Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery
Abstract Background 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 ident...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-08-01
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| Series: | EJNMMI Research |
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| Online Access: | https://doi.org/10.1186/s13550-025-01300-z |
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| author | Muath Almaslamani Jingyu Yang Chi Soo Kang Choong Mo Kang Jung Mi Park Sang-Keun Woo |
| author_facet | Muath Almaslamani Jingyu Yang Chi Soo Kang Choong Mo Kang Jung Mi Park Sang-Keun Woo |
| author_sort | Muath Almaslamani |
| collection | DOAJ |
| description | Abstract Background 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. Results 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. Conclusions 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. |
| format | Article |
| id | doaj-art-ef3d324fbd7a4d68966e45bb5b4428bf |
| institution | Kabale University |
| issn | 2191-219X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EJNMMI Research |
| spelling | doaj-art-ef3d324fbd7a4d68966e45bb5b4428bf2025-08-20T03:43:36ZengSpringerOpenEJNMMI Research2191-219X2025-08-0115111110.1186/s13550-025-01300-zDeep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discoveryMuath Almaslamani0Jingyu Yang1Chi Soo Kang2Choong Mo Kang3Jung Mi Park4Sang-Keun Woo5Division of Applied RI, Korea Institute of Radiological and Medical SciencesDivision of Applied RI, Korea Institute of Radiological and Medical SciencesDivision of Applied RI, Korea Institute of Radiological and Medical SciencesDivision of Applied RI, Korea Institute of Radiological and Medical SciencesDepartment of Nuclear Medicine, Soonchunhyang University Bucheon HospitalDivision of Applied RI, Korea Institute of Radiological and Medical SciencesAbstract Background 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. Results 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. Conclusions 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.https://doi.org/10.1186/s13550-025-01300-zBinding affinityRadiopharmaceutical discoveryCompound protein interactionGraph neural networkMitochondriaNDUFS1 |
| spellingShingle | Muath Almaslamani Jingyu Yang Chi Soo Kang Choong Mo Kang Jung Mi Park Sang-Keun Woo Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery EJNMMI Research Binding affinity Radiopharmaceutical discovery Compound protein interaction Graph neural network Mitochondria NDUFS1 |
| title | Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery |
| title_full | Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery |
| title_fullStr | Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery |
| title_full_unstemmed | Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery |
| title_short | Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery |
| title_sort | deep learning based radiolabelled compound protein interaction prediction for ndufs1 targeting radiopharmaceutical discovery |
| topic | Binding affinity Radiopharmaceutical discovery Compound protein interaction Graph neural network Mitochondria NDUFS1 |
| url | https://doi.org/10.1186/s13550-025-01300-z |
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