Preclinical efficacy of a cell division protein candidate gonococcal vaccine identified by artificial intelligence.

Vaccines to curb the global spread of multidrug-resistant gonorrhea are urgently needed. Here, 26 vaccine candidates identified by an artificial intelligence-driven platform (Efficacy Discriminative Educated Network[EDEN]) were screened for efficacy in the mouse vaginal colonization model. Complement-dependent bactericidal activity of antisera and the EDEN protective scores both correlated positively with the reduction in overall bacterial colonization burden. NGO1549 (FtsN) and NGO0265, both involved in cell division, displayed the best activity and were selected for further development. Both antigens, when fused to create a chimeric protein, elicited bactericidal antibodies against a wide array of gonococcal isolates and significantly attenuated the duration and burden of gonococcal colonization of mouse vaginas. Protection was abrogated in mice that lacked complement C9, the last step in the formation of the membrane attack complex pore, suggesting complement-dependent bactericidal activity as a mechanistic correlate of protection of the vaccine. FtsN and NGO0265 represent promising vaccine candidates against gonorrhea.