Identifying the Enzymatic Mode of Action for Cellulase Enzymes by Means of Docking Calculations and a Machine Learning Algorithm

• Main Authors: Somisetti V. Sambasivarao, David M. Granum, Hua Wang, C. Mark Maupin
• Format: Article
• Language: English
• Published: AIMS Press 2014-01-01
• Series: AIMS Molecular Science
• Subjects: Carboxymethyl cellulose; Cellobiohydrolase; Cellobiose; Cellulase; Docking; Endoglucanase; Machine learning; Product inhibition
• Online Access: http://www.aimspress.com/article/10.3934/molsci.2014.1.59/fulltext.html

Docking calculations have been conducted on 36 cellulase enzymes and the results were evaluated by a machine learning algorithm to determine the nature of the enzyme (i.e. endo- or exo- enzymatic activity). The docking calculations have also been used to identify crucial substrate-enzyme interactions, and establish structure-function relationships. The use of carboxymethyl cellulose as a docking substrate is found to correctly identify the endo- or exo- behavior of cellulase enzymes with 92% accuracy while cellobiose docking calculations resulted in an 86% predictive accuracy. The binding distributions for cellobiose have been classified into two distinct types; distributions with a single maximum or distributions with a bi-modal structure. It is found that the uni-modal distributions correspond to exo- type enzyme while a bi-modal substrate docking distribution corresponds to endo- type enzyme. These results indicate that the use of docking calculations and machine learning algorithms are a fast and computationally inexpensive method for predicting if a cellulase enzyme possesses primarily endo- or exo- type behavior, while also revealing critical enzyme-substrate interactions.