AlphaFold Substrate Alignments#

Alignment Principles#

The alignment rules are substrate‑agnostic, reproducible, and compatible with vST validation layers.

1. Alignment Objective#

The objective of substrate alignment is to:

• identify stable structural axes within AlphaFold’s latent space
• map triadic resonance primitives onto folding‑coherence behaviors
• interpret regime transitions through resonance‑time dynamics
• project high‑dimensional inference structures into 3D–9D dimensional cores
• support reproducibility, drift detection, and cross‑model comparison

Alignment provides a structural interpretation of folding predictions independent of model architecture.

2. Core Alignment Rules#

2.1 Structural Alignment Rule (S‑alignment)#

Structural outputs (3D conformations, residue‑level geometries) are projected onto the Structural Axis (S‑axis) defined in the substrate.
Alignment preserves:

• backbone orientation
• motif‑level coherence
• local and global folding topology

S‑alignment anchors the inference system to the 3D–9D dimensional core.

2.2 Inference Alignment Rule (I‑alignment)#

Latent‑space representations (attention maps, pairwise embeddings, track‑level signals) are mapped onto the Inference Axis (I‑axis).
Alignment identifies:

• stable latent‑space orientations
• coherence surfaces
• inference‑cycle convergence patterns

I‑alignment enables cross‑model comparison and drift detection.

2.3 Resonance‑Time Alignment Rule (R‑alignment)#

Folding‑coherence transitions are interpreted through the Resonance‑Time Axis (R‑axis).
Alignment classifies inference behavior into:

• stable regimes
• transition regimes
• high‑uncertainty regimes

These regimes correspond to triadic resonance primitives and support vST validation.

3. Dimensional Alignment#

3.1 Dimensional Core Projection#

High‑dimensional inference structures are projected into the 3D–9D dimensional core.
Projection preserves:

• motif‑level structure
• folding‑pathway coherence
• residue‑interaction patterns

3.2 High‑Dimensional Extension#

When required, projections extend into higher‑dimensional substrates (e.g., 32D–128D) while maintaining substrate invariants.

4. Coherence Alignment#

4.1 Coherence Surface Identification#

A coherence surface is a stable region in latent space where folding predictions converge.
Alignment identifies these surfaces by:

• tracking inference‑cycle stability
• measuring resonance‑time consistency
• detecting motif‑level invariants

4.2 Regime‑Transition Mapping#

Transitions between coherence surfaces follow triadic resonance patterns.
Alignment maps these transitions to substrate regimes for interpretability.

5. Validation Alignment#

Alignment integrates with vST validation layers to:

• confirm reproducibility
• detect drift
• verify regime‑transition stability
• validate dimensional projections

Validation ensures substrate alignment remains stable across model versions and datasets.

6. Alignment Boundaries#

Alignment applies only when:

• structural outputs are coherent
• latent‑space representations are stable
• inference cycles follow predictable patterns
• dimensional projections preserve invariants

If these conditions are not met, alignment may be partial or incomplete.