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REVIEW 4 major objections 6 minor 61 references

High-throughput Screening of the Mechanical Properties of Peptide Assemblies

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Coarse-grained molecular dynamics plus machine learning predicts the relative Young's modulus of self-assembled peptides well enough to screen large sequence libraries without synthesis.

desk verdict First exhaustive Young's modulus screen for di/tripeptides with a useful dataset, but the pentapeptide validation is overclaimed and the forced beta-sheet CG model undercuts the relative-modulus claim for diverse sequences. read the letter →

arxiv 2505.08850 v1 pith:RO3Q43R3 submitted 2025-05-13 q-bio.BM cond-mat.mtrl-sci

Sarah K. Yorke , Zhenze Yang , Aviad Levin , Alice Ray , Jeremy Owusu Boamah , Tuomas P. J. Knowles , Markus J. Buehler This is my paper · ORCID
classification q-bio.BMcond-mat.mtrl-sci
keywords peptideself-assemblyYoung'smoduluscoarse-grainedmoleculardynamicsmachinelearningscreeningatomicforcemicroscopysequence-propertyrelationshipspentapeptidepredictionaggregationpropensity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the mechanical stiffness of a self-assembled short peptide can be predicted from its amino-acid sequence quickly enough to screen huge sequence libraries. It combines coarse-grained molecular-dynamics simulations with atomic-force-microscopy measurements and machine learning, computing Young's moduli for all self-assembling di- and tripeptides and for a few thousand pentapeptides. The central validation claim is that the simulation-derived modulus hierarchy matches the experimentally measured relative stiffness for the peptides tested, even though absolute simulation values run higher than AFM values. If correct, this turns stiffness into a screenable sequence-design property, which matters because synthesizing and testing even a small fraction of the millions of possible pentapeptides is impractical.

What carries the argument

The central mechanism is a hydrostatic tension test on a coarse-grained self-assembled fibril, combined with an energy-balance formula that separates the pressure carried by the peptide assembly from the pressure carried by water. The Young's modulus is extracted as $E_s = 3(P_{\mathrm{tot}} - P_w^0 r_w)/(\lambda(1-r_w))$, where $P_{\mathrm{tot}}$ is the total pressure under strain, $P_w^0$ is the pressure from a pure-water control, $r_w$ is the water volume fraction, and $\lambda$ is the applied elongation. This quantity, computed from a single strained simulation box, is what allows thousands of sequences to be tested automatically. The workflow is completed by an aggregation-propensity screen that selects which sequences assemble and by a one-hot encoded Gaussian-process regressor that extrapolates modulus predictions to unmeasured pentapeptides.

What would settle it

Measure AFM nanomechanical maps for twenty or more pentapeptides spanning the predicted top and bottom modulus lists and compare the rank order to the simulation and machine-learning predictions; a low rank correlation would refute the screening claim, as would a demonstration that changing the forced secondary-structure flag from beta-sheet to another motif changes the predicted ordering of tested peptides.

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Extended reading notes

Core claim

The central claim is that coarse-grained molecular-dynamics simulations produce Young's moduli for short peptide assemblies that reliably rank the materials by relative stiffness, even though the absolute values are systematically higher than atomic-force-microscopy measurements. The paper computes modulus values for 27 aggregating dipeptides and 124 tripeptides via hydrostatic tension tests on simulated self-assembled fibrils, validates the predicted hierarchy against AFM nanomechanical mapping for nine selected peptides, then uses a Gaussian-process regression model trained on 2,990 simulated pentapeptides to predict Young's moduli for more than 25,000 self-assembling pentapeptide sequences. The author's own summary of the finding is that the modulus values calculated using MD for di- and tripeptides are an accurate indicator for the relative modulus of the materials.

Load-bearing premise

The load-bearing premise is that every peptide can be represented in the coarse-grained model as an extended beta-sheet assembly; if the true assembled morphology or deformation mechanism differs for some sequences, the computed relative moduli could be biased.

