer spacers allow a greater volume to search for binding partners but incur a greater entropy cost upon bond formation. The length dependent terms from these contributions cancel leaving a length-independent elastic term arising from the fact that a small amount of spacer stretching lowers the free energy by increasing the number of possible binding partners. The cancellation of spacer length from the theory justifies the decoupling approximation and supports mean-field treatments of heterogeneous spacers.

biophysics↗

bioRxiv2025

CASPULE: A computational tool to study sticker spacer polymer condensates

Phase separated condensates are recognized as a ubiquitous mechanism of spatial organization in cell biology. Biophysical modeling of condensates provides critical insights into the dynamics and functions of these subcellular structures that are difficult to extract via experiments. Here we present an efficient computational pipeline, CASPULE (Condensate Analysis of Sticker Spacer Polymers Using the LAMMPS Engine), to simulate and analyze the biological condensates made of sticker-spacer polymers. CASPULE implements a unique force field that combines traditional Langevin dynamics with a "detailed-balance proof" protocol for single-valent bond formation between stickers. This framework allows us to study the nontrivial biophysics that emerge from singlevalent sticker interactions, coupled with the effect of separating the energetic contributions of stickers and spacers. We provide detailed documentation on how to setup the simulation environment, perform simulations and analyze the results. Through case studies, we highlight the utility and efficacy of our pipeline. Importantly, we provide statistical parameters to characterize the cluster size distribution often observed in biological systems. We envision this tool to be broadly useful in decoding the interplay of kinetics and thermodynamics underlying the formation and function of biological condensates. Code and documentation: https://caspule.github.io/caspule/ Author SummaryLiving cells contain droplet-like compartments called biomolecular condensates that organize important molecules and reactions without a surrounding membrane. Understanding how these condensates form and behave helps us learn more about healthy cellular function and diseases. Condensate forming biopolymers (proteins and nucleic acids) often contain a sticker spacer architecture that gives rise to interesting emergent properties when they assemble into a condensate. Here we report an easy-to-use computational pipeline, called CASPULE, that allows us to simulate and analyze the condensation of polymer chains. Unlike previous tools, CASPULE models both the physical motion of these molecules and specific bonding interactions between sticker segments. This user-friendly tool will enable researchers to explore the governing principles underlying the formation and function of biomolecular condensates.

biophysics↗

bioRxiv2025

Biomolecular phase boundaries are described by a solubility product that accounts for variable stoichiometry and soluble oligomers

The solubility product is a rigorous description of the phase boundary for salt precipitation and has previously been shown to qualitatively describe the condensation of biomolecules. Here we present a derivation of the solubility product showing that the solubility product is also a robust description of biomolecule phase boundaries if care is taken to account for soluble oligomers and variable composition within the dense phase. Our calculation describes equilibrium between unbound monomers, the dense phase, and an ensemble of oligomer complexes with significant finite-size contributions to their free energy. The biomolecule phase boundary very nearly resembles the power law predicted by the solubility product when plotted as a function of the monomer concentrations. However, this simple form is concealed by the presence of oligomers in the dilute phase. Accounting for the oligomer ensemble introduces complexities to the power law phase boundary including re-entrant behavior and large shifts for stoichiometrically matched molecules. We show that allowing variable stoichiometry in the dense phase expands the two phase region, which appears as curvature of the phase boundary on a double-logarithmic plot. Furthermore, this curvature can be used to predict variations in the dense phase composition at different points along the phase boundary. Finally, we show how the solubility product power law can be identified in experiments by using dilute phase dissociation constants to account for the oligomer ensemble.

biophysics↗

bioRxiv2024

Measurement of solubility product in a model condensate reveals the interplay of small oligomerization and self-association

Cellular condensates often consist of 10s to 100s of distinct interacting molecular species. Because of the complexity of these interactions, predicting the point at which they will undergo phase separation into discrete compartments is daunting. Using experiments and computation, we therefore studied a simple model system consisting of 2 proteins, polySH3 and polyPRM, designed for pentavalent heterotypic binding. We tested whether the peak solubility product, the product of dilute phase monomer concentrations, is a predictive parameter for the onset of phase separation. Titrating up equal total concentrations of each component showed that the maximum solubility product does approximately coincide with the threshold for phase separation in both the experiments and models. However, we found that measurements of dilute phase concentration include contributions from small oligomers, not just monomers; therefore, a quantitative comparison of the experiments and models required inclusion of small oligomers in the model analysis. We also examined full phase diagrams where the model results were almost symmetric along the diagonal, but the experimental results were highly asymmetric. This led us to perform dynamic light scattering experiments, where we discovered a weak homotypic interaction for polyPRM; when this was added to the computational model, it was able to recapitulate the experimentally observed asymmetry. Thus, comparing experiments to simulation reveals that the solubility product can be predictive of phase separation, even if small oligomers and low affinity homotypic interactions preclude experimental measurement of monomer concentration.

