Dynamics simulations for engineering macromolecular interactions

Abstract

The predictable engineering of well-behaved transcriptional circuits is a central goal of synthetic biology. The artificial attachment of promoters to transcription factor genes usually results in noisy or chaotic behaviors, and such systems are unlikely to be useful in practical applications. Natural transcriptional regulation relies extensively on protein-protein interactions to insure tightly controlled behavior, but such tight control has been elusive in engineered systems. To help engineer proteinprotein interactions, we have developed a molecular dynamics simulation framework that simplifies features of proteins moving by constrained Brownian motion, with the goal of performing long simulations. The behavior of a simulated protein system is determined by summation of forces that include a Brownian force, a drag force, excluded volume constraints, relative position constraints, and binding constraints that relate to experimentally determined on-rates and off-rates for chosen protein elements in a system. Proteins are abstracted as spheres. Binding surfaces are defined radially within a protein. Peptide linkers are abstracted as small protein-like spheres with rigid connections. To address whether our framework could generate useful predictions, we simulated the behavior of an engineered fusion protein consisting of two 20 000 Da proteins attached by flexible glycine/serine-type linkers. The two protein elements remained closely associated, as if constrained by a random walk in three dimensions of the peptide linker, as opposed to showing a distribution of distances expected if movement were dominated by Brownian motion of the protein domains only. We also simulated the behavior of fluorescent proteins tethered by a linker of varying length, compared the predicted F€ orster resonance energy transfer with previous experimental observations, and obtained a good correspondence. Finally, we simulated the binding behavior of a fusion of two ligands that could simultaneously bind to distinct cell-surface receptors, and explored the landscape of linker lengths and stiffnesses that could enhance receptor binding of one ligand when the other ligand has already bound to its receptor, thus, addressing potential mechanisms for improving targeted signal transduction proteins. These specific results have implications for the design of targeted fusion proteins and artificial transcription factors involving fusion of natural domains. More broadly, the simulation framework described here could be extended to include more detailed system features such as nonspherical protein shapes and electrostatics, without requiring detailed, computationally expensive specifications. This framework should be useful in predicting behavior of engineered protein systems including binding and dissociation reactions. Synthetic biologists frequently take the elements of natural transcription systems and treat them as abstracted black boxes in which the protein elements are used as Nature provides them. Modeling the behavior of such systems typic...

Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

“…For example, as discussed in this issue, at subcellular level the binding of transcription factors to the DNA molecules presents significant challenges for both experimental and theoretical analyses. 76 Logical models provide a simplified way to capture the main genes, the transcription factors, and their interactions without specifying the detailed kinetics. An early example is the analysis of bacteriophage lambda that showed how the 025001- 4 Albert, Collins, and Glass Chaos 23, 025001 (2013) interaction of both positive and negative feedback loops were necessary to understand the interaction of the virus with a bacterium and whether the virus would cause lysis of the bacterium or would incorporate into the bacterium's genome leading to the state of lysogeny.…”

“…87,89 In another approach, kinetic equations for genetic networks are described by functions based on polynomial expressions. 111 Of course, it is also possible that simplified models will not offer adequate insights and that emerging theory will require realistic models for subcellular 76 or cellular 106 processes. Independent of the class of theoretical model that proves most useful to understand the structure of genetic networks, a profound problem involves the comparative structure of genetic networks in different organisms and the evolution of these networks.…”

Designing Cell-Targeted Therapeutic Proteins Reveals the Interplay between Domain Connectivity and Cell Binding

“…3, A and B) and the greater repulsion due to the increased negative charge. Although it is also possible for a linker to exhibit reduced binding enhancement because it is too short to span the two receptors (25,38), this was not expected in our system due to the inclusion of the SNAP domains (Fig. 1 B).…”

“…The simulation predicts the increase in effective IFNa concentration near the cell membrane as a result of CD20 binding (see Materials and Methods for details), which can then be used to compute the rate parameters. We developed the CBD simulation method by extending the molecular-dynamics simulation framework previously developed in our group (25). We added the capacity to handle nonspherical rigid structures for modeling rigid linkers and protein domain geometry.…”

“…Each simulation is run for 1 ms, the first 10 ms of which are discarded to allow the system to randomize. The measured quality of binding in arbitrary binding units (ABU) corresponds to the amount of time the IFN molecule spends near a productive binding configuration with respect to the distance from the cell membrane of the binding site on IFN receptors (25).…”