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MIRRAGGE – Minimum Information Required for Reproducible AGGregation Experiments

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Pedro M. Martins, Susanna Navarro, Alexandra Silva, Maria F. Pinto, Zsuzsa Sárkány, Francisco Figueiredo, Pedro José Barbosa Pereira, Francisca Pinheiro, Zuzana Bednarikova, Michał Burdukiewicz, Oxana V. Galzitskaya, Zuzana Gazova, Cláudio M. Gomes, Annalisa Pastore, Louise C. Serpell, Rostislav Skrabana, Vytautas Smirnovas, Mantas Ziaunys, Daniel E. Otzen, Salvador Ventura & 1 more Sandra Macedo-Ribeiro

Original languageEnglish
Article number582488
JournalFrontiers in Molecular Neuroscience
Published27 Nov 2020

King's Authors


Reports on phase separation and amyloid formation for multiple proteins and aggregation-prone peptides are recurrently used to explore the molecular mechanisms associated with several human diseases. The information conveyed by these reports can be used directly in translational investigation, e.g., for the design of better drug screening strategies, or be compiled in databases for benchmarking novel aggregation-predicting algorithms. Given that minute protocol variations determine different outcomes of protein aggregation assays, there is a strong urge for standardized descriptions of the different types of aggregates and the detailed methods used in their production. In an attempt to address this need, we assembled the Minimum Information Required for Reproducible Aggregation Experiments (MIRRAGGE) guidelines, considering first-principles and the established literature on protein self-assembly and aggregation. This consensus information aims to cover the major and subtle determinants of experimental reproducibility while avoiding excessive technical details that are of limited practical interest for non-specialized users. The MIRRAGGE table (template available in Supplementary Information) is useful as a guide for the design of new studies and as a checklist during submission of experimental reports for publication. Full disclosure of relevant information also enables other researchers to reproduce results correctly and facilitates systematic data deposition into curated databases.

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