Molecular Modeling of Self-Assembling Peptides

Abstract

Peptide epitopes mediate as many as 40% of protein− protein interactions and fulfill signaling, inhibition, and activation roles within the cell. Beyond protein recognition, some peptides can self-or coassemble into stable hydrogels, making them a readily available source of biomaterials. While these 3D assemblies are routinely characterized at the fiber level, there are missing atomistic details about the assembly scaffold. Such atomistic detail can be useful in the rational design of more stable scaffold structures and with improved accessibility to functional motifs. Computational approaches can in principle reduce the experimental cost of such an endeavor by predicting the assembly scaffold and identifying novel sequences that adopt said structure. Yet, inaccuracies in physical models and inefficient sampling have limited atomistic studies to short (two or three amino acid) peptides. Given recent developments in machine learning and advances in sampling strategies, we revisit the suitability of physical models for this task. We use the MELD (Modeling Employing Limited Data) approach to drive self-assembly in combination with generic data in cases where conventional MD is unsuccessful. Finally, despite recent developments in machine learning algorithms for protein structure and sequence predictions, we find the algorithms are not yet suited for studying the assembly of short peptides.

Injectable self-assembled GDF5-containing dipeptide hydrogels for enhanced tendon repair

“…In vivo animal experiments showed that 6 weeks after hydrogel injection, the DPH and GDF5@DPH groups had less hydrogel residue in the Achilles tendon than the SA group, along with reduced inflammatory cell infiltration when compared with the SA group; this was beneficial for fibrin growth and repair during tendon regeneration, and obstruction by implanted materials and foreign body reactions could be avoided. Several previous studies have also shown that polypeptide hydrogels have good biocompatibility and degradability, which are beneficial for cell proliferation and extracellular matrix production, accompanied by positive effects on inducing cell migration [ 22 , 28 ]. Yang et al [ 61 ] designed and synthesized a PAA-RGD polypeptide hydrogel.…”

“…Hydrogels exhibit high water absorption and good biocompatibility and can simulate the extracellular matrix to form a 3D scaffold, which is beneficial for cell growth and tissue regeneration [ [19] , [20] , [21] ]. Peptide molecules in self-assembling polypeptide hydrogels usually contain 10–20 amino acids; these peptide molecules can self-assemble into network structures owing to their unique properties [ 22 ]. This self-assembly process typically relies on non-covalent bonding interactions, such as hydrophobic interactions, electrostatic interactions, hydrogen bonding, and π–π interactions [ 23 , 24 ].…”

“…First, the hydrogel can be used as a drug delivery system to deliver drugs precisely to the repair site in a sustained, metered manner [ [25] , [26] , [27] ]. Second, it can support and promote cell proliferation, differentiation, and tissue regeneration, providing favorable conditions for successful tissue engineering [ 22 , 28 , 29 ]. However, despite the potential applications of self-assembled polypeptide hydrogels for tissue engineering and wound repair, the current therapeutic results are not ideal, especially for tendon repair.…”

Tips and Tricks in the Modeling of Supramolecular Peptide Assemblies

“…214 Another example is the work performed by Perez and Jones, who observed improved predictive performance when incorporating experimental NMR information into their MLbased predictions for self-assembling and coassembling peptides. 215 The Tuttle group utilized an active ML method to extend their material discovery approach from dipeptides and tripeptides to peptides up to hexapeptides. 216 In their study, they highlighted the impracticality of simulating all possible sequences due to the exponential growth in number with increasing peptide length.…”

“…For instance, Wang et al undertook the synthesis and evaluation of 160 peptides to assess their self-assembling capabilities, providing essential data for their ML approach . Another example is the work performed by Perez and Jones, who observed improved predictive performance when incorporating experimental NMR information into their ML-based predictions for self-assembling and coassembling peptides …”