Microscopic Insight into Self-Assembly of Amphiphilic Peptides: The Influence of The Hydrophilic Residues

not-yet-known not-yet-known not-yet-known unknown Unveiling the self-assembly mechanism of amphiphilic peptide is crucial for the development of functional supramolecular biomaterials. The chemical properties of hydrophilic amino acids play an essential role in this process. Our multiscale molecular dynamic (MD) simulations indicated that the hydrophilic residue, threonine (T) was an excellent candidate to balance the hydrophobicity of the peptide, which could enhance the peptide self-assembly performance. In addition, simulations demonstrated that the number of hydrogen bonds in peptide aggregates was irrelevant to the peptides self-assembly. Avoiding hydrophilic side chains from disrupting the hydrogen bond network between the peptide backbones can improve self-assembly stability. Together with the experimental validation, we believe that T is a promising amino acid to balance the hydrophobicity of amphiphilic peptides. This work highlighted the importance of hydrophilic amino acids in peptide self-assembly, which could be further utilized in designing amphiphilic peptides with different functions. 1. Introduction Molecular self-assembly is a spontaneous and thermodynamically favorable process that occurs in various areas, including biology (e.g., cellular processes) and materials science (e.g., crystal growth)1-4[]. This phenomenon has enabled the construction of supramolecular nanostructures from diverse building blocks, such as lipids5[], photosensitizers6[], nucleic acids7[], polymers8-10[], and peptides1112[, ]. Among these, short peptides—comprising fewer than 20–30 genetically encoded or synthetic amino acids—have garnered considerable attention due to their exceptional biocompatibility and functionality, which not only offer a rich diversity of chemical landscapes and properties, but also endow peptide self-assemblies with a vast spectrum of potential applications1213[, ]. A prominent biological example of peptide self-assembly is its pathological role in Alzheimer’s disease (AD)14[]. It has been demonstrated that the aggregation of amyloid β-peptides (Aβ) into cytotoxic intermediates (e.g., soluble oligomers, protofibrils) and insoluble amyloid plaques disrupts neuronal function, leading to synaptic degeneration and cell death15[]. Conversely, controlled peptide self-assembly holds therapeutic promise, particularly in drug delivery. By modulating the ratio of hydrophilic, hydrophobic, and charged residues in amphiphilic peptides, tunable intermolecular interactions can be achieved, thereby providing a conductive environment for self-assembly1617[, ]. For example, Xie et al. reported a self-assembling amphiphilic peptide capable of substantial drug loading capacity and biocompatibility, demonstrating its potential for therapeutic applications18[]. Notably, this bottom-up assembly is not only spontaneous but also most cases reversible, and stimuli-responsive, modulated by external conditions such as temperature, pH, and ionic strength19-21[]. This adaptability arises from the dynamic nature of non-covalent interactions and the structural plasticity of peptides, enabling the design of ”smart” peptide-based systems for applications in biosensing, bioimaging, and stimuli-responsive drug release2223[, ]. The mechanism of self-assembly process lies in a delicate interplay of non-covalent interactions, including electrostatic interactions, π-π stacking, van der Waals forces, and hydrophilic-hydrophobic equilibria2425[, ]. When non-covalent forces are sufficiently strong, small molecular units can coalesce into well-defined aggregates with unique shapes and