BiochemistryBiophysicsPeer-reviewed journal article
A novel computational method to design BH3-mimetic peptide inhibitors that can bind specifically to Mcl-1 or Bcl-XL
C. Narendra Reddy, Nishat ManzariD, Bushra AteeqiD, Ramasubbu Sankararamakrishnan
Abstract
Interactions between pro- and anti-apoptotic B cell lymphoma 2 (Bcl-2) proteins decide the fate of the cell. BH3 (Bcl-2 homology 3) domain of pro-apoptotic Bcl-2 proteins interacts with the exposed hydrophobic groove of anti-apoptotic counterparts. Design and development of BH3 mimetics that target the hydrophobic groove of specific anti-apoptotic Bcl-2 proteins have the potential to become anti-cancer drugs. We have developed a novel computational method to design sequences with BH3 domain features that can bind specifically to anti-apoptotic Mcl-1 or Bcl-XL. In this method, we retained the four highly conserved hydrophobic and aspartic residues of wild-type BH3 sequences and randomly substituted all other positions to generate a large number of BH3-like sequences. We modeled 20000 complex structures with Mcl-1 or Bcl-XL using the BH3-like sequences derived from five wild-type pro-apoptotic BH3 peptides. Peptide-protein interaction energies calculated from these models for each set of BH3-like sequences resulted in negatively-skewed extreme value distributions. The selected BH3-like sequences from the extreme negative tail regions have distinctly different distribution of charged residues for Mcl-1 and Bcl-XL. BH3-like sequences with highly favorable interaction energies prefer to have acidic residues for Mcl-1 and are enriched with basic residues when they bind to Bcl-XL. With the charged residues often away from the binding interface, the overall electric field generated by the charged residues result in highly favorable long-range electrostatic interaction energies between the peptide and the protein giving rise to high specificity. Cell viability studies of representative BH3-like peptides further validated the predicted specificity.
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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