news & views MOLECULAR SELF-ASSEMBLY
Searching sequence space
Short peptides are among the most intriguing building blocks in nanotechnology, but it would be very challenging to experimentally study the properties of large numbers of different sequences. Now, a computational analysis of all 8,000 possible tripeptides has been used to identify those with interesting self-assembly behaviour.
Ehud Gazit
T
14
Ranked AP list of 8,000 tripeptides
po
si tio
n
Highest
ird Th Second position
he study of very short peptide building blocks has proven to be a particularly promising area of research in molecular nanotechnology. Peptides are attractive compounds in this regard because of their simple (and often automated) synthesis, their rich chemical diversity and inherent biocompatibility, and their ability to adopt ordered secondary structures. It has been demonstrated that dipeptide and tripeptide motifs contain all of the structural information needed to form a range of well-ordered assemblies at the nanoscale1. Moreover, nanostructures made up from such extremely short peptides can exhibit useful physical properties including mechanical rigidity, piezoelectricity, semiconductivity and visible luminescence2,3. Yet, the identification of structural and functional short-peptide motifs is usually based on either known biological recognition motifs or by trial-and-error studies in which the properties of numerous peptide assemblies are explored experimentally. Now, writing in Nature Chemistry, Rein Ulijn, Tell Tuttle and colleagues describe4 a comprehensive computational analysis of all 8,000 tripeptides that can be built from the 20 canonical amino acids (Fig. 1), and use the results to identify peptide building blocks that could form well-ordered nanostructures. As well as focusing in on individual compounds with interesting self-assembly properties, analysis of the entire sequence space also means that the structural rules dictating whether a particular tripeptide is good at aggregating can be elucidated. Previous studies have identified the aggregation tendency of individual amino acids5, but a very important aspect of the current work — beyond the exhaustive unbiased exploration of the complete sequence space — is the ability to systematically understand the peptide association process in the context of the order of the amino acids within the peptide motifs. The complete aggregation-propensity (AP) analysis provided remarkable insights. It was found that all of the top 13
Computational analysis of aggregation propensity (AP) Entire tripeptide sequence space
First position
Aβ1–42 DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA Aβ42–1 AIVVGGVMLGIIAGKNSGVDEAFFVLKQHHVEYGSDHRFEAD
Lowest
Figure 1 | Identification of aggregation-prone tripeptide motifs. Computational analysis of the entire sequence space for all 8,000 naturally occurring tripeptides allowed their ranking according to aggregation propensity (AP). The ranking of peptide sequences rather than single amino acids may provide insights into the differential aggregation propensity of the 42-amino-acid β-amyloid polypeptide (Aβ1–42) implicated in Alzheimer disease and its non-aggregating reverse analogue (Aβ42–1). These two polypeptides have the same amino acid composition and distribution, but exhibit very different aggregation behaviour. The different AP ranking of the highlighted tripeptide motifs from the central recognition module may provide a partial explanation for the experimental observation.
