Rheostats and toggles switches for modulating protein function: A Cautionary Tale*

Proteins are heteropolymers – each assembled from a unique, linear sequence of amino acids – that fold into 3D shapes to perform their functions. Changes in protein sequences, such as the SARS-CoV2 changes frequently in headlines, can alter protein function. Some changes are biologically relevant, whereas other changes are neutral. To predict the outcomes of these changes, decades of biological, biochemical, and biophysical mutation experiments – along with bioinformatic studies of evolutionarily-related protein sequences – have been used to derive several common assumptions that underlie most computer prediction algorithms. However, most historical studies were biased to positions that are conserved during evolution. Most mutations at conserved positions are catastrophic (they “toggle off” structure/function), which is reliably predicted by most computer algorithms. In contrast, mutations at evolutionarily-changing positions (nonconserved) were largely overlooked, despite their critical roles in evolving functional variation. As a consequence, computer predictions about amino acid changes at functionally-important, nonconserved positions are poor. Our studies demonstrated one source of this failure: Amino acid substitutions at a special class of nonconserved protein positions do not follow the same substitution rules as conserved positions. These special “rheostat” positions are present in a wide range of protein types; in some proteins, rheostat positions comprise >40% of the protein positions. In crystallo and in silico structural studies of functional rheostat substitutions showed only local perturbations to the protein 3D structure. Dynamic coupling calculations for rheostat substitutions showed promising correlations with measured functional changes. Combined, results suggest that emergent properties of coupled amino acid networks could produce the complex outcomes observed for rheostat substitutions that must be understood to advance predictions.