Generative Landscapes and Dynamics to Design Multidomain Artificial Transmembrane Transporters

Area of Science:

• Computational biology
• Protein engineering
• Biophysics

Background:

• Designing proteins with specific functions is complex due to interconnected factors like fold, dynamics, and function.
• Evolutionary constraints in protein sequences hold patterns that can predict novel functional sequences.

Purpose of the Study:

• To develop and validate an integrated computational and experimental workflow for de novo protein design.
• To explore uncharted protein sequence space and create novel functional proteins.

Main Methods:

• Utilized the latent generative landscape (LGL) framework to learn evolutionary patterns and predict functional sequences.
• Employed molecular dynamics (MD) simulations to analyze the structure-dynamics relationship of designed proteins.
• Combined LGL and MD with biochemical characterization for a comprehensive design and validation process.

Main Results:

• Successfully designed and characterized two artificial multidomain ATP-driven transmembrane copper transporters.
• The designed transporters exhibited native-like functionality.
• The integrated workflow effectively revealed intricate relationships between protein sequence, structure, and function.

Conclusions:

• The synergized workflow combining LGL, MD, and biochemical characterization is effective for exploring sequence space and designing functional proteins.
• This integrative approach provides deeper insights into the interdependencies governing protein structure and dynamics.
• The methodology holds promise for advancing de novo protein design and engineering complex biological systems.