Identification of neuronatin as a SERCA2b regulin-like protein and assessment of its aggregation propensity via coarse grained simulations

Abstract Neuronatin (NNAT) is small transmembrane protein involved in a wide range of physiological processes, such as white adipose tissue browning and neuronal plasticity, as well as pathological ones, such as Lafora disease caused by the formation of NNAT aggregates. However, its 3D structure is unknown, and its mechanism of action is still unclear. In this study the two most well-known NNAT isoforms (α and β) were modelled and the interaction with the SERCA2b calcium pump was assessed using computational methods. First, molecular docking identified the same binding region as the one described for phospholamban, a thoroughly described SERCA inhibitor. Then, analyses of the flux of water molecules during molecular dynamics simulations highlighted significant similarities between the behavior of SERCA2b when in complex with phospholamban, and when in complex with either NNAT isoform. These results suggest that NNAT could be considered a “regulin-like” protein. Additional all-atom and coarse-grained simulations of multiple copies of NNAT highlighted a significant aggregation potential of both NNAT isoforms, supporting experimental data. Statement of significance This study presents the first structural model of neuronatin (NNAT) isoforms α and β. Through molecular docking and molecular dynamics simulations, we propose a NNAT interaction mechanism with the SERCA2b calcium pump similar to that of phospholamban, a known regulin and SERCA inhibitor. Our analyses also suggested a strong aggregation potential of NNAT based on all-atom and coarse-grained simulations, in line with experimental data on its involvement in Lafora disease. These insights suggest NNAT can be considered a “regulin-like” protein, advancing our understanding of its molecular function and contributing to new perspectives in targeting NNAT-related pathologies, as well as reinforcing the role of coarse-grained simulations as a valid tool to assess protein aggregation potential.