20.500.12556/DiRROS-19374 Learning deep representations of enzyme thermal adaptation Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes. bioinformatics deep neural networks enzyme catalytic temperatures optimal growth temperatures protein thermostability transfer learning bioinformatika optimalna temperatura rasti termostabilnost proteinov globoko ucenje true false true Angleški jezik Slovenski jezik Neznano 2024-07-17 03:55:28 2024-07-17 13:11:50 2024-07-18 03:53:59 0000-00-00 00:00:00 2022 0 0 Ostali avtorji: Filip Buric, Jan Zrimec, Sandra Viknander, Jens Nielsen, Aleksej Zelezniak, Martin KM Engqvist; Nasl. z nasl. zaslona; Opis vira z dne 21. 11. 2022; Štev. članka: e4480; 1-14 str. iss. 12 Vol. 31 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-12-01 577 1469-896X 10.1002/pro.4480 130304771 https://doi.org/10.1002/pro.4480 1 e0b953a6-43ce-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=26739 RAZ_Li_Gang_2022.pdf RAZ_Li_Gang_2022.pdf 1 03BF75E72E440D4ECA389BB101357A24 fb0d168b4c318431036db8d76540748b51ddc74ba9f36a176949d39a1e6f83db 3c77e10f-441d-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=26841 Nacionalni inštitut za biologijo 0 0 0