functionalities12[]. Hydrophobic interactions are a dominant force in protein folding and the formation of hierarchical structures. Yet, in aqueous solution, hydrophobic peptides tend to form amorphous aggregates lacking defined shape and size. This highlights the necessity for a delicate balance between hydrophobic and hydrophilic interactions to achieve peptide self-assemblies with uniform structures and reliable properties26[]. In addition, the assembly behaviors of the amphiphilic peptides are mediated by other non-covalent interactions, such as hydrogen bonds (e.g., in β-sheet formation), electrostatic forces, and aromatic interactions (e.g., in fibrillar assemblies of tryptophan- or phenylalanine-rich peptides)2728[, ]. For instance, Ghosh et al. demonstrated short peptides containing aromatic residues (W, F and Y) often assemble into nanofibrous structures through intermolecular π-π stacking29[]. Regrettably, previous studies have mainly focused on the impact of hydrophobic amino acids, largely overlooking the regulatory role of hydrophilic amino acids in assembly kinetics and thermodynamics. However, mechanistic insights into peptide interactions currently remain constrained by the limitations of macroscopic characterization techniques. Systematic studies are urgently needed to (i) correlate structural modifications with interaction dynamics and (ii) establish quantitative benchmarks for the delicate interplay and role of forces in self-assembly. Multiscale molecular dynamics (MD) simulations have emerged as a powerful tool to delve into the molecular interactions dynamics between biomolecules at atomistic level3031[, ]. It also could be utilized to screen different peptide sequences for their aggregation behaviors and analyze their self-assembly ability32-35[], while integration with experimental methods. (e.g., nuclear magnetic resonance (NMR)3637[, ], Fourier-transform infrared spectroscopy (FTIR)38[] and circular dichroism (CD) spectroscopy39[], etc.) provides a robust framework for mechanistic validation. In this work, we investigated the specific role of hydrophilic amino acids, particularly focusing on threonine (T), in modulating the self-assembly of amphiphilic peptides using integrated multiscale MD simulations and experiments. A classical 7-residue peptide (sequence: XXIIIII, XXI 5 ) was chosen as a template40[], where the first two residues (X) located at the N-terminal were hydrophilic amino acids (K, R, E, D, Q, N, T, S, and G). Based on the template, 81 amphiphilic peptides were generated, and their self-assembly processes were systematically investigated. 2. Results and Discussion 2.1 Coarse-Grained MD Simulations of Peptide Aggregation We first conducted 2000 ns-long coarse-grained MD (CGMD) simulations in aqueous solution on each amphiphilic peptide mentioned above. For each peptide, the simulation system included 400 peptide units, approximately 38900 water beads and 0.15 M NaCl. The detailed methods and parameters of the simulations were provided in the supplementary text and Figure S1 . CGMD simulations demonstrated that the chemical properties of hydrophilic amino acids could significantly impact the aggregation morphologies of amphiphilic peptides. For instance, the TGI 5 quickly aggregated within 50 ns and eventually formed a stable nanosphere ( Figure S2 ). In contrast, the peptide of DQI 5 showed no aggregation over the entire 2000 ns simulation ( Figure S2 ). These observations align with prior publications indicating that aggregation is a critical precursor to peptide self-assembly41[]. Figure 1. The AP score map of all 81 amphiphilic peptides after 2000 ns CGMD simulations. The representative aggregation morphologies were shown. To quantitatively evaluate