TY - JOUR
T1 - Computational analysis of protein–protein interactions of cancer drivers in renal cell carcinoma
AU - Pei, Jimin
AU - Zhang, Jing
AU - Cong, Qian
N1 - Publisher Copyright:
© 2023 The Authors. FEBS Open Bio published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - cancer drivers
KW - chromatin remodeling
KW - protein–protein interaction
KW - renal cell carcinoma
KW - ubiquitination
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U2 - 10.1002/2211-5463.13732
DO - 10.1002/2211-5463.13732
M3 - Article
C2 - 37964489
AN - SCOPUS:85177742336
SN - 2211-5463
VL - 14
SP - 112
EP - 126
JO - FEBS Open Bio
JF - FEBS Open Bio
IS - 1
ER -