Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Identifying druggable ligand‐binding sites on the surface of the macromolecular targets is an important process in structure‐based drug discovery. Deep‐learning models have been shown to successfully predict ligand‐binding sites of proteins. As a step toward predicting binding sites in RNA and RNA‐protein complexes, we employ three‐dimensional convolutional neural networks. We introduce a dataset splitting approach to minimize structure‐related bias in training data, and investigate the influence of protein‐based neural network pre‐training before fine‐tuning on RNA structures. Models that were pre‐trained on proteins considerably outperformed the models that were trained exclusively on RNA structures. Overall, 71 % of the known RNA binding sites were correctly located within 4 Å of their true centres.