Application of molecular simulations toward understanding cellulase mechanisms

Authors: ARORA, MANSI - Jaypee University Of Information Technology; YENNAMALLI, RAGOTHMAN - Jaypee University Of Information Technology; Sen, Taner

Submitted to: BioEnergy Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/6/2018
Publication Date: 10/16/2018
Citation: Arora, M., Yennamalli, R.M., Sen, T.Z. 2018. Application of molecular simulations toward understanding cellulase mechanisms. BioEnergy Research. 11:850-867. https://doi.org/10.1007/s12155-018-9944-x.
DOI: https://doi.org/10.1007/s12155-018-9944-x

Interpretive Summary: Computational methods automate and reduce the time for biological data analysis. They also help visualize and perform predictive modeling of existing data to extrapolate and infer new understandings of biological systems, especially for industrial enzymes. In the specific case of cellulases, computational approaches have provided new biological insights into how these enzymes perform chemical conversions during biofuel/bioethanol production. In this review, we highlight key recent scientific advances in understanding this crucial biofuel enzyme class’ chemistry and structural dynamics, as well as their significance in revealing its mechanism of action. Computational methods can complement and amplify the findings of experimental methods, which can be used in tandem to create more efficient industrial enzymes.

Technical Abstract: Computational methods automate and reduce the time for biological data analysis. They also help visualize and perform predictive modeling of existing data to extrapolate and infer new understandings of biological systems, especially for industrial enzymes. In the specific case of cellulases, computational approaches have provided new biological insights into chemical mechanism of action such as how these enzymes perform during biofuel/bioethanol production. Fine-grained methods such as Molecular Dynamics, Molecular Docking and Quantum Mechanical/Molecular Mechanics (QM/MM), and coarse-grained methods such as Elastic Network models have been used to delve into how the chemistry and structural dynamics of these enzymes contribute to their industrial performance. In this review, we highlight key recent scientific advances to understand this crucial biofuel enzyme class’ chemistry and structural dynamics, as well as their significance in revealing its mechanism of action. We also highlight additional methods that can be applied to achieve the same goal. Computational methods can complement and amplify the findings of experimental methods, which can be used in tandem to create more efficient industrial enzymes.