ArticleJournal of chemical information and modeling2023
Comparative Performance of Computer Simulation Models of Intrinsically Disordered Proteins at Different Levels of Coarse-Graining.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Physicochemical Principles Governing the Intrinsically Disordered Salivary Peptide Histatin 5: Ensemble Structure, Electrostatics, and Environmental Responsiveness.The journal of physical chemistry. B · 2026Review
- Sizes, conformational fluctuations, and SAXS profiles for intrinsically disordered proteins.Protein science : a publication of the Protein Society · 2025Article
- Molecular simulations integrated with experiments for probing the interaction dynamics and binding mechanisms of intrinsically disordered proteins.Current opinion in structural biology · 2024Review
- PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins.Nucleic acids research · 2024Article
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Authors and funding
2 authors.
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Abstract
Coarse-graining is commonly used to decrease the computational cost of simulations. However, coarse-grained models are also considered to have lower transferability, with lower accuracy for systems outside the original scope of parametrization. Here, we benchmark a bead-necklace model and a modified Martini 2 model, both coarse-grained models, for a set of intrinsically disordered proteins, with the different models having different degrees of coarse-graining. The SOP-IDP model has earlier been used for this set of proteins; thus, those results are included in this study to compare how models with different levels of coarse-graining compare. The sometimes naive expectation of the least coarse-grained model performing best does not hold true for the experimental pool of proteins used here. Instead, it showed the least good agreement, indicating that one should not necessarily trust the otherwise intuitive notion of a more advanced model inherently being better in model choice.
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