Margaret Johnson joined the Biophysics faculty at Johns Hopkins University in 2013. She received her B.S. in Applied Math from Columbia University and her PhD in BioEngineering from UC Berkeley. She completed postdoctoral training in the Laboratory of Chemical Physics at the National Institutes of Health in Bethesda, MD. Her research focuses on understanding how the individual interactions between thousands of diverse components in the cell generate order and collective function at the right time and the right place. She develops theoretical and computational approaches to study the evolution and mechanics of dynamic systems of interacting and assembling proteins.
In 2011 she received an NIH Pathway to Independence Award (K99/R00).
The dynamic assembly of multiple proteins into large functional complexes at specific times and places in the cell is a crucial step in cellular functions ranging from endocytic trafficking of membrane cargo to DNA transcription and actin polymerization. The protein-protein interactions that control such cellular processes are diverse and dynamic, forming a network of connections that can nucleate and stabilize extended protein complexes. Despite much experimental studies on the details of the proteins involved, their binding reactions, and the structure of completed assemblies, the evolution and mechanisms of many multi-protein assembly processes remains unresolved. Using computer simulation and theoretical models, we are able to build detailed models of the dynamics of protein assembly and provide predictions of how the timing of these events in the cell are controlled by the concentrations of proteins in the cytosol and membrane. Our approach combines systems level research on protein networks that investigates both general, governing principles of protein dynamics, and applications to several specific protein assembly systems.
A major research focus is on developing accurate physical models to describe the spatio-temporal dynamics of populations of proteins in the cell. Multi-component protein assembly in particular has received limited attention from the computational community because it spans nano to mesoscopic length scales and occurs over seconds to minutes. Reaction-diffusion models can handle the long time-scales of protein association but lack the resolution to capture the structure of molecular assembly. Conversely, molecular modeling approaches include structural detail but they are useful only for short time scales that at best allow observation of the association between two proteins. My lab addresses these issues by implementing new algorithms for building structural details into the reaction-diffusion framework without sacrificing accuracy in the dynamics, or a dramatic slow-down in the simulations
We complement these dynamic spatial simulations of protein interactions with biophysical and bioinformatics-based analysis on the evolution, specificity, and expression dynamics of systems of proteins. Using a framework of the cell as an optimized system, we predict how the global properties of the proteome, including its network topology and expression levels, arise from local constraints on individual proteins. We find that the requirement for proteins to evolve binding interfaces that are both selective for their specific functional partners and optimized against binding all other proteins places steep constraints on the structure of protein interface interaction networks that ultimately influences the protein-protein interaction network.
We are currently focusing on understanding assembly that is mediated through membrane recruitment of proteins, such as occurs in clathrin-mediated endocytosis (CME) and receptor-mediated signaling. These pathways are vital to the survival of all eukaryotes, both for internalizing nutrients and regulating communication between cells. Although both processes require membrane targeting, the proteins may have evolved in some cases to exploit the 2D surface for assembly, versus assembling in solution. With sufficient concentration of lipid recruiters, both weak and strong binding proteins can use the surface to dramatically enhance complex formation and assembly.
AS. 250.302 Models and Algorithms in Biophysics
View Margaret Johnson's profile on Google Scholar for a complete publications list.