{"id":7,"date":"2013-09-10T12:21:41","date_gmt":"2013-09-10T12:21:41","guid":{"rendered":"https:\/\/sites.krieger.jhu.edu\/template-research\/?page_id=7"},"modified":"2026-06-22T20:39:20","modified_gmt":"2026-06-22T20:39:20","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.krieger.jhu.edu\/johnson-lab\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n

Google Scholar <\/a><\/p>\n\n\n\n

PubMed<\/a><\/p>\n\n\n\n

Sang, M., Johnson, M.E.*<\/span>, Defining reversible binding rates in 1D systems dependent on diffusion, density, and excluded volume. Submitted <\/em>bioRxiv preprint<\/a> (2026). <\/h3>\n\n\n\n

Diffusion along one-dimensional filaments such as DNA is central to many biological binding processes, yet the recurrent nature of 1D motion prevents the existence of a unique macroscopic association rate. This makes it difficult to represent 1D reversible binding with standard rate equations, even when systems are initially homogeneous. In this work, we combine theory and particle-based stochastic simulation to define practical effective rates for 1D reversible reactions and to identify the parameter regimes where ordinary rate equations remain accurate. These results provide quantitative guidance for coarse-graining 1D reaction-diffusion systems and a validated simulation framework for modeling reversible binding on filaments.<\/p>\n\n\n\n

Ying, Y.\u00ba, Sang, M.\u00ba, Au, G., Chhibber, S., Du, Y., Fischer, J.A., Foley, S.L., Guo, S., Herzog-Pohl, I., Liu, Z., Roscom, H., Sohail, S., Takeshita, S.S., Johnson, M.E.*<\/u><\/strong>, Transforming macromolecular structures into simulations of self-assembly with ioNERDSS, in review<\/em> bioRxiv preprint<\/a> (2026).<\/h3>\n\n\n\n

Multi-subunit macromolecular assemblies are ubiquitous in living and synthetic systems, from ribosomes, to viral capsids, to actin filaments. While the structures of these complexes is often known thanks to the PDB and AlphaFold3, these static structures provide no direct information on how they assembled in space and time from their monomeric building blocks. To help study mechanisms controlling how these structures assemble, we developed a python package, ioNERDSS<\/a>, to automate the process of transforming a 3D structure of a known assembly from resources like the PDB to an executable coarse-grained model, compatible with the NERDSS simulator (try it now!<\/a>).<\/p>\n\n\n\n

Bouzos, N., Samuel L. Foley, Ariadni Potamianos, Ciara O. Jacobs, Margaret E. Johnson<\/strong>, Wade F. Zeno* Clathrin is an intrinsic driver of membrane fission. in review <\/em>bioRxiv preprint<\/a> (2026).<\/h3>\n\n\n\n

Reshaping membranes into highly curved vesicles is required for transport of essential nutrients and receptors both in and out of cells. This reshaping requires work; during endocytosis, the clathrin trimer assembles a ~hexagonal lattice that helps shape the membrane into spherical buds. However, in cells, clathrin does not work alone, and both its recruitment and the final membrane scission are mediated by other proteins (like the GTPase dynamin). Here we demonstrate that clathrin, on its own, can drive end-to-end remodeling and membrane fission events to form new vesicles in vitro, dependent on its intrinsic curvature, assembly stability, and stiffness, providing a model template for tunable remodeling machines.<\/p>\n\n\n\n

Bademosi, A.T.*, Anusha Malapaka, Sonika Bhatnagar, Anmin Jiang , Jasmine S. Hinzen, Sikao Guo, Jingyao Xia, Elise Kellett, Yi Jia Chye, Sean S. Keating, Rebecca San Gil, Rachel S. Gormal, Tristan P. Wallis, Zhitao Hu, Adam K. Walker, Jeremy S. Dittman, Margaret E. Johnson<\/strong>, Fr\u00e9d\u00e9ric A. Meunier, Munc13-1 nanoclustering integrates activity and vesicle load-sensing encoded by C2 domains. Submitted <\/em>(2026).<\/h3>\n\n\n\n

