Abstract

Toll-like receptors (TLRs) are one of the major sensors to regulate innate immunity. It is present in inactive form within immune cells. However, after recognizing the conserved region of the foreign body, it gets activated by the foreign body, such as bacteria, viruses, fungus, etc. Recently, it is reported that apart from participating in innate immunity, these TLRs also play an important role in apoptosis and cancer. Moreover, very few reported that it is cross-talk with p53 protein within the cell. P53 protein is a transcription factor for many cellular proteins involved in cellular transduction. It directly as well as indirectly regulates a wide variety of cellular processes such as apoptosis, senescence, cell cycle arrest, differentiation, and DNA repair and replication and cancer dynamics. Various studies reported genetic level interaction between p53 and TLRs. However, molecular interaction studies are still few reported. In the present work, we computationally characterized molecular interaction between p53 and toll-like receptors. We used open web resources for docking and analyzing the data. Our molecular docking and molecular dynamics simulation results suggest that there is a significant interaction between p53 and toll-like receptors. The study could important for the possible therapeutic intervention.

Highlights

  • Introduction of interferonsRecently, it is reported that apart fromToll-like receptors (TLRs) proteins are major sensor molecules within participating in innate immunity, these TLRs play the immune cells

  • It is observed that the p53 protein interacts with all the TLRs

  • It is noticed that from the results shown in Table 2 that there is a comparatively strong interaction between p53-TLR3, p53-TLR5, p53-TLR8 and p53-TLR9 complexes

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Summary

Materials and methods

We retrieve conserved sequences for p53 and TLR1 to TLR10 protein in FASTA format from the NCBI database (https://www.ncbi.nlm.nih.gov/). After obtaining these conserved sequences, we did multiple sequence analyses using CLUSTAL Omega, a web tool (https://www.ebi.ac.uk/Tools/msa/clustalo/). The interaction was downloaded for human p53 and TLR1 to TLR 10 is retrieved from the STRING database (https://stringdb.org/). We used the Cytoscape tool, an open web resource(https://cytoscape.org/)(15), to find the interaction between p53 and TLRs protein. We retrieve p53 protein structure data (PDB ID: 2OCJ). We retrieve PDB structure for all human TLRs (from TLR 1 to TLR 10) [PDB ID: 1FYV (TLR1), 1FYW (TLR2), 2MK9 (TLR3), 2Z63. (TLR10)].We did protein-protein molecular docking was done using CLUSPRO 2.0 (an open-source tools for protein-protein docking). (http://biocomp.chem.uw.edu.pl/CABSflex2)(17) for molecular dynamics study for 10ns to analyze the stability and flexibility of free p53 protein and TLRs bounded p53 protein

Results and discussion
Conclusions
27. Krysko
Full Text
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