bioRxivBioinformaticsPreprint
Bcl-xL dynamics and cancer-associated mutations under the lens of protein structure networks and biomolecular simulations
Abstract
Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research since the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. Particularly, Bcl-xL is a target of interest due to its forefront role of its dysfunctions in cancer development. Bcl-xL prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, as PUMA, and noncanonical partners such as p53 at different sites. An allosteric communication between the BH3-only proteins binding pocket and the p53 binding site has been postulated and supported by NMR and other biophysical data, mediating the release of p53 from Bcl-xL upon PUMA binding. The molecular details, especially at the residue level, of this mechanism remain unclear. In this work, we investigated the distal communication between these two sites in both Bcl-xL in its free state and bound to PUMA, and we evaluated how missense mutations of Bcl-xL found in cancer samples might impair the communication and thus the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.
- organism: human
- assay: protein structure
- research focus: protein structure prediction
- approach: mixed model
- Citations
- 4
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