Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Standard

Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma. / Grønning, Alexander G.B.; Schéele, Camilla.

Peptidomics: Methods and Protocols. ed. / Michael Schrader; Lloyd D. Fricker. Humana Press, 2024. p. 179-195 (Methods in Molecular Biology, Vol. 2758).

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Grønning, AGB & Schéele, C 2024, Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma. in M Schrader & LD Fricker (eds), Peptidomics: Methods and Protocols. Humana Press, Methods in Molecular Biology, vol. 2758, pp. 179-195. https://doi.org/10.1007/978-1-0716-3646-6_9

APA

Grønning, A. G. B., & Schéele, C. (2024). Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma. In M. Schrader, & L. D. Fricker (Eds.), Peptidomics: Methods and Protocols (pp. 179-195). Humana Press. Methods in Molecular Biology Vol. 2758 https://doi.org/10.1007/978-1-0716-3646-6_9

Vancouver

Grønning AGB, Schéele C. Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma. In Schrader M, Fricker LD, editors, Peptidomics: Methods and Protocols. Humana Press. 2024. p. 179-195. (Methods in Molecular Biology, Vol. 2758). https://doi.org/10.1007/978-1-0716-3646-6_9

Author

Grønning, Alexander G.B. ; Schéele, Camilla. / Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma. Peptidomics: Methods and Protocols. editor / Michael Schrader ; Lloyd D. Fricker. Humana Press, 2024. pp. 179-195 (Methods in Molecular Biology, Vol. 2758).

Bibtex

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title = "Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma",
abstract = "Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mass spectrometry techniques for high-throughput peptide quantitation has paved the way for the identification and discovery of numerous known and novel peptides. Though much has been achieved, scientists are still facing difficulties when it comes to reducing the search space of the large mass spectrometry-generated peptidomics datasets and focusing on the subset of functionally relevant peptides. Moreover, there is currently no straightforward way to analytically compare the distributions of bioactive peptides in distinct biological samples, which may reveal much useful information when seeking to characterize tissue- or fluid-specific peptidomes. In this chapter, we demonstrate how to identify, rank, and compare predicted bioactive peptides and bioactivity distributions from extensive peptidomics datasets. To aid this task, we utilize MultiPep, a multi-label deep learning approach designed for classifying peptide bioactivities, to identify bioactive peptides. The predicted bioactivities are synergistically combined with protein information from the UniProt database, which assist in navigating through the jungle of putative therapeutic peptides and relevant peptide leads.",
keywords = "Deep learning, Neuropeptide, Peptide bioactivity, Peptide hormone, Peptide therapeutics, Peptidomics",
author = "Gr{\o}nning, {Alexander G.B.} and Camilla Sch{\'e}ele",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024.",
year = "2024",
doi = "10.1007/978-1-0716-3646-6_9",
language = "English",
isbn = "978-1-0716-3648-0",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
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editor = "Michael Schrader and Fricker, {Lloyd D.}",
booktitle = "Peptidomics",
address = "United States",

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RIS

TY - CHAP

T1 - Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma

AU - Grønning, Alexander G.B.

AU - Schéele, Camilla

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024.

PY - 2024

Y1 - 2024

N2 - Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mass spectrometry techniques for high-throughput peptide quantitation has paved the way for the identification and discovery of numerous known and novel peptides. Though much has been achieved, scientists are still facing difficulties when it comes to reducing the search space of the large mass spectrometry-generated peptidomics datasets and focusing on the subset of functionally relevant peptides. Moreover, there is currently no straightforward way to analytically compare the distributions of bioactive peptides in distinct biological samples, which may reveal much useful information when seeking to characterize tissue- or fluid-specific peptidomes. In this chapter, we demonstrate how to identify, rank, and compare predicted bioactive peptides and bioactivity distributions from extensive peptidomics datasets. To aid this task, we utilize MultiPep, a multi-label deep learning approach designed for classifying peptide bioactivities, to identify bioactive peptides. The predicted bioactivities are synergistically combined with protein information from the UniProt database, which assist in navigating through the jungle of putative therapeutic peptides and relevant peptide leads.

AB - Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mass spectrometry techniques for high-throughput peptide quantitation has paved the way for the identification and discovery of numerous known and novel peptides. Though much has been achieved, scientists are still facing difficulties when it comes to reducing the search space of the large mass spectrometry-generated peptidomics datasets and focusing on the subset of functionally relevant peptides. Moreover, there is currently no straightforward way to analytically compare the distributions of bioactive peptides in distinct biological samples, which may reveal much useful information when seeking to characterize tissue- or fluid-specific peptidomes. In this chapter, we demonstrate how to identify, rank, and compare predicted bioactive peptides and bioactivity distributions from extensive peptidomics datasets. To aid this task, we utilize MultiPep, a multi-label deep learning approach designed for classifying peptide bioactivities, to identify bioactive peptides. The predicted bioactivities are synergistically combined with protein information from the UniProt database, which assist in navigating through the jungle of putative therapeutic peptides and relevant peptide leads.

KW - Deep learning

KW - Neuropeptide

KW - Peptide bioactivity

KW - Peptide hormone

KW - Peptide therapeutics

KW - Peptidomics

U2 - 10.1007/978-1-0716-3646-6_9

DO - 10.1007/978-1-0716-3646-6_9

M3 - Book chapter

C2 - 38549014

AN - SCOPUS:85189279776

SN - 978-1-0716-3648-0

T3 - Methods in Molecular Biology

SP - 179

EP - 195

BT - Peptidomics

A2 - Schrader, Michael

A2 - Fricker, Lloyd D.

PB - Humana Press

ER -

ID: 389551340