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A Computational Model for the PLP-Dependent Enzyme Methionine γ-Lyase
Xingyu Chen1, Pierre Briozzo2, David Machover3
1Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France.
Frontiers in Molecular Biosciences
|May 13, 2022
Summary
This study developed a simulation model for methionine gamma-lyase (MGL) to understand its pyridoxal-5'-phosphate (PLP) binding, revealing the deprotonated phosphate form is predominant for improved MGL activity in cancer therapy.
Area of Science:
- Biochemistry
- Computational Biology
- Enzyme Engineering
Background:
- Pyridoxal-5 -phosphate (PLP) is a crucial cofactor for over 160 enzymes, with dysregulation linked to various diseases.
- Methionine gamma-lyase (MGL) utilizes PLP and shows therapeutic potential in cancer treatment by depleting methionine, essential for cancer cell survival.
Purpose of the Study:
- To develop and validate a simulation model for MGL, focusing on understanding and engineering its pyridoxal-5 -phosphate (PLP) binding.
- To determine the optimal PLP binding state for enhanced MGL activity and therapeutic efficacy.
Main Methods:
- Development of force field parameters for PLP in various protonation and tautomeric states.
- Molecular dynamics simulations of MGL-PLP complexes.
- Alchemical free energy simulations to compare PLP forms.
- Extrapolation of protonation free energy to a macroscopic protein solution.
Main Results:
- Simulations indicated that a fully-deprotonated PLP phosphate, particularly the keto Schiff base form, best matches experimentally determined MGL structures.
- Free energy simulations confirmed a moderate preference for the keto tautomer and the deprotonated phosphate form.
- Calculated pK of 5.7 confirmed the predominance of the PLP -2 charged state.
Conclusions:
- The developed PLP force field and simulation methodologies accurately model MGL active site dynamics.
- These methods can be applied to other PLP-dependent enzymes for structural and dynamic insights.
- Optimized PLP binding in MGL can be achieved through computational approaches, advancing its therapeutic potential.
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