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.近期已有研究报道了肽基与蛋白基纤维及丝材中的长程电荷输运现象，使得该类材料成为连接生物系统与电子器件的极具应用前景的电荷传导界面。然而，在生物分子构筑基元的复杂分子环境中，究竟哪些化学与结构动态特征能够支撑电子传导，目前仍不明确。本研究针对由反平行卷曲螺旋六聚体（antiparallel coiled coil hexamer, ACC-Hex）作为构筑基元的肽基纤维材料，探究有限温度涨落对其电子结构的影响，以及该影响与材料导电性之间的关联。本研究结合全原子经典分子动力学（All-atom classical molecular dynamics, MD）、第一性原理密度泛函理论（first-principles density functional theory, DFT）与可解释机器学习（interpretable machine learning, ML），旨在阐明ACC-Hex的肽二聚体亚基的物理结构与电子结构之间的关联。针对ACC肽二聚体的1101组独特MD "snapshots"，杂化DFT计算结果显示，不同快照间的带隙附近轨道能量存在显著差异，且最高占据分子轨道（HOMO）附近的近简并态数量有所增加，这预示着材料导电性的提升。随后，研究借助可解释机器学习，探究哪些核构象会增加近简并态的数量。研究发现，正如预期那般，苯丙氨酸残基间距离与取向的分子构象描述符，与HOMO附近态密度的提升高度相关。而意外的是，研究还发现，紧密卷曲的肽主链描述符，以及表征肽二聚体周围静电环境变化的描述符，同样对预测HOMO附近的空穴可及态数量具有重要意义。本研究表明，可解释机器学习可作为有效工具，用于解析大规模从头算（ab initio）模拟中呈现的复杂趋势。