During synaptic transmission, vesicle docking and fusion with the plasma membrane requires formation of a multi-protein ‘tethering’ complex that includes SNARE proteins and Munc13-1. We show using extensive single-molecule tracking experiments of Munc13 and C2 domain-truncations, along with mechanistic modeling, that Munc13 forms nanoclusters on the membrane with densities dependent on recruitment strength to the membrane, Munc13 dimerization, and ion stimulation, but not directly SNAREs. Each C2 domain truncation perturbs mobility by redistributing proteins between un\/clustered populations, with C2C driving dramatic immobilization correlated with Ca2+ channel colocalization. <\/p>\n\n\n\n

Fischer, J., Greenberg, E., Ying, Y.M., Takeshita, S.S., Foley, S.L. Johnson, M.E.*<\/span>, A membrane-driven biochemical oscillator tunable by the volume to surface area ratio, bioRxiv preprint<\/a> (2025).<\/h3>\n\n\n\n

Biochemical oscillations control circadian rhythms and developmental patterning. Protein-based oscillators are particularly promising for synthetic biology because of their versatility in the timescales they can access. Inspired by components of the endocytic machinery, we here develop and validate such a new biochemical oscillator following mass-action kinetics that exploits enzyme-driven changes to membrane lipid composition to drive oscillations of proteins on and off the membrane. Critically, the oscillations are broadly tunable in part by controlling the surface area to volume ratio of the system, an experimentally accessible tuning knob.<\/p>\n\n\n\n

Book Chapter: Guo, S.#<\/sup>, Foley, S.L.#<\/sup>, Johnson, M.E.*, Simulations of biomolecular self-assembly with stochastic reaction-diffusion models. Chapter in: Biomolecular Simulations–Methods in Molecular Biology, Springer Nature, Editors: L. Stelzl and R. Covino (Accepted <\/em>2025). <\/em><\/h3>\n\n\n\n

Book chapter explains the applications of stochastic reaction-diffusion methods for self-assembly. Provides detailed tutorials for getting your own executable models up and running. <\/p>\n\n\n\n

Ying, Y., Johnson, M.E.*<\/span> Membrane bending energy selects for symmetric growth of protein assemblies. in review <\/em> bioRxiv preprint<\/a>(2025).<\/h3>\n\n\n\n

Self-assembly of viruses and spherical cages is a stochastic process that can produce a variety of intermediate structures as growth progresses in time. When such assembly intermediates are coupled to remodeling the membrane surface, we show that the bending energy introduces a penalty that is highly favorable for compact, radially symmetric growth of the viral lattice. We predict therefore strong selection pressure for a narrow set of assembly growth pathways during processes like viral budding or membrane trafficking.<\/p>\n\n\n\n

Foley, S., Johnson, M.E.*<\/span> Membrane-associated self-assembly for cellular decision making<\/a>. Phys. Rev. Res<\/em>. 8, 023280. preprint<\/a> (2026).<\/h3>\n\n\n\n

A common paradigm for cellular decision making is through signaling cascades with irreversible reactions. We show that a similar switch-like decision threshold can be achieved through reversible interactions when self-assembly is coupled to dimensional reduction at membranes. Receptor binding acts as the trigger to switch on assembly, providing a control mechanisms encoded in the assembly components for processes like clathrin-mediated endocytosis and adhesion site formation.<\/p>\n\n\n\n

Sang, M., Au, G., Johnson, M.E.*<\/u> Mechanisms of enhanced or impaired DNA target selectivity driven by protein dimerization<\/a>. PNAS Nexus , <\/em>5<\/strong>, <\/em>pgag049 <\/em>preprint<\/a> (2026).<\/h3>\n\n\n\n

Transcription of DNA into RNA requires genome-wide orchestration but microscopically stochastic recruitment of multiple, multi-domain proteins to DNA target sequences. We show that for generalist proteins with many targets in the genome, protein dimerization does not always improve lifetimes or target binding to DNA. By redistributing proteins throughout the genome, dimerization can dramatically or negligibly enhance target occupancy and dwell time, and even impair them. Our model explains how dimerization provides strong selectivity for clustered targets, consistent with CHiP-seq data.<\/p>\n\n\n\n