the aggregation of peptide, we calculated their aggregation propensity (AP) score. Higher AP scores represent greater aggregation performance of peptides, and the scores over 2 imply high degrees of aggregation (see supplementary text for detailed scoring methodology)41[]. The AP scores of all amphiphilic peptides were shown in Figure 1 . It is worth noting that the AP scores, while indicative of aggregation ability, does not necessarily correlate with improved self-assembly performance. For instance, the peptide SSI 5 (AP score=3.77) aggregated in a disordered and uncontrolled manner after 2000 ns CGMD. Consequently, we considered peptides with AP scores below 3.5 as potential candidates for effective self-assembly. Most of the amphiphilic peptides scored lower than 2 in AP, suggesting a reduced propensity for aggregate in solution. Notably, the peptides with uncharged hydrophilic residues generally exhibited higher AP scores than the ones with charged residues, implying that electrostatic repulsion prohibited peptide aggregation. Additionally, the combination of positively charged (K or R) and negatively charged (D or E) residues did not promote peptide aggregation, indicating that an inhomogeneous charge distribution may be not conducive to short peptide aggregation. Remarkably, our analysis revealed that S and T amino acids significantly contributed to the aggregation process, with average AP scores exceeding 2.5 for S or T-containing peptides. This underscores the role of hydroxyl-containing non-aromatic amino acids in facilitating amphiphilic peptide aggregation. In contrast, amino acids Q and N with amide groups did not exhibit similar aggregation tendencies. Since the amide group (both oxygen and nitrogen atoms can form hydrogen bonds) is more favorable for the formation of hydrogen bonds than the hydroxyl group, this observation implies that the hydrogen bond formation ability may not determine peptide self-assembly performance. Previous studies revealed that the position of amino acids could impact the peptide aggregation behaviour41[]. In this work, the AP scores dropped by 0.8 when the position of S and T were swapped (STI 5 and TSI 5 ). We speculate that positional constraint effects may influence the flexibility of amino acid side chains, thereby impacting the interactions between peptides. To further dissect the role of hydrophilic amino acid sidechains in peptide aggregation, we employed a modified aggregation propensity (AP s ) metric32[]. In this method, the AP s score of GGI 5 is 0, because glycine residues do not have a side chain (no side chain contribution during the aggregation process). Accordingly, the AP s scores greater than GGI 5 represented the hydrophilic sidechains could promote the aggregation of the peptides, whereas a negative AP s score indicated the hydrophilic sidechains would inhibit the aggregation of peptides. AP s score allowed us to quantify the influence of different hydrophilic sidechains in the peptide aggregation process. Additionally, the average value of AP s for different hydrophilic residues could be employed to evaluate their contribution to the aggregation of the peptide We found that 62.9% of the peptides had negative AP s scores ( Figure S3 ). This revealed that the hydrophilic sidechains in these amphiphilic peptides prevented spontaneous aggregation. Notably, the average AP s scores of the peptides containing S and T were greater than 0, suggesting their sidechains could assist the formation of self-assemblies. ( Figure S3 ). Furthermore, the average APs scores indicated that S and T at different positions had distinct contributions to the aggregation process. The highest average score was found when residue T was located at