Conformational ensemble-based framework enables rapid development of Lassa virus vaccine candidates

Abstract

Lassa virus (LASV), an arenavirus endemic to West Africa, poses a significant public health threat due to its high pathogenicity and expanding geographic risk zone. LASV glycoprotein complex (GPC) is the only known target of neutralizing antibodies, but its inherent metastability and conformational flexibility have hindered the development of GPC-based vaccines. We employed a variant of AlphaFold2 (AF2), called subsampled AF2, to generate diverse structures of LASV GPC that capture an array of potential conformational states using MSA subsampling and dropout layers. Conformational ensembles identified several metamorphic domains-areas of significant conformational flexibility-that could be targeted to stabilize the GPC in its immunogenic prefusion state. ProteinMPNN was then used to redesign GPC sequences to minimize the mobility of metamorphic domains. These redesigned sequences were further filtered using subsampled AF2, leading to the identification of promising GPC variants for further testing. A small library of redesigned GPC sequences was experimentally validated and showed significantly increased protein yields compared to controls. Antigenic profiles indicated these variants preserved essential epitopes for effective immune response, suggesting their potential for broad protective efficacy. Our results demonstrate that AI-driven approaches can predict the conformational landscape of complex pathogens. This knowledge can be used to stabilize viral proteins, such as LASV GPC, in their prefusion conformation, optimizing them for stability and expression, and offering a streamlined framework for vaccine design. Our deep learning / machine learning enabled framework contributes to global efforts to combat LASV and has broader implications for vaccine design and pandemic preparedness.

Figure 1.. Sampling of LASV GPC Conformational Diversity Using Subsampled AF2.

(A) Workflow illustrating the sampling strategy employed to enhance conformational heterogeneity. (B) Structural predictions with default AF2 parameters predominantly yield the pre-fusion conformation, with limited exploration of alternative conformations. (C) Subsampled AF2 pipeline captures a broader spectrum of LASV GPC conformations. (D-F) The AF2 predicted ensemble spans pre-fusion conformation while subsampled AF2 ensembles span varying conformations of post-fusion GPC.

Figure 2.. Conformational ensembles predict metamorphic domains within Lassa GPC.

(A) Superimposed predicted structures for uncleaved monomers for Lassa josiah strain highlight conformational plasticity within NFP, IFL and HR1 domains. (B) Superimposed predicted structures of post-fusion conformation highlighting conformational plasticity within NFP, IFL and T-loop during the post-fusion stage. (C) Trajectory analysis for all predicted structures showing conformationally dynamic regions of lassa GPC at different MSA subsamples (as denoted by varying lines). The known domains of GP2 are color-coded: NFP (red); IFL (orange); HR1 (yellow); T-loop (green); HR2 (blue); MPER (purple).

Figure 3.. Stabilization Strategies for Pre-Fusion LASV Glycoprotein Conformation.

(A) GP1-GP2 interface highlighted for stabilization, showing the α5 helix (from GP1) interaction with α6 and α7 helices (from GP2 subunit). (B) Residues (K320, R325, K327, E329) from the post-fusion 6HB conformational ensemble that form stabilizing interactions, illustrating their importance in pre-fusion stability. (C) Overview of the ProteinMPNN design protocol applied to the pre-fusion LASV GPC structure (PDB: 7PVD), showcasing the redesign strategy to enhance structural stability while preserving mAb binding epitopes and N-linked glycosylation sites. The generation of 2,306 sequences from varying temperature parameters and models is illustrated, leading to a refinement process that identified the top 24 designs for experimental evaluation.

Figure 4.. Antigenic characterization of ML designed LASV GPC constructs.

(A) Protein yield of 24 lead designs (with high pLDDT confidence predictions). (B) Fold change compared to GPC-CysR4-I53A for designs from panel A. (C) Antigenic profile of all 24 designs with a panel of conformation specific monoclonal antibodies.