Abstract
One of the long-standing holy grails of molecular evolution has been the ability to predict an organism's fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase's fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.
Highlights
The ability to predict how an organism’s fitness is influenced by mutations is central to being able to project and, in some cases, steer the course of natural evolution [1,2,3], engineer protein sequences with novel biological functions [4, 5], and treat genetic disorders [6]
In this work, we have analyzed how predictive thermodynamic biophysical indicators can be of organismal fitness, focusing in particular on how well protein folding and binding free energies can predict the fitness of β-lactamase mutants
Using MD+FoldX predictions and previously acquired β-lactamase fitness data, we demonstrated that large, positive ΔΔGfold values are highly predictive of low fitness, but that ΔΔGfold values only account for, at most, 24% of the variance in β-lactamase fitness based on linear models
Summary
The ability to predict how an organism’s fitness is influenced by mutations is central to being able to project and, in some cases, steer the course of natural evolution [1,2,3], engineer protein sequences with novel biological functions [4, 5], and treat genetic disorders [6]. Even in those rare instances, even the simplest protein’s fitness is influenced by a wide variety of factors [7] including protein and gene expression levels [8], interactions with chaperones [9,10,11], protein folding stability [12,13,14,15], protein folding dynamics [16, 17], and proteolytic susceptibility [18]—as well as many complex factors yet to be uncovered or understood Many of these even wellunderstood factors are often difficult, if not impossible, to model in vitro or in silico [19], limiting their overall utility. Simple, calculable indicators that can predict phenotypes, and organismal fitness, are of high value and in high demand
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