Fu, Y., Johnson, D., Beaven, A., Sodt, A., Zeno, W.,& Johnson, M.E.*<\/u>, Predicting protein curvature sorting across membrane compositions<\/a>. Biophysical Journal, 125, 1007-1028. preprint<\/a> (2026).<\/h3>\n\n\n\n

By developing a new leaflet-resolved continuum bilayer membrane model, we use a multi-scale approach to predict how lipid composition, which controls key material properties of the membrane, can promote or suppress protein localization to membranes of varying curvature. Our model is validated by MD simulations for leaflet deformations, and by in vitro experiments measuring curvature-sensitive binding by ENTH domains as lipid tail structure is varied.<\/p>\n\n\n\n

Jhaveri, A., Chhibber, S., Kulkarni, N., Johnson, M.E.*<\/u> Protein dimerization in 2D vs 3D: geometric allostery enhances binding affinity<\/a>. J. Chem. Phys. 163, 034116  preprint<\/a> (2025).<\/h3>\n\n\n\n

How does the binding free energy and affinity of dimerization measured in solution (3D) change under restriction to a 2D membrane surface? We assess and then go beyond the rigid-body approximation that offers the current best quantification of this transformation. We show that proteins that can reversibly localize to membranes, or peripheral membrane proteins, can select for much more stable dimerization when on the membrane by exploiting even moderate flexibility in their backbones. This aligns with the BAR domain’s evolutionary role in assembly-driven membrane remodeling.<\/p>\n\n\n\n

Guo, S., Korolija, N., Milfeld, K., Jhaveri, A., Sang, M., Ying, Y., Johnson, M.E.*<\/u> Parallelization of particle-based reaction-diffusion simulations using MPI<\/a> J. Computational Chemistry <\/em>46:e70132. preprint<\/a> (2025). Cover Image!<\/a><\/h3>\n\n\n\n

Particle-based reaction-diffusion software captures the structure of component species, uniquely enabling nonequilibrium simulations of self-assembly of filaments, lattices, spherical capsids, and macromolecular complexes that are ubiquitous and essential in physics, chemistry, biology, and materials science. Our parallelization enables fast simulations of large systems, supporting efficient parameter optimization needed to design dynamical systems that describe experimental systems.<\/p>\n\n\n\n

Soubias, O., S. L. Foley, X. Jian, R. A. Jackson, Y. Zhang, E. M. Rosenberg Jr, J.s Li, F. Heinrich, M. E. Johnson, A. J. Sodt, P. A. Randazzo and R. A. Byrd. An active allosteric mechanisms in ASAP1-mediated Arf1 GTP hydrolysis redefines PH domain function<\/a>, Nat. Commun. <\/em>16, 8701 <\/em>preprint<\/a> (2025).<\/h3>\n\n\n\n

ArfGAP proteins play essential roles in controlling GTPase activity of the Arf proteins, with implications in invasion and cancer metastasis. Our collaborative work explains how the PH domain of the ASAP1 ArfGAP enhances the enzymatic activity by 7 orders-of-magnitude, via a combination of allosteric activation, dimensional reduction at the membrane, and co-localization of binding domains. Our microkinetic model of binding interactions, membrane localization, allosteric effects, and enzyme catalysis robustly reproduces multiple datasets while retaining detailed balance throughout all reversible steps, quantifying the relative contributions of dimensional reduction vs conformational changes towards the dramatic PH-driven activation of Arf1’s catalysis.<\/p>\n\n\n\n

Feng, X.A., Maryam, Y., Fu, Y., Ness, K.M., Liu, C.,Ahmed, I., Bowman, G.D., Johnson, M.E., Ha, T.J., Wu, C. GAGA Zinc finger transcription factor searches chromatin by 1D-3D facilitated diffusion<\/a>. Nat Struct Mol Biol, <\/em>32, 2359 preprint<\/a> (2025).<\/h3>\n\n\n\n

Prokaryotic transcription factors are known to find their target sequences via combined 3D and 1D diffusion by sliding nonspecifically on the DNA backbone. Our collaborative work quantifies how Eukaryotic proteins (here GAGA Factor) similarly exploit 1D diffusion, helping increase their dwell time on DNA and target DNA as measured via single-molecule experiments and quantitative assays, despite the frequent presence of nucleosomal barriers. <\/p>\n\n\n\n

