The Binding Mechanism of Carbapenems in the Class A beta-lactamase IMI-1 : A Molecular Dynamics Study of Ligand Stability

Abstract: Antibiotic resistance is a global and accelerating matter. Over time, the bacteria have evolved several defense mechanisms against the antibiotics. One of the defense mechanisms is that the bacteria can produce enzymes with the ability to hydrolyze the characteristic b-lactam ring of the antibiotics. These enzymes are called b-lactamases. There are three different generations of antibiotics clinically available, and b-lactamases have co-evolved with the antibiotics over the generations. The third generation of antibiotics are called the carbapenems and b-lactamases which hydrolyze carbapenems are called carbapenemases. Carbapenemases are promiscuous, which means that they hydrolyze a variety of antibiotics. The b-lactamase IMI-1 is an imipenem-hydrolyzing enzyme and imipenem is a carbapenem, hence IMI-1 is a carbapenemase. In this project, IMI-1 was investigated in complex with the carbapenems imipenem, meropenem and biapenem using computational methods. More specifically, a homology model of IMI-1 was generated and the carbapenems were docked into the model. The system was then used for MD simulations where the important molecular interactions were identified, and the binding free energies were calculated using the LIE method. The results indicate that IMI-1 has flexible loops that enables an open and a closed conformation of IMI- 1. All three carbapenems were docked and simulated in both conformations of IMI-1. The results indicate that open and closed conformations confirms the promiscuity of carbapenemases since the flexibility enables various initial binding mechanisms. in other words, the hydrolysis may occur so quickly that the binding does not have much bearing of the activity of the enzyme. Furthermore, the calculated binding free energies indicate that IMI-1 is optimized for the catalytic process rather than the binding affinity. In conclusion, IMI-1 and similar systems requires further research using computational methods to counteract antibiotic resistance based on knowledge.