Skip to main content
SpringerLink
Log in

In Silico Prediction of Peptide Self-assembly into Nanostructures

  • Chapter
  • First Online:
Peptide Bionanomaterials

  • 366 Accesses

Abstract

Self-assembling peptides bear tremendous potential in the fields of material sciences, nanoscience, and medicine. In contrary to the popular building blocks used in supramolecular chemistry, which exploit rigid molecular structures with defined geometry, peptides are highly flexible. This feature renders the prediction of their most stable conformations and self-assembly ability, as well as an understanding of the mechanism behind aggregation, more challenging for experimental techniques. In this context, in silico techniques have progressed at a fast pace to provide highly valuable tools to study, predict, and visualize peptides’ behavior and their dynamics to assist with their design. In this chapter, we will provide an overview of popular computational techniques used to investigate the self-assembly of peptides and peptide-containing molecules. Together with the applications, we will briefly discuss the pros and cons of these methodologies and conclude with a perspective on the future directions that this exciting field can lead to.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adler-Abramovich L, Gazit E (2014) The physical properties of supramolecular peptide assemblies: from building block association to technological applications. Chem Soc Rev 43:6881–6893

    Article  CAS  PubMed  Google Scholar 

  • Alder BJ, Wainwright TE (1959) Studies in molecular dynamics. I. General method. J Chem Phys 31:459–466

    Article  CAS  Google Scholar 

  • Bera S, Xue B, Rehak P et al (2020) Self-assembly of aromatic amino acid enantiomers into supramolecular materials of high rigidity. ACS Nano 14:1694–1706

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bochicchio D, Pavan GM (2017) From cooperative self-assembly to water-soluble supramolecular polymers using coarse-grained simulations. ACS Nano 11:1000–1011

    Article  CAS  PubMed  Google Scholar 

  • Bottaro S, Lindorff-Larsen K (2018) Biophysical experiments and biomolecular simulations: a perfect match? Science 361:355–360

    Article  CAS  PubMed  Google Scholar 

  • Brooks CL, Case DA, Plimpton S et al (2021) Classical molecular dynamics. J Chem Phys 154:100401

    Article  CAS  PubMed  Google Scholar 

  • Brown N, Lei J, Zhan C et al (2018) Structural polymorphism in a self-assembled tri-aromatic peptide system. ACS Nano 12:3253–3262

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chakraborty P, Gazit E (2018) Amino acid based self-assembled nanostructures: complex structures from remarkably simple building blocks. ChemNanoMat 4:730–740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chandler D (1978) Statistical mechanics of isomerization dynamics in liquids and the transition state approximation. J Chem Phys 68:2959

    Article  CAS  Google Scholar 

  • Chen J, Zou X (2019) Self-assemble peptide biomaterials and their biomedical applications. Bioact Mater 4:120–131

    Article  PubMed  PubMed Central  Google Scholar 

  • Clover TM, O’Neill CL, Appavu R et al (2020) Self-assembly of block heterochiral peptides into helical tapes. J Am Chem Soc 142:19809–19813

    Article  PubMed  PubMed Central  Google Scholar 

  • Colombo G, Soto P, Gazit E (2007) Peptide self-assembly at the nanoscale: a challenging target for computational and experimental biotechnology. Trends Biotechnol 25:211–218

    Article  CAS  PubMed  Google Scholar 

  • Cringoli MC, Fornasiero P, Marchesan S (2021) Chapter 10. Minimalistic peptide self-assembly into supramolecular biomaterials. In: Azevedo HS, Mano JF, Borges J (eds) Soft matter series. Royal Society of Chemistry, Cambridge, pp 236–263

    Google Scholar 

  • Cui H, Webber MJ, Stupp SI (2010) Self-assembly of peptide amphiphiles: from molecules to nanostructures to biomaterials. Biopolymers 94:1–18

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Deng L, Wang Y (2021) Multiscale computational prediction of β-sheet peptide self-assembly morphology. Mol Simul 47:428–438

    Article  CAS  Google Scholar 

  • Eckes KM, Mu X, Ruehle MA et al (2014) β sheets not required: combined experimental and computational Studies of self-assembly and gelation of the ester-containing analogue of an Fmoc-dipeptide hydrogelator. Langmuir 30:5287–5296

