Phosphatidylethanolamine (PE), a major component of the cellular membrane across all domains of life, is synthesized exclusively by membrane-anchored phosphatidylserine decarboxylase (PSD) in most bacteria. The enzyme undergoes auto-cleavage for activation and utilizes the pyruvoyl moiety to form a Schiff base intermediate with PS to facilitate decarboxylation. However, the structural basis for self-maturation, PS binding, and decarboxylation processes directed by PSD remain unclear. Here, we present X-ray crystal structures of PSD from Escherichia coli, representing an apo form and a PE-bound complex, in which the phospholipid is chemically conjugated to the essential pyruvoyl residue, mimicking the Schiff base intermediate. The high-resolution structures of PE-complexed PSD clearly illustrate extensive hydrophobic interactions with the fatty acyl chains of the phospholipid, providing insights into the broad specificity of the enzyme over a wide range of cellular PS. Furthermore, these structures strongly advocate the unique topology of the enzyme in a lipid bilayer environment, where the enzyme associates with cell membranes in a monotopic fashion via the N-terminal domain composed of three amphipathic helices. Lastly, mutagenesis analyses reveal that E. coli PSD primarily employs D90/D142–H144–S254 to achieve auto-cleavage for the proenzyme maturation, where D90 and D142 act in complementary to each other.

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.