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Terminus-immobilization effect on peptide conformations and peptide–peptide interactions

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Abstract

Peptide-modified delivery systems are enabling the improvement of the targeting specificity, biocompatibility, stability, etc. However, the precise design of a peptide-decorated surface for a designated function has remained to be challenging due to a lack of mechanistic understanding of the interactions between surface-bound peptide ligands and their receptors. Enlightened by the recent report on pairwise interactions between peptides in the solution state and surface-immobilized state, we used computational simulations to explore the contributing mechanisms underlining the observed binding affinity characteristics. Molecular dynamics simulations were performed to sample and compare conformations of homo-octapeptides free in solution (mobile peptides) and bound to the surface (N-terminal fixed peptides). We found that peptides converged to more extended and rigid conformations when immobilized to the surface and confirmed that the extended structures could increase the space available to counter-interacting peptides during the peptide–peptide interactions. In addition, studies on interactions between stationary and mobile peptides revealed that main-chain/side-chain and side-chain/side-chain hydrogen bonds play an important role. The presented efforts in this work may provide supportive references for peptide design and modification on the nanoparticle surface as well as guidance for analyzing peptide–receptor interactions through an emphasis on hydrogen bonds during peptide design and an understanding of the influence on the binding affinity by the sequence-dependant conformational changes after peptide immobilization.

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References

  1. Rosenblum, D.; Joshi, N.; Tao, W.; Karp, J. M.; Peer, D. Progress and challenges towards targeted delivery of cancer therapeutics. Nat. Commun. 2018, 9, 1410.

    Article  Google Scholar 

  2. Mitchell, M. J.; Billingsley, M. M.; Haley, R. M.; Wechsler, M. E.; Peppas, N. A.; Langer, R. Engineering precision nanoparticles for drug delivery. Nat. Rev. Drug Discov. 2021, 20, 101–124.

    Article  CAS  Google Scholar 

  3. Lindberg, J.; Nilvebrant, J.; Nygren, P. Å.; Lehmann, F. Progress and future directions with peptide–drug conjugates for targeted cancer therapy. Molecules 2021, 26, 6042.

    Article  CAS  Google Scholar 

  4. Muttenthaler, M.; King, G. F.; Adams, D. J.; Alewood, P. F. Trends in peptide drug discovery. Nat. Rev. Drug Discov. 2021, 20, 309–325.

    Article  CAS  Google Scholar 

  5. Qiao, Z. Y.; Lin, Y. X.; Lai, W. J.; Hou, C. Y.; Wang, Y.; Qiao, S. L.; Zhang, D.; Fang, Q. J.; Wang, H. A general strategy for facile synthesis and in situ screening of self-assembled polymer-peptide nanomaterials. Adv. Mater. 2016, 28, 1859–1867.

    Article  CAS  Google Scholar 

  6. von Maltzahn, G.; Park, J. H.; Lin, K. Y.; Singh, N.; Schwöppe, C.; Mesters, R.; Berdel, W. E.; Ruoslahti, E.; Sailor, M. J.; Bhatia, S. N. Nanoparticles that communicate in vivo to amplify tumour targeting. Nat. Mater. 2011, 10, 545–552.

    Article  CAS  Google Scholar 

  7. Xiang, Z. C.; Yang, X. L.; Xu, J. J.; Lai, W. J.; Wang, Z. H.; Hu, Z. Y.; Tian, J. S.; Geng, L.; Fang, Q. J. Tumor detection using magnetosome nanoparticles functionalized with a newly screened EGFR/HER2 targeting peptide. Biomaterials 2017, 115, 53–64.

    Article  CAS  Google Scholar 

  8. Bai, L. L.; Du, Y. M.; Peng, J. X.; Liu, Y.; Wang, Y. M.; Yang, Y. L.; Wang, C. Peptide-based isolation of circulating tumor cells by magnetic nanoparticles. J. Mater. Chem. B 2014, 2, 4080–4088.

    Article  CAS  Google Scholar 

  9. Zhang, Y. J.; Zhang, H. R.; Ghosh, D.; Williams, R. O. Just how prevalent are peptide therapeutic products? A critical review. Int. J. Pharm. 2020, 587, 119491.

    Article  CAS  Google Scholar 

  10. Du, H. W.; Hu, X. Y.; Duan, H. Y.; Yu, L. L.; Qu, F. Y.; Huang, Q. X.; Zheng, W. S.; Xie, H. Y.; Peng, J. X.; Tuo, R. et al. Principles of inter-amino-acid recognition revealed by binding energies between homogeneous oligopeptides. ACS Cent. Sci. 2019, 5, 97–108.

