Multi-environment deep mutational scanning reveals the distribution of temperature-sensitive variants in a bacterial kinase

Abstract

Deep mutational scanning is a powerful technique for determining sequence-function landscapes of biological macromolecules, but it is often performed under a single condition. Thus, how changing environmental conditions affect these landscapes remains opaque. Here, we address this by performing multi-environment deep mutational scanning to characterize the functional landscape of a bacterial kinase at multiple temperatures. By doing so, we systematically identify temperature-sensitive (ts) and temperature-resistant variants, providing a global view of their prevalence and identities. Substitutions leading to temperature-associated changes in activity are rare, reflecting high mutational tolerance across conditions, but ts and temperature-resistant substitutions are identified. In contrast to existing paradigms, we find that substitutions causing temperature sensitivity are prevalent in both the protein core and the surface. Temperature-resistant variants also arise but exhibit increased enzymatic activity, not improved thermal stability. Our results could not be recapitulated by the state-of-the-art computational stability prediction, demonstrating the importance of systematic experimental approaches to identifying condition-dependent mutation effects.

Keywords: CP: Microbiology; activity-stability trade-off; deep mutational scanning; enzymatic activity; protein evolution; temperature sensitivity; thermal stability.