Dynamic Electronic Structure Fluctuations in the De Novo Peptide ACC-Dimer Revealed by First-Principles Theory and...

Area of Science:

• Biomaterials science
• Computational chemistry
• Molecular biophysics

Background:

• Peptide and protein fibers show potential for charge transport, bridging biological and electronic systems.
• Understanding the molecular features governing conductivity in these complex systems is crucial.

Purpose of the Study:

• Investigate the impact of temperature fluctuations on the electronic structure and conductivity of peptide-based materials.
• Identify specific structural and dynamic features that support electronic conductivity in antiparallel coiled coil hexamer (ACC-Hex) fibers.

Main Methods:

• Combined all-atom classical molecular dynamics (MD) and first-principles density functional theory (DFT) simulations.
• Utilized interpretable machine learning (ML) to analyze the relationship between physical and electronic structure.
• Analyzed 1101 unique snapshots of the ACC peptide dimer subunit.

Main Results:

• DFT calculations revealed significant variations in near-gap orbital energies with temperature fluctuations.
• An increased number of nearly degenerate states near the highest occupied molecular orbital (HOMO) were predicted, suggesting enhanced conductivity.
• Interpretable ML identified interphenylalanine distance/orientation, peptide backbone coiling, and electrostatic environment changes as key predictors of conductivity.

Conclusions:

• Finite temperature fluctuations play a significant role in modulating the electronic properties of peptide-based conductive materials.
• Interpretable ML is a powerful tool for deciphering complex structure-property relationships in large-scale computational simulations.
• Findings advance the design of peptide-based interfaces for bioelectronic applications.