An integrative structural biology approach to identify the
binding mode of a nanobody towards the pea ascorbate peroxidase

J 2025

An integrative structural biology approach to identify the binding mode of a nanobody towards the pea ascorbate peroxidase

D'ERCOLE, Claudia; Marco ORLANDO; Kristina Elersic FILIPIC a De Marco ARIO

Základní údaje

Originální název

An integrative structural biology approach to identify the binding mode of a nanobody towards the pea ascorbate peroxidase

Autoři

D'ERCOLE, Claudia; Marco ORLANDO; Kristina Elersic FILIPIC a De Marco ARIO

Vydání

Computational and Structural Biotechnology Journal, AMSTERDAM, Elsevier, 2025, 2001-0370

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10403 Physical chemistry

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.100 v roce 2024

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:90242/25:00144092

Organizační jednotka

CIISB III

Klíčová slova anglicky

Nanobodies; Ascorbate peroxidase; Hydrogen-deuterium exchange coupled to mass; spectrometry; Integrative structural biology; Protein cross-linking

Štítky

ne MU, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 9. 4. 2026 10:53, Mgr. Eva Dubská

Anotace

V originále

The optimization of diagnostic devices such as biosensors often requires understanding the molecular details of the interaction between capture and target biomolecules. This can be experimentally obtained by cryo-electron microscopy, the preferred method for the analysis of large protein complexes, while NMR and x-ray crystallography are effective for determining the structure of complexes formed by relatively small molecules. Nevertheless, all these approaches are demanding in terms of time and resources and, therefore, we explored the possibility to reduce the experimental load by compensating with in silico modelling. Here we demonstrate that an accurate prediction of the binding mode between a nanobody and its target pea ascorbate peroxidase, an oxidative stress biomarker in plants, can be obtained by combining cross-linking mass spectrometry, hydrogendeuterium exchange coupled to mass spectrometry and in silico modelling. Such model allowed to precisely design negative mutants that confirmed its accuracy. In conclusion, this study shows that an unconstrained prediction based on deep learning models is still not sufficiently reliable for new targets and difficult-to-model biomolecule classes such as nanobodies, while an experimental-guided approach can provide valuable structural information for lead optimization campaigns of such reagents.

Návaznosti

90242, velká výzkumná infrastruktura

Název: CIISB III

Citovat

D'ERCOLE, Claudia; Marco ORLANDO; Kristina Elersic FILIPIC a De Marco ARIO. An integrative structural biology approach to identify the binding mode of a nanobody towards the pea ascorbate peroxidase. Computational and Structural Biotechnology Journal. AMSTERDAM: Elsevier, 2025, roč. 27, Nov, s. 5078-5086. ISSN 2001-0370. Dostupné z: https://doi.org/10.1016/j.csbj.2025.11.024.

@article{2565589,
   author = {D'ercole, Claudia and Orlando, Marco and Filipic, Kristina Elersic and Ario, De Marco},
   article_location = {AMSTERDAM},
   article_number = {Nov},
   doi = {https://doi.org/10.1016/j.csbj.2025.11.024},
   keywords = {Nanobodies; Ascorbate peroxidase; Hydrogen-deuterium exchange coupled to mass; spectrometry; Integrative structural biology; Protein cross-linking},
   language = {eng},
   issn = {2001-0370},
   journal = {Computational and Structural Biotechnology Journal},
   title = {An integrative structural biology approach to identify the binding mode of a nanobody towards the pea ascorbate peroxidase},
   url = {https://spj.science.org/doi/10.1016/j.csbj.2025.11.024},
   volume = {27},
   year = {2025}
}

TY  - JOUR
ID  - 2565589
AU  - D'ercole, Claudia - Orlando, Marco - Filipic, Kristina Elersic - Ario, De Marco
PY  - 2025
TI  - An integrative structural biology approach to identify the binding mode of a nanobody towards the pea ascorbate peroxidase
JF  - Computational and Structural Biotechnology Journal
VL  - 27
IS  - Nov
SP  - 5078-5086
EP  - 5078-5086
PB  - Elsevier
SN  - 20010370
KW  - Nanobodies
KW  - Ascorbate peroxidase
KW  - Hydrogen-deuterium exchange coupled to mass
KW  - spectrometry
KW  - Integrative structural biology
KW  - Protein cross-linking
UR  - https://spj.science.org/doi/10.1016/j.csbj.2025.11.024
N2  - The optimization of diagnostic devices such as biosensors often requires understanding the molecular details of the interaction between capture and target biomolecules. This can be experimentally obtained by cryo-electron microscopy, the preferred method for the analysis of large protein complexes, while NMR and x-ray crystallography are effective for determining the structure of complexes formed by relatively small molecules. Nevertheless, all these approaches are demanding in terms of time and resources and, therefore, we explored the possibility to reduce the experimental load by compensating with in silico modelling. Here we demonstrate that an accurate prediction of the binding mode between a nanobody and its target pea ascorbate peroxidase, an oxidative stress biomarker in plants, can be obtained by combining cross-linking mass spectrometry, hydrogendeuterium exchange coupled to mass spectrometry and in silico modelling. Such model allowed to precisely design negative mutants that confirmed its accuracy. In conclusion, this study shows that an unconstrained prediction based on deep learning models is still not sufficiently reliable for new targets and difficult-to-model biomolecule classes such as nanobodies, while an experimental-guided approach can provide valuable structural information for lead optimization campaigns of such reagents.
ER  -

D'ERCOLE, Claudia; Marco ORLANDO; Kristina Elersic FILIPIC a De Marco ARIO. An integrative structural biology approach to identify the binding mode of a nanobody towards the pea ascorbate peroxidase. \textit{Computational and Structural Biotechnology Journal}. AMSTERDAM: Elsevier, 2025, roč.~27, Nov, s.~5078-5086. ISSN~2001-0370. Dostupné z: https://doi.org/10.1016/j.csbj.2025.11.024.

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