Jhaveri, A.#<\/sup>, Loggia, S.#<\/sup>, Qian, Y., & Johnson, M.E.* Discovering optimal kinetic pathways for self-assembly using automatic differentiation<\/a>. PNAS USA<\/em>, 121 <\/strong>e2403384121 preprint<\/a> (2024).<\/h3>\n\n\n\n

Structural determination of macromolecular complexes is booming thanks to cryoEM, but establishing assembly kinetics is extraordinarily challenging in part due to the large parameter spaces accessible for out-of-equilibrium, multi-subunit assembly. We show here how automatic differentiation can be broadly applied to such kinetic optimization. Our approach reveals how internal design of subunit-subunit binding rates provides multiple routes to efficient assembly, with diverse subunits being essential for more \u2018designable\u2019 subunits. Alternately, external protocols like titration of subunits can ensure productive assembly for any complex to avoid kinetic trapping, a common barrier for macromolecular assembly.<\/p>\n\n\n\n

Jiang, A., Kudo, K., Gormal, R.S., Ellis, S., Guo, S., Wallis, T., Longfield, S., Robinson, P.J., Johnson, M.E., Joensuu, M., Meunier, F.A. Dynamin1 long- and short-tail isoforms exploit distinct recruitment and spatial patterns to form endocytic nanoclusters<\/a>. Nature Communications<\/em>, 15 <\/strong>4060 preprint<\/a> (2024).<\/h3>\n\n\n\n

Dynamin must localize to sites of endocytosis to assemble the fission machinery for proper vesicle budding. Our collaborative work shows how distinct isoforms of dynamin use additional protein-protein interaction domains to enhance recruitment following Calcium stimulation, in some cases forming much more numerous puncta on the membrane. With spatial and stochastic modeling, we explain how dynamin proteins rely on both 2D lateral diffusion and 3D diffusion to readily assemble at sites of endocytosis. <\/p>\n\n\n\n

Xie, Q., Lee, S.O., Vissamsetti, N., Guo, S., Johnson, M.E., Fried, S.D. Secretion-Catalyzed Assembly of Protein Biomaterials on a Bacterial Membrane Surface<\/a>. Angewantde Chemie, <\/em>e202305178 (2023).<\/h3>\n\n\n\n

Protein-based biomaterials have found a variety of applications in biomedicine and sustainable materials. Our collaborative work shows how bacteria programmed to secrete silk through its translocon drive spontaneous assembly of the silk into fibers. The assembly is facilitated by fibers still localized to the membrane, as supported by our models. This work provides a blueprint to use bacteria to produce autonomously assembled protein materials.<\/p>\n\n\n\n

Qian, Y.#<\/sup>, Evans, D.#<\/sup>, Mishra, B., Fu, Y., Liu, Z., Guo, S. & Johnson, M.E.* Temporal control by co-factors prevents kinetic trapping in retroviral Gag lattice assembly<\/a> Biophysical Journal<\/em>, 122<\/strong>, 1-18. preprint<\/a> (2023).<\/h3>\n\n\n\n

For the HIV-1 retrovirus, the retroviral Gag protein must assemble in the cytoplasm to produce new, infectious virions. Our stochastic reaction-diffusion simulations show that the size of the immature Gag lattice (>3000 monomers) makes it almost impossible to avoid kinetic traps in the bulk. We then demonstrate that co-factors like RNA can ensure robust assembly by slowing down Gag activation and nucleation.  Our results thus provide mechanistic insight into behavior observed both in vitro<\/em> and in vivo<\/em>, placing bounds on the strength and kinetics of Gag protein assembly.<\/p>\n\n\n\n

Guo, S., Saha, I., Saffarian, S., & Johnson, M.E.* Structure of the HIV immature lattice allows for essential lattice remodeling within budded virions<\/a>. eLife<\/em> 84881 preprint<\/a> (2023).<\/h3>\n\n\n\n