position 1. It revealed that sequence TXI 5 was the most promising amphiphilic peptide for the self-assembly which merited in-depth study. not-yet-known not-yet-known not-yet-known unknown 2.2 All-Atom MD Simulations of Self-Assembly Mechanisms Following our CGMD simulations, we selected the peptide TGI5 (AP score of 2.8) for 500 ns-long all-atom MD (AAMD) investigation, with NGI5 (AP score of 2.2) as a comparison. For each peptide, the simulation system included 30 peptide units, approximately 32000 water molecules and 0.15 M NaCl. As shown in Table 1 and Figure 2a, both peptides formed ordered structures after 500 ns AAMD simulations. Notably, NGI5 and TGI5 exhibited globular structures at room temperature, which were similar to the aggregation morphologies obtained from CGMD simulations. Moreover, the simulations revealed a two-stage self-assembly process: initial formation of loose aggregates driven by hydrophobic interactions, followed by the establishment of stable hydrogen bonds between peptide backbones, leading to the formation of β-sheet structures. The secondary structure of peptides was crucial in the self-assembly process, which stabilizes the shape and size of nanostructure42[]. Thus, the aggregation of TGI5 and NGI5 can be regarded as a hierarchical course driven by hydrophobic and hydrogen bond interactions. Figure 2. AAMD simulations of peptide self-assembly. (a) The representative conformations of TGI5 (blue) and NGI5 (green) aggregates at different temperatures. (b) The Rg of TGI5 and NGI5 particles with respect to the temperature. Table 1 Structural details of TGI5 and NGI5 aggregates obtained from AAMD. not-yet-known not-yet-known not-yet-known unknown | Rg† (Å) | 18.2±0.3 | 19.3±0.3 | | H-bond‡ number | 115.3±8.8 | 136.7±10.2 | | H-bond‡ length (Å) | 3.0±0.3 | 3.1±0.4 | | β-sheet content: | || | anti-parallel | 31.0% | 21.9% | | parallel | 4.3% | 11.9% | † Radius of gyration. ‡ Intermolecular hydrogen bonds. The thermal stability of TGI 5 and NGI 5 aggregates, assessed by changes in morphology and radius of gyration (Rg) at varying temperatures ( Figures 2b ), highlighted the superior stability of TGI 5 aggregates. Obviously, with the increase of temperature, the NGI 5 aggregate gradually dissociate. In contrast, the morphology of TGI 5 aggregate remained unchanged at 500 K. Since NGI 5 and TGI 5 have the same hydrophobic segments, it is apparent that the non-covalent interactions between hydrophilic residues impacted the formation of the peptide aggregates. Interestingly, although NGI 5 formed more hydrogen bonds at room temperature (18% higher than that in TGI 5 ), these hydrogen bonds were less stable at elevated temperatures ( Figure 3a ), underscoring that hydrogen bond strength is more critical than quantity for peptide self-assembly. In addition, our analysis of secondary structures within the aggregates revealed a significant difference in the alignment of NGI 5 and TGI 5 . More than 31% of anti-parallel β-sheet structures were found in TGI 5 aggregate, whereas the parallel β-sheet structures only accounted for 4.3%. In contrast, the occupations of anti-parallel and parallel β-sheet structures in NGI 5 were 21.9% and 11.9%, respectively. Since the anti-parallel β-sheet structure is more stable than the parallel β-sheet structure43[], TGI 5 self-assembly could have higher stability. Detailed structural analysis showed that the amide groups in N were prone to form hydrogen bonds with the peptide backbones ( Figure 3b ). This may disturb the formation of hydrogen bond networks between the backbone atoms. Such distortion also interfered the normal alignment of the peptide backbones, resulting in higher parallel β-sheet structures in NGI 5 aggregate. Figure 3. (a) The number of hydrogen bonds in TGI5 and NGI5 particles with respect to the temperature. (b) The representative intermolecular hydrogen bond interactions in TGI5 and NGI5. The hydrogen bonds were depicted as black dash lines. The carbon atoms in T and N sidechains were colored in pink and cyan, respectively. 