Assessing the Utility of Infrared Spectroscopy as a Structural Diagnostic Tool for β-Sheets in Self-Assembling Aromatic Peptide Amphiphiles

Fleming, Scott W.; Frederix, Pim W. J. M.; Sasselli, Ivan R.; Hunt, Neil T.; Ulijn, Rein V.; Tuttle, Tell

doi:10.1021/la400994v

Cited by 134 publications

(191 citation statements)

References 51 publications

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“…1690 cm ¹1 may be attributed to an antiparallel β-sheet orientation; 19 however, the assignment of strand orientation based on this absorption has recently been shown to be unreliable for short peptides. 20 These bands were not observed in TFE, which is consistent with the monomolecular state of A in this solvent. The weak absorption at ca.…”

supporting

confidence: 71%

1D Self-assembly of Terthiophene (3T)–Naphthalenediimide (NDI) Dyad

Kim¹,

Parquette²

2014

Chemistry Letters

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This work describes a strategy to create terthiophene (3T) naphthalenediimide (NDI) nanostructures via the β-sheet selfassembly of a functionalized dipeptide. The dipeptide assembles into 1D nanofibers in organic solvents and forms a self-supporting organogel at low concentrations in CH 2 Cl 2 . The nanostructures engage in extensive intermolecular ππ stacking interactions.An important challenge in organic materials is to be able to program small and molecular building blocks to precisely assemble into multiscale and multifunctional superstructures that capture, transport, and process various forms of energy.1 Predictable strategies to create segregated and bicontinuous arrays of interpenetrating donor (D) and acceptor (A) chromophores are needed to optimize charge separation and transport within optoelectronic materials.2 Thus, the 1D self-assembly of DA molecules is emerging as a potential strategy to tune the properties of thin film materials and devices. 3 The cofacial π-stacking interactions that often comprise such nanostructures are thought to provide pathways for charge transport and energy migration and are expected to produce maximal charge carrier mobilities. 4 Although considerable progress has been realized over the last several years, precise control of the interactions of chromophores within self-assembled nanostructures remains difficult.Hydrogen-bonddriven self-assembly offers a directional strategy to predictably organize D and A chromophores over long distances and has recently led to new functional electronic materials.5 Peptide-based β-sheet aggregation provides a modular method to construct well-defined nanostructures that are stabilized by intermolecular hydrogen-bonding interactions.6 Notably, we have developed strategies based on peptide β-sheet formation and amphiphilic self-assembly in water to create a variety of 1D nanostructures ranging from nanotubes 7 to 1D fibers and ribbons 8 from very simple building blocks. This design strategy is based on prior studies demonstrating the selfassembly of a series of dilysine peptides functionalized at the ε-amino position with a single naphthalenediimide (NDI) chromophore in water. For example, the β-sheet self-assembly of a series of dilysine peptides functionalized at the ε-amino position with a single NDI chromophore produced nanofibers and nanoribbons in water.8d These dipeptide sequences reflect an alternating polar and nonpolar amphipathic pattern, which is commonly observed in natural and isolated synthetic, antiparallel β-sheet structures.9 The self-assembly strategy further capitalizes on an aromatic chromophore to drive aggregation via hydrophobic effects and ππ stacking interactions in aqueous media. Time-resolved fluorescence anisotropy experiments have shown that efficient energy migration occurs within the resultant π-aggregated NDI assemblies. 7c,10 This strategy was versatile with respect to the N-terminal substituent. For example, an Fmoc chromophore appended to this position of the dipeptide affords a hydrogel comprising ...

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supporting

confidence: 71%

1D Self-assembly of Terthiophene (3T)–Naphthalenediimide (NDI) Dyad

Kim¹,

Parquette²

2014

Chemistry Letters

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“…Particularly if the energetic difference between several possible conformations is within the limits of the model applied. [19] To overcome this problem, there has been an increasing shift towards the use of QM methods to predict properties that can be directly compared to the experimental observables. Experimentally, several techniques can be used to provide information about the types of non-covalent interactions that are involved in the self-assembled system.…”

Section: Preferred Packing Posesmentioning

confidence: 99%

“…Examples of this approach have been successfully employed for evaluating absorption spectra with TD-DFT, [20] calculating NMR chemical shifts to determine the conformation of a peptide, [16] and calculating Fourier transform infrared (FTIR) spectra to determine the nature of the H-bonding interactions. [19,21] An example case study of this approach is also discussed below (Section 2.2).…”

Section: Preferred Packing Posesmentioning

confidence: 99%

“…In a combined experimental and computational approach, Fleming, et al, [19] investigated the validity of assigning secondary structure motifs, parallel or anti-parallel β-sheet arrangements using FTIR. Within the field of protein structure determination, the amide I region of the IR spectra is known to be sensitive to changes in the H-bonding patterns of β-sheets.…”

Section: Structures Through Spectramentioning

confidence: 99%

“…Therefore, in order to investigate the origin of the split amide I peaks in Fmoc-dipeptides a number of computational and experimental studies were performed. [19] Initial calculations, at the B97-D/def2-SVP level of theory, [23] of the vibrational modes of the Fmocdialanine (Fmoc-AA) monomer unit were carried out to determine the IR spectra of the individual building block. This allowed the determination of the spectra in the absence of H-bonding that would be present in a self-assembled structure.…”

Section: Structures Through Spectramentioning

confidence: 99%

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Computational Approaches to Understanding the Self‐assembly of Peptide‐based Nanostructures

Tuttle

2015

Israel Journal of Chemistry

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This version is available at https://strathprints.strath.ac.uk/54165/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Computational Approaches to Understanding the SelfAssembly of Peptide-Based NanostructuresTell Tuttle* [a] Abstract: The interest in the self-assembly of peptide-based systems has grown significantly over the past 10 Ð 15 years as more and more applications are shown to benefit from the useful properties of the amino acid based monomers. With the desire to apply the principals of self-assembly to systems within new application areas there has been an increasing emphasis in understanding the governing forces involved in the self-assembly process and using this understanding to predict the behavior of, and design, new materials.To this end, computational approaches have played an increasingly important role over the past decade in helping to decode how small changes in the primary structure can lead to significantly different nanostructures with new function. In this review a brief survey of the different computational approaches employed in this quest for understanding are provided, along with representative examples of the types of questions that can be answered with each of the different approaches.

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