3.A.36.  The Artificial ATP-driven Multidomain Copper Transporter (AAMCT) Family

Evolutionary constraints are encoded in protein sequences and can be learned through the latent generative landscape (LGL) framework to predict functional sequences by leveraging evolutionary patterns, enabling exploration of uncharted sequence space. By simulating designed proteins through molecular dynamics (MD), one can gain deeper insights into the interdependencies governing structure and dynamics. Montalvillo Ortega et al. 2025 presented a synergized workflow combining LGL with MD and biochemical characterization, allowing one to explore the sequence space effectively. This approach has been applied to design and characterize two artificial multidomain ATP-driven transmembrane copper transporters, with native-like functionality. This integrative approach has proven effective in revealing the intricate relationships between sequence, structure, and function. This was achieved starting with a native P_1B-type copper-transporting ATPase (Montalvillo Ortega et al. 2025).