2.3 Experimental Validation To validate our simulation findings, the peptide self-assemblies of TGI 5 and NGI 5 were experimentally characterized using transmission electron microscopy (TEM), dynamic light scattering (DLS), atomic force microscopy (AFM), and circular dichroism (CD) spectroscopy. The TEM images unveiled that TGI 5 formed spherical particles in an aqueous solution at physiological pH ( Figure 4a ), while NGI 5 did not establish ordered nanostructures under the same conditions ( Figure 4b ). The TEM analysis further confirmed the monodispersity and uniformity of TGI 5, consistent with our simulation results. DLS intensity spectra showed that TGI 5 exhibited a uniform size distribution, characterized by a single peak with a normal distribution ranging from 230 to 300 nm, whereas NGI 5 displayed a broader size distribution from 100 to 1000 nm, indicative of varying degrees of aggregation ( Figure 4c ). This observation supports threonine’s role in facilitating the self-assembly of amphiphilic peptides. Moreover, AFM analysis indicated the width of TGI 5 aggregates at ~250 nm ( Figure S4 ). NGI 5 struggled to form ordered nanostructures, presenting widths over 1000 nm and heights below 3 nm. The secondary structures of the amphiphilic peptide aggregates in aqueous solution were further measured using CD spectroscopy ( Figure S5 ). Both TGI 5 and NGI 5 exhibited typical β-sheet structures, affirming our MD simulations and emphasizing the crucial role of secondary structure in the stability of peptide assemblies. not-yet-known not-yet-known not-yet-known unknown Figure 4. Experimental characterizations of peptides self-assembly in aqueous solution. TEM images of (a) TGI5 and (b) NGI5; DLS intensity spectra of (c) TGI5 and (d)NGI5. 3. Conclusion Overall, the chemical properties of hydrophilic amino acids have crucial effects on the self-assembly of amphiphilic peptides. In this work, multiscale MD simulations and experiments were used to study the role of hydrophilic amino acids in the self-assembly of amphiphilic peptides. By conducting CGMD simulations on a microsecond time scale, we found that maintaining the balance of hydrophilicity and hydrophobicity of peptides was essential for peptide self-assembly. Peptides with strong hydrophobicity would lead to disordered aggregation; otherwise, the strong hydrophilicity would inhibit the self-assembly. Our simulations revealed that the hydrophilic amino acid T is an excellent candidate to balance the amphiphilic nature of the short peptides, which could be introduced to the peptide sequence for self-assembly. Moreover, we found that hydrogen bond played a complex role in the self-assembly of peptides. Hydrogen bonds between peptide backbones could transit the disordered peptide aggregates into the hierarchical β-sheet dominated structures. However, the number of hydrogen bonds was not positively correlated with the self-assembly stability. Increasing the hydrogen bond number may disrupt the formation of the hydrogen bond network between the peptide backbones, resulting in unstable parallel β-sheet structures in the peptide aggregates. Furthermore, the TEM and DLS experiments revealed that the T containing peptides, TGI5, could form stable and uniform aggregates in solution, which is in accord with our simulation results. Additionally, the CD experiment proved that the β-sheet structure is mainly formed between