Literature DB >> 30612037

Structural dynamics of lytic polysaccharide monoxygenases reveals a highly flexible substrate binding region.

Radhika Arora1, Priya Bharval1, Sheena Sarswati1, Taner Z Sen2, Ragothaman M Yennamalli3.   

Abstract

Lytic polysaccharide monooxygenases (LPMOs), which are found in fungi, bacteria, and viruses, are redox enzymes utilizing copper to break glycosidic bonds in recalcitrant crystalline form of polysaccharides, such as chitin and cellulose. They are classified by the Carbohydrate-Active enZYmes (CAZy) database under various families. LPMOs's structure with a flat substrate binding region has been shown to contribute to its function, however, the role that LPMOs structural dynamics play during polysaccharide degradation and its mechanism of binding towards substrate are relatively unknown. Here, we report an exhaustive implementation of coarse-grained simulations using Elastic Network Models on multiple LPMO structures to shed light on how their structural dynamics contribute to their chemical function. Using Gaussian network models and Anisotropic network models, we show that the substrate binding region is highly flexible with significant and sustained micro-scale level conformational changes. Significantly, the loops on the binding side of the substrate are most mobile, in concert with the dynamic modes influencing the motions during binding. We also observed dynamic differences between four families of LPMO, namely AA9, AA10, AA11, and AA13 that consist of more than one structure. Specifically, the patterns of motion in the loop regions among the AA9 structures are distinct from those in the AA10 structures.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bioethanol; Conformational dynamics; Elastic network models; Lytic polysaccharide monooxygenases; Protein rigidity

Mesh:

Substances:

Year:  2018        PMID: 30612037     DOI: 10.1016/j.jmgm.2018.12.012

Source DB:  PubMed          Journal:  J Mol Graph Model        ISSN: 1093-3263            Impact factor:   2.518


  2 in total

1.  Minimalist De Novo Design of an Artificial Enzyme.

Authors:  Jahnu Saikia; Venugopal T Bhat; Lokeswara Rao Potnuru; Amay S Redkar; Vipin Agarwal; Vibin Ramakrishnan
Journal:  ACS Omega       Date:  2022-05-27

2.  Four cellulose-active lytic polysaccharide monooxygenases from Cellulomonas species.

Authors:  James Li; Laleh Solhi; Ethan D Goddard-Borger; Yann Mathieu; Warren W Wakarchuk; Stephen G Withers; Harry Brumer
Journal:  Biotechnol Biofuels       Date:  2021-01-23       Impact factor: 6.040

  2 in total

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