ArticleJournal of chemical theory and computation2024
Multiscale Simulations to Discover Self-Assembled Oligopeptides: A Benchmarking Study.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Predicting Supramolecular Self-Assembly of Peptide Structures with AlphaFold3.bioRxiv : the preprint server for biology · 2026Article
- Progressive Backmapping of Highly Coarse-Grained Protein Models.bioRxiv : the preprint server for biology · 2026Article
- Interplay of Hydrophobicity, Charge, and Sequence Length in Oligopeptide Coassembly.The journal of physical chemistry. B · 2025Article
- Nucleoprotein Phase-Separation Affinities Revealed via Atomistic Simulations of Short Peptide and RNA Fragments.The journal of physical chemistry letters · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Peptide self-assembly is critical for biomedical and material discovery and production. While it is costly to experimentally test every possible peptide design, computational assessment provides an affordable solution to evaluate many designs and prioritize synthesis and characterization. Following a theoretical investigation, we present a systematic analysis of all-atom and coarse-grained simulations to predict peptide self-assembly. Benchmarking studies of two model dipeptides allow us to assess the impacts of intrinsic properties (such as amino acids and terminal modifications) and external environment (such as salinity) on the simulated aggregation. Further examination of 20 oligopeptides containing two to five amino acids shows good agreement among our theory, simulations, and prior experimental observations. The success rate of our prediction is 90%. Therefore, our theory, simulation, and analysis can be useful to identify peptide designs that can self-assemble and predict the potential nanostructures. These findings lay the ground for future virtual screening of peptide-assembled nanostructures and computer-aided biologics design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.