I’ve decided to start summarizing literature topics on areas I’m interested in on my blog. I’m starting with peptide self-assembly of diphenylalanine. This is a very incomplete survey of work on it.

One of the most common self-assembling motifs is diphenylalanine, or FF in the amino acid code alphabet. FF is the shortest self-assembling peptide sequence. It is stable up to 100°C and 150°C with dry heating (Sedman et al., 2006), it has a Young’s modulous of 19 GPa (somewhere between human bone and concrete) (Kol et al., 2005) and is stable in harsh solvents such as acetone and alcohol (Adler-Abramovich et al., 2006). FF, or some variation of it, is thought to become an important building block in future biological applications (Yan et al., 2010).

Here’s what FF looks like:

Görbitz was the first to characterize the self-assembling motif (Görbitz, 2001). Interestingly, it is related to the beta-amyloid self-assembling proteins that are implicated in Alzheimer’s disease (Reches & Gazit, 2003). FF can form different structures including vesicles, nanotubes/nanowires, fibrils, and ribbons depending on solvent and concentration(Guo et al., 2012; Reches & Gazit, 2003). These component structures can be combined into larger hydrogels, an important material for tissue engineering (Mahler et al., 2006). One variation of FF peptides, QQKFQFQFEQQ, has been used to great success by Joel Collier at UChicago for designing materials for vaccines (Rudra et al., 2012). Zhou et al. created hydrogels as tissue scaffolding by using FF with an RGD group, a cell bindinig epitope (Zhou et al., 2009). What’s fascinating is that this experimental study and some others use Fmoc-FF instead of just FF, which means they leave the N-terminus protected after synthesis with an Fmoc group. Fmoc, short for Fluorenylmethyloxycarbonyl chloride, is a three-ringed ester group and has much stronger π-π interactions than FF alone. You have to be careful not to confuse such Fmoc work with modeling work, since no one models the Fmoc group that I know of.

Peptide-Inorganic Hybrid Materials

FF has also been combined with inorganic compounds to form some very interesting materials. Ryu et al. created photoluminescent nanotubes by combining FF with terbuium and europium, two lanthanide ions (Ryu et al., 2009). One of the first papers on non-biological applications was casting the self-assembled structures into silver nanowires (Reches & Gazit, 2003). You can also print self-assembled peptides from an inkjet printer onto surfaces, although I’m a little unsure on what the purpose is (Adler‐Abramovich & Gazit, 2008). There is lots of work in encapsulation applications, where for example a drug is encapsulated in a hydrogel (with a cross-linking compound) and slowly released. Newer work is in combining self-assembling peptides with conducting polymers, another fascinating application (Goldshtein et al., 2012)

Modeling Work

Molecule modeling of FF is challenging because the self-assembly process involves many rare events (binding of peptides) and a large length scale, two of the most difficult challenges in molecular modeling. One approach is to use simplified, or coarse-grained models. One example of coarse-graining is relative entropy matching, pioneered by M Scott Shell at UC Berkely. Joohyun, a student of his, studied the system in 2013 with this coarse-graining technique to see if there are differences between zwitterionic and neutral FF self-assembling peptides (Jeon et al., 2013). Using the coarse-graining technique they were able to simulate up to 168 peptides at an effective concentration of 151 mg/ml. The authors of found that hydrophobic clustering played a more important role than charges or even π-π ring-stacking which has been hypothesized to be important for forming oligmers. In the crystal structure they simulated, derived from NMR data, they did find some salt bridge contacts in the zwitterionic case.

Brute-force computation is another approach. Tamamis et al. studied self-assembling FF (Tamamis et al., 2009) with 12 or 8 peptides in their simulation. They observed ring-like structures forming between FF N- and C-termini, indicating strong salt bridging relative to hydrophobic interactions. It is unclear if the rings are actually precursor oligmers in the self-assembly process or due to an over-estimation of salt-bridges due to the implicit solvent model Tamamis et al. used (GBSW). Joohyun et al. noted their her work that the implicit solvent simulations (GBSA) produced invalid oligmer structures (Jeon et al., 2013), although they found no attraction between peptides, not over-attractive rings.

I read some about non-linear embedding or mannifold learning techniques from Andrew Ferguson, who is relatively new to the field and from University of Illinois at Urbana-Champaign. I’m interested to see if he applies these techniques to peptide self-assembly. Andrew, who worked with Pablo Debenedetti at Princeton, uses mannifold learning techniques to reduce the degrees of freedom in these self-assembling systems (Long & Ferguson, 2014). I haven’t seen any results for peptide systems from him yet, but I’m looking forward to it.

