Sailer, Z. R. | bioRxiv Science

bioRxiv2020

Exploring the evolutionary history of kinetic stability in the alpha-lytic protease family

In addition to encoding the final tertiary fold and stability, the primary sequence of a protein encodes the folding trajectory and kinetic barriers that determines the speed of folding. How these kinetic barriers are encoded by the sequence is not well understood. Here, we use evolutionary sequence variation in the alpha-lytic protease (LP) protein family to probe the relationship between sequence and energy landscape. LP has an unusual energy landscape: the native state of LP is not the most thermodynamically favored conformation and, instead, it remains folded due to a large kinetic barrier preventing unfolding. In order to fold, LP utilizes an N-terminal pro region of similar size to the protease itself that functions as a folding catalyst. Once folded, the pro region is removed, and the native state does not unfold on a biologically relevant timescale. Without the pro region, LP folds on the order of millennia. A phylogenetic search uncovers LP homologs with a wide range of pro-region sizes, including some with no pro region at all. In the resulting phylogenetic tree, these homologs cluster by pro-region size. Homologs naturally lacking pro regions are thermodynamically stable, fold much faster than LP, yet retain the same fold as LP. Key amino acids thought to contribute to LPs extreme kinetic stability are lost in these homologs, further supporting their role in kinetic stability. This study highlights how the entire energy landscape plays an important role in determining the evolutionary pressures on and changes to the protein sequence.

biophysics↗

bioRxiv2018

Uninterpretable interactions: epistasis as uncertainty

Epistasis is a common feature of genotype-phenotype maps. Understanding the patterns of epistasis is critical for predicting unmeasured phenotypes, explaining evolutionary trajectories, and for inferring the biological mechanisms that determine a map. One common approach is to use a linear model to decompose epistasis into specific pairwise and high-order interactions between mutations. Such interactions are then used to identify important biology or to explain how the genotype-phenotype map shapes evolution. Here we show that the coefficients extracted from such analyses are likely uninterpretable. They cannot be extracted reliably from experimental genotype-phenotype maps due to regression bias. Further, we can generate epistatic \"interactions\" indistinguishable from those in experimental maps using a completely random process. From this, we conclude that epistasis should be treated as a random, but quantifiable, variation in these maps. This perspective allows us to build predictive models with known error from a small number of measured phenotypes. It also suggests that we need mechanistic, nonlinear models to account for epistasis and decompose genotype-phenotype maps.

genetics↗