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Emamyari S, Kargar F, Sheikh-hasani V et al (2015) Mechanisms of the self-assembly of EAK16-family peptides into fibrillar and globular structures: molecular dynamics simulations from nano- to micro-seconds. Eur Biophys J 44:263–276

    Article  CAS  PubMed  Google Scholar 

  • Fichman G, Gazit E (2014) Self-assembly of short peptides to form hydrogels: design of building blocks, physical properties and technological applications. Acta Biomater 10:1671–1682

    Article  CAS  PubMed  Google Scholar 

  • Frederix PWJM, Ulijn RV, Hunt NT, Tuttle T (2011) Virtual screening for dipeptide aggregation: toward predictive tools for peptide self-assembly. J Phys Chem Lett 2:2380–2384

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Frederix PWJM, Scott GG, Abul-Haija YM et al (2015) Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels. Nat Chem 7:30–37

    Article  CAS  PubMed  Google Scholar 

  • Frederix PWJM, Patmanidis I, Marrink SJ (2018) Molecular simulations of self-assembling bio-inspired supramolecular systems and their connection to experiments. Chem Soc Rev 47:3470–3489

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fu IW, Nguyen HD (2015) Sequence-dependent structural stability of self-assembled cylindrical nanofibers by peptide amphiphiles. Biomacromolecules 16:2209–2219

    Article  CAS  PubMed  Google Scholar 

  • Fu IW, Markegard CB, Chu BK, Nguyen HD (2013) The role of electrostatics and temperature on morphological transitions of hydrogel nanostructures self-assembled by peptide amphiphiles via molecular dynamics simulations. Adv Healthc Mater 2:1388–1400

    Article  CAS  PubMed  Google Scholar 

  • Fu IW, Markegard CB, Chu BK, Nguyen HD (2014) Role of hydrophobicity on self-assembly by peptide amphiphiles via molecular dynamics simulations. Langmuir 30:7745–7754

    Article  CAS  PubMed  Google Scholar 

  • Fu IW, Markegard CB, Nguyen HD (2015) Solvent effects on kinetic mechanisms of self-assembly by peptide amphiphiles via molecular dynamics simulations. Langmuir 31:315–324

    Article  CAS  PubMed  Google Scholar 

  • Gan Z, Wu X, Zhu X, Shen J (2013) Light-induced ferroelectricity in bioinspired self-assembled diphenylalanine nanotubes/microtubes. Angew Chem Int Ed 52:2055–2059

    Article  CAS  Google Scholar 

  • Garcia AM, Iglesias D, Parisi E et al (2018) Chirality effects on peptide self-assembly unraveled from molecules to materials. Chem 4:1862–1876

    Article  CAS  Google Scholar 

  • Garcia AM, Melchionna M, Bellotto O et al (2021) Nanoscale assembly of functional peptides with divergent programming elements. ACS Nano 15:3015–3025

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ghadiri MR, Granja JR, Milligan RA et al (1993) Self-assembling organic nanotubes based on a cyclic peptide architecture. Nature 366:324–327

    Article  CAS  PubMed  Google Scholar 

  • Glielmo A, Husic BE, Rodriguez A et al (2021) Unsupervised learning methods for molecular simulation data. Chem Rev 121:9722–9758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Görbitz CH (2006) The structure of nanotubes formed by diphenylalanine, the core recognition motif of Alzheimer’s β-amyloid polypeptide. Chem Commun (22):2332–2334

    Google Scholar 

  • Guo C, Luo Y, Zhou R, Wei G (2012) Probing the self-assembly mechanism of diphenylalanine-based peptide nanovesicles and nanotubes. ACS Nano 6:3907–3918

    Article  CAS  PubMed  Google Scholar 

  • Guo C, Luo Y, Zhou R, Wei G (2014) Triphenylalanine peptides self-assemble into nanospheres and nanorods that are different from the nanovesicles and nanotubes formed by diphenylalanine peptides. Nanoscale 6:2800

    Article  CAS  PubMed  Google Scholar 

  • Guo C, Arnon ZA, Qi R et al (2016) Expanding the nanoarchitectural diversity through aromatic di- and tri-peptide coassembly: nanostructures and molecular mechanisms. ACS Nano 10:8316–8324

