L1 ⚛ L1-412 ⊙ Testnet

Protein Folding Energy Landscape Inversion

Computational Biology · Structural biology

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⬇ L1-412.md

# ⚛ L1 Principle — Protein Folding Energy Landscape Inversion **ID:** `L1-412` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Computational Biology — *Structural biology* > **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** 3D protein structure > **📡 Carrier:** N/A · **🌫 Noise:** shot poisson > **⚖ Difficulty (δ):** 50 · **⛓ Block:** 41555219 --- ## 🧠 1. Introduction **Protein Folding Energy Landscape Inversion** is a **nonlinear inverse problem** whose unknown lives in **3D protein structure** space, within the **Structural biology** sub-domain of **Computational Biology**. Measurements consist of N/A via a **cryo em nmr structure determination** sensing mechanism. The forward operator applies, in order: applies a smooth nonlinear function element-wise; S · md · molecular dynamics operator; O · rmsd · structure comparison operator. Observations are corrupted by Poisson shot noise from quantum-limited detection. Existence of the recovered 3D_protein_structure is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 10000000.0); conformational_heterogeneity dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Shot poisson sets the irreducible data-fidelity floor. ## ⚙ 2. Forward Model Physical chain: **x** → Pointwise nonlinearity → S · md · molecular dynamics → O · rmsd · structure comparison → **y** (detector). ``` y = `O.rmsd.structure_comparison` `S.md.molecular_dynamics` f(·) x, measurements ~ Poisson(αy) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `N.pointwise` | Applies a smooth nonlinear function element-wise | | `S.md.molecular_dynamics` | S · md · molecular dynamics operator | | `O.rmsd.structure_comparison` | O · rmsd · structure comparison operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Computational Biology | | Sub domain | Structural biology | | Carrier | N/A | | Problem class | nonlinear_inverse | | Solution space | 3D_protein_structure | | Noise model | shot_poisson | | Integration axis | conformation_space | | Difficulty delta | 50 | | L dag | 6.5 | ## 📡 4. Measurement Model Existence of the recovered 3D_protein_structure is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 10000000.0); conformational_heterogeneity dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Shot poisson sets the irreducible data-fidelity floor. | Metric | Value | |---|---| | Metric | TM_score | | Secondary | RMSD_Angstrom | ## 📏 5. Operating Range (Ω) **Center problem class:** `nonlinear_inverse` · **Forward operator:** `cryo_em_nmr_structure_determination` **Center point:** | Parameter | Unit | Value | |---|---|---| | N residues | — | 200 | | Map snr db | dB | 10 | | N conformations | — | 1 | | Resolution angstrom | — | 3 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N residues | — | 10 – 10000 | | Map snr db | dB | 5 – 30 | | N conformations | — | 1 – 100 | | Resolution angstrom | — | 1.5 – 10.0 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.70 TM_score | Metric | Range | |---|---| | Tm score | 0.1 – 1.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **TM_score**, with κ = `10000000000.0` and δ = `50`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x8d1ac8ba34fb77a3f6168e7a8f27664ced1e4082d8d748c18eb95cf2f7b5e847` - **Chain tx hash:** `0xbd019dc9b103ba59d579a469649c3cf3bd2a5873356fed950380c083728ccc27` - **Chain block:** `41555219` --- ## File Mapping This bundle consists of: `L1-412.md`, `L1-412.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-412.md` | Source of truth — edit this | Human or LLM | | `L1-412.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-412`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-412.md).
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L2-412-001 Protein Folding Energy Landscape Inversion — Nominal + Mismatch Specs

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📋 JSON metadata

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  "domain": "Computational Biology",
  "hardness_fn": {
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    "sensing_mechanism": "cryo_em_nmr_structure_determination",
    "solution_space": "3D_protein_structure",
    "sub_domain": "Structural biology",
    "title": "Protein Folding Energy Landscape Inversion"
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        "map_SNR_dB": 10,
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      ],
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      "map_SNR_dB": [
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        30
      ],
      "resolution_Angstrom": [
        1.5,
        10.0
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Structural biology",
  "title": "Protein Folding Energy Landscape Inversion"
}

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