aggregating tripeptides contain a pair of adjacent aromatic amino acids and at least one phenylalanine (F) residue (in decreasing order of AP score, these 13 tripeptides are: PFF, WFL, MFF, VFF, FFM, FWF, FFF, WWF, FWI, FYI, VFW, PWF, IFF; where P is proline; W is tryptophan; L is leucine; M is methionine; V is valine; Y is tyrosine and I is isoleucine). The notable occurrence of phenylalanine and tryptophan aromatic residues is in complete agreement with the calculated highaggregation propensity of these two amino acids5. When the entire sequence space was analysed, it was found that higher-scoring tripeptides favoured aromatic amino acids in the second and third positions; in addition, residues that were positively charged or capable of hydrogen-bonding were often found at the first position, whereas negatively-charged residues were
typically located in the third position. Moreover, these results clearly illustrate the remarkable aggregation propensity of the diphenylalanine (FF) motif — the core recognition motif of the Alzheimer disease β-amyloid polypeptide6 and one of the most studied dipeptide motifs1–3 — in the context of tripeptides. Of the 13 topscoring tripeptides, 6 contain this FF motif. Moreover the recent characterization7 of bacterial metalloproteases that specifically target the Phe–Phe bond offers some insight into the physiological significance of the extraordinary aggregation propensity of the FF motif. The identification of specific sequences that are responsible for self-assembling behaviour rather than just amino-acid composition is an important achievement. The findings of this study may also explain the difference in AP exhibited
NATURE CHEMISTRY | VOL 7 | JANUARY 2015 | www.nature.com/naturechemistry
© 2014 Macmillan Publishers Limited. All rights reserved
news & views by some peptides and proteins with identical amino-acid compositions and distributions. The ability to recognize aggregation-prone sequences could also prove useful for identifying modules within larger proteins and polypeptides that may undergo association in physiological and pathological states. For example, it is known that the 42-amino-acid β-amyloid polypeptide (Aβ1–42) implicated in Alzheimer disease readily forms amyloid fibrils, whereas its inverse analogue (Aβ42–1) does not aggregate (Fig. 1). This difference in behaviour is in spite of the fact that the two peptides have exactly the same amino acid composition, albeit in reversed order. The significant variance in AP between the VFF tripeptide from the original central recognition module of the polypeptide (AP ranking 3) and its inverse analogue FFV (AP ranking 89) provides a rationale for the observed difference. Another very important aspect of the current work is the identification of tripeptide building blocks that could form stable hydrogels at neutral pH. Although earlier studies demonstrated the ability of short peptides to form macroscopic hydrogels with nanoscale order, the previously identified dipeptide and tripeptide modules were modified and capped ones, typically incorporating the aromatic fluorenylmethyloxycarbonyl (Fmoc) protecting group. The unprotected tripeptide hydrogel-forming modules
(KYF, KYY, KFF and KYW) identified as part of the study by Ulijn, Tuttle and co-workers could play a key role in the utilization of natural tripepides for applications such as tissue engineering, controlled drug release and surgical reconstruction applications. In comparison to the use of other organic building blocks, a practical advantage of using peptides to assemble nanostructures is the availability of hundreds of various protected amino acids that can be combined by routine chemical synthesis to make millions of different tripeptides. The finding that aromatic amino acids play an important role in efficient tripeptide assembly suggests that a larger sequence space — comprising non-coded and nonnatural aromatic amino acids — should be explored. This could include natural, but non-coded, amino-acids such as l-Dopa and oxitriptan (5-hydroxytryptophan) together with artificial synthetic aromatic systems such as fluorophenylalanine, iodophenylalanine, bromophenylalanine, and nitrophenylalanine. Other possibilities include amino acids with larger aromatic systems, such as naphthylalanine and 4-phenyl-phenylalanine, as well as d-amino acid isomers. A far-reaching project may include the computational analysis of all possible dipeptide and tripeptide combinations from commercially available amino acids to build up a comprehensive toolbox for peptide nanotechnologists.
Taken together, the extensive computational efforts presented in the current work4 and their experimental validation, represent an important milestone in the development of short peptide building blocks as key structural and functional elements in modern organic nanotechnology. The combination of the newly defined structural rules, together with advancements in the automated synthesis of peptides, makes this technology especially attractive. The next stage of development may involve simulations that look beyond the aggregation of individual peptide sequences to the combination of different peptide building blocks. Such supramolecular co-assemblies may have structural features that are different to assemblies built from just a single peptide building block8. ❐ Ehud Gazit is in the Department of Molecular Microbiology and Biotechnology and the Department of Materials Science and Engineering at Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel. e-mail: [email protected] References
1. Yan, X., Zhu, P. & Li, J. Chem. Soc. Rev. 39, 1877–1890 (2010). 2. Hauser, C. A. & Zhang, S. Nature 468, 516–517 (2010). 3. Adler-Abramovich, L. & Gazit, E. Chem. Soc. Rev. 43, 6881–6893 (2014). 4. Frederix, P. W. J. M. et al. Nature Chem. 7, 30–37 (2015). 5. Pawar, A. P. et al. J. Mol. Biol. 350, 379–392 (2005). 6. Reches, M. & Gazit, E. Science 300, 625–627 (2003). 7. Lütticke, C. et al. Mol. Biosyst. 8, 1775–1782 (2012). 8. Aida, T., Meijer, E. W. & Stupp, S. I. Science 335, 813–817 (2012).
