Year: 2026
Type: preprint
Abstract: Thermal adaptation has evolved independently across diverse protein families, yet whether the information underlying this adaptation can be transferred between evolutionarily unrelated proteins remains unknown. Here, we demonstrate that thermostability can be learned from divergent protein families and transferred to enzymes without relying on their evolutionary ancestry. We developed THOR (THermally Optimised Representations), a generative framework that learns background-specific sequence transformations between unpaired mesophilic and thermophilic sequence populations while preserving the identity of the starting protein. Across evolutionarily divergent enzyme families, THOR transferred thermostability without exposure to thermophilic homologues during training. In malate dehydrogenase, excluded from cross-family training, zero-shot designs increased apparent melting temperature by up to 15 °C, with gains reaching 36 °C following limited family-specific fine-tuning. Majority of designed variants retained activity following prolonged thermal challenge, while selected variants exhibited nearly threefold higher catalytic turnover than wild type. Molecular dynamics simulations revealed that comparable thermal phenotypes emerge through distinct, background-dependent changes in protein flexibility and stabilising interactions. Together, these findings demonstrate that thermal adaptation encodes transferable information beyond lineage-specific evolutionary solutions, revealing evolution as a source of reusable adaptive principles for engineering phenotypes in proteins whose evolutionary lineages have never acquired them.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: Government of the United Kingdom, UK Research and Innovation, University of Bristol, Svenska Forskningsrådet Formas, National Science Council, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Directorate for Biological Sciences, Biotechnology and Biological Sciences Research Council, Science and Technology Facilities Council, National Supercomputer Centre, Linköpings Universitet
Awards: From data-driven novel enzyme discovery towards enzyme combinations for plastic degradation (PlastZyme), G2912470688, G402516054, G4632909464, G5404377299, Using AI to unravel “DNA grammar” for synthetic biology applications, Unlocking the Protein Production Potential of Non-Conventional Yeasts with Artificial Intelligence, G7318745325, Expanding genetic toolbox for heterologous protein expression in non-conventional yeasts using artificial intelligence., NAISS, G963556061
OpenAlex ID: W7223224909