Alphafold2 Predictions for Secreted Fungal Proteins from Rhizophagus and Fusarium

Alphafold2 prediction output data for three distinct collections of fungal proteins. Dataset A contains predictions for 753 secreted proteins from Rhizophagus irregularis DAOM197198, Dataset B covers 10 fungal effectors, and Dataset C includes predictions for 454 matches from a MycFOLD-HMM search across Glomeromycotina genomes. The data was contributed by Albin Teulet of the University of Cambridge.

Use Cases

• Predicting protein structure and function based on Alphafold2 model outputs for secreted fungal proteins.
• Studying fungal effector proteins and their potential roles in plant-microbe interactions.
• Comparative analysis of protein structures across different fungal species, including Rhizophagus irregularis and Fusarium oxysporum.
• Benchmarking or training machine learning models for protein structure prediction using pre-computed Alphafold2 data.

Strengths

• Contains predictions for a specific, biologically relevant set of 753 secreted proteins from Rhizophagus irregularis.
• Includes a curated subset of 10 fungal effector proteins with signal peptides removed, which suggests focused biological analysis.
• Integrates data from a systematic search (MycFOLD-HMM) across 36 Glomeromycotina fungal genomes, providing a comparative context.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown for the full collection, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode, contributed by Albin Teulet, University of Cambridge.

Collection Method

Computational predictions generated by the Alphafold2 model for protein sequences derived from genomic annotations and databases.