Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery

NDUFS1 is the largest subunit of OXPHOS complex I (MC-I) and mutations in this gene are associated with MC-I deficiency. This study aims to develop a graph neural network and attention mechanism-based radiopharmaceutical-protein (RP-protein) interaction prediction model for identifying an imaging candidate of mitochondrial function through targeting its core subunit NDUFS1. The estimated cell viability values for trastuzumab, 177Lu-DOTA-trastuzumab, and 225Ac-DOTA-trastuzumab were 290.1, 89.01, and 8.262 nM, respectively. The deep learning (DL) model was pretrained with normal compound-protein pairs. Afterwards, the model was fine-tuned with the dataset of RP-protein pairs and evaluated with five-fold cross validation. The prediction model trained with normal compound-protein pairs effectively predicted the binding affinity. The fine-tuned model incorporating radioactive properties outperformed the same model trained only on normal compounds. The model estimated the important substructure of a compound related to its binding to the target protein. NDUFS1 protein-targeting compounds were identified and BDBM210829 compound had the best binding affinities, binding rank, and LogP as it binds to the NDUFS1. This study proposed a DL-based radiolabelled compound-protein interaction prediction model to identify a radiopharmaceutical (RP) that binds to the mitochondrial core subunit NDUFS1. The proposed model shows good performance for predicting RP-protein interaction. BDBM210829 was identified as a top candidate for radiolabeling and targeting the mitochondrial core subunit NDUFS1. This model can be used as an effective virtual screening tool for RP discovery.