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dc.contributor.authorFeehan, Ryan
dc.contributor.authorFranklin, Meghan W.
dc.contributor.authorSlusky, Joanna S. G.
dc.identifier.citationFeehan, R., Franklin, M. W., & Slusky, J. (2021). Machine learning differentiates enzymatic and non-enzymatic metals in proteins. Nature communications, 12(1), 3712.
dc.description.abstractMetalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model’s ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.en_US
dc.publisherNature Researchen_US
dc.rights© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.subjectMachine learningen_US
dc.titleMachine learning differentiates enzymatic and non-enzymatic metals in proteinsen_US
kusw.kuauthorFeehan, Ryan
kusw.kuauthorFranklin, Meghan W.
kusw.kuauthorSlusky, Joanna S. G.
kusw.kudepartmentCenter for Computational Biologyen_US
kusw.kudepartmentMolecular Biosciencesen_US
kusw.oanotesPer Sherpa Romeo 12/10/2021:

Nature Communications [Open panel below]Publication Information TitleNature Communications [English] ISSNsElectronic: 2041-1723 URL PublishersNature Research [Commercial Publisher] DOAJ Listing Requires APCYes [Data provided by DOAJ] [Open panel below]Publisher Policy Open Access pathways permitted by this journal's policy are listed below by article version. Click on a pathway for a more detailed view.

Published Version NoneCC BYPMC Any Website, Journal Website OA PublishingThis pathway includes Open Access publishing EmbargoNo Embargo LicenceCC BY 4.0 Copyright OwnerAuthors Publisher Deposit PubMed Central Europe PMC Location Any Website Journal Website Conditions Must link to publisher version Publisher copyright and source must be acknowledged and DOI cited
dc.identifier.orcid 0000-0002-2435-671Xen_US
dc.identifier.orcid 0000-0003-0842-6340en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US

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© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License.
Except where otherwise noted, this item's license is described as: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License.