Komp, E., Phillips, C., Lee, L.M., Fallin, S.M., Alanzi, H.N., Zorman, M., McCully, M.E., Beck, D.A.C. (2025). Neural network conditioned to produce thermophilic protein sequences can increase thermal stability.  Sci. Rep. 15(1): 14124.

FBrf0262239

Research paper

This work presents Neural Optimization for Melting-temperature Enabled by Leveraging Translation (NOMELT), a novel approach for designing and ranking high-temperature stable proteins using neural machine translation. The model, trained on over 4 million protein homologous pairs from organisms adapted to different temperatures, demonstrates promising capability in targeting thermal stability. A designed variant of the Drosophila melanogaster Engrailed Homeodomain shows a melting temperature increase of 15.5 K. Furthermore, NOMELT achieves zero-shot predictive capabilities in ranking experimental melting and half-activation temperatures across a number of protein families. It achieves this without requiring extensive homology data or massive training datasets as do existing zero-shot predictors by specifically learning thermophilicity, as opposed to all natural variation. These findings underscore the potential of leveraging organismal growth temperatures in context-dependent design of proteins for enhanced thermal stability.

PMC12019596 (PMC) (EuropePMC)