biophysics↗

bioRxiv2024

Multi-condensate state as a functional strategy to optimize the cell signaling output

The existence of multiple biomolecular condensates inside living cells is a peculiar phenomenon not compatible with the predictions of equilibrium statistical mechanics. In this work, we address the problem of multiple condensates state (MCS) from a functional perspective. We combined Langevin dynamics, reaction-diffusion simulation, and dynamical systems theory to demonstrate that MCS can indeed be a function optimization strategy. Using Arp2/3 mediated actin nucleation pathway as an example, we show that actin polymerization is maximum at an optimal number of condensates. For a fixed amount of Arp2/3, MCS produces a greater response compared to its single condensate counterpart. Our analysis reveals the functional significance of the condensate size distribution which can be mapped to the recent experimental findings. Given the spatial heterogeneity within condensates and non-linear nature of intracellular networks, we envision MCS to be a generic functional solution, so that structures of network motifs may have evolved to accommodate such configurations.

biophysics↗

bioRxiv2023

Separation of sticker-spacer energetics governs the coalescence of biomolecular condensates

Biological condensates often emerge as a multi-droplet state and never coalesce into one large droplet within the experimental timespan. Previous work revealed that the sticker-spacer architecture of biopolymers may dynamically stabilize the multi-droplet state. Here, we simulate the condensate coalescence using metadynamics approach and reveal two distinct physical mechanisms underlying the fusion of droplets. Condensates made of sticker-spacer polymers readily undergo a kinetic arrest when stickers exhibit slow exchange while fast exchanging stickers at similar levels of saturation allow merger to equilibrium states. On the other hand, condensates composed of homopolymers fuse readily until they reach a threshold density. Increase in entropy upon inter-condensate mixing of chains drives the fusion of sticker-spacer chains. We map the range of mechanisms of kinetic arrest from slow sticker exchange dynamics to density mediated in terms of energetic separation of stickers and spacers. Our predictions appear to be in qualitative agreement with recent experiments probing dynamic nature of protein-RNA condensates. Statement of significanceA key conundrum of biological condensates is the coexistence of multiple droplets, in direct variance with classical predictions of mean-field theories of polymer solutions. Our current study uncovers that the merging of sticker-spacer condensate is an entropy driven process, as opposed to the surface energy driven fusion that are observed for canonical liquid droplets. This entropy, stemming from the inter-condensate polymer exchange, makes the droplet merging process dependent on inter-sticker dissociation kinetics. Stronger inter-sticker interaction triggers a kinetic arrest, preventing the condensate merger even at a low density. Our prediction starkly correlates with recent experimental findings on protein-RNA condensates in vitro and in vivo, highlighting the biological relevance of the interplay of kinetics and thermodynamics.

biophysics↗

bioRxiv2023

MolClustPy: A Python Package to Characterize Multivalent Biomolecular Clusters

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSSO_SCPLOWUMMARYC_SCPLOWC_ST_ABSLow-affinity interactions among multivalent biomolecules may lead to the formation of molecular complexes that undergo phase transitions to become extra-large clusters. Characterizing the physical properties of these clusters is important in recent biophysical research. Due to weak interactions such clusters are highly stochastic, demonstrating a wide range of sizes and compositions. We have developed a Python package to perform multiple stochastic simulation runs using NFsim (Network-Free stochastic simulator), characterize and visualize the distribution of cluster sizes, molecular composition, and bonds across molecular clusters and individual molecules of different types. AO_SCPLOWVAILABILITYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWANDC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWIMPLEMENTATIONC_SCPLOWThe software is implemented in Python. A detailed Jupyter notebook is provided to enable convenient running. Code, user guide and examples are freely available at https://molclustpy.github.io/ CO_SCPLOWONTACTC_SCPLOWachattaraj007@gmail.com, blinov@uchc.edu SO_SCPLOWUPPLEMENTARYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWINFORMATIONC_SCPLOWAvailable at https://molclustpy.github.io/

systems biology↗

bioRxiv2022

The maximum solubility product marks the threshold for condensation of multivalent biomolecules

