A Predictive Energy Landscape Model of MetamorphicProtein Conformational Specificity

Abstract

"Metamorphic" proteins challenge state-of-the-art structure prediction methods reliant on amino acid similarity. Unfortunately, this obviates a more effective thermodynamic approach necessary to properly evaluate the impact of amino acid changes on the stability of two different folds. A vital capability of such a thermodynamic approach would be the quantification of the free energy differences between 1) the energy landscape minima of each native fold, and 2) each fold and the denatured state. Here we develop an energetic framework for conformational specificity, based on an ensemble description of protein thermodynamics. This energetic framework was able to successfully recapitulate the structures of high-identity enginerered sequences experimentally shown to adopt either Streptococcus protein GA or GB folds, demonstrating that this approach indeed reflected the energetic determinants of fold. Residue-level decomposition of the conformational specificity suggested several testable hypotheses, notably among them that fold-switching could be affected by local de-stabilization of the populated fold at positions sensitive to equilibrium perturbation. Since this ensemble-based compatibility framework is applicable to any structure and any sequence, it may be practically useful for the future targeted design, or large-scale proteomic detection, of novel metamorphic proteins. Impact StatementMetamorphic proteins are single amino acid sequences capable of adopting more than one structure at equilibrium. Detection and design of these molecules hold great promise for biological understanding and materials engineering, but to do so requires a thermodynamic framework capable of estimating the free energy differences between the two structures and the denatured state. We present such a framework, show it to be effective for the well-studied metamorphic protein GA/GB system, and suggest testable hypotheses for engineering novel fold-switch proteins.