Real‐Time In‐Situ Monitoring of a Tunable Pentapeptide Gel–Crystal Transition

Guterman, Tom; Levin, Maayan; Kolusheva, Sofiya; Levy, Davide; Noor, Nadav; Roichman, Yael; Gazit, Ehud

doi:10.1002/ange.201907971

Cited by 10 publications

(6 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown by some models [ 11 ], even if the molecules are achiral it can assemble in a chiral way, if the molecules pack in a chiral way. We have shown that the tubes are metastable, and they transform into crystals after 28 h. Such transformations in organogels, have been observed for other gelators [ 25 , 26 ]. Given the shape of the aggregates, it is relevant to compare HUB-4 also with lipid compounds self-assembling in metastable nanotubes [ 27 ].…”

Section: Discussionsupporting

confidence: 75%

Structure of Nanotubes Self-Assembled from a Monoamide Organogelator

Zapién-Castillo

Díaz-Zavala

Melo-Banda

et al. 2020

IJMS

View full text Add to dashboard Cite

Some organic compounds are known to self-assemble into nanotubes in solutions, but the packing of the molecules into the walls of the tubes is known only in a very few cases. Herein, we study two compounds forming nanotubes in alkanes. They bear a secondary alkanamide chain linked to a benzoic acid propyl ester (HUB-3) or to a butyl ester (HUB-4). They gel alkanes for concentrations above 0.2 wt.%. The structures of these gels, studied by freeze fracture electron microscopy, exhibit nanotubes: for HUB-3 their external diameters are polydisperse with a mean value of 33.3 nm; for HUB-4, they are less disperse with a mean value of 25.6 nm. The structure of the gel was investigated by small- and wide-angle X-ray scattering. The evolution of the intensities show that the tubes are metastable and transit slowly toward crystals. The intensities of the tubes of HUB-4 feature up to six oscillations. The shape of the intensities proves the tubular structure of the aggregates, and gives a measurement of 20.6 nm for the outer diameters and 11.0 nm for the inner diameters. It also shows that the electron density in the wall of the tubes is heterogeneous and is well described by a model with three layers.

show abstract

Section: Discussionsupporting

confidence: 75%

Structure of Nanotubes Self-Assembled from a Monoamide Organogelator

Zapién-Castillo

Díaz-Zavala

Melo-Banda

et al. 2020

IJMS

View full text Add to dashboard Cite

show abstract

“…A set of simple guidelines were supplied (see Methods), and in response, a total of 29 pentapeptides were collected. Many literature examples of self-assembling pentapeptides include N-and C-terminii modification (acetylated or carbamidated, respectively) to facilitate assembly [43,44,45,46,47,48]. However, in this work the human-experts were directed to leave the pentapeptide terminii unmodified in alignment with the workflow adopted for the AI-expert.…”

Section: Screening Of Pentapeptidesmentioning

confidence: 99%

Machine learning overcomes human bias in the discovery of self-assembling peptides

Batra

Loeffler²,

Chan³

et al. 2022

Nat. Chem.

View full text Add to dashboard Cite

Peptide materials have a wide array of functions from tissue engineering, surface coatings to catalysis and sensing. This class of biopolymer is composed of a sequence, comprised of 20 naturally occurring amino acids whose arrangement dictate the peptide functionality. While it is highly desirable to tailor the amino acid sequence, a small increase in their sequence length leads to dramatic increase in the possible candidates (e.g., from tripeptide = 20 3 or 8,000 peptides to a pentapeptide = 20 5 or 3.2 M). Traditionally, peptide design is guided by the use of structural propensity tables, hydrophobicity scales, or other desired properties and typically yields <10 peptides per study, barely scraping the surface of the search space. These approaches, driven by human expertise and intuition, are not easily scalable and are riddled with human bias. Here, we introduce a machine learning workflow that combines Monte Carlo tree search and random forest, with molecular dynamics simulations to develop a fully autonomous computational search engine (named, AI-expert) to discover peptide sequences with high potential for self-assembly (as a representative target functionality). We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. Subsequent experiments on the proposed peptide sequences are performed to compare the predictability of the AI-expert with those of human experts. The AI performs on-par or better than human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.

show abstract

“…Interestingly, under identical solution conditions, Fmoc‐(3‐OH)‐ γ Phe self‐assembled into a transient hydrogel which was disrupted over time and finally formed crystalline aggregates ( Figure a). [ 52 ] Absorbance measurement at 405 nm demonstrated the self‐assembly kinetics. Initially, the absorbance decreased due to the formation of a semitransparent temporary hydrogel; however, after 15 min, the absorbance increased and returned to its initial value due to the collapse of the hydrogel and the precipitation of white clusters (Figure 3b).…”

Section: Resultsmentioning

confidence: 99%

Exploiting Minimalistic Backbone Engineered γ‐Phenylalanine for the Formation of Supramolecular Co‐Polymer

Misra

Tang

Chen

et al. 2022

Macromol. Rapid Commun.

View full text Add to dashboard Cite

Ordered supramolecular hydrogels assembled by modified aromatic amino acids often exhibit low mechanical rigidity. Aiming to stabilize the hydrogel and understand the impact of conformational freedom and hydrophobicity on the self‐assembly process, two building blocks based on 9‐fluorenyl‐methoxycarbonyl‐phenylalanine (Fmoc‐Phe) gelator which contain two extra methylene units in the backbone, generating Fmoc‐γPhe and Fmoc‐(3‐hydroxy)‐γPhe are designed. Fmoc‐γPhe spontaneously assembled in aqueous media forming a hydrogel with exceptional mechanical and thermal stability. Moreover, Fmoc‐(3‐hydroxy)‐γPhe, with an extra backbone hydroxyl group decreasing its hydrophobicity while maintaining some molecular flexibility, self‐assembled into a transient fibrillar hydrogel, that later formed microcrystalline aggregates through a phase transition. Molecular dynamics simulations and single crystal X‐ray analyses reveal the mechanism underlying the two residues' distinct self‐assembly behaviors. Finally, Fmoc‐γPhe and Fmoc‐(3‐OH)‐γPhe co‐assembly to form a supramolecular hydrogel with notable mechanical properties are demonstrated. It has been believed that the understanding of the structure‐assembly relationship will enable the design of new functional amino acid‐based hydrogels.

show abstract

Real‐Time In‐Situ Monitoring of a Tunable Pentapeptide Gel–Crystal Transition

Cited by 10 publications

References 58 publications

Structure of Nanotubes Self-Assembled from a Monoamide Organogelator

Structure of Nanotubes Self-Assembled from a Monoamide Organogelator

Machine learning overcomes human bias in the discovery of self-assembling peptides

Exploiting Minimalistic Backbone Engineered γ‐Phenylalanine for the Formation of Supramolecular Co‐Polymer

Contact Info

Product

Resources

About