Simulations of active zone structure and function at mammalian NMJs predict that loss of calcium channels alone is not sufficient to replicate LEMS effects.

Lambert-Eaton myasthenic syndrome (LEMS) is an autoimmune-mediated neuromuscular disease thought to be caused by autoantibodies against P/Q-type voltage-gated calcium channels (VGCCs), which attack and reduce the number of VGCCs within transmitter release sites (active zones; AZs) at the neuromuscular junction (NMJ), resulting in neuromuscular weakness. However, patients with LEMS also have antibodies to other neuronal proteins, and about 15% of patients with LEMS are seronegative for antibodies against VGCCs. We hypothesized that a reduction in the number of P/Q-type VGCCs alone is not sufficient to explain LEMS effects on transmitter release. Here, we used a computational model to study a variety of LEMS-mediated effects on AZ organization and transmitter release constrained by electron microscopic, pharmacological, immunohistochemical, voltage imaging, and electrophysiological observations. We show that models of healthy AZs can be modified to predict the transmitter release and short-term facilitation characteristics of LEMS and that in addition to a decrease in the number of AZ VGCCs, disruption in the organization of AZ proteins, a reduction in AZ number, a reduction in the amount of synaptotagmin, and the compensatory expression of L-type channels outside the remaining AZs are important contributors to LEMS-mediated effects on transmitter release. Furthermore, our models predict that antibody-mediated removal of synaptotagmin in combination with disruption in AZ organization alone could mimic LEMS effects without the removal of VGCCs (a seronegative model). Overall, our results suggest that LEMS pathophysiology may be caused by a collection of pathological alterations to AZs at the NMJ, rather than by a simple loss of VGCCs.NEW & NOTEWORTHY We used a computational model of the active zone (AZ) in the mammalian neuromuscular junction to investigate Lambert-Eaton myasthenic syndrome (LEMS) pathophysiology. This model suggests that disruptions in presynaptic active zone organization and protein content (particularly synaptotagmin), beyond the simple removal of presynaptic calcium channels, play an important role in LEMS pathophysiology.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptide/HLA (pHLA, the human MHC) structures has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within a curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these representative backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer peptide/HLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in terms of structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work provide a framework for linking conformational diversity with antigen immunogenicity and receptor cross-reactivity.