Computationally efficient and accurate enantioselectivity modeling by clusters of molecular dynamics simulations.

Area of Science:

• Biochemistry
• Computational Chemistry
• Enzyme Engineering

Background:

• High-throughput experimental screening is laborious for enzyme improvement via mutagenesis.
• Computational methods offer a potential alternative to reduce experimental workload.

Purpose of the Study:

• To develop and validate a cost-effective computational approach for modeling enzyme enantioselectivity.
• To accurately predict the enantioselective conversion of various substrates by haloalkane dehalogenases.

Main Methods:

• Utilized multiple short molecular dynamics (MD) simulations (10 ps) for conformational sampling.
• Quantified the frequency of catalytically productive conformations, focusing on the nucleophilic attack angle.
• Modeled enantioselectivity for four haloalkane dehalogenases using 45 different substrates.

Main Results:

• Accurate modeling of enantioselectivity was achieved using multiple short MD simulations.
• The angle of nucleophilic attack was identified as a critical variable for productive conformations.
• Short MD simulations provided better agreement with experimental data than single long MD simulations (22 ns).
• Computational costs were reduced 50-100 fold compared to single long MD simulations.

Conclusions:

• Multiple short MD simulations offer a computationally efficient and accurate method for predicting enzyme enantioselectivity.
• This approach significantly reduces the need for extensive experimental screening in enzyme engineering.
• The study highlights the importance of conformational sampling diversity for accurate modeling.