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Title: Thermodynamics, morphology, and kinetics of early-stage self-assembly of π-conjugated oligopeptides

Journal Article · · Molecular Simulation

Synthetic oligopeptides containing π-conjugated cores self-assemble novel materials with attractive electronic and photophysical properties. All-atom, explicit solvent molecular dynamics simulations of Asp-Phe-Ala-Gly-OPV3-Gly-Ala-Phe-Asp peptides were used to parameterize an implicit solvent model to simulate early-stage self-assembly. Under low-pH conditions, peptides assemble into β-sheet-like stacks with strongly favorable monomer association free energies of ΔF ≈ -25kBT. Aggregation at high-pH produces disordered aggregates destabilized by Coulombic repulsion between negatively charged Asp termini (ΔF ≈ -5kBT). In simulations of hundreds of monomers over 70 ns we observe the spontaneous formation of up to undecameric aggregates under low-pH conditions. Modeling assembly as a continuous-time Markov process, we infer transition rates between different aggregate sizes and microsecond relaxation times for early-stage assembly. Our data suggests a hierarchical model of assembly in which peptides coalesce into small clusters over tens of nanoseconds followed by structural ripening and diffusion limited aggregation on longer time scales. This work provides new molecular-level understanding of early-stage assembly, and a means to study the impact of peptide sequence and aromatic core chemistry upon the thermodynamics, assembly kinetics, and morphology of the supramolecular aggregates.

Research Organization:
Univ. of Illinois at Urbana-Champaign, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Contributing Organization:
Molecular simulations were partially conducted on University of Illinois Computational Science and Engineering Program parallel computing resources, which are supported by the University of Illinois.
Grant/Contract Number:
SC0004857
OSTI ID:
1357188
Alternate ID(s):
OSTI ID: 1357187
Journal Information:
Molecular Simulation, Vol. 42, Issue 12; ISSN 0892-7022
Publisher:
Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 28 works
Citation information provided by
Web of Science

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Cited By (2)

Machine learning and molecular design of self-assembling -conjugated oligopeptides journal April 2018
Nonlinear machine learning in simulations of soft and biological materials journal November 2017


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