ArticlePLoS computational biology2016
Defining the Product Chemical Space of Monoterpenoid Synthases.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Article
- Highly selective acid-catalyzed olefin isomerization of limonene to terpinolene by kinetic suppression of overreactions in a confined space of porous metal-macrocycle frameworks.Chemical science · 2022Article
- An Aromatic Cluster in the Active Site ofBiochemistry · 2020Article
- Exploring the catalytic cascade of cembranoid biosynthesis by combination of genetic engineering and molecular simulations.Computational and structural biotechnology journal · 2020Article
- Exploring novel bacterial terpene synthases.PloS one · 2020Article
- Terpene Synthases as Metabolic Gatekeepers in the Evolution of Plant Terpenoid Chemical Diversity.Frontiers in plant science · 2019Review
- New computational approaches to understanding molecular protein function.PLoS computational biology · 2018Article
- Structural and Chemical Biology of Terpenoid Cyclases.Chemical reviews · 2017Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Terpenoid synthases create diverse carbon skeletons by catalyzing complex carbocation rearrangements, making them particularly challenging for enzyme function prediction. To begin to address this challenge, we have developed a computational approach for the systematic enumeration of terpenoid carbocations. Application of this approach allows us to systematically define a nearly complete chemical space for the potential carbon skeletons of products from monoterpenoid synthases. Specifically, 18758 carbocations were generated, which we cluster into 74 cyclic skeletons. Five of the 74 skeletons are found in known natural products; some of the others are plausible for new functions, either in nature or engineered. This work systematizes the description of function for this class of enzymes, and provides a basis for predicting functions of uncharacterized enzymes. To our knowledge, this is the first computational study to explore the complete product chemical space of this important class of enzymes.
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Registered trials
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