Discoba Protein Sequences for Structure Prediction

Discoba protein sequences were gathered to improve protein structure predictions for Discoba species, including Trypanosoma and Leishmania, using AlphaFold and RoseTTAFold. The dataset was created by Richard John Wheeler at the University of Oxford. It is intended for use with the associated GitHub repository for computational biology research.

Use Cases

• Training protein structure prediction models like AlphaFold on Discoba-specific sequences.
• Benchmarking the performance of RoseTTAFold on non-model organism proteomes.
• Comparative genomics studies within the Discoba supergroup based on protein sequence data.
• Improving structural annotations for pathogenic species like Trypanosoma and Leishmania.

Strengths

• Dataset is associated with a specific, documented GitHub repository for reproducible research.
• Focuses on a specific biological supergroup (Discoba), providing targeted data for niche research.
• Explicitly created for use with state-of-the-art tools AlphaFold and RoseTTAFold.

Limitations

• Key metadata such as row count, file size, specific column names, and last update date are unknown across all sources.
• The dataset's scale and completeness are unclear, limiting assessment of its utility for large-scale modeling.
• Descriptions are repetitive across platforms, suggesting limited independent curation or validation.

Provenance

Source

Richard John Wheeler, University of Oxford

Collection Method

Gathered from unspecified sources for computational use.