    Article  CAS  PubMed  Google Scholar 

  • Gupta S, Singh I, Sharma AK, Kumar P (2020) Ultrashort peptide self-assembly: front-runners to transport drug and gene cargos. Front Bioeng Biotechnol 8:504

    Article  PubMed  PubMed Central  Google Scholar 

  • Hamley IW (2011) Self-assembly of amphiphilic peptides. Soft Matter 7:4122

    Article  CAS  Google Scholar 

  • Han W, Wan C-K, Jiang F, Wu Y-D (2010) PACE force field for protein simulations. 1. Full parameterization of version 1 and verification. J Chem Theory Comput 6:3373–3389

    Article  CAS  PubMed  Google Scholar 

  • Harvey MJ, De Fabritiis G (2009) An implementation of the smooth particle mesh Ewald method on GPU hardware. J Chem Theory Comput 5:2371–2377

    Article  CAS  PubMed  Google Scholar 

  • Hatip Koc M, Cinar Ciftci G, Baday S et al (2017) Hierarchical self-assembly of histidine-functionalized peptide amphiphiles into supramolecular chiral nanostructures. Langmuir 33:7947–7956

    Article  CAS  PubMed  Google Scholar 

  • Huggins DJ, Biggin PC, Dämgen MA et al (2019) Biomolecular simulations: from dynamics and mechanisms to computational assays of biological activity. WIREs Comput Mol Sci 9:e1393

    Article  Google Scholar 

  • Jain AN, Cleves AE, Gao Q et al (2019) Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen. J Comput Aided Mol Des 33:531–558

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jeon J, Mills CE, Shell MS (2013) Molecular insights into diphenylalanine nanotube assembly: all-atom simulations of oligomerization. J Phys Chem B 117:3935–3943

    Article  CAS  PubMed  Google Scholar 

  • Ji W, Yuan C, Zilberzwige-Tal S et al (2019) Metal-ion modulated structural transformation of amyloid-like dipeptide supramolecular self-assembly. ACS Nano 13:7300–7309

    Article  CAS  PubMed  Google Scholar 

  • Ji W, Yuan C, Chakraborty P et al (2020) Coassembly-induced transformation of dipeptide amyloid-like structures into stimuli-responsive supramolecular materials. ACS Nano 14:7181–7190

    Article  CAS  PubMed  Google Scholar 

  • Katyal P, Mahmoudinobar F, Montclare JK (2020) Recent trends in peptide and protein-based hydrogels. Curr Opin Struct Biol 63:97–105

    Article  CAS  PubMed  Google Scholar 

  • Kelly CM, Northey T, Ryan K et al (2015) Conformational dynamics and aggregation behavior of piezoelectric diphenylalanine peptides in an external electric field. Biophys Chem 196:16–24

    Article  CAS  PubMed  Google Scholar 

  • Kholkin A, Amdursky N, Bdikin I et al (2010) Strong piezoelectricity in bioinspired peptide nanotubes. ACS Nano 4:610–614

    Article  CAS  PubMed  Google Scholar 

  • Kutzner C, Páll S, Fechner M et al (2019) More bang for your buck: improved use of GPU nodes for GROMACS 2018. J Comput Chem arXiv:190305918. [physics, q-bio]

    Google Scholar 

  • Lai C-T, Rosi NL, Schatz GC (2017) All-atom molecular dynamics simulations of peptide amphiphile assemblies that spontaneously form twisted and helical ribbon structures. J Phys Chem Lett 8:2170–2174

    Article  CAS  PubMed  Google Scholar 

  • Lapshina N, Shishkin II, Nandi R et al (2019) Bioinspired amyloid nanodots with visible fluorescence. Adv Opt Mater 7:1801400

    Article  Google Scholar 

  • Lee O-S, Stupp SI, Schatz GC (2011) Atomistic molecular dynamics simulations of peptide amphiphile self-assembly into cylindrical nanofibers. J Am Chem Soc 133:3677–3683

    Article  CAS  PubMed  Google Scholar 

  • Lee O-S, Cho V, Schatz GC (2012) Modeling the self-assembly of peptide amphiphiles into fibers using coarse-grained molecular dynamics. Nano Lett 12:4907–4913

    Article  CAS  PubMed  Google Scholar 

  • Lee S, Trinh THT, Yoo M et al (2019) Self-assembling peptides and their application in the treatment of diseases. IJMS 20:5850