Editorial extensions

If this is right

  • Exhaustive modulus tables for 27 dipeptides and 124 tripeptides become available as design resources for stiff peptide materials.
  • Relative stiffness, not just self-assembly propensity, can be used as a sequence-design criterion.
  • A Gaussian-process model trained on 2,990 simulated pentapeptides lets the modulus of tens of thousands of untested sequences be ranked in silico.
  • Amino-acid trends, such as aromatic and hydrophobic residues favoring assembly, charged residues contributing to modulus, and proline at the central pentapeptide position favoring assembly, provide design heuristics beyond the specific peptides tested.
  • The accuracy-versus-cost hierarchy of machine learning, simulation, and experiment gives a practical route to screen longer peptide libraries without exhaustive synthesis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the relative-ranking claim holds, the identical pipeline could screen chemically modified or non-canonical peptides, where experimental libraries are even more expensive to build.
  • The paper's own data leave open whether the forced beta-sheet flag distorts rankings for assemblies that prefer spherical, helical, or amorphous morphologies; testing that would require simulations without the fixed secondary-structure constraint.
  • A natural next step, not taken here, is to feed AFM-derived moduli back into the machine-learning training set, which could convert the validated relative rankings into approximate absolute design targets.
  • Synthesizing and measuring the top-five predicted pentapeptides would directly test whether the machine-learning extrapolation beyond the 2,990 simulated sequences survives contact with experiment.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper proposes a high-throughput workflow for predicting the Young's modulus of self-assembled short peptides. The authors use coarse-grained MARTINI molecular dynamics with a hydrostatic tension test to compute moduli for all di- and tripeptides with aggregation propensity above 2, validate the relative ranking against AFM nanomechanical measurements on seven assembled peptides, and then train a Gaussian process regression model on 2,990 CG-MD pentapeptide simulations to screen roughly 25,000 self-assembling pentapeptide sequences. The central claim is that the simulation-derived moduli are an accurate indicator of the relative modulus of peptide materials, which would justify using the workflow for sequence screening.

Significance. If the relative-modulus claim holds, this is a valuable contribution to peptide materials discovery: it provides exhaustive di/tripeptide modulus tables, a public code and data repository, and a machine-learning model that extends mechanical screening to the pentapeptide sequence space. The study is not circular: the ML models are evaluated on held-out test sets, and the modulus formula does not fit parameters to the target experimental moduli. The main strength is the transferable ranking rather than absolute modulus prediction, a distinction the authors themselves draw. However, the breadth of the screening claim currently rests on a narrow validation set and on a strong structural assumption about the assembled state, so the result is promising but not yet established.

major comments (4)
  1. [Methods: CG MD simulations; Experimental investigation] The screening claim rests on the assumption that every peptide can be modeled as an extended beta-sheet assembly: the Methods state that 'the input flag for secondary structures is set to "E" (extended beta secondary structure) for all amino acids' in martinize.py. This forces a beta-like backbone before the hydrostatic tension test, so the computed modulus reflects an assumed morphology rather than the morphology each sequence actually adopts. The experimental validation is too narrow to detect failure of this assumption: only seven di/tripeptides assembled in the AFM experiments (FF, LF, PF, WW, FFF, CWF, WLL), the authors note that all AP>2 dipeptides contain at least one of F or W, and the pentapeptide screen applies the same forced-E pipeline to a much more compositionally diverse set including charged and histidine-rich candidates. I ask for a concrete test of the assumption, for example unbiased assembly simulations without the forced 'E' flag for a diverse subset of pentapeptides, or experimental modulus measurements on at least a few non-aromatic or charged candidates, so that the relative-modulus ranking is not an artifact of the imposed secondary structure.
  2. [Methods: CG MD simulations; Results: Mechanical properties of di- and tri-peptides] All moduli in Tables S2 and S3 and Figure 4 appear to come from a single CG MD run per sequence: the Methods describe one random placement, one equilibration, and one deformation trajectory, and no error bars or replicate statistics are reported for the simulation data. Since the paper's central claim is a relative ranking of moduli, the absence of replicate runs leaves ranking stability unknown; differences as small as about 0.2 GPa between adjacent dipeptides in Table S2 may be within run-to-run noise. I request at least 3-5 independent replicas for a representative subset spanning the modulus range, with means and standard deviations, and an assessment of whether the reported ranking is preserved.
  3. [Experimental investigation of selected di- and tri-peptides; Figure 3e] The experimental validation consists of seven assembled peptides with very large standard deviations (for example, CWF 10.3 +/- 8.09 GPa and WW 7.94 +/- 5.78 GPa), and the claim that the experimental ordering is 'consistent with' the prediction is made visually without a rank-correlation statistic or uncertainty propagation. With error bars of this size, the ordering of CWF, WW, and FFF may not be statistically significant, so the central validation of the relative-modulus claim is not yet quantitative. I ask for a rank-correlation measure, such as Kendall's tau with a confidence interval, between predicted and measured moduli, and for reporting of the number of fibrils, locations, and force curves underlying each mean.
  4. [Modulus calculation of polypeptide self-assemblies, Eqs. (2)-(6)] Equation (2) uses E_s V_s lambda^2 for the deformational work of the solid. For linear elastic behavior under strain lambda, the strain-energy density is (1/2) E_s lambda^2, so the derivation should state whether a factor of 1/2 is intentionally omitted or whether the reported absolute moduli are systematically overestimated by a factor of 2; this matters because the experimental values are consistently lower than the simulation values. I note that Eq. (6) follows algebraically from Eq. (2) under the stated small-deformation approximations, but the physical content of E_s V_s lambda^2 needs clarification. In addition, the same equation is applied to all assemblies even though the Methods acknowledge that non-fiber assemblies should be treated with the bulk modulus, and PF is experimentally observed to form sheets (Figure 3). The relative-modulus screening would be more robust if the analysis either restricted the claim to fiber-forming assemblies or corrected for morphology.
minor comments (6)
  1. [Methods: CG MD simulations] The deformation speed is given as '1 Å/fs', but the stated box size, strain, and simulation time imply 1 Å/ns; please correct the unit.
  2. [Methods: CG MD simulations] The text says 'no pressure coupling is used during the equilibration process', which contradicts the preceding step 2 that uses a Berendsen barostat during equilibration; this likely should read 'during the deformation process'.
  3. [Abstract and AFM methods] There are typographical errors such as 'expectional' in the abstract and 'quantative' in the AFM section; these should be corrected.
  4. [References] Reference 54 for martinize.py is incomplete, as it lacks the access year; please provide full access information.
  5. [Figure 4d] For the GPR test set, please report the coefficient of determination or Pearson correlation in addition to the MSE, since MSE alone does not convey the strength of the predicted-versus-actual correlation.
  6. [Experimental investigation, AFM analysis] The sentence 'The average RMSD was also calculated using Gwyddian and is presented as error bars' is ambiguous; please specify whether the reported error bars are the standard deviation of the modulus distribution, the standard error of the mean, or a surface-roughness quantity.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the modulus formula is a self-contained energy balance, AFM and held-out ML splits provide external checks, and the minor Batra et al. self-citation is not load-bearing.

