CAT-SiteHafner Petrovski, Žan (Avtor) Hribar-Lee, Barbara (Avtor) Bosnić, Zoran (Avtor) protein binding site predictionligandsmolecular dockingmachine learningconvolutional neural networkIdentifying 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.20222024-01-25 15:17:10Članek v reviji154114UDK: 004.8:615ISSN pri članku: 1999-4923DOI: 10.3390/pharmaceutics15010119COBISS_ID: 135845635sl