SYSTEMS CHEMISTRY
Selecting complex behaviour
Creating chemical systems that can model living systems is far from easy. However, the evolution of oil droplets in water through the application of artificial selective pressure to produce droplets with dramatically different — yet specific — behaviours, is an encouraging step in this direction.
Andrew J. Bissette and Stephen P. Fletcher
N
atural selection is a primary mechanism by which biological species evolve, develop new abilities, and adapt to different ecological niches over time. Although biological systems likely emerged from mixtures of relatively simple chemicals, how this ‘prebiotic evolution’ occurred is still unknown, despite a growing body of research aimed at probing chemical systems which display emergent behaviour 1,2. Non-natural chemical systems typically operate under thermodynamic control, which has made the design of responsive non-equilibrium systems that evolve or show
dynamic functions exceedingly challenging. Now, writing in Nature Communications, Leroy Cronin and colleagues report 3 a chemo-robotic platform that uses artificial evolution to select for desired traits in chemical systems. The behaviour of oil droplets in water is governed by a range of factors, including the relative solubility of each phase in the other, the surface tension at the interface, the osmotic pressure inside the droplets as well as relevant chemical equilibria in the system as a whole. Combinations of these properties give rise to phenomena such as
NATURE CHEMISTRY | VOL 7 | JANUARY 2015 | www.nature.com/naturechemistry
© 2014 Macmillan Publishers Limited. All rights reserved
the Marangoni effect (macroscopic droplet motion, well-known for creating ‘legs’ or ‘tears’ inside wine glasses) and, under the right conditions, to complex macroscopic behaviours ranging from biomimetic motion to rudimentary computation. In the work described by Cronin and co-workers, droplets made up of various ratios of four chemicals (1-pentanol, 1-octanol, diethyl phthalate and either dodecane or octanoic acid) are suspended in an alkaline solution of a cationic surfactant. These particular compounds were chosen to produce a chemically simple system: under these 15
Searching sequence space
Short peptides are among the most intriguing building blocks in nanotechnology, but it would be very challenging to experimentally study the properties of large numbers of different sequences. Now, a computational analysis of all 8,000 possible tripeptides has been used to identify those with interesting self-assembly behaviour.
Ehud Gazit
T
14
Ranked AP list of 8,000 tripeptides
po
si tio
n
Highest
ird Th Second position
he study of very short peptide building blocks has proven to be a particularly promising area of research in molecular nanotechnology. Peptides are attractive compounds in this regard because of their simple (and often automated) synthesis, their rich chemical diversity and inherent biocompatibility, and their ability to adopt ordered secondary structures. It has been demonstrated that dipeptide and tripeptide motifs contain all of the structural information needed to form a range of well-ordered assemblies at the nanoscale1. Moreover, nanostructures made up from such extremely short peptides can exhibit useful physical properties including mechanical rigidity, piezoelectricity, semiconductivity and visible luminescence2,3. Yet, the identification of structural and functional short-peptide motifs is usually based on either known biological recognition motifs or by trial-and-error studies in which the properties of numerous peptide assemblies are explored experimentally. Now, writing in Nature Chemistry, Rein Ulijn, Tell Tuttle and colleagues describe4 a comprehensive computational analysis of all 8,000 tripeptides that can be built from the 20 canonical amino acids (Fig. 1), and use the results to identify peptide building blocks that could form well-ordered nanostructures. As well as focusing in on individual compounds with interesting self-assembly properties, analysis of the entire sequence space also means that the structural rules dictating whether a particular tripeptide is good at aggregating can be elucidated. Previous studies have identified the aggregation tendency of individual amino acids5, but a very important aspect of the current work — beyond the exhaustive unbiased exploration of the complete sequence space — is the ability to systematically understand the peptide association process in the context of the order of the amino acids within the peptide motifs. The complete aggregation-propensity (AP) analysis provided remarkable insights. It was found that all of the top 13
Computational analysis of aggregation propensity (AP) Entire tripeptide sequence space
First position
Aβ1–42 DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA Aβ42–1 AIVVGGVMLGIIAGKNSGVDEAFFVLKQHHVEYGSDHRFEAD
Lowest
Figure 1 | Identification of aggregation-prone tripeptide motifs. Computational analysis of the entire sequence space for all 8,000 naturally occurring tripeptides allowed their ranking according to aggregation propensity (AP). The ranking of peptide sequences rather than single amino acids may provide insights into the differential aggregation propensity of the 42-amino-acid β-amyloid polypeptide (Aβ1–42) implicated in Alzheimer disease and its non-aggregating reverse analogue (Aβ42–1). These two polypeptides have the same amino acid composition and distribution, but exhibit very different aggregation behaviour. The different AP ranking of the highlighted tripeptide motifs from the central recognition module may provide a partial explanation for the experimental observation.