full rationale

The central derivation is not circular. The Young's modulus formula (Eq. 6) follows from energy conservation (Eq. 2) together with a water-only control simulation used to subtract the water contribution (Eqs. 3-5); no parameter is fitted to the experimental or target moduli. The simulated di- and tripeptide moduli are then compared with independent AFM QNM measurements, providing an external rank-order benchmark for the relative modulus claim. The pentapeptide ML models are trained on CG-MD-generated data and evaluated on held-out training/validation/test splits, so the ML predictions are learned surrogates for the MD pipeline rather than re-labeled inputs. The only group-self-referential element is the use of the pentapeptide AP>2 pool and the forced 'E' secondary-structure flag, both adopted from prior work including Batra et al. (ref. 35, with overlapping authorship); this is a methodological transfer and a screening input, not a derivation of modulus values. The experimental validation set is narrow and the forced beta-sheet assumption is a genuine correctness risk, but no circular reduction of the paper's central claims to its own inputs is exhibited.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central modulus values depend on the MARTINI force field, the imposed beta-sheet secondary structure, the chosen concentration and strain, and the volume estimation cutoff. None of these are fitted to experimental moduli; they are modeling choices. The ML predictor adds hyperparameters chosen by grid search. No new physical entities are introduced.

free parameters (4)
  • Peptide concentration in CG MD box = di: 0.748 M, tri: 0.498 M, penta: 0.299 M
    Chosen by hand to ensure assemblies span the simulation box; affects AP and modulus values.
  • Tensile strain lambda = 0.02 (2%)
    Small-deformation point at which modulus is extracted; assumes linear elastic response.
  • Volume sampling cutoff = 5 Å
    Used in Monte Carlo seeds to estimate nanostructure volume; determines r_w and hence E_s.
  • GPR hyperparameters = selected by grid search (see Table S4)
    Optimized on validation data; affects pentapeptide modulus predictions.
assumptions (6)
  • domain assumption MARTINI 2.2 coarse-grained force field faithfully represents the interactions and mechanical response of peptide assemblies.
    All computed moduli depend on MARTINI parameters; no comparison to atomistic simulations or experimental benchmarks is provided.
  • ad hoc to paper Forcing the secondary structure flag 'E' (extended beta) for all amino acids in martinize.py is appropriate for the assembled state of every sequence.
    This imposes beta-sheet-like conformations on all peptides regardless of their actual assembled morphology, biasing the mechanical response.
  • domain assumption During hydrostatic tension, assembled nanostructures deform as uniaxial fibers, so the energy balance can be written in terms of Young's modulus rather than bulk modulus.
    Eq 2 uses E_s V_s lambda^2 for the assembly contribution; the paper acknowledges spheres require bulk modulus but applies the same formula to all structures.
  • domain assumption The pressure and volume of a pure-water control simulation accurately represent the water contribution inside the peptide-containing box.
    The water term P0_w r_w is subtracted using a separate water box; any difference in water structure or density in the peptide system is unaccounted for.
  • domain assumption The aggregation propensity threshold AP > 2 from prior literature correctly identifies sequences that self-assemble into defined nanostructures.
    Used to select 27 dipeptides and 124 tripeptides; the paper itself notes LPF (AP=2.07) did not assemble experimentally, indicating the threshold is imperfect.
  • domain assumption The Gaussian process regression model for pentapeptide AP (reimplemented from Batra et al.) accurately predicts self-assembling sequences across the full pentapeptide space.
    The pentapeptide candidate pool is defined by this model; errors in AP prediction propagate directly into the modulus screening.

