Proposal Title: Establishing novel signaling transmission modes of LOV photoreceptors
Summary: Photoreceptors are complex protein machines that respond to a fundamental sensory input: light. They are found in nearly all organisms and are critical to behavior, circadian rhythms, metabolism, and photosynthesis. Thus, elucidating what they do and how they work (i) informs how organisms from bacteria to humans adapt to environmental changes and sustain life, and (ii) provides design rules for engineering "optogenetic" protein tools for controlling cellular function with light. The overall goal of this CAREER Award is to reveal novel mechanisms and applications of light/oxygen/voltage (LOV) photoreceptor signaling in natural and engineered systems, through a unified research and educational program at the interface of photobiology, synthetic biology, and bioengineering.
This project will establish the signaling function and signal transmission mode of LOV proteins with regulators of G-protein signaling (RGS) as their ontological effectors. The existence of RGS-LOV has been hypothesized based on bioinformatics but never proven by biochemical and biophysical characterization. Preliminary data suggests that the light-driven structural rearrangements unmask a membrane localization sequence, which enhances protein-protein interaction between the RGS and its G-alpha signaling partner to terminate G-protein coupled receptor signaling upon translocation. RGS-LOV structure-function relationships and functional signaling activity will be studied (i) in vitro using purified recombinant protein produced in bacteria and (ii) using cellular assays in heterologously expressing mammalian cells, as assessed by spectroscopy, immunohistochemistry, and high-resolution microscopy. The research will impact broad research communities through the dissemination of optogenetic tools and open-source hardware. Educational activities will extend LOV photobiology studies and application into undergraduate research and STEM training, through activities that focus on hands-on learning and combining wet laboratory experience with mathematical models of biological signaling.
Reliance Industries Term Assistant Professor
Chemical and Biomolecular Engineering
Proposal Title: Computational Characterization of Protein Hydration and Interactions
Summary: Because all of biology happens in water, every biomolecular binding process involves protein-water interactions being disrupted, and replaced by direct interactions between the binding partners. Protein-water interactions thus play a crucial role in protein hydration, its stability, as well as its interactions with ligands and other proteins. However, characterizing protein-water interactions is challenging, because they depend not only on the chemistry, but also on the precise topography and the chemical pattern presented by the protein surface. As a result, a fundamental understanding of how water structure is perturbed at protein interfaces, and how this perturbation in turn affects protein interactions, is lacking. The goal of this proposal is to characterize and understand the interactions between protein surfaces and their hydration waters, and to introduce students ranging from elementary to graduate school, especially those from disadvantaged backgrounds, to aspects of protein hydration and interactions through the use of innovative teaching methods and materials. The results from this project will be broadly disseminated to the research community and the general public.
To enable a comprehensive characterization of protein-water interactions, the PI will employ novel simulation techniques to apply an unfavorable biasing potential to water molecules in the entire protein hydration shell. As the strength of the potential is increased, protein-water interactions are systematically disrupted, and the response of the hydration shell waters to the applied potential contains a wealth of information that can be analyzed to: (1) identify regions of the protein that have the weakest interactions with water, and investigate whether they can serve as predictors of protein interaction interfaces, (2) estimate free energies for hydrating diverse ligand-shaped cavities in the protein hydration shell, which inform protein-ligand interactions, and (3) identify super-hydrophilic patches on the protein that allow them to have strong interactions with water, and interrogate whether such interactions are able to stabilize the protein upon dehydration. The proposed research promises to break new ground by providing molecular level understanding and uncovering its impact on protein interactions and assembly. As novel protein structures continue to be solved at an incredible rate, modulating protein interactions promises to be an exciting frontier in the search for novel therapeutics.
Raj and Neera Singh Term Assistant Professor
Electrical and Systems Engineering
Proposal Title: Scalable Algorithms for Spectral Analysis of Massive Networked Systems
Summary: Social, financial, and biological networks are examples of large-scale systems composed by a large number of units coupled through a complex network of interactions. Understanding the relationship between the structure of a complex network and the performance of dynamical processes taking place in the network, such as the spread of diseases in human contact networks, the propagation of shock in financial networks, and coordination protocols in robotic networks, is of critical societal importance. From an engineering perspective, we are interested in using this knowledge to design secure and resilient critical networked systems and infrastructures. In particular, we would like to provide answers to the following questions: What structural factors make a communication network efficient in the coordination of a group of robotic agents? What network properties are useful while controlling the spread of a disease in a human contact network? How can we design critical networked infrastructures able to self-contain the spread of a cascading failure? Since networks are ubiquitous across science and engineering, developing efficient tools for network analysis and design is of great relevance to many scientific disciplines. The proposed research program will be complemented with a comprehensive educational agenda spanning K-12, undergraduate, and graduate level education at the University of Pennsylvania.
During the last decade, Network Science has matured into an established research field, providing a plethora of tools for modeling and analyzing complex systems. In particular, the field of spectral graph theory has been instrumental in the development of a wide array of powerful network analysis techniques, such as spectral graph partitioning, community detection, and ranking techniques (including Google's PageRank). Furthermore, spectral-graph properties are directly related to the dynamical behavior of many networked processes, such as synchronization of oscillators, multi-agent coordination, and viral spreading processes. The goal of this project is to develop a novel computational framework based on recent results from algebraic graph theory and real algebraic geometry to infer global spectral properties of dynamical relevance from local structural information. Theoretical advancements in this proposal will go hand-in-hand with the development of scalable algorithms for spectral analysis of large networked systems. Furthermore, an important part of this proposal will be focused on developing efficient strategies to design secure and resilient critical networked systems and infrastructures. Examples of particular design problems that will be considered are the design of efficient topologies to facilitate in-network coordination in multi-agent robotic systems, as well as the design of network interventions to contain viral processes in human contact networks, and financial shock in economic networks. During the course of this proposal, the PI will address a number of fundamental open theoretical problems, as well as explore the application of newly developed techniques to a diverse array of network control problems in collaborations with domain-specific experts.