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. 2023 Dec;12(6):e1393.
doi: 10.1002/mbo3.1393.

Examining the functional space of gut microbiome-derived peptides

Affiliations

Examining the functional space of gut microbiome-derived peptides

Ying-Chiang J Lee. Microbiologyopen. 2023 Dec.

Abstract

The human gut microbiome contains thousands of small, novel peptides that could play a role in microbe-microbe and host-microbe interactions, contributing to human health and disease. Although these peptides have not yet been systematically characterized, computational tools can be used to elucidate the bioactivities they may have. This article proposes probing the functional space of gut microbiome-derived peptides (MDPs) using in silico approaches for three bioactivities: antimicrobial, anticancer, and nucleomodulins. Machine learning programs that support peptide and protein queries are provided for each bioactivity. Considering the biases of an activity-centric approach, activity-agnostic tools using structural and chemical similarity and target prediction are also described. Gut MDPs represent a vast functional space that can not only contribute to our understanding of microbiome interactions but potentially even serve as a source of life-changing therapeutics.

Keywords: anticancer; antimicrobial; bioactivity; machine learning; microbiome; peptide.

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Conflict of interest statement

The authors declare no conflict of interest.

References

    1. Bernhofer, M. , Goldberg, T. , Wolf, S. , Ahmed, M. , Zaugg, J. , Boden, M. , & Rost, B. (2018). NLSdb‐major update for database of nuclear localization signals and nuclear export signals. Nucleic Acids Research, 46, D503–D508. - PMC - PubMed
    1. Bragina, M. E. , Daina, A. , Perez, M. A. S. , Michielin, O. , & Zoete, V. (2022). The SwissSimilarity 2021 Web Tool: Novel chemical libraries and additional methods for an enhanced ligand‐based virtual screening experience. International Journal of Molecular Sciences, 23, 811. - PMC - PubMed
    1. Brameier, M. , Krings, A. , & MacCallum, R. M. (2007). NucPred—Predicting nuclear localization of proteins. Bioinformatics, 23, 1159–1160. - PubMed
    1. Chen, J. , Cheong, H. H. , & Siu, S. W. I. (2021). xDeep‐AcPEP: Deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning. Journal of Chemical Information and Modeling, 61, 3789–3803. - PubMed
    1. Cohen, L. J. , Kang, H. S. , Chu, J. , Huang, Y.‐H. , Gordon, E. A. , Reddy, B. V. B. , Ternei, M. A. , Craig, J. W. , & Brady, S. F. (2015). Functional metagenomic discovery of bacterial effectors in the human microbiome and isolation of commendamide, a GPCR G2A/132 agonist. Proceedings of the National Academy of Sciences of the United States of America, 112(35), E4825–E4834. - PMC - PubMed

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