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Cite this review

Pith. "Pith review of High-throughput Screening of the Mechanical Properties of Peptide Assemblies." pith.science (2026). https://pith.science/paper/RO3Q43R3

@misc{pith2026250508850,
  author       = {Pith},
  title        = {Pith review of: High-throughput Screening of the Mechanical Properties of Peptide Assemblies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RO3Q43R3}},
  note         = {Machine review of arXiv:2505.08850}
}
read the original abstract

Peptides are recognized for their varied self-assembly behaviors, forming a wide array of structures and geometries, such as spheres, fibers, and hydrogels, each presenting a unique set of material properties. The functionalities of these materials hold exceptional interest for applications in biology, medicine, photonics, nanotechnology and the food industry. In specific, the ability to exploit peptides as viable and sustainable mechanical materials requires sequence design that enables superior performance, notably a high Young's modulus. As the peptide sequence space is vast, however, even a slight increase in sequence length leads to an exponential increase in the number of potential peptide sequences to be characterized. Here, we combine coarse-grained molecular dynamics simulations, atomic force microscopy experiments and machine learning models to correlate the sequence length and composition with the mechanical properties of self-assembled peptides. We calculate the Young's modulus for all possible amino acid sequences of di- and tripeptides using high-throughput coarse-grained methods, and validate these calculations through in-situ mechanical characterization. For pentapeptides, we select and calculate properties for a subset of sequences to train a machine learning model, which allows us to predict the modulus for other sequences. The combined workflow not only identifies promising peptide candidates with exceptional mechanical performances, but also extends current understanding of the sequence-to-function relationships for peptide materials, for specific applications.

Figures

Figures reproduced from arXiv: 2505.08850 by the authors.

Figure 1
Figure 1. Full workflow. The workflow used in this work to investigate the mechanical properties of polypeptide self-assemblies can be divided into three major steps: 1) Sequence selection: Based on the number of amino acids in the peptide sequence, we investigate di-, tri- and pentapeptides. We first screen these peptides to collect sequences that can form aggregates for mechanical testing with results from previous studies … view at source ↗
Figure 2
Figure 2. Data statistics of dipeptides and tripeptides. a, Example self-assembled nanostructures for dipeptides (first row) and tripeptides (second row), and corresponding strain-stress response under hydrostatic tensile test. (Pxx, Pyy and Pzz are pressure along x, y and z directions)b, Aggregation propensity and hydrophobicity of dipeptides and tripeptides. c, Modulus statistics of dipeptides (left) and tripeptides (right)… view at source ↗
Figure 3
Figure 3. Modulus validation in di- and tri-peptides. a, Optical density measurements at 400 nm. Filled circles indicate assembly and empty circles indicate no assembly. SEM micrographs or AFM images for named, assembled tripeptides b, and dipeptides c, with corresponding CG-MD projections inset. d, Schematic indicating QNM-AFM methodology with a high projection of an LF fibre and corresponding modulus map. e, Calculated pept… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: ML model for pentapeptide modulus screening. a, Occurrence frequency of amino acids within the 2990 pentapeptide sequences collected. b, Distribution of Young’s modulus for pentapeptides calculated from CG MD simulations. c, Performance comparison of 5 different classi…

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Works this paper leans on

61 extracted references · 49 canonical work pages

  1. [1]

    W., Tarakanova, A., Pugno, N

    Cranford, S. W., Tarakanova, A., Pugno, N. M. & Buehler, M. J. Nonlinear material behaviour of spider silk yields robust webs.Nature482, 72–76, DOI: 10.1038/nature10739 (2012). 11/28

    doi:10.1038/nature10739 2012

  2. [2]

    & Buehler, M

    Nova, A., Keten, S., Pugno, N., Redaelli, A. & Buehler, M. Molecular and nanostructural mechanisms of deformation, strength and toughness of spider silk fibrils.Nature PrecedingsDOI: 10.1038/npre.2010.4336.1 (2010)

    work page arXiv 2010

  3. [3]

    & Buehler, M

    Lu, W., Yang, Z. & Buehler, M. J. Rapid mechanical property prediction and de novo design of three-dimensional spider webs through graph and GraphPerceiver neural networks.Journal Applied Physics132, 074703, DOI: 10.1063/5.0097589 (2022). https://pubs.aip.org/aip/jap/article-pdf/doi/10.1063/5.0097589/19809709/074703_1_online.pdf

    work page doi:10.1063/5.0097589 2022

  4. [4]