    Article  CAS  Google Scholar 

  11. Zou, Y. M.; Yu, L. L.; Fang, X. C.; Zheng, Y. F.; Yang, Y. L.; Wang, C. Position-coded multivalent peptide–peptide interactions revealed by tryptophan-scanning mutagenesis. J. Pept. Sci. 2020, 26, e3273.

    Article  CAS  Google Scholar 

  12. Zou, Y. M.; Tu, B.; Yu, L. L.; Zheng, Y. F.; Lin, Y. C.; Luo, W. D.; Yang, Y. L.; Fang, Q. J.; Wang, C. Peptide conformation and oligomerization characteristics of surface-mediated assemblies revealed by molecular dynamics simulations and scanning tunneling microscopy. RSC Adv. 2019, 9, 41345–41350.

    Article  CAS  Google Scholar 

  13. Samieegohar, M.; Sha, F.; Clayborne, A. Z.; Wei, T. ReaxFF MD simulations of peptide-grafted gold nanoparticles. Langmuir 2019, 35, 5029–5036.

    Article  CAS  Google Scholar 

  14. Ma, W. W.; Saccardo, A.; Roccatano, D.; Aboagye-Mensah, D.; Alkaseem, M.; Jewkes, M.; Di Nezza, F.; Baron, M.; Soloviev, M.; Ferrari, E. Modular assembly of proteins on nanoparticles. Nat. Commun. 2018, 9, 1489.

    Article  Google Scholar 

  15. González-Díaz, N. E.; López-Rendón, R.; Ireta, J. Insight into the dipeptide self-assembly process using density functional theory. J. Phys. Chem. C 2019, 123, 2526–2532.

    Article  Google Scholar 

  16. Zheng, Y. F.; Luo, W. D.; Yu, L. L.; Chen, S. X.; Mao, K. J.; Fang, Q. J.; Yang, Y. L.; Wang, C.; Zhu, H.; Tu, B. Heterochirality-mediated cross-strand nested hydrophobic interaction effects manifested in surface-bound peptide assembly structures. J. Phys. Chem. B 2022, 126, 723–733.

    Article  CAS  Google Scholar 

  17. Gasteiger, E.; Hoogland, C.; Gattiker, A.; Duvaud, S.; Wilkins, M. R.; Appel, R. D.; Bairoch, A. Protein identification and analysis tools on the ExPASy server. In The Proteomics Protocols Handbook; Walker, J. M., Ed.; Humana Press: Totowa, NJ, 2005; pp 571–607.

    Chapter  Google Scholar 

  18. IBM SPSS Statistics for Windows, Version 26.0. I. Corp., Ed.; Armonk, NY: IBM Corp, 2019.

  19. Maier, J. A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K. E.; Simmerling, C. ff14SB:Improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 2015, 11, 3696–3713.

    Article  CAS  Google Scholar 

  20. Feldman, H. J.; Hogue, C. W. V. A fast method to sample real protein conformational space. Proteins: Struct. Funct. Bioinf. 2000, 39, 112–131.

    Article  CAS  Google Scholar 

  21. Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. J. C. Numerical integration of the Cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J. Comput. Phys. 1977, 23, 327–341.

    Article  CAS  Google Scholar 

  22. Roe, D. R.; Cheatham III, T. E. PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 2013, 9, 3084–3095.

    Article  CAS  Google Scholar 

  23. Finkelstein, A. V.; Badretdinov, A. Y.; Gutin, A. M. Why do protein architectures have boltzmann-like statistics. Proteins: Struct. Funct. Bioinf. 1995, 23, 142–150.

    Article  CAS  Google Scholar 

  24. Shao, J. Y.; Tanner, S. W.; Thompson, N.; Cheatham, T. E. Clustering molecular dynamics trajectories: 1. Characterizing the performance of different clustering algorithms. J. Chem. Theory Comput. 2007, 3, 2312–2334.

    Article  CAS  Google Scholar 

  25. Hou, T. J.; Wang, J. M.; Li, Y. Y.; Wang, W. Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J. Comput. Chem. 2011, 32, 866–877.

    Article  CAS  Google Scholar 

  26. Yang, T. Y.; Wu, J. C.; Yan, C. L.; Wang, Y. F.; Luo, R.; Gonzales, M. B.; Dalby, K. N.; Ren, P. Y. Virtual screening using molecular simulations. Proteins: Struct. Funct. Bioinf. 2011, 79, 1940–1951.

