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Articles

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

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Pages 930-945 | Received 22 Feb 2018, Accepted 21 Apr 2018, Published online: 09 May 2018
 

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.

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].

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