20.500.12556/RUL-154114 CAT-Site predicting protein binding sites using a convolutional neural network Identifying binding sites on the protein surface is an important part of computer-assisted drug design processes. Reliable prediction of binding sites not only assists with docking algorithms, but it can also explain the possible side-effects of a potential drug as well as its efficiency. In this work, we propose a novel workflow for predicting possible binding sites of a ligand on a protein surface. We use proteins from the PDBbind and sc-PDB databases, from which we combine available ligand information for similar proteins using all the possible ligands rather than only a special sub-selection to generalize the work of existing research. After performing protein clustering and merging of ligands of similar proteins, we use a three-dimensional convolutional neural network that takes into account the spatial structure of a protein. Lastly, we combine ligandability predictions for points on protein surfaces into joint binding sites. Analysis of our model’s performance shows that its achieved sensitivity is 0.829, specificity is 0.98, and F$_1$ score is 0.517, and that for 54% of larger and pharmacologically relevant binding sites, the distance between their real and predicted centers amounts to less than 4 Å. protein binding site prediction ligands molecular docking machine learning convolutional neural network napovedovanje veznih mest proteinov ligandi molekulsko sidranje strojno učenje konvolucijska nevronska mreža true false true Angleški jezik Slovenski jezik Članek v reviji 2024-01-25 15:17:10 2024-01-25 15:17:11 2024-01-27 03:24:02 0000-00-00 00:00:00 2022 0 0 21 str. iss. 1, art. 119 Vol. 15 Jan. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8:615 1999-4923 10.3390/pharmaceutics15010119 135845635 pharmaceutics-15-00119-v2.pdf pharmaceutics-15-00119-v2.pdf 1 9CF506D196522647EF664ADFA41D5D33 3e7b1b98dfca998c424e3c59cd2c90722aee11776b02b4be2d86fd1258143ce2 bf09a112-bb8c-11ee-9ead-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=180301 https://www.mdpi.com/1999-4923/15/1/119 1 6f4a0434-bb8c-11ee-9ead-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=180300 Fakulteta za računalništvo in informatiko Fakulteta za kemijo in kemijsko tehnologijo 0 0 0