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liang L, Wang L-W, Shen J-W (2016) The self-assembly mechanism of tetra-peptides from the motif of β-amyloid peptides: a combined coarse-grained and all-atom molecular dynamics simulation. RSC Adv 6:100072–100078

    Article  CAS  Google Scholar 

  • Manandhar A, Kang M, Chakraborty K et al (2017) Molecular simulations of peptide amphiphiles. Org Biomol Chem 15:7993–8005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mandal D, Nasrolahi Shirazi A, Parang K (2014) Self-assembly of peptides to nanostructures. Org Biomol Chem 12:3544–3561

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mansbach RA, Ferguson AL (2018) Patchy particle model of the hierarchical self-assembly of Ď€-conjugated optoelectronic peptides. J Phys Chem B 122:10219–10236

    Article  CAS  PubMed  Google Scholar 

  • Marchesan S, Easton CD, Kushkaki F et al (2012a) Tripeptide self-assembled hydrogels: unexpected twists of chirality. Chem Commun 48:2195–2197

    Article  CAS  Google Scholar 

  • Marchesan S, Waddington L, Easton CD et al (2012b) Unzipping the role of chirality in nanoscale self-assembly of tripeptide hydrogels. Nanoscale 4:6752

    Article  CAS  PubMed  Google Scholar 

  • Marchesan S, Styan KE, Easton CD et al (2015a) Higher and lower supramolecular orders for the design of self-assembled heterochiral tripeptide hydrogel biomaterials. J Mater Chem B 3:8123–8132

    Article  CAS  PubMed  Google Scholar 

  • Marchesan S, Vargiu A, Styan K (2015b) The Phe-Phe motif for peptide self-assembly in nanomedicine. Molecules 20:19775–19788

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mazza M, Notman R, Anwar J et al (2013) Nanofiber-based delivery of therapeutic peptides to the brain. ACS Nano 7:1016–1026

    Article  CAS  PubMed  Google Scholar 

  • Meli M, Morra G, Colombo G (2008) Investigating the mechanism of peptide aggregation: insights from mixed Monte Carlo-molecular dynamics simulations. Biophys J 94:4414–4426

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Moitra P, Subramanian Y, Bhattacharya S (2017) Concentration dependent self-assembly of TrK-NGF receptor derived tripeptide: new insights from experiment and computer simulations. J Phys Chem B 121:815–824

    Article  CAS  PubMed  Google Scholar 

  • Monticelli L, Kandasamy SK, Periole X et al (2008) The MARTINI coarse-grained force field: extension to proteins. J Chem Theory Comput 4:819–834

    Article  CAS  PubMed  Google Scholar 

  • Moreira IP, Scott GG, Ulijn RV, Tuttle T (2019) Computational prediction of tripeptide-dipeptide co-assembly. Mol Phys 117:1151–1163

    Article  CAS  Google Scholar 

  • Mu Y, Yu M (2014) Effects of hydrophobic interaction strength on the self-assembled structures of model peptides. Soft Matter 10:4956–4965

    Article  CAS  PubMed  Google Scholar 

  • Muthusivarajan R, Allen WJ, Pehere AD et al (2020) Role of alkylated residues in the tetrapeptide self-assembly—a molecular dynamics study. J Comput Chem 41:2634–2640

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • NoĂ© F, Tkatchenko A, MĂĽller K-R, Clementi C (2020) Machine learning for molecular simulation. Annu Rev Phys Chem 71:361–390

    Article  PubMed  Google Scholar 

  • Panda JJ, Chauhan VS (2014) Short peptide based self-assembled nanostructures: implications in drug delivery and tissue engineering. Polym Chem 5:4431–4449

    Article  CAS  Google Scholar 

  • Pappas CG, Shafi R, Sasselli IR et al (2016) Dynamic peptide libraries for the discovery of supramolecular nanomaterials. Nat Nanotech 11:960–967

    Article  CAS  Google Scholar 

  • Phillips JC, Hardy DJ, Maia JDC et al (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 153:044130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rissanou AN, Georgilis E, Kasotakis E et al (2013) Effect of solvent on the self-assembly of dialanine and diphenylalanine peptides. J Phys Chem B 117:3962–3975

