(394a) Insights to Nanobody-Induced Surface-Layer Protein Lattice Inhibition from Multiscale Molecular Dynamics and Data-Driven Classification

Surface-layer proteins (SLPs) are multi-domain proteins that self-assemble into a nanoporous lattice, the so-called S-layer, on the exterior of many bacteria and archaea. The S-layer protects and aids signaling between cells and has been identified as a virulence factor in pathogens. Recently, a subset of camelid nanobodies targeting the B. anthracis SLP were found to disarm the pathogen by disassembling the lattice; infections in murine models were cleared within 6 days, suggesting that nanobodies may be used as therapeutics to prevent pathogenesis. However, several nanobodies bound to the same site of the SLP did not cause lattice disassembly, suggesting that binding affinity alone cannot account for the functional outcome of all nanobody therapeutics. We hypothesize that inhibitory nanobodies enact strain across the lattice to a greater degree than noninhibitory nanobodies, causing lattice disassembly. To test our hypothesis, we combine insights from atomistic molecular dynamics (MD), coarse-grained (CG) MD, and data-driven classification. Using systematically derived CG models, we discuss how nanobodies bind to and restrict collective motions throughout the assembled S-layer. We also discuss the use of gradient-boosting decision trees to classify non-binding, binding-noninhibitory, and binding-inhibitory nanobodies based only on the dynamics of the SLP binding region. These interpretable decision trees will be inspected in conjunction with dynamical insights from CGMD to uncover the specific biophysical mechanisms that drive lattice inhibition and direct identification of nanobody motifs that lead to inhibitory action in binding nanobodies. In the future, we will leverage these insights to design nanobodies with enhanced inhibitory behavior to target S-layer virulence factors in pathogenic bacteria.