RUI: A Systematic, Computational Study of the Effect of Macromolecular Crowding on Electrostatic Interactions, Biomolecular Recognition, and Molecular Design

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In recent years, sophisticated computer modeling has enabled scientists to understand and address challenging problems in materials science, engineering, environmental science, and other fields. In order for a computational model to be effective, it must be accurate and efficient. In this project, the research team will evaluate, improve upon, and apply computational models used to study molecular interactions in biological environments. Specifically, they will focus on computational models used to study how interactions between biological molecules are affected by the physical nature of other molecules in their immediate environment. It has been shown that cells are highly crowded environments, and the nature of this "crowding" can significantly affect how crucial molecules interact with each other. A robust way to model such environments will allow for better predictions of cellular processes, which can have important impacts on society's collective understanding of biological systems and its ability to develop solutions when things go "wrong" in such systems. This research will be conducted at Wellesley College, an all-female undergraduate institution, catalyzing women's contribution to computational science, a discipline in which women have long been very underrepresented. The research team will also conduct outreach activities at a local diverse high school, introducing youth to the field of computational modeling of biological systems. The activities developed as part of this collaborative, outreach effort will be made available online to help excite students across the country about using computers and physical science to address real-world issues in biology.In more technical terms, the goal of the project is to study how macromolecular crowding within the cell affects electrostatic interactions and molecular recognition between biomolecules via a controlled set of computational models that are informed through experimental data. Through computation, the team will first study systems in which the interacting biomolecules and macromolecular crowders are "virtual", whose physical properties can be exhaustively and systematically sampled, in order to understand how molecular and crowding agent properties such as shape, size, and charge distribution can affect interaction energies. They will use multiple computational models, including ones that treat the solvent implicitly and explicitly, in order to assess the extent to which the method used to model the system affects the predictions made. The team will also simulate experimentally realizable biological systems, including DNA-protein and protein-protein complexes within crowded environments. Through a tight cycle of comparing simulation predictions with experimental outcomes, they will assess and improve computational models of crowded biological environments. Finally, the team will assess whether macromolecular crowding can affect the outcome of a molecular design application. Through computationally designing molecules meant to bind with specificity to a partner in either a crowded or uncrowded environment and experimentally testing those designs, they will determine whether accounting for macromolecular crowding in the design process is necessary for ensuring the desired binding properties of the designed molecule. As a whole, this project will increase our understanding of how biological environments can crucially affect biomolecular recognition.