    Article  CAS  PubMed  Google Scholar 

  • Rozhin P, Charitidis C, Marchesan S (2021) Self-assembling peptides and carbon nanomaterials join forces for innovative biomedical applications. Molecules 26:4084

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Salomon-Ferrer R, Götz AW, Poole D et al (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. explicit solvent particle mesh Ewald. J Chem Theory Comput 9:3878–3888

    Article  CAS  PubMed  Google Scholar 

  • Sasselli IR, Moreira IP, Ulijn RV, Tuttle T (2017) Molecular dynamics simulations reveal disruptive self-assembly in dynamic peptide libraries. Org Biomol Chem 15:6541–6547

    Article  CAS  PubMed  Google Scholar 

  • Scott GG, McKnight PJ, Tuttle T, Ulijn RV (2016) Tripeptide emulsifiers. Adv Mater 28:1381–1386

    Article  CAS  PubMed  Google Scholar 

  • Scott GG, Börner T, Leser ME et al (2022) Directed discovery of tetrapeptide emulsifiers. Front Chem 10:822868

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shaw DE, Grossman JP, Bank JA et al (2014) Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In: SC14: international conference for high performance computing, networking, storage and analysis. IEEE, New Orleans, LA, pp 41–53

    Chapter  Google Scholar 

  • Shmilovich K, Mansbach RA, Sidky H et al (2020) Discovery of self-assembling Ď€-conjugated peptides by active learning-directed coarse-grained molecular simulation. J Phys Chem B 124:3873–3891

    Article  CAS  PubMed  Google Scholar 

  • Sinibaldi A, Della Penna F, Ponzetti M et al (2021) Asymmetric organocatalysis accelerated via self-assembled minimal structures. Eur J Org Chem 2021:5403–5406

    Article  CAS  Google Scholar 

  • Song Y, Challa SR, Medforth CJ et al (2004) Synthesis of peptide-nanotube platinum-nanoparticle composites. Chem Commun (9):1044–1045

    Google Scholar 

  • Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151

    Article  CAS  Google Scholar 

  • Sun J, Zhang H, Guo K, Yuan S (2015a) Self-assembly of dipeptide sodium salts derived from alanine: a molecular dynamics study. RSC Adv 5:102182–102190

    Article  CAS  Google Scholar 

  • Sun Y, Qian Z, Guo C, Wei G (2015b) Amphiphilic peptides A 6 K and V 6 K display distinct oligomeric structures and self-assembly dynamics: a combined all-atom and coarse-grained simulation study. Biomacromolecules 16:2940–2949

    Article  CAS  PubMed  Google Scholar 

  • Sun M, Zhang X, Gao Z et al (2019) Probing a dipeptide-based supramolecular assembly as an efficient camptothecin delivering carrier for cancer therapy: computational simulations and experimental validations. Nanoscale 11:3864–3876

    Article  CAS  PubMed  Google Scholar 

  • Tamamis P, Adler-Abramovich L, Reches M et al (2009a) Self-assembly of phenylalanine oligopeptides: insights from experiments and simulations. Biophys J 96:5020–5029

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tamamis P, Kasotakis E, Mitraki A, Archontis G (2009b) Amyloid-like self-assembly of peptide sequences from the adenovirus fiber shaft: insights from molecular dynamics simulations. J Phys Chem B 113:15639–15647

    Article  CAS  PubMed  Google Scholar 

  • Tang Y, Yao Y, Wei G (2020) Expanding the structural diversity of peptide assemblies by coassembling dipeptides with diphenylalanine. Nanoscale 12:3038–3049

    Article  CAS  PubMed  Google Scholar 

  • Tuttle T (2015) Computational approaches to understanding the self-assembly of peptide-based nanostructures. Isr J Chem 55:724–734

    Article  CAS  Google Scholar 

  • Ung P, Winkler DA (2011) Tripeptide motifs in biology: targets for peptidomimetic design. J Med Chem 54:1111–1125

    Article  CAS  PubMed  Google Scholar 

  • van Teijlingen A, Tuttle T (2021) Beyond tripeptides two-step active machine learning for very large data sets. J Chem Theory Comput 17:3221–3232

