Daisy: An integrated repeat protein curation service

Manuel Bezerra-Brandao, Ronaldo Romario Tunque Cahui, Layla Hirsh

Research output: Contribution to journalArticlepeer-review


Tandem repeats in proteins identification, classification and curation is a complex process that requires manual processing from experts, processing power and time. There are recent and relevant advances applying machine learning for protein structure prediction and repeat classification that are useful for this process. However, no service contemplates required databases and software to supplement researching on repeat proteins. In this publication we present Daisy, an integrated repeat protein curation web service. This service can process Protein Data Bank (PDB) and the AlphaFold Database entries for tandem repeats identification. In addition, it uses an algorithm to search a sequence against a library of Pfam hidden Markov model (HMM). Repeat classifications are associated with the identified families through RepeatsDB. This prediction is considered for enhancing the ReUPred algorithm execution and hastening the repeat units identification process. The service can also operate every associated PDB and AlphaFold structure with a UniProt proteome registry. Availability: The Daisy web service is freely accessible at daisy.bioinformatica.org.

Original languageEnglish
Article number108033
JournalJournal of Structural Biology
Issue number4
StatePublished - Dec 2023


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