Computational modelling of supramolecular metallopeptide assemblies.

Area of Science:

• Supramolecular chemistry
• Computational biophysics
• Metallopeptide self-assemblies

Background:

• Interactions between metallic compounds/ions and peptide self-assemblies are crucial but structurally challenging to study experimentally.
• Understanding these interactions is vital for both natural systems (e.g., neurodegenerative diseases) and de novo designed systems (e.g., catalysts).

Purpose of the Study:

• To present general multiscale computational strategies for studying metallopeptide assemblies.
• To apply these strategies to peptides implicated in neurodegenerative diseases and catalytic de novo fibrillar systems.

Main Methods:

• Development and application of multiscale computational strategies.
• Includes: metal binding site prediction, metallic moiety docking, classical and accelerated molecular dynamics, and QM/MM calculations.

Main Results:

• Successful application of the multiscale computational protocol to model metallopeptide systems.
• Provides structural insights into metallopeptide assemblies relevant to disease and catalysis.

Conclusions:

• Multiscale computational chemistry offers a powerful complementary approach to experimental methods for studying metallopeptide assemblies.
• The presented strategies can be adapted for diverse metallopeptide systems, highlighting both successes and areas for future development.