A general computational design strategy for stabilizing viral class I fusion proteins

Area of Science:

• Structural biology
• Virology
• Immunology

Background:

• Class I fusion proteins are critical for viral entry into host cells.
• Antibodies targeting the prefusion conformation of these proteins are highly effective for vaccines.
• Stabilizing the prefusion state is a key strategy for developing potent viral vaccines.

Purpose of the Study:

• To develop a computational protocol for stabilizing viral class I fusion proteins in their prefusion conformation.
• To destabilize the postfusion conformation of these proteins.
• To accelerate vaccine design for viruses utilizing class I fusion proteins.

Main Methods:

• A computational design protocol was established to stabilize prefusion conformations.
• The protocol was applied to fusion proteins from Respiratory Syncytial Virus (RSV), human Metapneumovirus (hMPV), and SARS-CoV-2.
• Structural analysis and immunological studies were performed on designed proteins.

Main Results:

• The computational protocol successfully stabilized the prefusion state of RSV, hMPV, and SARS-CoV-2 fusion proteins.
• Solved structures confirmed the atomic accuracy of the designed proteins.
• The redesigned RSV F protein elicited a humoral response comparable to an approved vaccine in a mouse model.

Conclusions:

• The developed computational protocol is effective in stabilizing viral prefusion fusion proteins.
• This approach significantly reduces the time and resources required for immunogen optimization.
• The protocol has broad applicability for developing vaccines against viruses employing class I fusion proteins.