Structure prediction of transferrin receptor protein 1 (TfR1) by homology modelling, docking, and molecular dynamics simulation studies

Transferrin receptor protein 1 (TfR1) is an important molecule in anti-cancer therapy. Targeted delivery of such therapeutic compounds improves their cellular uptake and circulation time, thereby enhancing therapeutic efficacy. Drug designing is therefore used to engineer molecules with structures that facilitate specific interactions. However, this process requires a thorough knowledge of all the interactions, including the three-dimensional (3D) and quaternary structures (QS) of the interacting molecules. Since structural information is available for only a part of the full TfR1 sequence, in the present study, we predicted the whole structure of TfR1 using homology modelling, docking, and molecular dynamics simulations. Homology modelling is used to generate 3D structures of TfR1 using MODELLER, I-TASSER, and RaptorX programs. Verify3D and Rampage server evaluated the quality of the resultant models. According to this evaluation, the model built by the RaptorX server and validated by Verify3D (compatibility: 83.82%) had the highest number of residues (95.5%) within the favoured regions of the Ramachandran plot, making it the most reliable 3D protein structure for TfR1 compared with others. The QS of TfR1 was built using HADDOCK and SymmDock docking software, and the results were evaluated by the ligand root mean square deviation (l-RMSD) value computed using the ProFit software. This showed that both HADDOCK and SymmDock gave acceptable results. However, the HADDOCK result was more stable and closest to the native complex structure with disulfide bonds. Therefore, the HADDOCK complex was further refined using both SymmRef and GalaxyRefineComplex until the medium l-RMSD rank was reached. This QS was successfully verified using nanoscale molecular dynamics (NAMD) energy minimization. This model could pave the way for further functional, structural, and therapeutic studies on TfR1.

Abstract licence: CC BY-NC

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.