De Novo Design and Experimental Characterization of Ultrashort Self-Associating Peptides

Abstract

Self-association is a common phenomenon in biology and one that can have positive and negative impacts, from the construction of the architectural cytoskeleton of cells to the formation of fibrils in amyloid diseases. Understanding the nature and mechanisms of self-association is important for modulating these systems and in creating biologically-inspired materials. Here, we present a two-stage de novo peptide design framework that can generate novel self-associating peptide systems. The first stage uses a simulated multimeric template structure as input into the optimization-based Sequence Selection to generate low potential energy sequences. The second stage is a computational validation procedure that calculates Fold Specificity and/or Approximate Association Affinity (K*association) based on metrics that we have devised for multimeric systems. This framework was applied to the design of self-associating tripeptides using the known self-associating tripeptide, Ac-IVD, as a structural template. Six computationally predicted tripeptides (Ac-LVE, Ac-YYD, Ac-LLE, Ac-YLD, Ac-MYD, Ac-VIE) were chosen for experimental validation in order to illustrate the self-association outcomes predicted by the three metrics. Self-association and electron microscopy studies revealed that Ac-LLE formed bead-like microstructures, Ac-LVE and Ac-YYD formed fibrillar aggregates, Ac-VIE and Ac-MYD formed hydrogels, and Ac-YLD crystallized under ambient conditions. An X-ray crystallographic study was carried out on a single crystal of Ac-YLD, which revealed that each molecule adopts a β-strand conformation that stack together to form parallel β-sheets. As an additional validation of the approach, the hydrogel-forming sequences of Ac-MYD and Ac-VIE were shuffled. The shuffled sequences were computationally predicted to have lower K*association values and were experimentally verified to not form hydrogels. This illustrates the robustness of the framework in predicting self-associating tripeptides. We expect that this enhanced multimeric de novo peptide design framework will find future application in creating novel self-associating peptides based on unnatural amino acids, and inhibitor peptides of detrimental self-aggregating biological proteins.

Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels

“…We propose that positive and negative residues near the N-and C-terminus, respectively, create a 'doubly charged' head group, which could drive alignment of the molecules by repulsion of equal charges and strong intermolecular salt bridge formation. Moreover, rules 1 and 2 are congruent with several reports in the literature: for protected tripeptides it has been hypothesized that two sequential aromatic residues can provide a strong tendency to aggregate because of the conformation of the backbone and aromatic interactions, while negative residues at the end of the peptide provide the amphiphilicity to provide an ordered self-assembled structure (Table 1 and Supplementary Tables 4 and 5) 18 . It is also interesting to compare these rules with those created by Dobson and co-workers 41,42 to search for aggregation-prone sequences in peptides and proteins.…”

“…One common approach to alter the self-assembly properties of short peptides is protection of the terminal amine or acid groups with acetyl [14][15][16][17][18] , t-butyloxycarbonyl 19,20 or large aromatic groups [21][22][23][24] , thereby reducing charge repulsions and introducing π-stacking/hydrophobic contributions to favour self-assembly and gelation. Simple rules have been described for assembling peptides based on repeating sequences based on biological systems 25,26 .…”

“…In previous work, we have shown that the propensity of dipeptides (two amino acids) to aggregate can be predicted using coarse-grain (CG) molecular dynamics (MD) 29 . Several other studies comprising short peptide fragments of biological relevance, such as NFGAIL 14,30 (a fragment of human islet amyloid polypeptide) or FF 29,31 , FFF 32 and KLVFFAE 33 (parts of amyloid β [16][17][18][19][20][21][22], have shown the usefulness of CG-MD for studying peptide self-assembly. However, in all these cases, the focus was on systems that were experimentally known to self-assemble.…”

“…On an atomistic level, Smadbeck et al have recently developed a computational protocol for the discovery of self-assembling protected tripeptides from a library based on conservative mutations of template molecule Ac-IVD (128 peptides) 18 . However, we have chosen to utilize the CG MARTINI force field, which has been extensively parameterized for amino acids (Fig.…”

BWM*: A Novel, Provable, Ensemble-Based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design

“…However, protein design is NP-hard (Kingsford et al, 2005), making algorithms that guarantee optimality expensive for larger designs where many residues are allowed to mutate simultaneously. Therefore, researchers have developed tractable approximations of the protein design problem to obtain provably good approximate solutions (Leach and Lemon, 1998;Roberts et al, 2012;Chen et al, 2009;Lilien et al, 2005;Georgiev and Donald, 2007;Donald, 2011, Smadbeck et al, 2014, or employed heuristic approaches to rapidly generate candidate solutions (Lee and Subbiah, 1991;Kuhlman and Baker, 2000;Jones, 1994;Desjarlais and Handel, 1995;Koehl and Delarue, 1994;Jiang et al, 2000;Donald, 2011). Heuristic sampling of sequences quickly generates locally optimal candidate sequences, whereas provable algorithms are guaranteed to return the global minimum energy conformation (GMEC).…”