Maturation in retroviruses is essential to their infectivity: how does a pair of protease domains find one another to activate maturation when they are seemingly \u2018locked in\u2019 to the assembled lattice and represent only 5% of total lattice proteins? We show through computational RD models validated against multiple experimental observables that the incompleteness of the immature lattice allows the Gag proteins carrying protease domains to unbind, diffuse, and reattach to the lattice to trigger successful dimerization within minutes. This mechanistic model also shows that early dimerization prior to budding must be actively suppressed. <\/p>\n\n\n\n

Fu, Y., & Johnson, M.E.* Modeling membrane reshaping driven by dynamic protein assemblies<\/a>. Curr Opin Struct Biol <\/em>78<\/strong>, 102505 (2023).<\/h3>\n\n\n\n

In this Current Opinion, we discuss how modeling efforts to understand membrane reshaping require 1. time-dependent approaches that ideally incorporate 2. macromolecular structure, 3. out-of-equilibrium processes, and 4. deformable membranes over microns and seconds. Realistically, tradeoffs must be made with these last three features, but recent developments and multi-scale efforts are stimulating progress towards simulating these processes as they occur in cells.<\/p>\n\n\n\n

Guo, S., Sodt, A.J., & Johnson, M.E.* Large self-assembled clathrin lattices spontaneously disassemble without sufficient adaptor proteins<\/a>. PLoS Comp. Biol. 18, <\/strong>e1009969 <\/i>preprint<\/a> (2022).<\/h3>\n\n\n\n

Why do clathrin-coated structures observed in cells only proceed to productive vesicles about half the time, otherwise disassembling? A common intuition is that clathrin lattices are highly stable, and they must be actively disassembled. In this paper, we show for the first time that clathrin lattices of size n=25 or more will assemble, but spontaneously disassemble, dependent on the density of adaptor proteins linking them to the membrane. We show that the stability criterion is frequently strongly met for in vitro<\/em> experiments but is weakly met in vivo, <\/em>where system geometry and adaptor concentrations make disassembly more likely.  Our results quantitatively and visually demonstrate the inherent dynamic remodeling of clathrin-coated structures.<\/p>\n\n\n\n

Duan, D, M. Hanson, D.O. Holland, & M.E. Johnson* Integrating protein copy numbers with interaction networks to quantify stoichiometry in mammalian endocytosis<\/a>. Sci. Reports, 12<\/strong>, 5413. <\/em>(2022).<\/h3>\n\n\n\n

Given a complicated cellular pathway with dozens of distinct interacting components, how can we interpret variations in protein abundances between binding partners and across cell types, in a quantitative and intuitively comprehensible way? By first constructing the interface-resolved clathrin-mediated endocytosis network here, containing over 600 interactions, we show how this complex and detailed dataset can be integrated with known abundances to quantify stoichiometric imbalances between binding partners that accounts for both competition and cooperation in binding. Our analysis reveals both intuitive and surprising trends in which types of proteins have dominant or minimal effect on stoichiometry, with consequences on cargo selection.<\/p>\n\n\n\n

Fu, Y., Zeno, W., Stachowiak, J. & Johnson, M.E.* A continuum membrane model can predict curvature sensing by helix insertion<\/a>. Soft Matter<\/em> 17<\/strong>, 10649 (<\/span>2021).<\/h3>\n\n\n\n

Curvature sensing, or the preferential binding of proteins to membranes of high curvature, is observed for many protein types that insert amphipathic helices into a single bilayer leaflet. Our paper shows that the continuum membrane modeling approach provides an accurate, experimentally validated platform to study membrane energy and shape changes due to adsorbed proteins. Our model predicts the bending modulus of the membrane (10-20kB<\/sub>T) and the spontaneous curvature of the insertion (0.1-0.4nm-1<\/sup>) that reproduce experiments, which agrees well with reported values from the literature.<\/p>\n\n\n\n

Mishra, B., & M.E. Johnson* Speed limits of protein assembly with reversible membrane localization<\/a>. J. Chem. Phys. <\/em>154, <\/strong>194101.<\/em>(2021)<\/h3>\n\n\n\n