these amphiphilic peptides. In summary, the hybrid approach in this study highlights the crucial role of hydrophilic amino acids in modulating the delicate balance of forces that govern the self-assembly of amphiphilic peptides in aqueous environments, and offer a more in-depth understanding of the factors such as such as the hydrophilic-to-hydrophobic residue ratio and their position that influence this process. These findings provide key insights for designing multifunctional systems that integrate amphiphilic peptides, polymers, and composite materials. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment We gratefully acknowledge financial supports from the Natural Science Foundation of China (22173020, 22102027 and 22403023) and the Natural Science Foundation of Fujian Province (2019J06007).

[1] S. Kim, H. Choi, S. Jin, S. Son, Y. Lee, K. Kim, J.-H. Ryu, Self-assembly inside cellular organelles: Aspects of functions and various strategies for cancer therapy, Giant, 16 (2023) 100189.https://doi.org/10.1016/j.giant.2023.100189[2] P.-A. Monnard, D.W. Deamer, Membrane self-assembly processes: Steps toward the first cellular life, The Anatomical Record, 268 (2002) 196-207.https://doi.org/10.1002/ar.10154[3] G. Singh, H. Chan, A. Baskin, E. Gelman, N. Repnin, P. Král, R. Klajn, Self-assembly of magnetite nanocubes into helical superstructures, Science, 345 (2014) 1149-1153.doi:10.1126/science.1254132[4] G.M. Whitesides, B. Grzybowski, Self-Assembly at All Scales, Science, 295 (2002) 2418-2421.doi:10.1126/science.1070821[5] Y.K. Go, C. Leal, Polymer–Lipid Hybrid Materials, Chem. Rev., 121 (2021) 13996-14030.10.1021/acs.chemrev.1c00755[6] G. Xu, C. Li, C. Chi, L. Wu, Y. Sun, J. Zhao, X.-H. Xia, S. Gou, A supramolecular photosensitizer derived from an Arene-Ru(II) complex self-assembly for NIR activated photodynamic and photothermal therapy, Nature Communications, 13 (2022) 3064.10.1038/s41467-022-30721-w[7] M. Kwak, A. Herrmann, Nucleic acid amphiphiles: synthesis and self-assembled nanostructures, Chem. Soc. Rev., 40 (2011) 5745-5755.10.1039/C1CS15138J[8] D. Trung Bui, A. Maksimenko, D. Desmaële, S. Harrisson, C. Vauthier, P. Couvreur, J. Nicolas, Polymer Prodrug Nanoparticles Based on Naturally Occurring Isoprenoid for Anticancer Therapy, Biomacromolecules, 14 (2013) 2837-2847.10.1021/bm400657g[9] M. Lages, N. Gil, P. Galanopoulo, J. Mougin, C. Lefay, Y. Guillaneuf, M. Lansalot, F. D’Agosto, J. Nicolas, Degradable Vinyl Copolymer Nanoparticles/Latexes by Aqueous Nitroxide-Mediated Polymerization-Induced Self-Assembly, Macromolecules, 55 (2022) 9790-9801.10.1021/acs.macromol.2c01734[10] G. Srinivas, D.E. Discher, M.L. Klein, Self-assembly and properties of diblock copolymers by coarse-grain molecular dynamics, Nature Mater, 3 (2004) 638-644.10.1038/nmat1185[11] D. Mao, Q. Li, Q. Li, P. Wang, C. Mao, A conformational study of the 10–23 DNAzyme via programmed DNA self-assembly, Chem. Commun., 58 (2022) 6188-6191.10.1039/D2CC01144A[12] N.J. Sinha, M.G. Langenstein, D.J. Pochan, C.J. Kloxin, J.G. Saven, Peptide Design and Self-assembly into Targeted Nanostructure and Functional Materials, Chemical Reviews, 121 (2021) 13915-13935.https://doi.org/10.1021/acs.chemrev.1c00712[13] I. Insua, J. Montenegro, 1D to 2D Self Assembly of Cyclic Peptides, Journal of the American Chemical Society, 142 (2020) 300-307.https://doi.org/10.1021/jacs.9b10582[14] C. Ballard, S. Gauthier, A. Corbett, C. Brayne, D. Aarsland, E. Jones, Alzheimer’s disease, The Lancet, 377 (2011) 1019-1031.10.1016/S0140-6736(10)61349-9[15] D.R. Thal, M. Fändrich, Protein aggregation in Alzheimer’s disease: Aβ and τ and their potential roles in the pathogenesis of AD, Acta Neuropathologica, 129 (2015) 163-165.10.1007/s00401-015-1387-2[16] C. Zhao, H. Chen, F. Wang, X. Zhang, Amphiphilic self-assembly peptides: Rational strategies to design and delivery for drugs in biomedical applications, COLLOID SURFACE B, 208 (2021) 112040.https://doi.org/10.1016/j.colsurfb.2021.112040[17] B. Yang, T.