    Buehler, M. J. Nature designs tough collagen: Explaining the nanostructure of collagen fibrils.Proceedings National Academy Sciences103, 12285–12290, DOI: 10.1073/pnas.0603216103 (2006). https://www.pnas.org/doi/pdf/10.1073/ pnas.0603216103

    work page doi:10.1073/pnas.0603216103 2006

  5. [5]

    & Buehler, M

    Gautieri, A., Vesentini, S., Redaelli, A. & Buehler, M. J. Hierarchical structure and nanomechanics of collagen microfibrils from the atomistic scale up.Nano Letters11, 757–766, DOI: 10.1021/nl103943u (2011). PMID: 21207932, https: //doi.org/10.1021/nl103943u

    work page doi:10.1021/nl103943u 2011

  6. [6]

    Nature Communications12, 3529, DOI: 10.1038/s41467-021-23813-6 (2021)

    Kamada, A.et al.Controlled self-assembly of plant proteins into high-performance multifunctional nanostructured films. Nature Communications12, 3529, DOI: 10.1038/s41467-021-23813-6 (2021)

    work page doi:10.1038/s41467-021-23813-6 2021

  7. [7]

    & de la Caba, K

    Guerrero, P., Retegi, A., Gabilondo, N. & de la Caba, K. Mechanical and thermal properties of soy protein films processed by casting and compression.Journal F ood Engineering100, 145–151, DOI: https://doi.org/10.1016/j.jfoodeng.2010.03.039 (2010)

    work page doi:10.1016/j.jfoodeng.2010.03.039 2010

  8. [8]

    & Lele, A

    Nagarkar, S., Nicolai, T., Chassenieux, C. & Lele, A. Structure and gelation mechanism of silk hydrogels.Phys. Chem. Chem. Phys.12, 3834–3844, DOI: 10.1039/B916319K (2010)

    work page doi:10.1039/b916319k 2010

Show all 61 references
  1. [9]

    G., Toprakcioglu, Z., Jayaram, A

    Wiita, E. G., Toprakcioglu, Z., Jayaram, A. K. & Knowles, T. P. J. Selenium-silk microgels as antifungal and antibacterial agents.Nanoscale Horiz.–, DOI: 10.1039/D3NH00385J (2024)

    2024 doi

  2. [10]

    P.et al.Silk fibroin-based biomaterials for biomedical applications: A review.Polymers11, DOI: 10.3390/ polym11121933 (2019)

    Nguyen, T. P.et al.Silk fibroin-based biomaterials for biomedical applications: A review.Polymers11, DOI: 10.3390/ polym11121933 (2019)

    2019

  3. [11]

    & Gazit, E

    Reches, M. & Gazit, E. Casting metal nanowires within discrete self-assembled peptide nanotubes.Science300, 625–627, DOI: doi:10.1126/science.1082387 (2003)

    2003 doi

  4. [12]

    R., Granja, J

    Ghadiri, M. R., Granja, J. R., Milligan, R. A., McRee, D. E. & Khazanovich, N. Self-assembling organic nanotubes based on a cyclic peptide architecture.Nature366, 324–327, DOI: 10.1038/366324a0 (1993)

    1993 doi

  5. [13]

    https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.200501522

    Jayawarna, V .et al.Nanostructured hydrogels for three-dimensional cell culture through self-assembly of fluorenyl- methoxycarbonyl–dipeptides.Advanced Materials18, 611–614, DOI: https://doi.org/10.1002/adma.200501522 (2006). https://onlinelibrary.wiley.com/doi/pdf/10.1002/adm...

    2006 doi

  6. [14]

    & Gazit, E

    Mahler, A., Reches, M., Rechter, M., Cohen, S. & Gazit, E. Rigid, self-assembled hydrogel composed of a modified aromatic dipeptide.Advanced Materials18, 1365–1370, DOI: https://doi.org/10.1002/adma.200501765 (2006). https: //onlinelibrary.wiley.com/doi/pdf/10.1002/adma.200501765

    2006 doi

  7. [15]

    & Accardo, A

    Diaferia, C., Morelli, G. & Accardo, A. Fmoc-diphenylalanine as a suitable building block for the preparation of hybrid materials and their potential applications.J. Mater . Chem. B7, 5142–5155, DOI: 10.1039/C9TB01043B (2019)

    2019 doi

  8. [16]

    https://onlinelibrary

    Smith, A.et al.Fmoc-diphenylalanine self assembles to a hydrogel via a novel architecture based on π–π interlocked β-sheets.Advanced Materials20, 37–41, DOI: https://doi.org/10.1002/adma.200701221 (2008). https://onlinelibrary. wiley.com/doi/pdf/10.1002/adma.200701221

    2008 doi

  9. [17]

