Systematic computer-aided disulfide design as a general strategy to stabilize prefusion class I fusion proteins.

Numerous enveloped viruses, such as coronaviruses, influenza, and respiratory syncytial virus (RSV), utilize class I fusion proteins for cell entry. During this process, the proteins transition from a prefusion to a postfusion state, undergoing substantial and irreversible conformational changes. The prefusion conformation has repeatedly shown significant potential in vaccine development. However, the instability of this state poses challenges for its practical application in vaccines. While non-native disulfides have been effective in maintaining the prefusion structure, identifying stabilizing disulfide bonds remains an intricate task. Here, we present a general computational approach to systematically identify prefusion-stabilizing disulfides. Our method assesses the geometric constraints of disulfide bonds and introduces a ranking system to estimate their potential in stabilizing the prefusion conformation. We hypothesized that disulfides restricting the initial stages of the conformational switch could offer higher stability to the prefusion state than those preventing unfolding at a later stage. The implementation of our algorithm on the RSV F protein led to the discovery of prefusion-stabilizing disulfides that supported our hypothesis. Furthermore, the evaluation of our top design as a vaccine candidate in a cotton rat model demonstrated robust protection against RSV infection, highlighting the potential of our approach for vaccine development.

Systematic computer-aided disulfide design as a general strategy to stabilize prefusion class I fusion proteins.

Numerous enveloped viruses, such as coronaviruses, influenza, and respiratory syncytial virus (RSV), utilize class I fusion proteins for cell entry. During this process, the proteins transition from a prefusion to a postfusion state, undergoing substantial and irreversible conformational changes. The prefusion conformation has repeatedly shown significant potential in vaccine development. However, the instability of this state poses challenges for its practical application in vaccines. While non-native disulfides have been effective in maintaining the prefusion structure, identifying stabilizing disulfide bonds remains an intricated task. Here, we present a general computational approach to systematically identify prefusion-stabilizing disulfides. Our method assesses the geometric constraints of disulfide bonds and introduces a ranking system to estimate their potential in stabilizing the prefusion conformation. We found, for the RSV F protein, that disulfides restricting the initial stages of the conformational switch can offer higher stability to the prefusion state than those preventing unfolding at a later stage. The implementation of our algorithm on the RSV F protein led to the discovery of prefusion-stabilizing disulfides, providing evidence that supports our hypothesis. Furthermore, the evaluation of our top design as a vaccine candidate in a cotton rat model demonstrated robust protection against RSV infection, highlighting the potential of our approach for vaccine development.

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

Many pathogenic viruses rely on class I fusion proteins to fuse their viral membrane with the host cell membrane. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more stable postfusion state. Mounting evidence underscores that antibodies targeting the prefusion conformation are the most potent, making it a compelling vaccine candidate. Here, we establish a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. With this protocol, we stabilize the fusion proteins of the RSV, hMPV, and SARS-CoV-2 viruses, testing fewer than a handful of designs. The solved structures of these designed proteins from all three viruses evidence the atomic accuracy of our approach. Furthermore, the humoral response of the redesigned RSV F protein compares to that of the recently approved vaccine in a mouse model. While the parallel design of two conformations allows the identification of energetically sub-optimal positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.