20.500.12556/DKUM-86438 Naive prediction of protein backbone phi and psi dihedral angles using deep learning Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone φ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the φ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies. protein structure prediction backbone dihedral angles deep neural network fully connected neural network FCNN protein secondary structure prediction globoke nevronske mreže proteinske strukture kotne napovedi true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/ Članek v reviji 2023-12-01 09:54:42 2023-12-01 10:01:09 2024-05-08 14:04:15 0000-00-00 00:00:00 2023 0 0 19 str. iss. 20, [article no.] 7046 Vol. 28 Okt. 2023 0000-00-00 Zaloznikova Objavljeno apc 2023-09-01 2023-10-09 2023-10-12 54 168255235 10.3390/molecules28207046 1420-3049 molecules-28-07046.pdf molecules-28-07046.pdf 1 9667B704BA6EECBE99A508438ACE2A15 6040cd840f2c903bfe8c597b05e48ed318091916255a239dc8accbe4b3d6d030 04a1cca2-9028-11ee-bc08-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=174474 https://www.mdpi.com/1420-3049/28/20/7046 0 145bca49-9028-11ee-bc08-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=174475 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0