Komp Evan, Phillips Christian, Lee Lauren M, Fallin Shayna M, Alanzi Humood N, Zorman Marlo, McCully Michelle E, Beck David A C
Chemical Engineering, University of Washington, Seattle, WA, USA.
Chemistry, University of Washington, Seattle, WA, USA.
Sci Rep. 2025 Apr 23;15(1):14124. doi: 10.1038/s41598-025-90828-0.
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.
这项工作提出了基于翻译的熔解温度神经优化方法（NOMELT），这是一种利用神经机器翻译设计高温稳定蛋白质并对其进行排序的新方法。该模型在来自适应不同温度的生物体的400多万对蛋白质同源序列上进行训练，在提高热稳定性方面展现出了良好的能力。一个设计的黑腹果蝇Engrailed同源结构域变体的熔解温度提高了15.5K。此外，NOMELT在对多个蛋白质家族的实验熔解温度和半激活温度进行排序时实现了零样本预测能力。与现有的零样本预测器不同，它不需要大量的同源数据或海量训练数据集，而是通过专门学习嗜热性（而非所有自然变异）来实现这一点。这些发现强调了在蛋白质的上下文相关设计中利用生物体生长温度来提高热稳定性的潜力。
