GraphBNC Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins

GraphBNC : Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins

© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 47 vom: 24. Nov., Seite e2407046
1. Verfasser: Pihlajamäki, Antti (VerfasserIn)
Weitere Verfasser: Matus, María Francisca, Malola, Sami, Häkkinen, Hannu
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article graphs machine learning metal nanoclusters molecular dynamics nano–bio interface Gold 7440-57-5 Blood Proteins
Beschreibung
Zusammenfassung:© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano-bio interface. This work introduces and validates a strategy to predict atom-scale interactions between water-soluble gold nanoclusters (AuNCs) and a set of blood proteins (albumin, apolipoprotein, immunoglobulin, and fibrinogen). Graph theory and neural networks are utilized to predict the strengths of interactions in AuNC-protein complexes on a coarse-grained level, which are then optimized in Monte Carlo-based structure search and refined to atomic-scale structures. The training data is based on extensive molecular dynamics (MD) simulations of AuNC-protein complexes, and the validating MD simulations show the robustness of the predictions. This strategy can be generalized to any complexes of inorganic nanostructures and biomolecules provided that one generates enough data about the interactions, and the bioactive parts of the nanostructure can be coarse-grained rationally
Beschreibung:Date Completed 25.11.2024
Date Revised 27.11.2024
published: Print-Electronic
Citation Status MEDLINE
ISSN:1521-4095
DOI:10.1002/adma.202407046