EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning

Banerjee, Deepro; Jindra, Michael A.; Linot, Alec J.; Pfleger, Brian F.; Maranas, Costas D.

doi:10.1016/j.crbiot.2021.12.002

EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning

Journal Article · Thu Dec 16 00:00:00 EST 2021 · Current Research in Biotechnology

DOI:https://doi.org/10.1016/j.crbiot.2021.12.002· OSTI ID:1855996

Banerjee, Deepro ^[1]; Jindra, Michael A. ^[2]; Linot, Alec J. ^[2]; Pfleger, Brian F. ^[2]; Maranas, Costas D. ^[3]

Pennsylvania State Univ., University Park, PA (United States); University of Illinois
Univ. of Wisconsin, Madison, WI (United States)
Pennsylvania State Univ., University Park, PA (United States)

Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.

View Accepted Manuscript (DOE)

Research Organization:: CABBI, Urbana, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018420

OSTI ID:: 1855996

Journal Information:: Current Research in Biotechnology, Journal Name: Current Research in Biotechnology Vol. 4; ISSN 2590-2628

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Enzyme classification
Machine learning
Medium-chain oleochemicals
Substrate specificity
Synthetic biology
Thioesterase

EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning

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