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Navigating protein fitness landscapes in multiple dimensions

Christian Landry, Universit茅 Laval
Tuesday February 13, 12-1pm
Zoom Link:听
In Person: 550 Sherbrooke, Room 189

Abstract:听Proteins could have as many fitness landscapes as they have functions. The relationship between mutations and the performance of any two functions could be positively or negatively correlated, making evolution on one landscape contingent on evolution on the other. Understanding the evolution of proteins, therefore, requires that we characterize how mutations affect the multiple dimensions of the landscapes of protein functions. Here, I will share our recent work on the deep mutational scanning of two drug-resistance enzymes in which we examine more than one dimension at a time. In one project, we examine the trade-off between resistance to an antifungal drug and activity on the canonical substrate of the drug target. We show that the trade-off between the two functions is very steep and shaped by the difference in drug resistance and nutrient use dose-response functions. The two fitness functions are nearly perfectly negatively correlated, meaning no single mutation can lead to resistance while maintaining growth in conditions that require this enzyme. In the second project, we examine how expression level affects the fitness landscape of an antibiotic resistance protein. By measuring the effect of all single mutations at optimal and several suboptimal expression levels, we show that optimal expression can mask the effects of many deleterious mutations on enzyme function. Surprisingly, some beneficial mutations also appear to be masked at optimal expression levels. Protein expression level is, therefore, a critical factor shaping the fitness landscape of proteins. These two studies illuminate complex protein evolution in heterogeneous growth and cellular environments.