Cited References

  1. Sedman, V. L., Adler-Abramovich, L., Allen, S., Gazit, E., & Tendler, S. J. B. (2006). Direct Observation of the Release of Phenylalanine from Diphenylalanine Nanotubes. J. Am. Chem. Soc., 128(21), 6903–6908. https://doi.org/10.1021/ja060358g
  2. Kol, N., Adler-Abramovich, L., Barlam, D., Shneck, R. Z., Gazit, E., & Rousso, I. (2005). Self-Assembled Peptide Nanotubes Are Uniquely Rigid Bioinspired Supramolecular Structures. Nano Lett., 5(7), 1343–1346. https://doi.org/10.1021/nl0505896
  3. Adler-Abramovich, L., Reches, M., Sedman, V. L., Allen, S., Tendler, S. J. B., & Gazit, E. (2006). Thermal and Chemical Stability of Diphenylalanine Peptide Nanotubes:  Implications for Nanotechnological Applications. Langmuir, 22(3), 1313–1320. https://doi.org/10.1021/la052409d
  4. Yan, X., Zhu, P., & Li, J. (2010). Self-assembly and application of diphenylalanine-based nanostructures. Chem. Soc. Rev., 39(6), 1877–1890. https://doi.org/10.1039/b915765b
  5. Görbitz, C. H. (2001). Nanotube Formation by Hydrophobic Dipeptides. Chem. Eur. J., 7(23), 5153–5159. https://doi.org/10.1002/1521-3765(20011203)7:23%3C5153::aid-chem5153%3E3.0.co;2-n
  6. Reches, M., & Gazit, E. (2003). Casting Metal Nanowires Within Discrete Self-Assembled Peptide Nanotubes. Science, 300(5619), 625–627. https://doi.org/10.1126/science.1082387
  7. Guo, C., Luo, Y., Zhou, R., & Wei, G. (2012). Probing the Self-Assembly Mechanism of Diphenylalanine-Based Peptide Nanovesicles and Nanotubes. ACS Nano, 6(5), 3907–3918. https://doi.org/10.1021/nn300015g
  8. Mahler, A., Reches, M., Rechter, M., Cohen, S., & Gazit, E. (2006). Rigid, Self-Assembled Hydrogel Composed of a Modified Aromatic Dipeptide. Adv. Mater., 18(11), 1365–1370. https://doi.org/10.1002/adma.200501765
  9. Rudra, J. S., Sun, T., Bird, K. C., Daniels, M. D., Gasiorowski, J. Z., Chong, A. S., & Collier, J. H. (2012). Modulating Adaptive Immune Responses to Peptide Self-Assemblies. ACS Nano, 6(2), 1557–1564. https://doi.org/10.1021/nn204530r
  10. Zhou, M., Smith, A. M., Das, A. K., Hodson, N. W., Collins, R. F., Ulijn, R. V., & Gough, J. E. (2009). Self-assembled peptide-based hydrogels as scaffolds for anchorage-dependent cells . Biomaterials , 30(13), 2523–2530. https://doi.org/http://dx.doi.org/10.1016/j.biomaterials.2009.01.010
  11. Ryu, J., Lim, S. Y., & Park, C. B. (2009). Photoluminescent Peptide Nanotubes. Advanced Materials, 21(16), 1577–1581. https://doi.org/10.1002/adma.200802700
  12. Reches, M., & Gazit, E. (2003). Casting metal nanowires within discrete self-assembled peptide nanotubes. Science (New York, N.Y.), 300(5619), 625–627. https://doi.org/10.1126/science.1082387
  13. Adler‐Abramovich, L., & Gazit, E. (2008). Controlled patterning of peptide nanotubes and nanospheres using inkjet printing technology. Journal of Peptide Science, November 2007, 217–223. https://doi.org/10.1002/psc
  14. Goldshtein, K., Golodnitsky, D., Peled, E., Adler-Abramovich, L., Gazit, E., Khatun, S., Stallworth, P., & Greenbaum, S. (2012). Effect of peptide nanotube filler on structural and ion-transport properties of solid polymer electrolytes. Solid State Ionics, 220, 39–46. https://doi.org/10.1016/j.ssi.2012.05.028
  15. Jeon, J., Mills, C. E., & Shell, M. S. (2013). Molecular Insights into Diphenylalanine Nanotube Assembly: All-Atom Simulations of Oligomerization. The Journal of Physical Chemistry B, 117(15), 3935–3943. https://doi.org/10.1021/jp308280d
  16. Tamamis, P., Adler-Abramovich, L., Reches, M., Marshall, K., Sikorski, P., Serpell, L., Gazit, E., & Archontis, G. (2009). Self-Assembly of Phenylalanine Oligopeptides: Insights from Experiments and Simulations. Biophysical Journal, 96(12), 5020–5029. https://doi.org/10.1016/j.bpj.2009.03.026
  17. Long, A. W., & Ferguson, A. L. (2014). Nonlinear Machine Learning of Patchy Colloid Self-Assembly Pathways and Mechanisms. The Journal of Physical Chemistry B, 118(15), 4228–4244. https://doi.org/10.1021/jp500350b