Geometry-Aware Design Framework for DNA Origami Scaffold Optimization

Overview

DNA origami design automation has advanced rapidly, but scaffold sequence is typically treated as fixed, despite emerging methods enabling custom scaffold synthesis. This project presents a geometry-aware, budget-constrained computational framework for post-routing scaffold sequence optimization that complements existing routing tools. Cadnano designs were converted into graph representations in which duplex binding windows are nodes and crossover couplings are edges. Window hybridization free energies were computed using nearest-neighbor thermodynamic models, and scaffold redesign was formulated as a minimal-edit optimization problem under explicit mutation budgets. Multiple optimization strategies were evaluated, including weak-window rescue, crossover-local stabilization, cooperativity smoothing, beam search, Markov Random Field simulated annealing, and machine learning–guided ranking. Across a benchmark set of DNA origami designs, scaffold-side optimization produced consistent, non-random improvements in local thermodynamic stability. Machine learning–guided prioritization improved low-budget efficiency relative to heuristic and random baselines, increasing weak-window rescue and gain per edit. However, improved prioritization did not substantially reduce high-tail discontinuity metrics (p95 EDI), and accepted edits saturated at approximately 3–4 effective modifications even as nominal mutation budgets increased. Structural diagnostics further showed that persistent instability concentrates at short-to-long duplex junctions imposed by routing topology. These results indicate the presence of a topology-limited stability ceiling that cannot be overcome by scaffold sequence optimization alone. This work establishes scaffold redesign as an effective post-routing refinement layer while demonstrating that residual instability is dominated by routing topology, motivating future routing–sequence co-design and experimental validation.

Competition history

• CSEF 2026 Computational Science (Senior Division) · Entry S-07-40

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