Modelling substrate specificity and enantioselectivity for lipases and esterases by substrate-imprinted docking

Area of Science:

• Biochemistry
• Computational Chemistry
• Enzymology

Background:

• Traditional docking methods for inhibitor-receptor interactions require adaptation for modeling enzyme catalysis.
• Modeling enzyme catalysis necessitates using substrate reaction intermediates and incorporating protein flexibility for accurate predictions.

Purpose of the Study:

• To develop a predictive and robust computational method for modeling substrate specificity and enantioselectivity in lipases and esterases.
• To address limitations in existing docking approaches by integrating reaction intermediates and protein flexibility.

Main Methods:

• Substrate-imprinted docking: covalent docking of reaction intermediates, followed by geometry optimization.
• A second docking round using the optimized structures, with productive poses identified by geometric filters and docking scores.
• Application to model enantioselectivity and substrate specificity of various lipases and esterases.

Main Results:

• The substrate-imprinted docking method accurately modeled the enantioselectivity and substrate specificity of lipases and esterases.
• Experimental observations of enzyme selectivity and specificity differences were reproduced with 81% accuracy.
• The method demonstrated robustness against minor variations in initial protein structures.

Conclusions:

• Substrate-imprinted docking is a reliable method for predicting enzyme selectivity and specificity.
• The integration of reaction intermediates and protein flexibility is crucial for accurate enzyme modeling.
• While robust, significant catalytic residue displacements can affect the accuracy of predicted substrate poses.