aggregating tripeptides contain a pair of adjacent aromatic amino acids and at least one phenylalanine (F) residue (in decreasing order of AP score, these 13 tripeptides are: PFF, WFL, MFF, VFF, FFM, FWF, FFF, WWF, FWI, FYI, VFW, PWF, IFF; where P is proline; W is tryptophan; L is leucine; M is methionine; V is valine; Y is tyrosine and I is isoleucine). The notable occurrence of phenylalanine and tryptophan aromatic residues is in complete agreement with the calculated highaggregation propensity of these two amino acids5. When the entire sequence space was analysed, it was found that higher-scoring tripeptides favoured aromatic amino acids in the second and third positions; in addition, residues that were positively charged or capable of hydrogen-bonding were often found at the first position, whereas negatively-charged residues were
typically located in the third position. Moreover, these results clearly illustrate the remarkable aggregation propensity of the diphenylalanine (FF) motif — the core recognition motif of the Alzheimer disease β-amyloid polypeptide6 and one of the most studied dipeptide motifs1–3 — in the context of tripeptides. Of the 13 topscoring tripeptides, 6 contain this FF motif. Moreover the recent characterization7 of bacterial metalloproteases that specifically target the Phe–Phe bond offers some insight into the physiological significance of the extraordinary aggregation propensity of the FF motif. The identification of specific sequences that are responsible for self-assembling behaviour rather than just amino-acid composition is an important achievement. The findings of this study may also explain the difference in AP exhibited
NATURE CHEMISTRY | VOL 7 | JANUARY 2015 | www.nature.com/naturechemistry
© 2014 Macmillan Publishers Limited. All rights reserved
news & views by some peptides and proteins with identical amino-acid compositions and distributions. The ability to recognize aggregation-prone sequences could also prove useful for identifying modules within larger proteins and polypeptides that may undergo association in physiological and pathological states. For example, it is known that the 42-amino-acid β-amyloid polypeptide (Aβ1–42) implicated in Alzheimer disease readily forms amyloid fibrils, whereas its inverse analogue (Aβ42–1) does not aggregate (Fig. 1). This difference in behaviour is in spite of the fact that the two peptides have exactly the same amino acid composition, albeit in reversed order. The significant variance in AP between the VFF tripeptide from the original central recognition module of the polypeptide (AP ranking 3) and its inverse analogue FFV (AP ranking 89) provides a rationale for the observed difference. Another very important aspect of the current work is the identification of tripeptide building blocks that could form stable hydrogels at neutral pH. Although earlier studies demonstrated the ability of short peptides to form macroscopic hydrogels with nanoscale order, the previously identified dipeptide and tripeptide modules were modified and capped ones, typically incorporating the aromatic fluorenylmethyloxycarbonyl (Fmoc) protecting group. The unprotected tripeptide hydrogel-forming modules
(KYF, KYY, KFF and KYW) identified as part of the study by Ulijn, Tuttle and co-workers could play a key role in the utilization of natural tripepides for applications such as tissue engineering, controlled drug release and surgical reconstruction applications. In comparison to the use of other organic building blocks, a practical advantage of using peptides to assemble nanostructures is the availability of hundreds of various protected amino acids that can be combined by routine chemical synthesis to make millions of different tripeptides. The finding that aromatic amino acids play an important role in efficient tripeptide assembly suggests that a larger sequence space — comprising non-coded and nonnatural aromatic amino acids — should be explored. This could include natural, but non-coded, amino-acids such as l-Dopa and oxitriptan (5-hydroxytryptophan) together with artificial synthetic aromatic systems such as fluorophenylalanine, iodophenylalanine, bromophenylalanine, and nitrophenylalanine. Other possibilities include amino acids with larger aromatic systems, such as naphthylalanine and 4-phenyl-phenylalanine, as well as d-amino acid isomers. A far-reaching project may include the computational analysis of all possible dipeptide and tripeptide combinations from commercially available amino acids to build up a comprehensive toolbox for peptide nanotechnologists.
