Clarus × AlphaFold
Adding stability behaviour to structure prediction
Summary
AlphaFold predicts protein geometry. Clarus predicts how that geometry behaves.
This page introduces the Clarus × AlphaFold pipeline: a stability‑aware modelling regime where AlphaFold supplies the fold and Clarus evaluates drift, recovery, metastability, and transition behaviour under defined perturbations. The result is a continuous K stability trajectory that reveals how reliably a predicted structure holds under real‑world conditions.
Clarus × AlphaFold — Short Index
Clarus × AlphaFold — Short Index
1. The Stability Gap
AlphaFold predicts structure; Clarus measures stability.
2. Why Structure Isn’t Enough
Static folds miss drift, recovery, metastability, collapse, and environmental sensitivity.
3. K — The Stability Signal
Trajectory‑coherence measure capturing robustness, drift, recovery, transitions.
4. The Combined Pipeline
AlphaFold → geometry
Clarus compiler → constraint matrix
C64 → continuous settling
K → stability trajectory
5. Modeled Gains
Higher stability accuracy, better metastability detection, improved drift sensitivity, lower energy cost vs MD.
6. High‑Value Use Cases
Drug safety, misfolding detection, variant scoring, antibody/enzyme engineering, thermal/solvent robustness, high‑throughput screening.
7. Why It Works
Proteins are dynamical systems; C64 is dynamical; K links behaviour to structure.
8. Roadmap
Phase 1: K‑annotated AlphaFold
Phase 2: Software C64
Phase 3: Hardware‑accelerated C64
9. Limitations
Behavioural stability only; simplified environments; pre‑silicon C64; not a replacement for MD or wet‑lab assays.
10. Conclusion
AlphaFold predicts the fold.
Clarus shows how it behaves.
Download paper as pdf
Download paper as pdf