SilverSight/formal/BindingSite/BindingSiteCodec.lean

/-
  BindingSiteCodec.lean — Deterministic Pipeline: PDB → Binding Site Receipt

  The protein-structure analog of HachimojiCodec.lean.
  Takes a PDB identifier, fetches the structure (or uses local file),
  computes the entropy profile, classifies residues via the 8-state
  Hachimoji system, and emits a typed receipt compatible with the
  PVGS-DQ receipt system.

  This is NOT a machine learning model. It is a library function
  that deterministically maps protein structure → classification.
  The ML (Void-X) is only needed for the entropy computation step;
  everything else is deterministic geometry.

  Pipeline:
    PDB ID → fetch structure → extract residues → compute entropy
    → classify via Hachimoji → Sidon address → PVGS params
    → DQ energy → receipt
-/}

import Mathlib
import BindingSiteHachimoji
import BindingSiteEntropy
import pvgs.PVGS_DQ_Bridge_fixed

namespace BindingSiteCodec

open BindingSiteHachimoji
open BindingSiteEntropy
open Semantics.PVGS_DQ_Bridge

-- =================================================================
-- §1. PDB DATA INTERFACE (placeholders for RCSB API)
-- =================================================================

/-- Fetch a PDB structure from the RCSB database.
    In production, this calls the RCSB REST API:
    https://data.rcsb.org/rest/v1/core/entry/{pdbId}
    For now, placeholder that returns empty data. -/
def fetchPDB (pdbId : String) : IO (List (String × String × ℝ × List ℝ)) := do
  -- In production:
  -- 1. Download mmCIF from https://files.wwpdb.org/pub/pdb/data/structures/
  -- 2. Parse with Bio.PDB or similar
  -- 3. Extract: (residue_type, modification, b_factor, neighbor_b_factors)
  -- 4. Return list ordered by residue sequence number
  IO.println s!"[BindingSiteCodec] Fetching PDB {pdbId}..."
  -- Placeholder: return empty (caller must handle)
  pure []

/-- Fetch sequence cluster membership from RCSB.
    https://cdn.rcsb.org/resources/sequence/clusters/clusters-by-entity-40.txt
    Tells us which proteins are structurally similar (same cluster). -/
def fetchClusterMembership (pdbId : String) : IO (Option (ℕ × ℕ)) := do
  -- Returns (entity_id, cluster_id) or none if not found
  IO.println s!"[BindingSiteCodec] Fetching cluster for {pdbId}..."
  pure none

-- =================================================================
-- §2. THE PIPELINE (deterministic, no ML except entropy step)
-- =================================================================

/-- Step 1: Extract residue data from PDB structure. -/
def extractResidues (pdbData : List (String × String × ℝ × List ℝ))
    : List (AminoAcidToken × ℝ × BindingSiteState) :=
  pdbData.map (λ (residueType, mod, bFactor, neighborBFs) =>
    let token := residueToToken residueType mod
    let entropy := entropyFromBFactor bFactor neighborBFs
    let state := entropyToHachimoji entropy false true
    (token, entropy, state)
  )

/-- Step 2: Build the binding site profile. -/
def buildProfile (residues : List (AminoAcidToken × ℝ × BindingSiteState))
    : BindingSiteProfile :=
  let entropies := residues.map (λ (_, e, _) => e)
  let states := residues.map (λ (_, _, s) => s)
  let dominant := states.headD .Ζ
  { residues := residues.map (λ (t, e, s) =>
      { token := t, entropy := e, state := s
      , position := (0, 0, 0)  -- placeholder: actual coords from PDB
      , bindability := 50.0 })
  , totalEntropy := match entropies with | [] => 0 | es => List.sum es / es.length
  , maxEntropy := match entropies with | [] => 0 | es => es.maximumD 0
  , minEntropy := match entropies with | [] => 0 | es => es.minimumD 0
  , siteState := dominant
  , druggable := dominant = .Π ∨ dominant = .Λ
  , receiptHash := "PENDING" }

/-- Step 3: Build PVGS parameters from the profile.
    The stellar rank k = number of distinct Hachimoji states present.
    The displacement μ = average entropy (real part), entropy variance (imag).
    The squeezing ζ = 0 (no squeezing in protein context, placeholder).
    The sign t = +1 if druggable, -1 otherwise. -/
def profileToPVGS (profile : BindingSiteProfile) : PVGSParams :=
  let distinctStates := profile.residues.map (λ r => r.state) |>.eraseDups |>.length
  let avgEntropy := profile.totalEntropy
  let varEntropy := 0.0  -- placeholder: compute variance
  { φ := Q16_16.zero
  , μ_re := Q16_16.ofFloat avgEntropy.toFloat
  , μ_im := Q16_16.ofFloat varEntropy.toFloat
  , ζ_mag := Q16_16.zero
  , ζ_angle := Q16_16.zero
  , k := distinctStates
  , t := if profile.druggable then 1 else -1 }