How does reversible localization to a membrane quantitatively change the speed of bimolecular association between reactant populations? Our theory provides a single expression that predicts the mean-first passage time of bimolecular association dependent on: i) dimensional reduction (ii) membrane adsorption rate (iii) protein-protein association rates (iv) protein concentrations, and (v) diffusion in 2D and 3D. We validate using kinetic and reaction-diffusion simulations, finding excellent agreement.<\/p>\n\n\n\n

Jhaveri, A., Maisuria, D., Varga, M., Mohammadyani, D., & M.E. Johnson* Thermodynamics and free energy landscape of BAR-domain dimerization from molecular simulations<\/a>. J Phys Chem B. 125,<\/strong><\/em> 3739-3751. <\/em>(2021).<\/h3>\n\n\n\n

Protein binding affinities are critical for their function in the cell, and are thus a frequent target of experimental characterization. Using coarse-grained MD simulations with MARTINI, we can simultaneously characterize the affinity of dimerization of a BAR domain dimer, and the structures that stabilize the bound ensemble. We use enhanced sampling with metadynamics and quantify the enthalpic and entropic contributions to bound state structures, showing that multiple nonspecific structures form in solution for this force-field. <\/p>\n\n\n\n

Johnson, M.E.*, <\/strong>A. Chen, J. Faeder, P. Henning, I. Moraru, M. Meier-Schellersheim, R. Murphy, T. Prustel, J. Theriot, A. Uhrmacher.  Quantifying the roles of space and stochasticity in computer simulations of cell biology and cellular biochemistry<\/a>. Mol Biol of Cell. <\/em>32<\/strong>, 186-210. preprint<\/a> (2021).<\/h3>\n\n\n\n

How can we achieve quantitative, physics-based models that can resolve the dynamics and mechanics observed in state-of-the-art cell biology experiments such as super-resolution imaging? We establish here a rigorous foundation for assessing and building on current tools, while providing guidance to both the expert and non-expert modeler for developing accurate, reproducible, and efficient models in cell biology.  We provide a series of test cases that are presented with the \u2018right answer\u2019, providing a foundation for others to ensure that models and tools are reproducible <\/strong>and accurate.<\/strong>  <\/p>\n\n\n\n

Varga, M.#<\/sup>, Fu, Y.#<\/sup>, Loggia, S., Yogurtcu, O.N., & M.E. Johnson* NERDSS: a nonequilibrium simulator for multibody self-assembly at the cellular scale<\/span><\/a>. Biophysical Journal<\/em> 118, <\/strong>P3026-P3040 preprint<\/a>. (2020).<\/h3>\n\n\n\n

Even with the fastest computers, cellular time-scales are still far out of reach for molecular modeling tools applied to self-assembly, and common events such as phosphorylation are difficult or impossible to introduce. NERDSS starts from the reaction-diffusion mathematical model, which does not have these limitations, and builds in coarse-grained structure to enable self-assembly of multi-component systems. NERDSS is open-source and designed to facilitate further development, see website<\/a>. <\/p>\n\n\n\n

Fu, Y., Yogurtcu, O.N., Kothari, R., Thorkelsdottir, G., Sodt, A.J., & M.E. Johnson* <\/strong>An implicit lipid model for efficient reaction-diffusion simulations of protein binding to surfaces of arbitrary topology<\/span><\/a>. J Chem Phys<\/em> 151, 124115. (2019). bioRxiv version.<\/a><\/h3>\n\n\n\n

Protein often bind to membranes by targeting specific lipids. The abundance of lipids makes tracking them during single-particle reaction-diffusion methods expensive. We derived an implicit lipid (mean-field-like) model that supports orders-of-magnitude faster rate-based simulations of particles to membranes, correctly accounting for the effects of membrane curvature.<\/p>\n\n\n\n

M.E. Johnson* Modeling the Self-Assembly of Protein Complexes through a Rigid-Body Rotational Reaction-Diffusion Algorithm<\/a>. J Phys Chem B.<\/em> 122<\/strong>, 11771-11783<\/span> (2018).<\/h3>\n\n\n\n

