Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma
Renal cell carcinoma (RCC) is the most common type of kidney cancer with rising cases in recent years. Extensive research has identified various cancer driver proteins associated with different subtypes of RCC. Most RCC drivers are encoded by tumor suppressor genes and exhibit enrichment in function...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | FEBS Open Bio |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/2211-5463.13732 |
| _version_ | 1797367589828034560 |
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| author | Jimin Pei Jing Zhang Qian Cong |
| author_facet | Jimin Pei Jing Zhang Qian Cong |
| author_sort | Jimin Pei |
| collection | DOAJ |
| description | Renal cell carcinoma (RCC) is the most common type of kidney cancer with rising cases in recent years. Extensive research has identified various cancer driver proteins associated with different subtypes of RCC. Most RCC drivers are encoded by tumor suppressor genes and exhibit enrichment in functional categories such as protein degradation, chromatin remodeling, and transcription. To further our understanding of RCC, we utilized powerful deep‐learning methods based on AlphaFold to predict protein–protein interactions (PPIs) involving RCC drivers. We predicted high‐confidence complexes formed by various RCC drivers, including TCEB1, KMT2C/D and KDM6A of the COMPASS‐related complexes, TSC1 of the MTOR pathway, and TRRAP. These predictions provide valuable structural insights into the interaction interfaces, some of which are promising targets for cancer drug design, such as the NRF2‐MAFK interface. Cancer somatic missense mutations from large datasets of genome sequencing of RCCs were mapped to the interfaces of predicted and experimental structures of PPIs involving RCC drivers, and their effects on the binding affinity were evaluated. We observed more than 100 cancer somatic mutations affecting the binding affinity of complexes formed by key RCC drivers such as VHL and TCEB1. These findings emphasize the importance of these mutations in RCC pathogenesis and potentially offer new avenues for targeted therapies. |
| first_indexed | 2024-03-08T17:20:32Z |
| format | Article |
| id | doaj.art-22df5761c5ea4f968f76fc547471fb31 |
| institution | Directory Open Access Journal |
| issn | 2211-5463 |
| language | English |
| last_indexed | 2024-03-08T17:20:32Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | FEBS Open Bio |
| spelling | doaj.art-22df5761c5ea4f968f76fc547471fb312024-01-03T05:28:43ZengWileyFEBS Open Bio2211-54632024-01-0114111212610.1002/2211-5463.13732Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinomaJimin Pei0Jing Zhang1Qian Cong2Eugene McDermott Center for Human Growth and Development University of Texas Southwestern Medical Center Dallas TX USAEugene McDermott Center for Human Growth and Development University of Texas Southwestern Medical Center Dallas TX USAEugene McDermott Center for Human Growth and Development University of Texas Southwestern Medical Center Dallas TX USARenal cell carcinoma (RCC) is the most common type of kidney cancer with rising cases in recent years. Extensive research has identified various cancer driver proteins associated with different subtypes of RCC. Most RCC drivers are encoded by tumor suppressor genes and exhibit enrichment in functional categories such as protein degradation, chromatin remodeling, and transcription. To further our understanding of RCC, we utilized powerful deep‐learning methods based on AlphaFold to predict protein–protein interactions (PPIs) involving RCC drivers. We predicted high‐confidence complexes formed by various RCC drivers, including TCEB1, KMT2C/D and KDM6A of the COMPASS‐related complexes, TSC1 of the MTOR pathway, and TRRAP. These predictions provide valuable structural insights into the interaction interfaces, some of which are promising targets for cancer drug design, such as the NRF2‐MAFK interface. Cancer somatic missense mutations from large datasets of genome sequencing of RCCs were mapped to the interfaces of predicted and experimental structures of PPIs involving RCC drivers, and their effects on the binding affinity were evaluated. We observed more than 100 cancer somatic mutations affecting the binding affinity of complexes formed by key RCC drivers such as VHL and TCEB1. These findings emphasize the importance of these mutations in RCC pathogenesis and potentially offer new avenues for targeted therapies.https://doi.org/10.1002/2211-5463.13732cancer driverschromatin remodelingprotein–protein interactionrenal cell carcinomaubiquitination |
| spellingShingle | Jimin Pei Jing Zhang Qian Cong Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma FEBS Open Bio cancer drivers chromatin remodeling protein–protein interaction renal cell carcinoma ubiquitination |
| title | Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma |
| title_full | Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma |
| title_fullStr | Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma |
| title_full_unstemmed | Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma |
| title_short | Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma |
| title_sort | computational analysis of protein protein interactions of cancer drivers in renal cell carcinoma |
| topic | cancer drivers chromatin remodeling protein–protein interaction renal cell carcinoma ubiquitination |
| url | https://doi.org/10.1002/2211-5463.13732 |
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