Biology subjects

Jhaveri, A.

Publications and source records attributed to Jhaveri, A..

4 recordsLinked to original sources

bioRxiv2025

Binding affinities for 2D protein dimerization benefit from enthalpic stabilization

Dimerization underpins all macromolecular assembly processes both on and off the membrane. While the strength of dimerization, KD, is commonly quantified in solution (3D), many proteins like the soluble BAR domain-containing proteins also reversibly dimerize while bound to a membrane surface (2D). The ratio of dissociation constants, [Formula], defines a lengthscale that is essential for determining whether dimerization is more favorable in solution or on the membrane surface, particularly for these proteins that reversibly transition between 3D and 2D. While purely entropic rigid-body estimates of h apply well to transmembrane adhesion proteins, we show here using Molecular Dynamics simulations that even moderate flexibility in BAR domains dramatically alters the free energy landscape from 3D to 2D, driving enhanced selectivity and stability of the native dimer in 2D. By simulating BAR homodimerization in three distinct environments, 1) solution (3D), 2) bound to a lipid bilayer (2D), and 3) fully solvated but restrained to a pseudo membrane (2D), we show that both 2D environments induce backbone configurations that better match the crystal structure and produce more enthalpically favorable dimer states, violating the rigid-body estimates to drive h<<hRIGID. Remarkably, contact with an explicit lipid bilayer is not necessary to drive these changes, as the solvated pseudo membrane induces this same result. We show this outcome depends on the stability of the protein interaction, as a parameterization that produces exceptionally stable binding in 3D does not induce systematic improvements on the membrane. With h lengthscales calculated here that are well below a physiological volume-to-surface-area lengthscale, assembly will be dramatically enhanced on the membrane, which aligns with BAR domain function as membrane remodelers. Our approach provides simple metrics to move beyond rigid-body estimates of 2D affinities and assess whether conformational flexibility selects for enhanced stability on membranes.

biophysics↗

bioRxiv2024

Parallelization of particle-based reaction-diffusion simulations using MPI

Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into higher-order structures like filaments, spherical cages, or heterogeneous macromolecular complexes, which are ubiquitous across living systems and in materials design. The disadvantage of these high-resolution methods is their increased computational cost. Here we present a parallel implementation of the particle-based NERDSS software using the Message Passing Interface (MPI) and spatial domain decomposition, achieving close to linear scaling for up to 96 processors in the largest simulation systems. The scalability of parallel NERDSS is evaluated for bimolecular reactions in 3D and 2D, for self-assembly of trimeric and hexameric complexes, and for protein lattice assembly from 3D to 2D, with all parallel test cases producing accurate solutions. We demonstrate how parallel efficiency depends on the system size, the reaction network, and the limiting timescales of the system, showing optimal scaling only for smaller assemblies with slower timescales. The formation of very large assemblies represents a challenge in evaluating reaction updates across processors, and here we restrict assembly sizes to below the spatial decomposition size. We provide the parallel NERDSS code open source, with detailed documentation for developers and extension to other particle-based reaction-diffusion software.

biophysics↗

bioRxiv2023

Discovering optimal kinetic pathways for self-assembly using automatic differentiation

During self-assembly of macromolecules ranging from ribosomes to viral capsids, the formation of long-lived intermediates or kinetic traps can dramatically reduce yield of the functional products. Understanding biological mechanisms for avoiding traps and efficiently assembling is essential for designing synthetic assembly systems, but learning optimal solutions requires numerical searches in high-dimensional parameter spaces. Here, we exploit powerful automatic differentiation algorithms commonly employed by deep learning frameworks to optimize physical models of reversible self-assembly, discovering diverse solutions in the space of rate constants for 3-7 subunit complexes. We define two biologically-inspired protocols that prevent kinetic trapping through either internal design of subunit binding kinetics or external design of subunit titration in time. Our third protocol acts to recycle intermediates, mimicking energy-consuming enzymes. Preventative solutions via interface design are the most efficient and scale better with more subunits, but external control via titration or recycling are effective even for poorly evolved binding kinetics. Whilst all protocols can produce good solutions, diverse subunits always helps; these complexes access more efficient solutions when following external control protocols, and are simpler to design for internal control, as molecular interfaces do not need modification during assembly given sufficient variation in dimerization rates. Our results identify universal scaling in the cost of kinetic trapping, and provide multiple strategies for eliminating trapping and maximizing assembly yield across large parameter spaces. SIGNIFICANCEMacromolecular complexes are frequently composed of diverse subunits. While evolution may favor repeated subunits and symmetry, we show how diversity in subunits generates an expansive parameter space that naturally improves the expressivity of self-assembly, much like a deeper neural network. By using automatic differentiation algorithms commonly used in deep learning, we searched these parameter spaces to identify classes of kinetic protocols that mimic biological solutions for productive self-assembly. Our results reveal how high-yield complexes that easily become kinetically trapped in incomplete intermediates can instead be steered by internal design of rate constants or external and active control of subunits to efficiently assemble, exploiting nonequilibrium control of these ubiquitous dynamical systems.

biophysics↗

bioRxiv2020

Thermodynamics and free energy landscape of BAR-domain dimerization from molecular simulations

Nearly all proteins interact specifically with other proteins, often forming reversible bound structures whose stability is critical to function. Proteins with BAR domains function to bind to, bend, and remodel biological membranes, where the dimerization of BAR domains is a key step in this function. Here we characterize the binding thermodynamics of homodimerization between the LSP1 BAR domain proteins in solution, using Molecular Dynamics (MD) simulations. By combining the MARTINI coarse-grained protein models with enhanced sampling through metadynamics, we construct a two-dimensional free energy surface quantifying the bound versus unbound ensembles as a function of two distance variables. Our simulations portray a heterogeneous and extraordinarily stable bound ensemble for these modeled LSP1 proteins. The proper crystal structure dimer has a large hydrophobic interface that is part of a stable minima on the free energy surface, which is enthalpically the minima of all bound structures. However, we also find several other stable nonspecific dimers with comparable free energies to the specific dimer. Through structure-based clustering of these bound structures, we find that some of these nonspecific contacts involve extended tail regions that help stabilize the higher-order oligomers formed by BAR-domains, contacts that are separated from the homodimer interface. We find that the known membrane-binding residues of the LSP1 proteins rarely participate in any of the bound interfaces, but that both patches of residues are aligned to interact with the membrane in the specific dimer. Hence, we would expect a strong selection of the specific dimer in binding to the membrane. The effect of a 100mM NaCl buffer reduces the relative stability of nonspecific dimers compared to the specific dimer, indicating that it would help prevent aggregation of the proteins. With these results, we provide the first free energy characterization of interaction pathways in this important class of membrane sculpting domains, revealing a variety of interfacial contacts outside of the specific dimer that may help stabilize its oligomeric assemblies.

biophysics↗