biostudies-literatureKomp ENational Institute of General Medical SciencesNIGMS NIH HHSNational Science Foundation14124https://www.ebi.ac.uk/biostudies/studies/S-EPMC12019596biostudies-literatureUnknown15(1)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.Scientific reportsNeural network conditioned to produce thermophilic protein sequences can increase thermal stability.PMC12019596OAC-1934292GM134439R15 GM134439Fallin SMKomp EAlanzi HNBeck DACZorman MPhillips CLee LMMcCully MEfalseNeural network conditioned to produce thermophilic protein sequences can increase thermal stability.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.2025-01-01T00:00:00Z2025 Apr2025-07-10T03:09:01.841Z2025-07-10T03:09:01.841ZS-EPMC120195964026897010.1038/s41598-025-90828-0