    K.et al.Hydrogel formation by short d-peptide for cell-culture scaffolds.Materials Science Engineering: C 111, 110746, DOI: https://doi.org/10.1016/j.msec.2020.110746 (2020)

    Restu, W. K.et al.Hydrogel formation by short d-peptide for cell-culture scaffolds.Materials Science Engineering: C 111, 110746, DOI: https://doi.org/10.1016/j.msec.2020.110746 (2020)

    2020

  10. [18]

    & Zhang, S

    Gelain, F., Luo, Z., Rioult, M. & Zhang, S. Self-assembling peptide scaffolds in the clinic.npj Regenerative Medicine6, 9, DOI: 10.1038/s41536-020-00116-w (2021)

    2021 doi

  11. [19]

    & Das, D

    Das, S. & Das, D. Rational design of peptide-based smart hydrogels for therapeutic applications.Frontiers Chemistry9, DOI: 10.3389/fchem.2021.770102 (2021)

    2021

  12. [20]

    Oliveira, C. B. P., Gomes, V ., Ferreira, P. M. T., Martins, J. A. & Jervis, P. J. Peptide-based supramolecular hydrogels as drug delivery agents: Recent advances.Gels8, DOI: 10.3390/gels8110706 (2022)

    2022 doi

  13. [21]

    Commun.56, 11977–11980, DOI: 10.1039/D0CC04299D (2020)

    Castelletto, V .et al.Peptide nanotubes self-assembled from leucine-rich alpha helical surfactant-like peptides.Chem. Commun.56, 11977–11980, DOI: 10.1039/D0CC04299D (2020)

    2020 doi

  14. [22]

    Proceedings National Academy Sciences100, 10258–10262, DOI: 10.1073/pnas.1730609100 (2003)

    Valéry, C.et al.Biomimetic organization: Octapeptide self-assembly into nanotubes of viral capsid-like dimension. Proceedings National Academy Sciences100, 10258–10262, DOI: 10.1073/pnas.1730609100 (2003). https://www.pnas. org/doi/pdf/10.1073/pnas.1730609100. 12/28

    2003 doi

  15. [23]

    Frederix, P. W. J. M., Ulijn, R. V ., Hunt, N. T. & Tuttle, T. Virtual screening for dipeptide aggregation: Toward predictive tools for peptide self-assembly.The Journal Physical Chemistry Letters2, 2380–2384, DOI: 10.1021/jz2010573 (2011). PMID: 23795243, https://doi.org/10.1...

    2011 doi

  16. [25]

    Smadbeck, J.et al.De novo design and experimental characterization of ultrashort self-associating peptides.PLOS Computational Biol.10, 1–17, DOI: 10.1371/journal.pcbi.1003718 (2014)

    2014 doi

  17. [27]

    Xu, T.et al.Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop.Nature Communica- tions14, 3880, DOI: 10.1038/s41467-023-39648-2 (2023)

    2023 doi

  18. [28]

    Wang, J.et al.Artificial superstrong silkworm silk surpasses natural spider silks.Matter5, 4396–4406, DOI: https: //doi.org/10.1016/j.matt.2022.08.028 (2022)

    2022 doi

  19. [29]

    https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/ adfm.202305040

    Schmuck, B.et al.Strategies for making high-performance artificial spider silk fibers.Advanced Functional Materials34, 2305040, DOI: https://doi.org/10.1002/adfm.202305040 (2024). https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/ adfm.202305040

    2024 doi

  20. [30]

    Journal86, 3269–3283, DOI: 10.1016/S0006-3495(04)74375-1 (2004)

    Stolz, M.et al.Dynamic Elastic Modulus of Porcine Articular Cartilage Determined at Two Different Levels of Tissue Organization by Indentation-Type Atomic Force Microscopy.Biophys. Journal86, 3269–3283, DOI: 10.1016/S0006-3495(04)74375-1 (2004)

    2004 doi

  21. [31]

    J., Schaap, I

    de Pablo, P. J., Schaap, I. A. T., MacKintosh, F. C. & Schmidt, C. F. Deformation and collapse of microtubules on the nanometer scale.Phys. Rev. Lett.91, 098101, DOI: 10.1103/PhysRevLett.91.098101 (2003)

    2003 doi

  22. [32]

    & Gelain, F

    Fontana, F. & Gelain, F. Probing mechanical properties and failure mechanisms of fibrils of self-assembling peptides. Nanoscale Adv.2, 190–198, DOI: 10.1039/C9NA00621D (2020)

    2020 doi

  23. [33]

    N., Laudisio, F

    Schwarzl, R., Liese, S., Brünig, F. N., Laudisio, F. & Netz, R. R. Force response of polypeptide chains from water-explicit md simulations.Macromolecules53, 4618–4629, DOI: 10.1021/acs.macromol.0c00138 (2020). https://doi.org/10.1021/ acs.macromol.0c00138