    Article  CAS  Google Scholar 

  27. Oehme, D. P.; Brownlee, R. T. C.; Wilson, D. J. D. Effect of atomic charge, solvation, entropy, and ligand protonation state on MM-PB(GB)SA binding energies of HIV protease. J. Comput. Chem. 2012, 33, 2566–2580.

    Article  CAS  Google Scholar 

  28. Wang, J. M.; Hou, T. J. Develop and test a solvent accessible surface area-based model in conformational entropy calculations. J. Chem. Inf. Model. 2012, 52, 1199–1212.

    Article  CAS  Google Scholar 

  29. Hou, T. J.; Wang, J. M.; Li, Y. Y.; Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model. 2011, 51, 69–82.

    Article  CAS  Google Scholar 

  30. Rastelli, G.; Rio, A. D.; Degliesposti, G.; Sgobba, M. Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. J. Comput. Chem. 2010, 31, 797–810.

    Article  CAS  Google Scholar 

  31. Sun, H. Y.; Li, Y. Y.; Tian, S.; Xu, L.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys. 2014, 16, 16719–16729.

    Article  CAS  Google Scholar 

  32. Genheden, S. MM/GBSA and LIE estimates of host–guest affinities:Dependence on charges and solvation model. J. Comput. Aided Mol. Des. 2011, 25, 1085–1093.

    Article  CAS  Google Scholar 

  33. Onufriev, A.; Bashford, D.; Case, D. A. Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins: Struct. Funct. Bioinf. 2004, 55, 383–394.

    Article  CAS  Google Scholar 

  34. Weiser, J.; Shenkin, P. S.; Still, W. C. Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J. Comput. Chem. 1999, 20, 217–230.

    Article  CAS  Google Scholar 

  35. Cock, P. J. A.; Antao, T.; Chang, J. T.; Chapman, B. A.; Cox, C. J.; Dalke, A.; Friedberg, I.; Hamelryck, T.; Kauff, F.; Wilczynski, B. et al. Biopython: Freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 2009, 25, 1422–1423.

    Article  CAS  Google Scholar 

  36. Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865–3868.

    Article  CAS  Google Scholar 

  37. Xu, F.; Deng, Z. X.; Lin, S. J. Tryptophan, an important starting material in biosynthesis of microbial natural products. Microbiol. China 2013, 40, 1796–1809.

    CAS  Google Scholar 

  38. Sanchez, K. M.; Kang, G.; Wu, B. J.; Kim, J. E. Tryptophan–lipid interactions in membrane protein folding probed by ultraviolet resonance Raman and fluorescence spectroscopy. Biophys. J. 2011, 100, 2121–2130.

    Article  CAS  Google Scholar 

  39. Song, Z. H.; Chen, X.; You, X. R.; Huang, K. Q.; Dhinakar, A.; Gu, Z. P.; Wu, J. Self-assembly of peptide amphiphiles for drug delivery: The role of peptide primary and secondary structures. Biomater. Sci. 2017, 5, 2369–2380.

    Article  CAS  Google Scholar 

  40. Huang, F.; Nau, W. M. A conformational flexibility scale for amino acids in peptides. Angew. Chem., Int. Ed. 2003, 42, 2269–2272.

    Article  CAS  Google Scholar 

  41. Wang, G. L.; Dunbrack, R. L.Jr. PISCES: A protein sequence culling server. Bioinformatics 2003, 19, 1589–1591.

    Article  CAS  Google Scholar 

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Acknowledgments

This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDB36000000), the National Key R&D Program of China (No. 2022YFA1203200), and the National Natural Science Foundation of China (No. 32027801).

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Authors and Affiliations

  1. Laboratory of Theoretical and Computational Nanoscience, CAS Key Laboratory for Biological Effects of Nanomaterials & Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China

    Wendi Luo, Xiaocui Fang, Chen Wang, Yanlian Yang, Bin Tu & Qiaojun Fang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Wendi Luo, Xiaocui Fang, Chen Wang, Yanlian Yang, Bin Tu & Qiaojun Fang

  3. Sino-Danish Center for Education and Research, Beijing, 101408, China

    Chen Wang & Qiaojun Fang

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  1. Wendi Luo
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  2. Xiaocui Fang
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  5. Bin Tu
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Correspondence to Bin Tu or Qiaojun Fang.

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Luo, W., Fang, X., Wang, C. et al. Terminus-immobilization effect on peptide conformations and peptide–peptide interactions. Nano Res. 16, 13498–13508 (2023). https://doi.org/10.1007/s12274-023-5787-7

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  • DOI: https://doi.org/10.1007/s12274-023-5787-7

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