    Article  PubMed  PubMed Central  Google Scholar 

  • Vargiu AV, Iglesias D, Styan KE et al (2016) Design of a hydrophobic tripeptide that self-assembles into amphiphilic superstructures forming a hydrogel biomaterial. Chem Commun 52:5912–5915

    Article  CAS  Google Scholar 

  • Velichko YS, Stupp SI, de la Cruz MO (2008) Molecular simulation study of peptide amphiphile self-assembly. J Phys Chem B 112:2326–2334

    Article  CAS  PubMed  Google Scholar 

  • Villa A, Peter C, van der Vegt NFA (2009) Self-assembling dipeptides: conformational sampling in solvent-free coarse-grained simulation. Phys Chem Chem Phys 11:2077

    Article  CAS  PubMed  Google Scholar 

  • Wang J, Liu K, Xing R, Yan X (2016) Peptide self-assembly: thermodynamics and kinetics. Chem Soc Rev 45:5589–5604

    Article  CAS  PubMed  Google Scholar 

  • Wang M, Zhou P, Wang J et al (2017) Left or right: how does amino acid chirality affect the handedness of nanostructures self-assembled from short amphiphilic peptides? J Am Chem Soc 139:4185–4194

    Article  CAS  PubMed  Google Scholar 

  • Wang J, Peng C, Yu Y et al (2020) Exploring conformational change of adenylate kinase by replica exchange molecular dynamic simulation. Biophys J 118:1009–1018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wychowaniec JK, Patel R, Leach J et al (2020) Aromatic stacking facilitated self-assembly of ultrashort ionic complementary peptide sequence: β-sheet nanofibers with remarkable gelation and interfacial properties. Biomacromolecules 21:2670–2680

    Article  CAS  PubMed  Google Scholar 

  • Xiong Q, Jiang Y, Cai X et al (2019) Conformation dependence of diphenylalanine self-assembly structures and dynamics: insights from hybrid-resolution simulations. ACS Nano 13:4455–4468

    Article  CAS  PubMed  Google Scholar 

  • Yang YI, Shao Q, Zhang J et al (2019) Enhanced sampling in molecular dynamics. J Chem Phys 151:070902

    Article  PubMed  Google Scholar 

  • Yuan C, Li S, Zou Q et al (2017) Multiscale simulations for understanding the evolution and mechanism of hierarchical peptide self-assembly. Phys Chem Chem Phys 19:23614–23631

    Article  CAS  PubMed  Google Scholar 

  • Zaldivar G, Samad MB, Conda-Sheridan M, Tagliazucchi M (2018) Self-assembly of model short triblock amphiphiles in dilute solution. Soft Matter 14:3171–3181

    Article  CAS  PubMed  Google Scholar 

  • Zhao Y, Yang W, Chen C et al (2018) Rational design and self-assembly of short amphiphilic peptides and applications. Curr Opin Colloid Interface Sci 35:112–123

    Article  CAS  Google Scholar 

  • Zheng Y, Mao K, Chen S, Zhu H (2021) Chirality effects in peptide assembly structures. Front Bioeng Biotechnol 9:703004

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhou P, Deng L, Wang Y et al (2016) Different nanostructures caused by competition of intra- and inter- β -sheet interactions in hierarchical self-assembly of short peptides. J Colloid Interface Sci 464:219–228

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Physics Department, University of Cagliari, Cittadella Universitaria, Monserrato, Italy

    Attilio Vittorio Vargiu & Giuliano Malloci

  2. Chemical and Pharmaceutical Sciences Department, University of Trieste, Trieste, Italy

    Silvia Marchesan

  3. INSTM, University of Trieste, Trieste, Italy

    Silvia Marchesan

Authors
  1. Attilio Vittorio Vargiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giuliano Malloci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Silvia Marchesan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Attilio Vittorio Vargiu .

Editor information

Editors and Affiliations

  1. Leicester School of Pharmacy, Leicester Institute for Pharmaceutical Innovation, De Montfort University, Leicester, UK

    Mohamed A. Elsawy

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vargiu, A.V., Malloci, G., Marchesan, S. (2023). In Silico Prediction of Peptide Self-assembly into Nanostructures. In: Elsawy, M.A. (eds) Peptide Bionanomaterials. Springer, Cham. https://doi.org/10.1007/978-3-031-29360-3_9

Download citation

Publish with us

Policies and ethics

Search

Navigation