Engineered BCL6 BTB Domain of the Bcl-2 Protein Family shows Dynamic Structural Behavior: Insights from Molecular Dynamics Simulations
Saddam, M.; Ahsan Habib, M.; Abrar Fahim, M.; Mimi, A.; Islam, S.; Mostofa Uddin Helal, M.
Show abstract
Apoptosis is crucially regulated by the Bcl-6 protein, and mutations in this protein can have a significant impact on many malignancies. In this study, we used molecular dynamics simulations to examine the effects of specific mutations (Q8C, R67C, and N84C) in the crystal structure of the BCL6 BTB domain in a compound with pyrazole-pyrimidine ligand. We concentrated on comprehending the dynamics of these alterations and their possible effects on the emergence of cancer. To explore the structural and dynamic changes induced by these mutations, we performed in silico simulations using the GROMACS software suite (version 5.2, 2020.1) on Google Colabs Tesla T4 GPU. The crystal structure of the BCL6 BTB domain in complex with the pyrazole-pyrimidine ligand (PDB ID: 5N20) served as the wild-type reference structure. Mutations were imposed using the Rotamer functions of Chimera. The simulations were carried out for a total duration of 20 ns using a time step of 2 femtoseconds (0.002 ps). The Trajectory profiles of the BCL6 BTB domain protein and its three mutations, Q8C, R67C and N84C, were shown to differ from each other. Based on the analysis of RMSD, RMSF, and Rg, it was determined that the mutant 2 (R67C) protein exhibited increased instability and greater flexibility. In contrast, mutant 3 (N84C) demonstrates a heightened level of compactness and greater stability compared to the remaining protein mutant. PCA also provides information regarding the structural dynamics of these mutants. In addition, the SASA and SASA autocorrelation provides a distinct view of the solvent accessibility of these proteins.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Investigating the role of N-terminal domain in phosphodiesterase 4B-inhibition by molecular dynamics simulation 99%
- An insight into SARS-CoV-2 Membrane protein interaction with Spike, Envelope, and Nucleocapsid proteins 98%
- Ab initio modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1) 97%
Similar papers in this journal
- Molecular Dynamics Study on the Effects of Charged Amino Acid Distribution Under low pH Condition to the Unfolding of Hen Egg White Lysozyme and Formation of Beta Strands. 98%
- Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8 97%
- iBRAB: in silico based-designed Broad-spectrum Fab against H1N1 Influenza A Virus 96%
Similar papers in this journal
- Mechanistic insights into the deleterious role of nasu-hakola disease associated TREM2 variants 97%
- Curvature increases permeability of the plasma membrane for ions, water and the anti-cancer drugs cisplatin and gemcitabine 96%
- Time Dependent Dihedral Angle Oscillations of the Spike Protein of SARS-CoV-2 Reveal Favored Frequencies of Dihedral Angle Rotations 96%
Similar papers in this journal
- Possible link between higher transmissibility of B.1.617 and B.1.1.7 variants of SARS-CoV-2 and increased structural stability of its spike protein and hACE2 affinity 98%
- Effect of Delta and Omicron mutations on the RBD-SD1 do-main of the Spike protein in SARS-CoV-2 and the Omicron mutations on RBD-ACE2 interface complex 97%
- Hot-spots and their contribution to the self-assembly of the viral capsid: in-silico prediction and analysis 95%
Similar papers in this journal
- Characterization of the NiRAN domain from RNA-dependent RNA polymerase provides insights into a potential therapeutic target against SARS-CoV-2 94%
- A new machine learning method for cancer mutation analysis 94%
- Large-scale, dynamin-like motions of the human guanylate binding protein 1 revealed by multi-resolution simulations 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.