Clustering of weakly interacting multivalent biomolecules underlies the formation of membraneless compartments known as condensates. As opposed to single component (homotypic) systems, the concentration dependence of multi-component (heterotypic) condensate formation is not well understood. We previously proposed the solubility product (SP), the product of monomer concentrations in the dilute phase, as a tool for understanding the concentration dependence of multi-component systems. In the current study, we further explore the limits of the SP concept using spatial Langevin dynamics and rule-based stochastic simulations. We show, for a variety of idealized molecular structures, how the maximum SP coincides with the onset of the phase transition, i.e., the formation of large clusters. We reveal the importance of intra-cluster binding in steering the free and cluster phase molecular distributions. We also show how structural features of biomolecules shape the solubility product profiles. The interplay of flexibility, length and steric hindrance of linker regions controls the phase transition threshold. Remarkably, when solubility products are normalized to non-dimensional variables and plotted against the concentration scaled to the threshold for phase transition, the curves all coincide independent of the structural features of the binding partners. Similar coincidence is observed for the normalized clustering vs. concentration plots. Overall, the principles derived from these systematic models will help guide and interpret in vitro and in vivo experiments on the biophysics of biomolecular condensates. Significance StatementBiomolecular condensates are macroscopic intracellular structures that are composed of weakly interacting macromolecules. Because their composition can be complex, there are no simple rules for how condensates form as a function of the concentrations of their individual components. In this work, we show how the solubility product (SP), the product of monomer concentrations in the dilute phase, might serve as a tool for predicting the concentration dependence for condensation of multi-component systems. Specifically, Langevin dynamics simulations of the clustering of a series of multivalent binding partners reveals how the maximum SP is always attained at the same concentration as the appearance of large clusters. Experimental application of the SP concept should help rationalize the cellular formation of biomolecular condensates.

biophysics↗

bioRxiv2020

Solubility product constant directs the formation of biomolecular condensates

Biomolecular condensates, formed by liquid-liquid phase separation (LLPS), are important cellular structures. Using stochastic network-free kinetic models, we establish a physical-chemical basis for the concentration threshold of heterotypic multivalent molecules required for LLPS. We associate phase separation with a bimodal partitioning of the cluster distribution into small oligomers vs. huge polymers. The simulations reveal that LLPS obeys the solubility product constant (Ksp): the product of monomer concentrations, accounting for ideal stoichiometries, does not exceed a threshold no matter how much additional monomer is added to the system - additional monomer is funneled into large clusters. The Ksp applies over a range of valencies and stoichiometries. However, consistent with the importance of disordered domains for LLPS, removing flexible linker domains funnels valency-matched monomers into a "dimer trap", and Ksp no longer defines a threshold for large cluster formation. We propose Ksp as a new tool for elucidating biomolecular condensate biophysics.

biophysics↗

bioRxiv2018

A biophysical model of molecular clusters: interplay of multivalency, membrane localization, steric hindrance, molecular flexibility and intracellular crowding

Dynamic molecular clusters are assembled through weak multivalent interactions and are platforms for cellular functions, especially receptor-mediated signaling. Clustering is also a prerequisite for liquid-liquid phase separation. But it is not well understood how molecular structure and cellular organization control clustering. Using coarse-grain kinetic Langevin dynamics, we performed computational experiments on a prototypical ternary system modeled after membrane-bound nephrin, the adaptor Nck1 and the actin nucleation promoting factor NWASP. Steady state cluster size distributions favored stoichiometries that optimized binding (stoichiometry matching), but still were quite broad. At high concentrations, the system can be driven beyond the saturation boundary such that cluster size is limited only by the number of available molecules. This behavior would be predictive of phase separation. Domains close to binding sites sterically inhibited clustering much less than terminal domains because the latter effectively restrict access to the cluster interior. Increased flexibility of interacting molecules diminished clustering by shielding binding sites within compact conformations. Membrane association of nephrin increased the cluster size distribution in a density-dependent manner. These properties provide insights into how molecular ensembles function to localize and amplify cell signaling.

biophysics↗