Fast approximative methods for study of ligand transport and rational design of improved enzymes for biotechnologies
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Periodical |
| Magazine / Source | Biotechnology Advances |
| MU Faculty or unit | |
| Citation | |
| web | https://www.sciencedirect.com/science/article/pii/S0734975022001057 |
| Doi | http://dx.doi.org/10.1016/j.biotechadv.2022.108009 |
| Keywords | ART-RRT; Binding; Biotechnology; CaverDock; Catalysis; Cytochrome P450 CYP153A; Fe/a-ketoglutarate-dependent hydroxylase; GPathFinder; Channel; Docking; Ligand; MoMA-LigPath; Monoamine oxidase; Nanomotors; Protein engineering; SLITHER |
| Attached files | |
| Description | Acceleration of chemical reactions by the enzymes optimized using protein engineering represents one of the key pillars of the contribution of biotechnology towards sustainability. Tunnels and channels of enzymes with buried active sites enable the exchange of ligands, ions, and water molecules between the outer environment and active site pockets. The efficient exchange of ligands is a fundamental process of biocatalysis. Therefore, enzymes have evolved a wide range of mechanisms for repetitive conformational changes that enable periodic opening and closing. Protein-ligand interactions are traditionally studied by molecular docking, whereas molecular dynamics is the method of choice for studying conformational changes and ligand transport. However, computational demands make molecular dynamics impractical for screening purposes. Thus, several approximative methods have been recently developed to study interactions between a protein and ligand during the ligand transport process. Apart from identifying the best binding modes, these methods also provide information on the energetics of the transport and identify problematic regions limiting the ligand passage. These methods use approximations to simulate binding or unbinding events rapidly (calculation times from minutes to hours) and provide energy profiles that can be used to rank ligands or pathways. Here we provide a critical comparison of available methods, showcase their results on sample systems, discuss their practical applications in molecular biotechnologies and outline possible future developments. |
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