    2020 doi

  24. [34]

    Basavalingappa, V .et al.Diphenylalanine-derivative peptide assemblies with increased aromaticity exhibit metal-like rigidity and high piezoelectricity.ACS Nano14, 7025–7037, DOI: 10.1021/acsnano.0c01654 (2020)

    2020 doi

  25. [35]

    Batra, R.et al.Machine learning overcomes human bias in the discovery of self-assembling peptides.Nature Chemistry 14, 1427–1435, DOI: 10.1038/s41557-022-01055-3 (2022)

    2022 doi

  26. [36]

    J., Risselada, H

    Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P. & de Vries, A. H. The martini force field: Coarse grained model for biomolecular simulations.The Journal Physical Chemistry B111, 7812–7824, DOI: 10.1021/jp071097f (2007). PMID: 17569554, https://doi.org/10.1021/jp071097f

    2007 doi

  27. [37]

    Adamcik, J.et al.Measurement of intrinsic properties of amyloid fibrils by the peak force qnm method.Nanoscale4, 4426–4429, DOI: 10.1039/C2NR30768E (2012)

    2012 doi

  28. [38]

    Ruggeri, F. S.et al.Influence of the β-sheet content on the mechanical properties of aggregates during amyloid fibrillization.Angewandte Chemie International Edition54, 2462–2466, DOI: https://doi.org/10.1002/anie.201409050 (2015). https://onlinelibrary.wiley.com/doi/pdf/10.10...

    2015 doi

  29. [39]

    & Fields, G

    Yu, Y .-C., Berndt, P., Tirrell, M. & Fields, G. B. Self-assembling amphiphiles for construction of protein molecular architecture.Journal American Chemical Society118, 12515–12520, DOI: 10.1021/ja9627656 (1996). https://doi.org/10. 1021/ja9627656

    1996 doi

  30. [40]

    Effect of proline residues on protein folding.Journal Molecular Biol.145, 251–263, DOI: https://doi.org/10

    Levitt, M. Effect of proline residues on protein folding.Journal Molecular Biol.145, 251–263, DOI: https://doi.org/10. 1016/0022-2836(81)90342-9 (1981)

    1981

  31. [41]

    Levin, A.et al.Ostwald’s rule of stages governs structural transitions and morphology of dipeptide supramolecular polymers.Nature Communications5, 5219, DOI: 10.1038/ncomms6219 (2014)

    2014 doi

  32. [42]

    P., Jones, J

    Jung, J. P., Jones, J. L., Cronier, S. A. & Collier, J. H. Modulating the mechanical properties of self-assembled peptide hydrogels via native chemical ligation.Biomaterials29, 2143–2151, DOI: https://doi.org/10.1016/j.biomaterials.2008.01. 008 (2008). 13/28

    2008 doi

  33. [43]

    & Matson, J

    Li, Z., Zhu, Y . & Matson, J. B. ph-responsive self-assembling peptide-based biomaterials: Designs and applications.ACS Applied Bio Materials5, 4635–4651, DOI: 10.1021/acsabm.2c00188 (2022). PMID: 35505454, https://doi.org/10.1021/ acsabm.2c00188

    2022 doi

  34. [44]

    P.et al.Controlling the self-assembly and material properties of β-sheet peptide hydrogels by modulating intermolecular interactions.Gels9, DOI: 10.3390/gels9060441 (2023)

    Warren, J. P.et al.Controlling the self-assembly and material properties of β-sheet peptide hydrogels by modulating intermolecular interactions.Gels9, DOI: 10.3390/gels9060441 (2023)

    2023 doi

  35. [45]

    R.et al.Temperature triggered self-assembly of polypeptides into multivalent spherical micelles.Journal American Chemical Society130, 687–694, DOI: 10.1021/ja0764862 (2008)

    Dreher, M. R.et al.Temperature triggered self-assembly of polypeptides into multivalent spherical micelles.Journal American Chemical Society130, 687–694, DOI: 10.1021/ja0764862 (2008). PMID: 18085778, https://doi.org/10.1021/ ja0764862

    2008 doi

  36. [46]

    & Ueda, M

    Nandakumar, A., Ito, Y . & Ueda, M. Solvent effects on the self-assembly of an amphiphilic polypeptide incorporating α-helical hydrophobic blocks.Journal American Chemical Society142, 20994–21003, DOI: 10.1021/jacs.0c03425 (2020). PMID: 33272014, https://doi.org/10.1021/jacs.0c03425

    2020 doi

  37. [47]

    J.et al.Self-assembly of peptide nanotubes in an organic solvent.Langmuir24, 8158–8162, DOI: 10.1021/la800942n (2008)

    Krysmann, M. J.et al.Self-assembly of peptide nanotubes in an organic solvent.Langmuir24, 8158–8162, DOI: 10.1021/la800942n (2008). PMID: 18572891, https://doi.org/10.1021/la800942n