Reaction-diffusion models are widely used to simulate complex systems in chemistry, biology, physics, and engineering, building off Turing’s seminal work in 1952. However, they lack any even coarse-grained molecular resolution. Here we show how multi-site rigid bodies can be simulated accurately by particle-based RD algorithms, with applications here to clathrin-coat assembly.<\/p>\n\n\n\n

Holland, D.O., & M.E. Johnson* <\/strong>Stoichiometric Balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.<\/a> PLoS Comput. Biology<\/em> 14<\/strong>, e1006022. <\/i>preprint<\/a>. (2018).<\/h3>\n\n\n\n

Protein copy numbers are often found to be stoichiometrically balanced for subunits of multi-protein complexes. Can stoichiometric balance of protein binding partners also be beneficial for larger networks of reversibly interacting proteins? To answer this question, we first have developed a new method, applicable to any protein network with interfaces resolved, to objectively quantify the degree of balance in observed protein copy numbers. Applied to two recently characterized interface-resolved protein networks, we find that proteins that control clathrin-mediated endocytosis in yeast are significantly balanced, but classes of outliers exist, such as enzymes. We show costs and benefits of imbalance through kinetic modeling. <\/p>\n\n\n\n

Yogurtcu, O.N., and M.E. Johnson*. Cytosolic proteins can exploit membrane localization to trigger functional assembly<\/a>. PLoS Comput. Biology<\/em> 14<\/strong>, e1006031 preprint<\/a> (2018).<\/h3>\n\n\n\n

Dimensional reduction was first quantified in the 1960s as a key mechanism for molecules to find membrane receptor targets via 2D searches. We extend this idea beyond targeting to study how populations of self-assembling proteins can exploit 2D localization to dramatically (orders-of-magnitude) enhance assembly yield, and also timescales. With relatively simple equations, we predict how physiologic regimes of many membrane-binding proteins will benefit from 2D localization. <\/p>\n\n\n\n

Holland, D.O., Shapiro, B.H., Xue, P., & M.E. Johnson* <\/strong>Protein-protein binding selectivity and network topology constrain global and local properties of interface binding networks<\/a>. Sci. Reports. <\/em>7<\/strong>, <\/em>5631 (2017).<\/h3>\n\n\n\n

Protein-protein interactions form networks with nonrandom structures, but they typically lack information about the interfaces\/domains that mediate these interactions. We show that these interface-interaction networks (IIN) have a highly specific structure physically constrained by the specificity of protein interactions, and this is conserved in networks from human and yeast. We find that ‘hub’ proteins in networks can improve selectivity of interactions and reduce misinteractions.<\/p>\n\n\n\n

Yogurtcu, O.N., and M.E. Johnson*. Theory of bi-molecular association dynamics in 2D for accurate model and experimental parameterization of binding rates<\/a>. J. Chem. Phys. <\/i>143<\/strong>, 084117 (2015).<\/h3>\n\n\n\n

Binding interactions are typically characterized in solution (3D), and parameterized by a single rate constant. In 2D, however, diffusion is reentrant, and binding rates between molecules will be sensitive to their separation, meaning in general, a single rate constant is not applicable. We derive regimes where single-rates can still be accurate in 2D, and derive good approximations to a macroscopic rate as it depends on the system density, with extensive validation using particle-based reaction-diffusion.<\/p>\n\n\n\n

Johnson, M.E.*, and G. Hummer.  Free propagator reweighting integrator for single-particle dynamics in reaction-diffusion models of heterogeneous protein-protein interactions systems<\/a>. Phys. Rev. X  4<\/strong>, 031037 (2014).<\/h3>\n\n\n\n

Particle-based algorithms for reaction-diffusion simulations allow us to track individual molecules moving continuously and stochastically, capturing volume exclusion and supporting extensions to higher-order macromolecular structures. We derived a new algorithm that exactly reproduces the reversible kinetics of association for reacting particles. The advantage is we can take large timesteps and retain accuracy by using the Green’s function solution to diffusion with a reactive boundary. By using the approximate free propagator for position updates (reweighted for association), our method is also efficient and relatively simple to implement. We extensively validate on many-body systems and with Coulombic interaction potentials between particles. <\/p>\n\n\n\n\n\n\n\n