XL-MS-Guided Structure Prediction of Disordered Encephalitozoon hellem Proteins

Area of Science:

• Structural biology
• Parasitology
• Computational biology

Background:

• Microsporidia, such as Encephalitozoon hellem, are human parasites with genetically intractable proteomes.
• Limited sequence similarity hinders structural studies of microsporidian proteins using traditional methods.

Purpose of the Study:

• To develop a novel approach for predicting the structure of microsporidian proteins.
• To overcome limitations in homology-based modeling and deep learning for proteins with low sequence similarity.

Main Methods:

• Incorporated cross-linking mass spectrometry (XL-MS) data into protein structure prediction.
• Utilized deep learning-based modeling and docking with distance constraints from XL-MS.
• Applied the AlphaLink-Modeller framework to model interacting spore wall proteins (Swp1b and EnP1) of E. hellem.

Main Results:

• Generated a structural model for two interacting E. hellem spore wall proteins (Swp1b and EnP1).
• The model is consistent with experimental XL-MS data and validated by independent data.
• Demonstrated the interaction between Swp1b and EnP1, previously unknown.

Conclusions:

• The AlphaLink-Modeller framework effectively predicts structures of proteins with limited homology and flexible regions.
• This integrative modeling approach expands structural characterization to previously inaccessible microsporidian targets.
• Enables functional characterization of essential parasite proteins.