AlphaFold Substrate Alignments#

Folding Regimes#

1. Purpose of Folding Regimes#

Folding regimes provide a structural lens for interpreting:

• convergence behavior during inference
• motif‑level stability
• latent‑space coherence
• resonance‑time transitions
• dimensional‑core alignment

Regimes allow folding predictions to be analyzed independently of model architecture or training data.

2. Triadic Folding Regime Structure#

Folding regimes follow the triadic resonance pattern used throughout RSM:

1. Stable Regime (R₁)
2. Transition Regime (R₂)
3. High‑Uncertainty Regime (R₃)

These regimes apply to both structural outputs and latent‑space inference signals.

3. Regime Definitions#

3.1 Stable Regime (R₁)#

A region where folding predictions converge consistently across inference cycles.

Characteristics:

• high motif‑level coherence
• stable backbone geometry
• consistent residue‑interaction patterns
• low variance across inference iterations
• alignment with 3D–9D dimensional cores

R₁ corresponds to resonance‑time stability and forms the substrate’s primary coherence surface.

3.2 Transition Regime (R₂)#

A region where the model shifts between candidate conformations or latent‑space orientations.

Characteristics:

• moderate structural variance
• partial motif stability
• detectable shifts in latent‑space orientation
• increased sensitivity to input features
• resonance‑time oscillation patterns

R₂ represents the dynamic region between stable conformations and is essential for interpreting folding pathways.

3.3 High‑Uncertainty Regime (R₃)#

A region where folding predictions exhibit low coherence and high variance.

Characteristics:

• unstable or conflicting structural outputs
• weak motif‑level signals
• diffuse latent‑space representations
• inconsistent inference‑cycle behavior
• sensitivity to noise or MSA variability

R₃ corresponds to resonance‑time divergence and often indicates insufficient structural information.

4. Regime Transitions#

Regime transitions follow predictable resonance‑time patterns:

• R₁ → R₂: onset of structural reorientation
• R₂ → R₁: convergence to a stable coherence surface
• R₂ → R₃: breakdown of motif‑level structure
• R₃ → R₂: partial recovery of structural coherence

Transitions are detectable through latent‑space orientation shifts, dimensional‑core projections, and vST validation signals.

5. Dimensional‑Core Behavior#

Folding regimes interact with dimensional cores as follows:

• R₁: fully aligns with 3D–9D cores
• R₂: partially aligns; projections reveal transitional geometry
• R₃: weak alignment; projections show high‑dimensional dispersion

Dimensional‑core mapping provides a stable substrate for interpreting regime behavior.

6. Inference‑Cycle Behavior#

Folding regimes correspond to characteristic inference‑cycle patterns:

• R₁: monotonic convergence
• R₂: oscillatory or branching behavior
• R₃: divergent or unstable trajectories

These patterns support reproducibility analysis and drift detection.

7. Regime‑Based Interpretation#

Regimes enable:

• structural interpretation of folding pathways
• identification of stable motifs
• detection of ambiguous or low‑confidence regions
• substrate‑level comparison across models
• integration with vST validation layers

Regime analysis provides a consistent framework for interpreting high‑dimensional biological inference systems.