Our lack of total understanding of the intricacies of how enzymes behave has constrained our ability to robustly engineer substrate specificity. Furthermore, the mechanisms of natural evolution leading to improved or novel substrate specificities are not wholly defined. Here we generate near-comprehensive single-mutation fitness landscapes comprising >96.3% of all possible single nonsynonymous mutations for hydrolysis activity of an amidase expressed in E. coli with three different substrates. For all three selections, we find that the distribution of beneficial mutations can be described as exponential, supporting a current hypothesis for adaptive molecular evolution. Beneficial mutations in one selection have essentially no correlation with fitness for other selections and are dispersed throughout the protein sequence and structure. Our results further demonstrate the dependence of local fitness landscapes on substrate identity and provide an example of globally distributed sequence-specificity determinants for an enzyme.

To address the ongoing SARS-CoV-2 pandemic and prepare for future coronavirus outbreaks, understanding the protective potential of epitopes conserved across SARS-CoV-2 variants and coronavirus lineages is essential. We describe a highly conserved, conformational S2 domain epitope present only in the prefusion core of β-coronaviruses: SARS-CoV-2 S2 apex residues 980–1006 in the flexible hinge. Antibody RAY53 binds the native hinge in MERS-CoV and SARS-CoV-2 spikes on the surface of mammalian cells and mediates antibody-dependent cellular phagocytosis and cytotoxicity against SARS-CoV-2 spike in vitro. Hinge epitope mutations that ablate antibody binding compromise pseudovirus infectivity, but changes elsewhere that affect spike opening dynamics, including those found in Omicron BA.1, occlude the epitope and may evade pre-existing serum antibodies targeting the S2 core. This work defines a third class of S2 antibody while providing insights into the potency and limitations of S2 core epitope targeting.

It is incompletely understood how biophysical properties like protein stability impact molecular evolution and epistasis. Epistasis is defined as specific when a mutation exclusively influences the phenotypic effect of another mutation, often at physically interacting residues. In contrast, nonspecific epistasis results when a mutation is influenced by a large number of nonlocal mutations. As most mutations are pleiotropic, the in vivo folding probability—governed by basal protein stability—is thought to determine activity-enhancing mutational tolerance, implying that nonspecific epistasis is dominant. However, evidence exists for both specific and nonspecific epistasis as the prevalent factor, with limited comprehensive data sets to support either claim. Here, we use deep mutational scanning to probe how in vivo enzyme folding probability impacts local fitness landscapes. We computationally designed two different variants of the amidase AmiE with statistically indistinguishable catalytic efficiencies but lower probabilities of folding in vivo compared with wild-type. Local fitness landscapes show slight alterations among variants, with essentially the same global distribution of fitness effects. However, specific epistasis was predominant for the subset of mutations exhibiting positive sign epistasis. These mutations mapped to spatially distinct locations on AmiE near the initial mutation or proximal to the active site. Intriguingly, the majority of specific epistatic mutations were codon dependent, with different synonymous codons resulting in fitness sign reversals. Together, these results offer a nuanced view of how protein folding probability impacts local fitness landscapes and suggest that transcriptional–translational effects are as important as stability in determining evolutionary outcomes.

Significance Proteins have shown promise as therapeutics and diagnostics, but their effectiveness is limited by our inability to spatially target their activity. To overcome this limitation, we developed a computationally guided method to design inactive proenzymes or zymogens, which are activated through cleavage by a protease. Since proteases are differentially expressed in various tissues and disease states, including cancer, these proenzymes could be targeted to the desired microenvironment. We tested our method on the therapeutically relevant protein carboxypeptidase G2 (CPG2). We designed Pro-CPG2s that are inhibited by 80 to 98% and are partially to fully reactivatable following protease treatment. The developed methodology, with further refinements, could pave the way for routinely designing protease-activated protein-based therapeutics and diagnostics that act in a spatially controlled manner.

It is incompletely understood how biophysical properties like protein stability impact molecular evolution and epistasis. Epistasis is defined as specific when a mutation exclusively influences the phenotypic effect of another mutation, often at physically interacting residues. By contrast, nonspecific epistasis results when a mutation is influenced by a large number of non-local mutations. As most mutations are pleiotropic, basal protein stability is thought to determine activity-enhancing mutational tolerance, which implies that nonspecific epistasis is dominant. However, evidence exists for both specific and nonspecific epistasis as the prevalent factor, with limited comprehensive datasets to validate either claim. Here we use deep mutational scanning to probe how initial enzyme stability impacts local fitness landscapes. We computationally designed two different variants of the amidase AmiE in which catalytic efficiencies are statistically indistinguishable but the enzyme variants have lower probabilities of folding in vivo.Local fitness landscapes show only slight alterations among variants, with essentially the same global distribution of fitness effects. However, specific epistasis was predominant for the subset of mutations exhibiting positive sign epistasis. These mutations mapped to spatially distinct locations on AmiE near the initial mutation or proximal to the active site. Most intriguingly, the majority of specific epistatic mutations were codondependent, with different synonymous codons resulting in fitness sign reversals.Together, these results offer a nuanced view of how protein stability impacts local fitness landscapes, and suggest that transcriptional-translational effects are an equally important determinant as thermodynamic stability in determining evolutionary outcomes.