Successful Molecular Dynamics Simulation of the Zinc-bound Farnesyltransferase Using the Cationic Dummy Atom Approach
Overview
Authors
Affiliations
Farnesyltransferase (FT) inhibitors can suppress tumor cell proliferation without substantially interfering with normal cell growth, thus holding promise for cancer treatment. A structure-based approach to the design of improved FT inhibitors relies on knowledge of the conformational flexibility of the zinc-containing active site of FT. Although several X-ray structures of FT have been reported, detailed information regarding the active site conformational flexibility of the enzyme is still not available. Molecular dynamics (MD) simulations of FT can offer the requisite information, but have not been applied due to a lack of effective methods for simulating the four-ligand coordination of zinc in proteins. Here, we report in detail the problems that occurred in the conventional MD simulations of the zinc-bound FT and a solution to these problems by employing a simple method that uses cationic dummy atoms to impose orientational requirement for zinc ligands. A successful 1.0 ns (1.0 fs time step) MD simulation of zinc-bound FT suggests that nine conserved residues (Asn127alpha, Gln162alpha, Asn165alpha, Gln195alpha, His248beta, Lys294beta, Leu295beta, Lys353beta, and Ser357beta) in the active site of mammalian FT are relatively mobile. Some of these residues might be involved in the ligand-induced active site conformational rearrangement upon binding and deserve attention in screening and design of improved FT inhibitors for cancer chemotherapy.
Wang K, Zhang L, Zhang S, Liu Y, Mao J, Liu Z Nat Commun. 2024; 15(1):7459.
PMID: 39198440 PMC: 11358137. DOI: 10.1038/s41467-024-51979-2.
Ng N, Ghosh S, Bok C, Ching C, Low B, Chen J Nat Commun. 2024; 15(1):4288.
PMID: 38909044 PMC: 11193738. DOI: 10.1038/s41467-024-48647-w.
Molecular Dynamics as a Tool for Virtual Ligand Screening.
Menchon G, Maveyraud L, Czaplicki G Methods Mol Biol. 2023; 2714:33-83.
PMID: 37676592 DOI: 10.1007/978-1-0716-3441-7_3.
Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins.
Durr S, Levy A, Rothlisberger U Nat Commun. 2023; 14(1):2713.
PMID: 37169763 PMC: 10175565. DOI: 10.1038/s41467-023-37870-6.
Ma M, Feng Y, Miao Y, Shen Q, Tang S, Dong J Foods. 2023; 12(8).
PMID: 37107368 PMC: 10137938. DOI: 10.3390/foods12081573.