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

Recent studies have reported long-range charge transport
in peptide-
and protein-based fibers and wires, rendering this class of materials
as promising charge-conducting interfaces between biological systems
and electronic devices. In the complex molecular environment of biomolecular
building blocks, however, it is unclear which chemical and structural
dynamic features support electronic conductivity. Here, we investigate
the role of finite temperature fluctuations on the electronic structure
and its implications for conductivity in a peptide-based fiber material
composed of an antiparallel coiled coil hexamer, ACC-Hex, building
block. All-atom classical molecular dynamics (MD) and first-principles
density functional theory (DFT) are combined with interpretable machine
learning (ML) to understand the relationship between physical and
electronic structure of the peptide dimer subunit of ACC-Hex. For
1101 unique MD “snapshots” of the ACC peptide dimer,
hybrid DFT calculations predict a significant variation of near-gap
orbital energies among snapshots, with an increase in the predicted
number of nearly degenerate states near the highest occupied molecular
orbital (HOMO), which suggests improved conductivity. Interpretable
ML is then used to investigate which nuclear conformations increase
the number of nearly degenerate states. We find that molecular conformation
descriptors of interphenylalanine distance and orientation are, as
expected, highly correlated with increased state density near the
HOMO. Unexpectedly, we also find that descriptors of tightly coiled
peptide backbones, as well as those describing the change in the electrostatic
environment around the peptide dimer, are important for predicting
the number of hole-accessible states near the HOMO. Our study illustrates
the utility of interpretable ML as a tool for understanding complex
trends in large-scale ab initio simulations.