Computational and Experimental Characterization of NF023, A Candidate Anticancer Compound Inhibiting cIAP2/TRAF2 Assembly.
Federica CossuLuca SorrentinoElisa FagnaniMattia ZaffaroniMario MilaniToni GiorginoEloise MastrangeloPublished in: Journal of chemical information and modeling (2020)
Protein-protein interactions are the basis of many important physiological processes and are currently promising, yet difficult, targets for drug discovery. In this context, inhibitor of apoptosis proteins (IAPs)-mediated interactions are pivotal for cancer cell survival; the interaction of the BIR1 domain of cIAP2 with TRAF2 was shown to lead the recruitment of cIAPs to the TNF receptor, promoting the activation of the NF-κB survival pathway. In this work, using a combined in silico-in vitro approach, we identified a drug-like molecule, NF023, able to disrupt cIAP2 interaction with TRAF2. We demonstrated in vitro its ability to interfere with the assembly of the cIAP2-BIR1/TRAF2 complex and performed a thorough characterization of the compound's mode of action through 248 parallel unbiased molecular dynamics simulations of 300 ns (totaling almost 75 μs of all-atom sampling), which identified multiple binding modes to the BIR1 domain of cIAP2 via clustering and ensemble docking. NF023 is, thus, a promising protein-protein interaction disruptor, representing a starting point to develop modulators of NF-κB-mediated cell survival in cancer. This study represents a model procedure that shows the use of large-scale molecular dynamics methods to typify promiscuous interactors.
Keyphrases
- molecular dynamics
- signaling pathway
- molecular dynamics simulations
- lps induced
- oxidative stress
- protein protein
- pi k akt
- nuclear factor
- small molecule
- drug discovery
- papillary thyroid
- molecular docking
- density functional theory
- inflammatory response
- rheumatoid arthritis
- cell cycle arrest
- cell death
- emergency department
- minimally invasive
- deep learning
- childhood cancer
- free survival
- neural network
- electronic health record