In exponentially growing bacteria, expression of heterologous protein impedes cellular growth rates. Recently, a scaling law was proposed by Terence Hwa's group that could be used to quantitatively predict this dependence. We were struck by this model because it had no free parameters and suggested a simplicity in this relationship. In this work we tested this scaling law using synthetic promoters to drive expression of two different heterologous proteins in E. coli under different growth conditions. In all cases, the growth rate dependence on protein expression was consistent with this scaling law. Because these results validate the quantitative prediction of the fitness cost upon protein expression, we suspect that this work will have broad utility in formulating design of optimal synthetic metabolic pathways. This work has been published in Plos One: Bienick MS, Young KW, Klesmith JR, Detwiler EE, Tomek KJ, Whitehead TA 2014 The interrelationship between promoter strength, gene expression, and growth rate. PLoS One, DOI: 10.1371/journal.pone.0109105
We describe a powerful new method using DNA deep sequencing to optimize protein affinity, specificity, and function. This method was applied to computationally designed inhibitors of Influenza Hemagglutinin to rapidly optimize their sequences for function.
We developed a new computational method for designing protein interactions and applied it to the design of anti-flu hemagglutinin inhibitors. The proteins were designed using computational resources generously provided by Rosetta @ Home participants and inhibited the function of the hemagglutinin flu coat protein that is crucial for viral infectivity. An experimentally determined molecular structure of the designed protein interacting with Spanish flu hemagglutinin (Figure) shows unprecedented level of agreement between model and experiment for a designed interface.