-t. Wang, Y.-s. Yang, H.-l. Zhu, J.-h. Li, The application progress of peptides in drug delivery systems in the past decade, Journal of Drug Delivery Science and Technology, 66 (2021) 102880.https://doi.org/10.1016/j.jddst.2021.102880[18] X. Xie, F. Meng, Z. Zhang, X. Xu, M. Guo, Y. Jiang, T. Wang, Self-assembled peptide-based nanoblocks for drug delivery, New Journal of Chemistry, 47 (2023) 18721-18728.https://doi.org/10.1039/D3NJ03414C[19] K.A. Carter, S. Shao, M.I. Hoopes, D. Luo, B. Ahsan, V.M. Grigoryants, W. Song, H. Huang, G. Zhang, R.K. Pandey, J. Geng, B.A. Pfeifer, C.P. Scholes, J. Ortega, M. Karttunen, J.F. Lovell, Porphyrin–phospholipid liposomes permeabilized by near-infrared light, Nat. Commun., 5 (2014) 3546.https://doi.org/10.1038/ncomms4546[20] J. Zhou, L. Sun, L. Wang, Y. Liu, J. Li, J. Li, J. Li, H. Yang, Self-Assembled and Size-Controllable Oligonucleotide Nanospheres for Effective Antisense Gene Delivery through an Endocytosis-Independent Pathway, Angewandte Chemie International Edition, 58 (2019) 5236-5240.https://doi.org/10.1002/anie.201813665[21] P. Ji, X. Wang, J. Yin, Y. Mou, H. Huang, Z. Ren, Selective delivery of curcumin to breast cancer cells by self-targeting apoferritin nanocages with pH-responsive and low toxicity, Drug Delivery, 29 (2022) 986-996.https://doi.org/10.1080/10717544.2022.2056662[22] J.D. Tang, C. Mura, K.J. Lampe, Stimuli-Responsive, Pentapeptide, Nanofiber Hydrogel for Tissue Engineering, Journal of the American Chemical Society, 141 (2019) 4886-4899.https://doi.org/10.1021/jacs.8b13363[23] P. Liang, J. Zheng, S. Dai, J. Wang, Z. Zhang, T. Kang, C. Quan, pH triggered re-assembly of nanosphere to nanofiber: The role of peptide conformational change for enhanced cancer therapy, J. Control. Release, 260 (2017) 22-31.https://doi.org/10.1016/j.jconrel.2017.05.018[24] M.J. Webber, E.A. Appel, E.W. Meijer, R. Langer, Supramolecular biomaterials, Nature Materials, 15 (2016) 13-26.https://doi.org/10.1038/nmat4474[25] L. Zhao, C. Gu, Y. Gan, L. Shao, H. Chen, H. Zhu, Exosome-mediated siRNA delivery to suppress postoperative breast cancer metastasis, J. Control. Release, 318 (2020) 1-15.https://doi.org/10.1016/j.jconrel.2019.12.005[26] C.J.C. Edwards-Gayle, I.W. Hamley, Self-assembly of bioactive peptides, peptide conjugates, and peptide mimetic materials, Organic & Biomolecular Chemistry, 15 (2017) 5867-5876.https://doi.org/10.1039/C7OB01092C[27] A. Mosseri, M. Sancho-Albero, F.A. Mercurio, M. Leone, L. De Cola, A. Romanelli, Tryptophan-PNA gc Conjugates Self-Assemble to Form Fibers, Bioconjugate Chem., 34 (2023) 1429-1438.10.1021/acs.bioconjchem.3c00200[28] D. Tomar, S. Chaudhary, K.C. Jena, Self-assembly of l-phenylalanine amino acid: electrostatic induced hindrance of fibril formation, RSC Adv., 9 (2019) 12596-12605.10.1039/C9RA00268E[29] G. Ghosh, K.K. Kartha, G. Fernández, Tuning the mechanistic pathways of peptide self-assembly by aromatic interactions, Chem. Commun., 57 (2021) 1603-1606.https://doi.org/10.1039/D0CC07199D[30] K. Chen, Y. Zhao, Molecular recognition of enzymes and modulation of enzymatic activity by nanoparticle conformational sensors, Chem. Commun., 58 (2022) 1732-1735.https://doi.org/10.1039/D1CC05699A[31] S. Xie, Y. Zhou, H. Zhu, X. Xu, H. Zhang, C. Yuan, M. Huang, P. Xu, J. Li, Y. Liu, Interface-driven structural evolution on diltiazem as novel uPAR inhibitors: from in silico design to in vitro evaluation, Molecular Diversity, (2024).https://doi.org/10.1007/s11030-024-10908-7[32] T. Hu, Z. Zhang, H. Hu, S.R. Euston, S. Pan, A Comprehensive Study on Self-Assembly and Gelation of C13-Dipeptides—From Design Strategies to Functionalities, Biomacromolecules, 21 (2020) 670-679.https://doi.org/10.1021/acs.biomac.9b01386[33] A.