Conformation-programmed DNA computing

Area of Science:

• Synthetic biology
• Biophysics
• DNA computing

Background:

• Biological systems use multidimensional signaling for cellular control.
• Current synthetic DNA networks primarily rely on sequence programmability, limiting their regulatory scope.
• Integrating conformational dynamics offers a new dimension for DNA-based computation.

Purpose of the Study:

• To develop an allosteric DNA computing framework combining sequence and conformational signals.
• To enable integrated, multilevel signal processing in synthetic DNA networks.
• To bridge DNA computing with genetic control for in vivo applications.

Main Methods:

• Encoding conformational signals using polythymidine loops of varying lengths.
• Utilizing catalytic allosteric hairpin assemblies for signal amplification.
• Designing allosteric DNA neural networks for conformational signal discrimination.
• Developing microRNA-responsive DNA frameworks for gene expression regulation.

Main Results:

• The framework executes loop-dependent logic operations with expanded signaling ranges.
• Catalytic assemblies achieved ~30-fold signal amplification and improved signal-to-noise ratios.
• DNA neural networks discriminated conformational signals with two-nucleotide resolution based on loop lengths.
• MicroRNA-responsive systems demonstrated in vivo gene expression regulation.

Conclusions:

• Established a novel conformational signal-processing paradigm for adaptive DNA computing.
• Demonstrated the potential for integrating sequence and conformational dynamics in DNA networks.
• Paved the way for advanced synthetic biology applications and precision therapeutics.