Abstract
Extensive investigation and characterisation of nanoparticle-protein conjugates are imperative to assess potential nanoparticle-induced hazards for humans and the environment, predict adverse biological effects, and identify suitable nanoparticles for medical applications. Investigating the formation of the nanoparticle protein corona solely based on experimental analysis is currently very time-consuming and cost-intensive. Therefore, development of prediction tools based on in silico modelling is much-needed in order to provide viable alternative approaches and accelerate nanomaterial risk assessment at the early development stage. This work aimed to validate currently emerging in silico protein corona modelling tools with experimental results and to reveal the models’ potentials and limitations thereby contributing to the improvement of their predictive power. Comprehensive data and metadata sets of the obtained in vitro and in silico results were collected and annotated in the NanoCommons Knowledge Base to facilitate data Findability, Accessibility, Interoperability, and Reusability (FAIRness) in nanosafety assessment. In silico protein corona predictions (in silico modelling with UnitedAtom) and in vitro investigation of corona formation (binding and selectivity studies with eight different proteins, mixtures thereof, and an allergenic effector cell degranulation assay) on differently coated SiO2 nanoparticles were aligned and the results, in the first run, revealed substantial deviations. Therefore, we attempted to identify the potential and limitations in the modelling and provided recommendations to improve the model. Similar iteractive approaches, as described here, based on the verification versus rebuttal of data from in silico procedures by in vitro analyses, complemented by comprehensive data and metadata collection according to the FAIR principles, are expected to help optimise future prediction certainties and improve in silico modelling.
| Original language | English |
|---|---|
| Article number | 101561 |
| Number of pages | 15 |
| Journal | Nano Today |
| Volume | 46 |
| Early online date | 23 Jul 2022 |
| DOIs | |
| Publication status | Published - Oct 2022 |
Bibliographical note
Funding Information:This work was funded by the European Commission Horizon 2020 (H2020) research infrastructure for nanosafety project NanoCommons (grant agreement 731032), the H2020 Research and Innovation grant NanoSolveIT (grant agreement 814572), the International Doctoral School “Immunity in Cancer and Allergy - ICA” of the Austrian Science Fund, Austria (FWF, grant W01213), and the Allergy-Cancer-BioNano Research Center of the PLUS. We would like to acknowledge Milena Schenck for providing the PTMO-coated SiO2 NPs. The authors acknowledge the Transnational Access provision from NanoCommons to Robert Mills-Goodlet. IH performed the experimental work for Figs. 1–5 and Tables 2–4 and wrote the first draft of the manuscript. RMG performed the in silico modelling with UnitedAtom for Fig. 6, Figs. S1-S8 and Table 5. LJ contributed to the experimental work for Fig. 5. MG operated the TEM, DLS and ELS equipment. IR and VL provided and helped with use of the in silico tool UnitedAtom and performed predictions of corona abundances and kinetic MC calculations. DM provided access and support for working with the NanoCommons Knowledge Base. AD and IL provided funding and infrastructure for performing this study. MH supervised and conceived the study and provided infrastructure and funding for the study. All authors critically reviewed and edited the manuscript. The collection and usage of patient samples for the huRBL assay was approved by the local Ethics Committee of the Allergy Clinic Salzburg (No. 415-E/1398/4–2011).
Publisher Copyright:
© 2022 The Authors
Keywords
- Metadata completeness
- Multiscale modelling
- Nanoparticle
- Protein corona
ASJC Scopus subject areas
- Biotechnology
- Bioengineering
- Biomedical Engineering
- Materials Science(all)
- Pharmaceutical Science