Molecular and Coarse-Grained Analysis of Flap Motion In Lipase Enzymes

Lipase enzymes are a common component of most pathways for metabolizing lipids. They catalyze the breakdown of large lipid structures by means of hydrolyzing ester bonds.  Their importance in this role explains why human lipases have been implicated in a variety of health conditions, including heart disease and diabetes. Industrial utilization of lipases (purified from various microorganisms) has predominantly been for food production and in detergents, yet in the past decade for the production of biodiesel and other sustainable fuels from plant-derived substrates.

Despite their importance in both technological application and human health, little is known about the dynamics of lipases at the atomic level. Previous experimental investigations have shown a common structural motif among lipases is a flap-like domain that protects the active site from the solvent until in the enzyme contacts a solvent-lipid interface. The C. rugosa lipase (CRL) structure has been resolved for both the flap-open and flap-closed states. The two states have almost identical structure, except for the 26 amino-acid flap, which differs by 17 Angstrom between the open and closed states. This similarity has led to the conclusion that the flap-opening mechanism is a hinge-like motion.  Given the importance of this conformational change in regulating lipase activity, the molecular details of the flap mechanism are essential knowledge for engineering improved lipases.  However, direct observation of this process is difficult or impossible via experiment alone.  Therefore, we have used molecular and coarse-grained simulations to investigate the behavior of CRL in explicit water.  The major challenge of using classical MD in investigating conformational rearrangement and rare events (e.g., flap opening/closing) lies in sufficient sampling.  As we demonstrate, even microsecond-long simulations of the all-atom (AA) system cannot provide enough sampling to calculate the equilibrium probability distribution of this process. We use the AA simulation trajectories to develop coarse-grain (CG) models based on the recently developed ED-CG [1] method.  The ED-CG models help identify dynamically similar domains within the open and closed states.  Finally, we use the metadynamics [2] algorithm to calculate the free-energy landscape and identify key structural transitions in the opening and closing process.

[1] Z. Zhang, L. Lu, W.G. Noid, V. Krishna, J. Pfaendtner, and G.A. Voth (2008). "A Systematic Methodology for Defining Course-Grained Sites in Large Biomolecules." Biophysical Journal 95: 5073-5083.

[2] A. Laio and M. Parrinello (2002). "Escaping free-energy minima." PNAS 99(20): 12562-12566.    

---