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This article discusses the importance of high-temperature proteins (HTPs) and their applications in various fields. It introduces a new dataset, learn2thermDB, which is orders of magnitude larger than the current largest and allows for the study of high-temperature stability in a sequence-dependent manner. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. It also shows that the data contains signal for deep learning, offering a new doorway towards thermal stability design models.
