Exploration of Multiconformers to Extract Information About Structural Deformation Undergone by a Protein Target: Illustration on the Bcl-xL Target
We previously developed SA-conf, a method designed to quantify backbone structural variability in protein targets. This approach is based on the HMM-SA structural alphabet, which enables efficient and rapid comparison of local backbone conformations across multiple structures of a given target. In this study, SA-conf (version for python2.7) was applied to a dataset of 130 crystallographic chains of Bcl-xL, a protein involved in promoting cell survival. SA-conf quantified and mapped backbone structural variability, revealing the protein's capacity for conformational rearrangement. Our results showed that while most mutations had minimal impact on backbone conformation, some were associated with long-range structural effects. By jointly analyzing residue flexibility and backbone rearrangements across apo and holo structures, SA-conf identified key regions where the backbone undergoes structural adjustments upon ligand binding. Notably, the α2-α3 region was shown to be a hotspot of structural plasticity, exhibiting ligand-specific conformational signatures. Furthermore, SA-conf enabled the construction of a structural map of the binding site, distinguishing a conserved anchoring core from flexible peripheral regions that contribute to ligand specificity. Overall, this study highlights SA-conf's capacity to detect conformational changes in protein backbones upon ligand binding and to uncover structural determinants of selective ligand recognition.
Authors
- Leslie Regad (ORCID: https://orcid.org/0000-0002-9292-5283)
- Anne Badel (ORCID: https://orcid.org/0000-0002-2777-5979)
- Marine Baillif
- Eliott Tempez
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Université Paris Cité (FR)
- Unit of Functional and Adaptive Biology (FR)
Publication Details
- Journal
- Molecules
- Published
- 2025-08-12
- DOI
- https://doi.org/10.3390/molecules30163355
- Citations
- 2
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.01