-E. Molza, P. Gao, J. Jakpou, J. Nicolas, N. Tsapis, T. Ha-Duong, Simulations of the Upper Critical Solution Temperature Behavior of Poly(ornithine-co-citrulline)s Using MARTINI-Based Coarse-Grained Force Fields, J. Chem. Theory Comput., 17 (2021) 4499-4511.10.1021/acs.jctc.1c00140[34] P. Gao, T. Ha-Duong, J. Nicolas, Coarse-Grained Model-Assisted Design of Polymer Prodrug Nanoparticles with Enhanced Cytotoxicity: A Combined Theoretical and Experimental Study, Angewandte Chemie International Edition, 63 (2024) e202316056.10.1002/anie.202316056[35] Y. Song, S. Selmani, J.A. Freites, Z. Guan, D.J. Tobias, Multiscale Molecular Dynamics Simulations of an Active Self-Assembling Material, J. Phys. Chem. B, 128 (2024) 1266-1274.10.1021/acs.jpcb.3c06572[36] I.W. Hamley, D.R. Nutt, G.D. Brown, J.F. Miravet, B. Escuder, F. Rodríguez-Llansola, Influence of the Solvent on the Self-Assembly of a Modified Amyloid Beta Peptide Fragment. II. NMR and Computer Simulation Investigation, J. Phys. Chem. B, 114 (2010) 940-951.10.1021/jp906107p[37] J. Hjalte, S. Hossain, A. Hugerth, H. Sjögren, M. Wahlgren, P. Larsson, D. Lundberg, Aggregation Behavior of Structurally Similar Therapeutic Peptides Investigated by 1H NMR and All-Atom Molecular Dynamics Simulations, Mol Pharm, 19 (2022) 904-917.10.1021/acs.molpharmaceut.1c00883[38] B. Banerji, M. Chatterjee, U. Pal, N.C. Maiti, Formation of Annular Protofibrillar Assembly by Cysteine Tripeptide: Unraveling the Interactions with NMR, FTIR, and Molecular Dynamics, J. Phys. Chem. B, 121 (2017) 6367-6379.10.1021/acs.jpcb.7b04373[39] C. Shi, J. Kaffy, T. Ha-Duong, J.-F. Gallard, A. Pruvost, A. Mabondzo, L. Ciccone, S. Ongeri, N. Tonali, Proteolytically Stable Diaza-Peptide Foldamers Mimic Helical Hot Spots of Protein–Protein Interactions and Act as Natural Chaperones, J. Med. Chem., 66 (2023) 12005-12017.10.1021/acs.jmedchem.3c00611[40] Y. Zhao, J. Wang, L. Deng, P. Zhou, S. Wang, Y. Wang, H. Xu, J.R. Lu, Tuning the Self-Assembly of Short Peptides via Sequence Variations, Langmuir, 29 (2013) 13457-13464.https://doi.org/10.1021/la402441w[41] P.W.J.M. Frederix, G.G. Scott, Y.M. Abul-Haija, D. Kalafatovic, C.G. Pappas, N. Javid, N.T. Hunt, R.V. Ulijn, T. Tuttle, Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels, Nature Chemistry, 7 (2015) 30-37.https://doi.org/10.1038/nchem.2122[42] A.E. Hauser Charlotte, R. Deng, A. Mishra, Y. Loo, U. Khoe, F. Zhuang, W. Cheong Daniel, A. Accardo, B. Sullivan Michael, C. Riekel, Y. Ying Jackie, A. Hauser Ulrich, Natural tri- to hexapeptides self-assemble in water to amyloid β-type fiber aggregates by unexpected α-helical intermediate structures, P. Natl. Acad. Sci., 108 (2011) 1361-1366.https://doi.org/10.1073/pnas.1014796108[43] W. Kabsch, C. Sander, Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features, Biopolymers, 22 (1983) 2577-2637.https://doi.org/10.1002/bip.360221211 Information & Authors Information Version history Peer review timeline Published Physical Chemistry Chemical Physics Version of Record1 Jan 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 215views 160downloads Citations Download citation Yichang Liu, Song Xie, Ziwei Chen, et al. Microscopic Insight into Self-Assembly of Amphiphilic Peptides: The Influence of The Hydrophilic Residues. Authorea. 09 March 2025. DOI: https://doi.org/10.22541/au.174150723.36443578/v1 DOI: https://doi.org/10.22541/au.174150723.36443578/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.