Integrative genomic analyses combined with molecular dynamics simulations reveal the impact of deleterious mutations of Bcl-2 gene on the apoptotic machinery and implications in carcinogenesis

Abstract

Objectives: Unlike other diseases, cancer is not just a genome disease but should broadly be viewed as a disease of the cellular machinery. Therefore, integrative multifaceted approaches are crucial to understanding the complex nature of cancer biology. Bcl-2 (B-cell lymphoma 2), encoded by the human BCL-2 gene, is an anti-apoptotic molecule that plays a key role in apoptosis and genetic variation of Bcl-2 proteins and is vital in disrupting the apoptotic machinery. Single nucleotide polymorphisms (SNPs) are considered viable diagnostic and therapeutic biomarkers for various cancers. Therefore, this study explores the association between SNPs in Bcl-2 and the structural, functional, protein-protein interactions (PPIs), drug binding and dynamic characteristics.

Methods: Comprehensive cross-validated bioinformatics tools and molecular dynamics (MD) simulations. Multiple sequence, genetic, structural and disease phenotype analyses were applied in this study.

Results: Analysis revealed that out of 130 mutations, approximately 8.5% of these mutations were classified as pathogenic. Furthermore, two particular variants, namely, Bcl-2^G101V and Bcl-2^F104L, were found to be the most deleterious across all analyses. Following 500 ns, MD simulations showed that these mutations caused a significant distortion in the protein conformational, protein-protein interactions (PPIs), and drug binding landscape compared to Bcl-2^WT.

Conclusion: Despite being a predictive study, the findings presented in this report would offer a perspective insight for further experimental investigation, rational drug design, and cancer gene therapy.

Keywords: Bcl-2; genomic analyses; molecular dynamics simulations; mutations; nsSNPs.

FIGURE 1

Flowchart of the different types of analyses and approaches employed in this study.

FIGURE 2

Deleterious and tolerated variations in Bcl-2 predicted through sequence-based algorithms.

FIGURE 3

Destabilizing and stabilizing variations in Bcl-2 predicted through structure-based algorithms.

FIGURE 4

Disease and neutral variations in Bcl-2 predicted through disease phenotype prediction algorithms.

FIGURE 5

Bcl-2 Gene interactions with other genes predicted by (A) GeneMANIA and (B) STRING.

FIGURE 6

G101 and F104 residue interactions network of Bcl-2; (A) wild G101, (A) G101V variant, (B) wild F104, and (B) F104L variant as predicted by mCSM-PPI2.

FIGURE 7

Sequence conservation plot of Bcl-2 protein generated using ConSurf web server.

FIGURE 8

FT-site server prediction of the Bcl-2 protein ligand binding sites represented in mesh-like structure: pink (binding site 1), green (binding site 2), and purple (binding site 3).

FIGURE 9

Close-ups (different angles) of the mutant and wild system; (A) Bcl-2^G101V and (B) Bcl-2^F104L.

FIGURE 10

The secondary structural analysis of the Bcl-2WT, Bcl-2G101V, and Bcl-2F104L at 10, 100, 200, 300, 400, and 500 ns using the STRIDE web server.

FIGURE 11

(A) RMSD, (B) RMSF, (C) Rg, and (D) SASA values across Cα of Bcl-2WT (gray), Bcl-2F104L (orange), and Bcl-2G101V (green) over 500 ns MD simulations.

FIGURE 12

Intramolecular hydrogen bonding in Bcl-2WT (gray), Bcl-2F104L (orange), and Bcl-2G101V (green) over 500 ns MD simulations.

FIGURE 13

DCCM analyses for Bcl-2^WT, Bcl-2^G101V, and Bcl-2^F104L over 500 ns MD simulations.

FIGURE 14

PCA for Bcl-2^WT (gray), Bcl-2^F104L (orange), and Bcl-2^G101V (green) over the 500ns MD simulations.