{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Komp E"],"funding":["National Institute of General Medical Sciences","NIGMS NIH HHS","National Science Foundation"],"pagination":["14124"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12019596"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["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."],"journal":["Scientific reports"],"pubmed_title":["Neural network conditioned to produce thermophilic protein sequences can increase thermal stability."],"pmcid":["PMC12019596"],"funding_grant_id":["OAC-1934292","GM134439","R15 GM134439"],"pubmed_authors":["Fallin SM","Komp E","Alanzi HN","Beck DAC","Zorman M","Phillips C","Lee LM","McCully ME"],"additional_accession":[]},"is_claimable":false,"name":"Neural network conditioned to produce thermophilic protein sequences can increase thermal stability.","description":"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.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2025-07-10T03:09:01.841Z","creation":"2025-07-10T03:09:01.841Z"},"accession":"S-EPMC12019596","cross_references":{"pubmed":["40268970"],"doi":["10.1038/s41598-025-90828-0"]}}