BCL-2
September 2021
The B cell lymphoma-2 (BCL-2) family proteins are key biomolecules responsible for the regulation of cell death (apoptosis). One of the members is called BCL-2 protein and prevents apoptosis by binding and inhibiting pro-apoptotic proteins, together with 5 structurally similar homologs (Bcl-xL, Bcl-w, Mcl-1, Bfl-1, and cl-B). Nevertheless, the overexpression of this group of BCL-2 family proteins causes dysregulation and promotes the survival of different cancer cells. In the case of the BLC-2 protein, the excess most commonly leads to lymphoma and leukemia.
The structure of the BCL-2 protein consists of 4 BCL-2 homolog domains (BH 1-4). The BH3 domain is particularly important since it forms the binding groove where BCL-2 interacts with pro-apoptotic proteins to control cell death. Therefore, blocking this protein-protein interaction (PPI) with small molecule drugs is an attractive therapeutic approach. The first-ever FDA-approved BCL-2 selective PPI inhibitor is Venetoclax (Venclexta™, Abbvie). The other small molecules targeting BCL-2 are in development (one of them, S55746, is currently under clinical trials) intending to achieve an even more efficient therapy.
3decision Get ligands that bind in the pocket feature
Pharmacophore-based virtual screening is an efficient strategy to identify new active and potent binders. The first step in the generation of pharmacophore models is the preparation of a curated 3D dataset. With 3decision, you can buildsuch a dataset with a simple click. The “Get all ligands that bind in the pocket” feature can retrieve the list of aligned structures and ligands in a matter of seconds.
For the case of BLC-2, you can use the structure of BCL-2 co-crystallized with Venetoclax (PDB ID: 6o0K) as starting query. As an output from the search, you get a library of 26 compounds bound to structures from the public domain protein 3D structures stored in 3decision. You can then automatically superpose all the ligands in the pocket of interest and export the lists of aligned structures in different formats (sdf., xlsx., csv.). This way you can highly speed up the data set preparation and proceed faster with creating pharmacophore models.
Reference: Krishna S, Kumar SB, Murthy TPK, Murahari M. Structure-based design approach of potential BCL-2 inhibitors for cancer chemotherapy. Comput Biol Med. 2021 Jul;134:104455. doi: 10.1016/j.compbiomed.2021.104455. Epub 2021 Apr 30. PMID: 33962088.