Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2

Area of Science:

• Structural Biology
• Computational Biology
• Biochemistry

Background:

• Deep neural networks, such as AlphaFold2, are increasingly used for protein structure prediction.
• Myocilin (MYOC) is implicated in glaucoma, and understanding its structure, especially variants, is clinically relevant.

Purpose of the Study:

• To evaluate AlphaFold2-modeled wild-type and variant myocilin (MYOC) protein structures against experimentally determined structures.
• To compare molecular dynamics and ligand binding properties of modeled versus experimental MYOC structures.

Main Methods:

• Utilized AlphaFold2 for in silico modeling of MYOC wild-type and variant protein structures.
• Performed comparative analysis with experimentally determined MYOC structures.
• Conducted molecular dynamics simulations and analyzed ligand binding site properties.

Main Results:

• AlphaFold2-modeled MYOC variant structures exhibited high overall similarity to experimental structures.
• Observed subtle differences in amino acid side chain orientations and geometries between modeled and experimental structures.
• Identified variations in molecular dynamics and ligand binding sites between modeled and experimental, and wild-type and variant MYOC structures.

Conclusions:

• AlphaFold2-modeled MYOC variant structures can closely resemble experimentally determined folds.
• Discrepancies in side chain details and functional properties necessitate careful comparison with experimental data.
• Experimental validation is essential before relying on in silico modeled MYOC variant structures for further applications.