Machine learning reveals signatures of promiscuous microbial amidases for micropollutant biotransformations
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Autor:in (Körperschaft)
Publikationsdatum
04.12.2024
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
ACS Environmental Au
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
5
Ausgabe / Nummer
1
Seiten / Dauer
114-127
Patentnummer
Verlag / Herausgebende Institution
American Chemical Society
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Organic micropollutants, including pharmaceuticals, personal care products, pesticides, and food additives, are widespread in the environment, causing potentially toxic effects. Human waste is a direct source of micropollutants, with the majority of pharmaceuticals being excreted through urine. Urine contains its own microbiota with the potential to catalyze micropollutant biotransformations. Amidase signature (AS) enzymes are known for their promiscuous activity in micropollutant biotransformations, but the potential for AS enzymes from the urinary microbiota to transform micropollutants is not known. Moreover, the characterization of AS enzymes to identify key chemical and enzymatic features associated with biotransformation profiles is critical for developing benign-by-design chemicals and micropollutant removal strategies. Here, to uncover the signatures of AS enzyme–substrate specificity, we tested 17 structurally diverse compounds against a targeted enzyme library consisting of 40 AS enzyme homologues from diverse urine microbial isolates. The most promiscuous enzymes were active on nine different substrates, while 16 enzymes had activity on at least one substrate and exhibited diverse substrate specificities. Using an interpretable gradient boosting machine learning model, we identified chemical and amino acid features associated with AS enzyme biotransformations. Key chemical features from our substrates included the molecular weight of the amide carbonyl substituent and the number of formal charges in the molecule. Four of the identified amino acid features were located in close proximity to the substrate tunnel entrance. Overall, this work highlights the understudied potential of urine-derived microbial AS enzymes for micropollutant biotransformation and offers insights into substrate and protein features associated with micropollutant biotransformations for future environmental applications.
Schlagwörter
Micropollutant biotransformations, Amidase signature enzymes, Urinary microbiota, Paracetamol, Acetylsulfamethoxazole, Capecitabine, Machine learning
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
2694-2518
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
Marti, T. D., Schweizer, D., Yu, Y., Schärer, M. R., Probst, S. I., & Robinson, S. L. (2024). Machine learning reveals signatures of promiscuous microbial amidases for micropollutant biotransformations. ACS Environmental Au, 5(1), 114–127. https://doi.org/10.1021/acsenvironau.4c00066