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

Shmilovich, Kirill ; Mansbach, Rachael A ; Sidky, Hythem ; Dunne, Olivia E ; Panda, Sayak Subhra ; Tovar, John D ; Ferguson, Andrew L

The journal of physical chemistry. B, 2020-05, Vol.124 (19), p.3873-3891 [Periódico revisado por pares]

United States: American Chemical Society

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  • Título:
    Discovery of Self-Assembling π‑Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation
  • Autor: Shmilovich, Kirill ; Mansbach, Rachael A ; Sidky, Hythem ; Dunne, Olivia E ; Panda, Sayak Subhra ; Tovar, John D ; Ferguson, Andrew L
  • Assuntos: Bayes Theorem ; embedding ; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY ; Molecular Dynamics Simulation ; molecular modeling ; molecules ; monomers ; Oligopeptides ; Peptides ; peptides and proteins
  • É parte de: The journal of physical chemistry. B, 2020-05, Vol.124 (19), p.3873-3891
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    LA-UR-19-27326
    89233218CNA000001; DMR-1841807; DGE-1746045; DMR-1828629; DMS-1440415
    National Science Foundation (NSF)
    USDOE Laboratory Directed Research and Development (LDRD) Program
  • Descrição: 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.
  • Editor: United States: American Chemical Society
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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