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Abstract hnRNPA2 is a major component of mRNA transport granules in oligodendrocytes and neurons. However, the structural details of how hnRNPA2 binds the A2 recognition element (A2RE) and if this sequence stimulates granule formation by enhancing phase separation of hnRNPA2 has not yet been studied. Using solution NMR and biophysical studies, we find that each of the two individual RRMs retain the domain structure observed in complex with RNA but are not rigidly confined (i.e. they move independently) in solution in the absence of RNA. hnRNPA2 RRMs bind the minimal rA2RE11 weakly but at least, and most likely, two hnRNPA2 molecules are able to simultaneously bind the longer 21mer myelin basic protein A2RE. Upon binding of the RNA, NMR chemical shift deviations are observed in both RRMs, suggesting both play a role in binding the A2RE11. Interestingly, addition of short A2RE RNAs or longer RNAs containing this sequence completely prevents in vitro phase separation of full-length hnRNPA2 and aggregation of the disease-associated mutants. These findings suggest that RRM interactions with specific recognition sequences alone do not account for nucleating granule formation, consistent with models where multivalent protein:RNA and protein:protein contacts form across many sites in granule proteins and long RNA transcripts. 

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