Literature DB >> 32119077

Sparse Epistatic Patterns in the Evolution of Terpene Synthases.

Aditya Ballal1, Caroline Laurendon2,3, Melissa Salmon2,3,4, Maria Vardakou2,3,5, Jitender Cheema6, Marianne Defernez7, Paul E O'Maille2,3,8, Alexandre V Morozov1.   

Abstract

We explore sequence determinants of enzyme activity and specificity in a major enzyme family of terpene synthases. Most enzymes in this family catalyze reactions that produce cyclic terpenes-complex hydrocarbons widely used by plants and insects in diverse biological processes such as defense, communication, and symbiosis. To analyze the molecular mechanisms of emergence of terpene cyclization, we have carried out in-depth examination of mutational space around (E)-β-farnesene synthase, an Artemisia annua enzyme which catalyzes production of a linear hydrocarbon chain. Each mutant enzyme in our synthetic libraries was characterized biochemically, and the resulting reaction rate data were used as input to the Michaelis-Menten model of enzyme kinetics, in which free energies were represented as sums of one-amino-acid contributions and two-amino-acid couplings. Our model predicts measured reaction rates with high accuracy and yields free energy landscapes characterized by relatively few coupling terms. As a result, the Michaelis-Menten free energy landscapes have simple, interpretable structure and exhibit little epistasis. We have also developed biophysical fitness models based on the assumption that highly fit enzymes have evolved to maximize the output of correct products, such as cyclic products or a specific product of interest, while minimizing the output of byproducts. This approach results in nonlinear fitness landscapes that are considerably more epistatic. Overall, our experimental and computational framework provides focused characterization of evolutionary emergence of novel enzymatic functions in the context of microevolutionary exploration of sequence space around naturally occurring enzymes.
© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  enzyme kinetics; epistasis; molecular evolution; terpene synthases

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Year:  2020        PMID: 32119077     DOI: 10.1093/molbev/msaa052

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  2 in total

1.  On the sparsity of fitness functions and implications for learning.

Authors:  David H Brookes; Amirali Aghazadeh; Jennifer Listgarten
Journal:  Proc Natl Acad Sci U S A       Date:  2022-01-04       Impact factor: 12.779

2.  Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions.

Authors:  Amirali Aghazadeh; Hunter Nisonoff; Orhan Ocal; David H Brookes; Yijie Huang; O Ozan Koyluoglu; Jennifer Listgarten; Kannan Ramchandran
Journal:  Nat Commun       Date:  2021-09-01       Impact factor: 17.694

  2 in total

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