/-- Step 4: Emit the receipt. -/
def emitReceipt (pdbId : String) (profile : BindingSiteProfile)
    : BindingSiteReceipt :=
  let pvgs := profileToPVGS profile
  let dq := pvgsToDQ pvgs
  let energy := (dualQuatEnergy dq).toInt
  { version := "BindingSite:v1"
  , pdbId := pdbId
  , entityId := 0
  , clusterId := 0
  , profile := profile
  , pvgsParams := pvgs
  , dqEnergy := energy
  , stellarRank := pvgs.k
  , helstromBound := 0.0  -- computed from pairwise discrimination
  , sha256 := "TBD" }

-- =================================================================
-- §3. THE ONE-FUNCTION API
-- =================================================================

/-- `pdb_to_receipt : PDB ID → BindingSiteReceipt`

    The complete pipeline in one call. This is the protein-structure
    analog of `equation_to_emit` from HachimojiCodec.lean.

    Usage:
      let receipt ← pdbToReceipt "1YY9"
      IO.println receipt.profile.siteState
      -- prints: Π (potential binding site)

    The receipt plugs directly into the PVGS-DQ system:
    - receipt.dqEnergy links to dual quaternion energy
    - receipt.stellarRank links to stellar rank / photon variation count
    - receipt.sha256 links to the hash-chained receipt system -/
def pdbToReceipt (pdbId : String) : IO BindingSiteReceipt := do
  let pdbData ← fetchPDB pdbId
  let classified := extractResidues pdbData
  let profile := buildProfile classified
  let cluster ← fetchClusterMembership pdbId
  let receipt := emitReceipt pdbId profile
  -- Update cluster info if available
  match cluster with
  | some (entity, clusterId) =>
    pure { receipt with entityId := entity, clusterId := clusterId }
  | none => pure receipt

-- =================================================================
-- §4. BATCH PROCESSING (for screening libraries)
-- =================================================================

/-- Process a list of PDB IDs and return only the druggable ones.
    This is the screening workflow: given a library of protein
    structures, find which have bindable pockets.

    Analog: `equation_to_emit` filtered for ADMIT results. -/
def screenDruggable (pdbIds : List String) : IO (List BindingSiteReceipt) := do
  let receipts ← pdbIds.mapM pdbToReceipt
  pure (receipts.filter (λ r => r.profile.druggable))

/-- Rank binding sites by DQ energy (lower = more ordered = better pocket).
    This uses the dual quaternion energy as a scoring function,
    exactly like spectral binning ranks equations by profile energy. -/
def rankByEnergy (receipts : List BindingSiteReceipt) : List BindingSiteReceipt :=
  receipts.insertionSort (λ r1 r2 => r1.dqEnergy < r2.dqEnergy)

-- =================================================================
-- §5. TEST CASES (from Void-X paper)
-- =================================================================

/-- Test: EGFR (PDB 1YY9) — the example from Yang et al. 2025 Fig. S8.
    Known epitopes: antibody/nanobody binding sites circled in red.
    Expected result: siteState = Π (potential), druggable = true. -/
def testEGFR : IO Unit := do
  let receipt ← pdbToReceipt "1YY9"
  IO.println s!"EGFR: siteState = {receipt.profile.siteState}"
  IO.println s!"EGFR: druggable = {receipt.profile.druggable}"
  IO.println s!"EGFR: DQ energy = {receipt.dqEnergy}"
  IO.println s!"EGFR: stellar rank = {receipt.stellarRank}"

/-- Test: Tautomerase (PDB 9MUA) — from Void-X Fig. S4A.
    Generated atoms reconstruct GKL, TV, FL fragments.
    Expected: moderate entropy, Λ or Π state. -/
def testTautomerase : IO Unit := do
  let receipt ← pdbToReceipt "9MUA"
  IO.println s!"Tautomerase: siteState = {receipt.profile.siteState}"

/-- Test: KIR2DL1/nanobody (PDB 9HML) — from Void-X Fig. S9A.
    Ground truth entropy: 1.18. AF3 predictions: 2.08-2.66.
    Expected: ground truth = Π, AF3 = Ω (collision, unmodelable). -/
def testKIR2DL1 : IO Unit := do
  let gt ← pdbToReceipt "9HML"
  IO.println s!"KIR2DL1 ground truth: entropy = {gt.profile.totalEntropy}"
  IO.println s!"KIR2DL1 ground truth: state = {gt.profile.siteState}"

end BindingSiteCodec