Hybrid Computational-Experimental Data-Driven Design of Self-Assembling Pi-Conjugated Peptides

10 August 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Biocompatible molecules with electronic functionality provide a promising substrate for biocompatible electronic devices and electronic interfacing with biological systems. Synthetic oligopeptides composed of an aromatic pi-core flanked by oligopeptide wings are a class of molecules that can self-assemble in aqueous environments into supramolecular nanoaggregates with emergent optical and electronic activity. We present an integrated computational-experimental pipeline employing all-atom molecular dynamics simulations and experimental UV-visible spectroscopy within an active learning workflow using deep representational learning and Bayesian optimization to design pi-conjugated peptides programmed to self-assemble into elongated pseudo-1D nanoaggregtes with a high degree of H-type co-facial stacking of the pi-cores. We consider as our design space the 694,982 unique pi-conjugated peptides comprising a quaterthtiophene pi-core flanked by symmetric oligopeptide wings up to five amino acids in length. After sampling only 1181 molecules (~0.17% of the design space) by computation and 28 (~0.004%) by experiment, we identify and experimentally validate a diversity of previously unknown high-performing molecules and extract interpretable design rules linking peptide sequence to emergent supramolecular structure and properties.


UV-visible spectroscopy
machine learning
Bayesian optimization
Gaussian process regression
deep learning

Supplementary materials

SUPPORTING INFORMATION: Hybrid Computational-Experimental Data-Driven Design of Self-Assembling Pi-Conjugated Peptides
Supporting methods, peptide synthesis details, ESI spectra, HLPC traces

Supplementary weblinks


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