Deorphanizing Peptides Using Structure Prediction

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Deorphanizing Peptides Using Structure Prediction. / Teufel, Felix; Refsgaard, Jan C.; Kasimova, Marina A.; Deibler, Kristine; Madsen, Christian T.; Stahlhut, Carsten; Grønborg, Mads; Winther, Ole; Madsen, Dennis.

In: Journal of Chemical Information and Modeling, Vol. 63, No. 9, 2023.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Teufel, F, Refsgaard, JC, Kasimova, MA, Deibler, K, Madsen, CT, Stahlhut, C, Grønborg, M, Winther, O & Madsen, D 2023, 'Deorphanizing Peptides Using Structure Prediction', Journal of Chemical Information and Modeling, vol. 63, no. 9. https://doi.org/10.1021/acs.jcim.3c00378

APA

Teufel, F., Refsgaard, J. C., Kasimova, M. A., Deibler, K., Madsen, C. T., Stahlhut, C., Grønborg, M., Winther, O., & Madsen, D. (2023). Deorphanizing Peptides Using Structure Prediction. Journal of Chemical Information and Modeling, 63(9). https://doi.org/10.1021/acs.jcim.3c00378

Vancouver

Teufel F, Refsgaard JC, Kasimova MA, Deibler K, Madsen CT, Stahlhut C et al. Deorphanizing Peptides Using Structure Prediction. Journal of Chemical Information and Modeling. 2023;63(9). https://doi.org/10.1021/acs.jcim.3c00378

Author

Teufel, Felix ; Refsgaard, Jan C. ; Kasimova, Marina A. ; Deibler, Kristine ; Madsen, Christian T. ; Stahlhut, Carsten ; Grønborg, Mads ; Winther, Ole ; Madsen, Dennis. / Deorphanizing Peptides Using Structure Prediction. In: Journal of Chemical Information and Modeling. 2023 ; Vol. 63, No. 9.

Bibtex

@article{29521e81515f4a00882f65aa734d369c,
title = "Deorphanizing Peptides Using Structure Prediction",
abstract = "Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold{\textquoteright}s confidence metrics have strong performance for prioritizing true peptide-receptor interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.",
author = "Felix Teufel and Refsgaard, {Jan C.} and Kasimova, {Marina A.} and Kristine Deibler and Madsen, {Christian T.} and Carsten Stahlhut and Mads Gr{\o}nborg and Ole Winther and Dennis Madsen",
note = "Publisher Copyright: {\textcopyright} 2023 American Chemical Society.",
year = "2023",
doi = "10.1021/acs.jcim.3c00378",
language = "English",
volume = "63",
journal = "Journal of Chemical Information and Modeling",
issn = "1549-9596",
publisher = "American Chemical Society",
number = "9",

}

RIS

TY - JOUR

T1 - Deorphanizing Peptides Using Structure Prediction

AU - Teufel, Felix

AU - Refsgaard, Jan C.

AU - Kasimova, Marina A.

AU - Deibler, Kristine

AU - Madsen, Christian T.

AU - Stahlhut, Carsten

AU - Grønborg, Mads

AU - Winther, Ole

AU - Madsen, Dennis

N1 - Publisher Copyright: © 2023 American Chemical Society.

PY - 2023

Y1 - 2023

N2 - Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold’s confidence metrics have strong performance for prioritizing true peptide-receptor interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.

AB - Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold’s confidence metrics have strong performance for prioritizing true peptide-receptor interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.

U2 - 10.1021/acs.jcim.3c00378

DO - 10.1021/acs.jcim.3c00378

M3 - Journal article

C2 - 37092865

AN - SCOPUS:85156247565

VL - 63

JO - Journal of Chemical Information and Modeling

JF - Journal of Chemical Information and Modeling

SN - 1549-9596

IS - 9

ER -

ID: 346593473