Using Evolution to Guide Protein Engineering: The Devil IS in the Details

Abstract

For decades, protein engineers have endeavored to reengineer existing proteins for novel applications. Overall, protein folds and gross functions can be readily transferred from one protein to another by transplanting large blocks of sequence (i.e., domain recombination). However, predictably fine-tuning function (e.g., by adjusting ligand affinity, specificity, catalysis, and/or allosteric regulation) remains a challenge. One approach has been to use the sequences of protein families to identify amino acid positions that change during the evolution of functional variation. The rationale is that these nonconserved positions could be mutated to predictably fine-tune function. Evolutionary approaches to protein design have had some success, but the engineered proteins seldom replicate the functional performances of natural proteins. This Biophysical Perspective reviews several complexities that have been revealed by evolutionary and experimental studies of protein function. These include 1) challenges in defining computational and biological thresholds that define important amino acids; 2) the co-occurrence of many different patterns of amino acid changes in evolutionary data; 3) difficulties in mapping the patterns of amino acid changes to discrete functional parameters; 4) the nonconventional mutational outcomes that occur for a particular group of functionally important, nonconserved positions; 5) epistasis (nonadditivity) among multiple mutations; and 6) the fact that a large fraction of a protein's amino acids contribute to its overall function. To overcome these challenges, new goals are identified for future studies.

Homolog comparisons further reconcile in vitro and in vivo correlations of protein activities by revealing over‐looked physiological factors

“…We previously used the rank order of ligand affinities to assess whether changes in the region altered ligand specificity. 12,76 The current work shows that this definition was too narrow. In his seminal textbook, Creighton stated "Specific binding by a protein of one ligand, and not another, depends on their relative affinities, their concentrations, and whether they bind at the same site."…”

“…Finally, these results lead us to consider what lessons can be learned about ligand specificity changes. We previously used the rank order of ligand affinities to assess whether changes in the region altered ligand specificity . The current work shows that this definition was too narrow.…”

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

“…Here, we extend those concepts to accommodate the continuum between classes, from neutral‐ to rheostat‐ to toggle‐like behaviors, that were observed in experimental data (Swint‐Kruse, ). Analyses of data for model proteins show that the neutral, rheostat, and toggle RheoScale scores (i) recapitulated the findings of previously published manual analyses, and (ii) have proven useful for condensing outcomes from multiple amino acid substitutions into a simple quantitative descriptor for each substituted position.…”

“…We called these “rheostat” positions (Meinhardt et al., ) because, similar to a dimmer switch on a light, substitutions at such positions could be exploited by evolutionary processes to “dial” function up or down as organisms adapt to new niches. A review of other experimental studies suggests that rheostat positions occur in many proteins and that their effects could manifest in many parameters including binding affinities, rate constants, allosteric coupling, and/or protein stability (Swint‐Kruse, ). We have reasoned that some variants at rheostat positions are likely to be medically important: “Dead” variants at rheostat positions are catastrophic, and medically important substitutions have been documented for various nonconserved positions (e.g., de Beer et al., ; Pendergrass, Williams, Blair, & Fenton, ).…”

fuNTRp: Identifying protein positions for variation driven functional tuning

“…Identifying disease-association of the roughly 10,000 protein sequence changing genetic variants of every individual (Bromberg, 2013) is like looking for the needle in a haystack. Finding variants that alter protein function may help, but variant effects are not black and white, having a range of outcomes (Swint-Kruse, 2016). While some variants may only marginally alter ligand affinity, others can induce drastic changes (Walker, et al, 2010).…”