Machine learning and molecular design of self-assembling -conjugated oligopeptides

Abstract

Self-assembling oligopeptides present a means to fabricate biocompatible supramolecular aggregates with engineered electronic and optical functionality. We conducted molecular dynamics simulations of self-assembling synthetic oligopeptides with Asp-X-X-X--X-X-X-Asp architectures. Dimerisation and trimerisation free energies were computed for a range of Asp-X-X-X amino acid sequences, and for perylenediimide (PDI) and naphthalenediimide (NDI) conjugated cores that mediate hydrophobic stacking and electron delocalisation within the self-assembled nanostructure. The larger PDI cores elevated oligomerisation free energies by a factor of 2-3 relative to NDI and also improved alignment of the oligopeptides within the stack. Training of a quantitative structure–property relationship (QSPR) model revealed key physicochemical determinants of the oligomerisation free energies and produced a predictive model for the oligomerisation thermodynamics. Oligopeptides with moderate dimerisation and trimerisation free energies of (-25) produced aggregates with the best in-register parallel stacking, and we used this criterion within our QSPR model to perform high-throughput virtual screening to identify promising candidates for the spontaneous assembly of ordered nanoaggregates. We identified a small number of oligopeptide candidates for direct testing in large scale molecular simulations, and predict a novel chemistry DAVG-PDI-GVAD previously unstudied by experiment or simulation to produce well-aligned nanoaggregates expected to possess good optical and electronic functionality.

Keywords:

• -conjugated oligopeptides
• molecular dynamics simulation
• supramolecular peptides
• self-assembly
• quantitative structure-property relationships

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This material is based upon work supported by the National Science Foundation [grant number DMR-1729011].