Taken together, the extensive computational efforts presented in the current work4 and their experimental validation, represent an important milestone in the development of short peptide building blocks as key structural and functional elements in modern organic nanotechnology. The combination of the newly defined structural rules, together with advancements in the automated synthesis of peptides, makes this technology especially attractive. The next stage of development may involve simulations that look beyond the aggregation of individual peptide sequences to the combination of different peptide building blocks. Such supramolecular co-assemblies may have structural features that are different to assemblies built from just a single peptide building block8. ❐ Ehud Gazit is in the Department of Molecular Microbiology and Biotechnology and the Department of Materials Science and Engineering at Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel. e-mail: [email protected] References
1. Yan, X., Zhu, P. & Li, J. Chem. Soc. Rev. 39, 1877–1890 (2010). 2. Hauser, C. A. & Zhang, S. Nature 468, 516–517 (2010). 3. Adler-Abramovich, L. & Gazit, E. Chem. Soc. Rev. 43, 6881–6893 (2014). 4. Frederix, P. W. J. M. et al. Nature Chem. 7, 30–37 (2015). 5. Pawar, A. P. et al. J. Mol. Biol. 350, 379–392 (2005). 6. Reches, M. & Gazit, E. Science 300, 625–627 (2003). 7. Lütticke, C. et al. Mol. Biosyst. 8, 1775–1782 (2012). 8. Aida, T., Meijer, E. W. & Stupp, S. I. Science 335, 813–817 (2012).
SYSTEMS CHEMISTRY
Selecting complex behaviour
Creating chemical systems that can model living systems is far from easy. However, the evolution of oil droplets in water through the application of artificial selective pressure to produce droplets with dramatically different — yet specific — behaviours, is an encouraging step in this direction.
Andrew J. Bissette and Stephen P. Fletcher
N
atural selection is a primary mechanism by which biological species evolve, develop new abilities, and adapt to different ecological niches over time. Although biological systems likely emerged from mixtures of relatively simple chemicals, how this ‘prebiotic evolution’ occurred is still unknown, despite a growing body of research aimed at probing chemical systems which display emergent behaviour 1,2. Non-natural chemical systems typically operate under thermodynamic control, which has made the design of responsive non-equilibrium systems that evolve or show
dynamic functions exceedingly challenging. Now, writing in Nature Communications, Leroy Cronin and colleagues report 3 a chemo-robotic platform that uses artificial evolution to select for desired traits in chemical systems. The behaviour of oil droplets in water is governed by a range of factors, including the relative solubility of each phase in the other, the surface tension at the interface, the osmotic pressure inside the droplets as well as relevant chemical equilibria in the system as a whole. Combinations of these properties give rise to phenomena such as
NATURE CHEMISTRY | VOL 7 | JANUARY 2015 | www.nature.com/naturechemistry
© 2014 Macmillan Publishers Limited. All rights reserved
the Marangoni effect (macroscopic droplet motion, well-known for creating ‘legs’ or ‘tears’ inside wine glasses) and, under the right conditions, to complex macroscopic behaviours ranging from biomimetic motion to rudimentary computation. In the work described by Cronin and co-workers, droplets made up of various ratios of four chemicals (1-pentanol, 1-octanol, diethyl phthalate and either dodecane or octanoic acid) are suspended in an alkaline solution of a cationic surfactant. These particular compounds were chosen to produce a chemically simple system: under these 15
