Discovery of Self-Assembling π‑Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

Electronically
active organic molecules have demonstrated great
promise as novel soft materials for energy harvesting and transport.
Self-assembled nanoaggregates formed from π-conjugated oligopeptides
composed of an aromatic core flanked by oligopeptide wings offer emergent
optoelectronic properties within a water-soluble and biocompatible
substrate. Nanoaggregate properties can be controlled by tuning core
chemistry and peptide composition, but the sequence–structure–function
relations remain poorly characterized. In this work, we employ coarse-grained
molecular dynamics simulations within an active learning protocol
employing deep representational learning and Bayesian optimization
to efficiently identify molecules capable of assembling pseudo-1D
nanoaggregates with good stacking of the electronically active π-cores.
We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an
Asp residue and OPV3 is an oligophenylenevinylene oligomer (1,4-distyrylbenzene),
to identify the top performing XXX tripeptides within all 203 = 8000 possible sequences. By direct simulation of only 2.3% of
this space, we identify molecules predicted to exhibit superior assembly
relative to those reported in prior work. Spectral clustering of the
top candidates reveals new design rules governing assembly. This work
establishes new understanding of DXXX-OPV3-XXXD assembly, identifies
promising new candidates for experimental testing, and presents a
computational design platform that can be generically extended to
other peptide-based and peptide-like systems.