AlphaFold added new 3D protein structures of parasitic worms

The latest AlphaFold database update in January 2022, added three-dimensional (3D) structures of complete proteomes for 27 new organisms relevant to neglected tropical diseases and antimicrobial resistance including 7 parasitic worms from WormBase ParaSite.

Determining the three-dimensional (3D) structure of a protein has been a computational challenge for decades, and can provide essential insights into the underlying mechanisms of the proteins’ functions. AlphaFold is an AI system, created in partnership between DeepMind and the EMBL-European Bioinformatics Institute (EMBL-EBI), that makes state-of-the-art accurate predictions of a protein’s structure from its amino-acid sequence. Launched in July 2021, the database initially released ~350,000 3D structures of the human proteome and other 20 biologically-significant organisms such as C. elegans, E. coli, fruit fly, mouse, zebrafish, malaria parasite and tuberculosis bacteria.

AlphaFold’s latest release, announced on the 28th January 2022, focused on organisms with a UniProt reference proteome that are relevant to Neglected Tropical Disease or antimicrobial resistance. The selection of 27 new species was based on priority lists compiled by the World Health Organisation and included 7 parasitic worms (Table 1). AlphaFold predicted these structures based on their Uniprot reference proteomes, provided through WormBase ParaSite.

SpeciesPredicted structuresLinks
Brugia malayi8,743WormBase Parasite, AlphaFold
Dracunculus medinensis10,834WormBase Parasite, AlphaFold
Onchocerca volvulus12,047WormBase Parasite, AlphaFold
Schistosoma mansoni13,865WormBase Parasite, AlphaFold
Strongyloides stercoralis12,613WormBase Parasite, AlphaFold
Trichuris trichiura9,564WormBase Parasite, AlphaFold
Wuchereria bancrofti12,721WormBase Parasite, AlphaFold
Table 1.Structural predictions for complete proteomes of parasitic worms in AlphaFold DB v.2.1.2

As the majority of helminth proteins do not currently have data from direct protein characterisation studies, the prediction of their 3D structures will provide researchers with a powerful tool in predicting their mechanisms of function and their role within the cell. Scientists can also develop in silico screening assays against drugs that work with the protein’s unique shape.

Figure 1. AlphaFold predicted 3D structure of Schistosoma Mansoni’s Malate dehydrogenase. You can find it here.

The database is expected to grow further in 2022 and cover additional proteomes, as well as a much larger proportion of all proteins in Uniprot (UniRef90).

If you cannot find the AlphaFold predicted structure for the protein of interest of your favourite worm, here are some suggestions:

  • Multiple isoforms are not covered in AlphaFold DB, so make sure you are using the most appropriate protein from the reference proteome of your species.
  • Try searching by protein or gene name rather than specific UniProt accession.
  • Check if your protein is in the reference proteome of one of the covered organisms or in Swiss-Prot.
  • Proteins with high sequence similarity will most likely have identical 3D structure predictions. If you don’t see the sequence you are looking for, try searching for it using the EBI Protein Similarity Search tool against the sequences in the AlphaFold DB and/or using the WormBase ParaSite BLASTp tool against species which already have their proteins in the AlphaFold database. If the query sequence is not available then a structure prediction with a similar sequence to the query may be available.
  • Contact us! If your favourite species or protein is not available yet, keep watching for further announcements (EMBL-EBI news, EMBL-EBI Twitter, DeepMind Twitter) , or let us know.

Undoubtedly, AlphaFold opens new research horizons and we would like to encourage our users to go and explore this ground-breaking dataset ( by searching and testing the 3D protein models of your favourite worm. Over time, we are planning to create a deeper integration of this dataset into WormBase ParaSite, so it will be easier to search, analyse and interpret.

We would love to hear the feedback of the helminth research community on the AlphaFold resource, the structure predictions, how you think WormBase ParaSite could facilitate your interaction with this unique dataset, or anything else. So please feel free to contact us (


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