Peptide adsorption on silica surfaces: Simulation and experimental insights

Area of Science:

• Biomaterials Science
• Computational Chemistry
• Surface Chemistry

Background:

• Understanding protein-surface interactions is vital for applications like medical devices, drug delivery, and biotechnology.
• Silica surfaces are common in biomedical and biotechnological contexts, necessitating research into their interactions with biomolecules.
• Predicting these interactions computationally can accelerate the development of new materials and processes.

Purpose of the Study:

• To apply the Effective Implicit Surface Model (EISM) for high-speed computational screening of peptide interactions with silica surfaces.
• To model the binding affinity of small peptides to silica surfaces using EISM.
• To validate the EISM model's predictive capabilities through experimental data.

Main Methods:

• Utilized the Effective Implicit Surface Model (EISM) for computational screening of peptide-silica interactions.
• Performed high-speed computational modeling to assess binding affinities.
• Complemented simulations with experimental data obtained from microscale thermophoresis and infrared spectroscopy of peptides interacting with silica nanoparticles.

Main Results:

• The EISM model successfully predicted peptide interactions with silica surfaces.
• Computational results showed excellent agreement with experimental data, validating the EISM.
• Screened 57 peptides, identifying those with favorable adsorption properties for silica surfaces.

Conclusions:

• The EISM is a reliable tool for predicting peptide-surface interactions, particularly with silica.
• The findings provide guidance for future experimental and theoretical studies in peptide-surface interactions.
• The EISM platform offers broad applicability for various surfaces and biomolecules beyond silica and peptides.