Predictable fold switching by the SARS-CoV-2 protein ORF9b

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• Fold-switching proteins dynamically alter their structures and functions in response to environmental cues.
• These proteins are implicated in critical biological processes and various diseases, including cancer, tuberculosis, and Alzheimer's disease.
• Current methods for identifying fold-switching proteins are largely serendipitous, necessitating the development of predictive approaches.

Purpose of the Study:

• To evaluate the efficacy of two recently developed high-throughput computational methods for predicting protein fold switching.
• To test these predictive methods on ORF9b, a fold-switching protein from SARS-CoV-2, and its homolog ORF9b1 from SARS-CoV-1.

Main Methods:

• Application of two distinct high-throughput computational prediction methods.
• Testing the methods on known and suspected fold-switching proteins, specifically ORF9b (SARS-CoV-2) and ORF9b1 (SARS-CoV-1).

Main Results:

• Both computational methods accurately predicted fold switching for ORF9b from SARS-CoV-2.
• The methods also correctly predicted fold switching for ORF9b1 from SARS-CoV-1, aligning with experimental binding data.
• These findings validate the predictive power of computational approaches for identifying fold-switching proteins.

Conclusions:

• High-throughput computational methods are effective for predicting protein fold-switching capabilities.
• Fold switching may be a common characteristic among ORF9b homologs, suggesting broader implications for viral protein function and evolution.
• This predictive capability can accelerate the discovery of novel therapeutic targets for diseases associated with protein misfolding.