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Molecular Modeling of Self-Assembling Peptides.

Authors
  • Jones, Stephen J1
  • Perez, Alberto1
  • 1 Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States. , (United States)
Type
Published Article
Journal
ACS applied bio materials
Publication Date
Feb 19, 2024
Volume
7
Issue
2
Pages
543–552
Identifiers
DOI: 10.1021/acsabm.2c00921
PMID: 36795608
Source
Medline
Keywords
Language
English
License
Unknown

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.

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