    2008 doi

  38. [48]

    O.et al.Expanding the solvent chemical space for self-assembly of dipeptide nanostructures.ACS Nano8, 1243–1253, DOI: 10.1021/nn404237f (2014)

    Mason, T. O.et al.Expanding the solvent chemical space for self-assembly of dipeptide nanostructures.ACS Nano8, 1243–1253, DOI: 10.1021/nn404237f (2014). PMID: 24422499, https://doi.org/10.1021/nn404237f

    2014 doi

  39. [49]

    Salomon-Ferrer, R., Case, D. A. & Walker, R. C. An overview of the amber biomolecular simulation package.WIREs Computational Molecular Science3, 198–210, DOI: https://doi.org/10.1002/wcms.1121 (2013). https://wires.onlinelibrary. wiley.com/doi/pdf/10.1002/wcms.1121

    2013 doi

  40. [50]

    21367 (2010)

    Vanommeslaeghe, K.et al.Charmm general force field: A force field for drug-like molecules compatible with the charmm all-atom additive biological force fields.Journal Computational Chemistry31, 671–690, DOI: https://doi.org/10.1002/jcc. 21367 (2010). https://onlinelibrary.wile...

    2010 doi

  41. [51]

    Sun, B.et al.Photoactive properties of supramolecular assembled short peptides.Chem. Soc. Rev.48, 4387–4400, DOI: 10.1039/C9CS00085B (2019). 52.Schrödinger, LLC. The PyMOL molecular graphics system, version 1.8 (2015)

    2019 doi

  42. [53]

    & Schulten, K

    Humphrey, W., Dalke, A. & Schulten, K. Vmd: Visual molecular dynamics.Journal Molecular Graphics14, 33–38, DOI: https://doi.org/10.1016/0263-7855(96)00018-5 (1996)

    1996 doi

  43. [54]

    http://cgmartini.nl/index.php/tools2/proteins-and-bilayers/204-martinize (Access Year)

    martinize.py. http://cgmartini.nl/index.php/tools2/proteins-and-bilayers/204-martinize (Access Year). Accessed: date-of- access

  44. [55]

    Frederix, P. W. J. M.et al.Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels.Nature Chemistry7, 30–37, DOI: 10.1038/nchem.2122 (2015)

    2015 doi

  45. [56]

    Abraham, M. J.et al.Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX1-2, 19–25, DOI: https://doi.org/10.1016/j.softx.2015.06.001 (2015)

    2015 doi

  46. [57]

    P-lincs: A parallel linear constraint solver for molecular simulation.Journal Chemical Theory Computation4, 116–122, DOI: 10.1021/ct700200b (2008)

    Hess, B. P-lincs: A parallel linear constraint solver for molecular simulation.Journal Chemical Theory Computation4, 116–122, DOI: 10.1021/ct700200b (2008). PMID: 26619985, https://doi.org/10.1021/ct700200b

    2008 doi

  47. [58]

    & Parrinello, M

    Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling.The Journal Chemical Physics 126, 014101, DOI: 10.1063/1.2408420 (2007). https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/1.2408420/15393604/ 014101_1_online.pdf

    2007 doi

  48. [59]

    Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath.The Journal Chemical Physics81, 3684–3690, DOI: 10.1063/1.448118 (1984). https: //pubs.aip.org/aip/jcp/article-pdf/81/8/3684/18950084/3684...

    1984 doi

  49. [60]

    White, S. H. & Wimley, W. C. Hydrophobic interactions of peptides with membrane interfaces.Biochimica et Biophys. Acta (BBA) - Reviews on Biomembranes1376, 339–352, DOI: https://doi.org/10.1016/S0304-4157(98)00021-5 (1998)

    1998 doi

  50. [61]

    C., Creamer, T

    Wimley, W. C., Creamer, T. P. & White, S. H. Solvation energies of amino acid side chains and backbone in a family of host-guest pentapeptides.Biochemistry35, 5109–5124, DOI: 10.1021/bi9600153 (1996). PMID: 8611495, https: //doi.org/10.1021/bi9600153

    1996 doi

  51. [62]

    Pedregosa, F.et al.Scikit-learn: Machine learning in Python.Journal Machine Learning Research12, 2825–2830 (2011)

    2011

  52. [63]

    Software available from tensorflow.org

    Abadi, M.et al.TensorFlow: Large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org

    2015

  53. [64]

    & Toporov, Y

    Derjaguin, B., Muller, V . & Toporov, Y . Effect of contact deformations on the adhesion of particles.Journal Colloid Interface Science53, 314–326, DOI: https://doi.org/10.1016/0021-9797(75)90018-1 (1975). 14/28 Acknowledgements The authors acknowledge the support from the fol...

    1975 doi

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.