A study of protein targeting reveals insights into mitigating protein aggregation

A unique chloroplast Signal Recognition Particle (SRP) in green plants is primarily dedicated to the post-translational targeting of light harvesting chlorophyll-a/b binding (LHC) proteins. Our study of the thermodynamics and kinetics of the GTPases of the system demonstrates that GTPase complex assembly and activation are highly coupled in the chloroplast GTPases, suggesting they may forego the GTPase activation step as a key regulatory point. This reflects adaptations of the chloroplast SRP to the delivery of their unique substrate protein. Devotion to one highly hydrophobic family of proteins also may have allowed the chloroplast SRP system to evolve an efficient chaperone in the cpSRP43 subunit. To understand the mechanism of disaggregation, we showed that LHC proteins form micellar, disc-shaped aggregates that present a recognition motif (L18) on the aggregate surface. Further molecular genetic and structure-activity analyses reveal that the action of cpSRP43 can be dissected into two steps: (i) initial recognition of L18 on the aggregate surface; and (ii) aggregate remodeling, during which highly adaptable binding interactions of cpSRP43 with hydrophobic transmembrane domains of the substrate protein compete with the packing interactions within the aggregate. We also tested the adaptability of cpSRP43 for alternative substrates, specifically in attempts to improve membrane protein expression and inhibition of amyloid beta fibrillization. These preliminary results attest to cpSRP43’s potential as a molecular chaperone and provides the impetus for further engineering endeavors to address problems that stem from protein aggregation.

A Model For Residential Adoption of Photovoltaic Systems

The rapid rise in the residential photo voltaic (PV) adoptions in the past half decade has created a need in the electricity industry for a widely-accessible model that estimates PV adoption based on a combination of different business and policy decisions. This work analyzes historical adoption patterns and finds fiscal savings to be the single most important factor in PV adoption, with significantly greater predictive power compared to all other socioeconomic factors including income and education. We can create an application available on Google App Engine (GAE) based on our findings that allows all stakeholders including policymakers, power system researchers and regulators to study the complex and coupled relationship between PV adoption, utility economics and grid sustainability. The application allows users to experiment with different customer demographics, tier structures and subsidies, hence allowing them to tailor the application to the geographic region they are studying. This study then demonstrates the different type of analyses possible with the application by studying the relative impact of different policies regarding tier structures, fixed charges and PV prices on PV adoption.