Mican analysis

MICAN Protein Structure Analysis Skill

MICAN compares protein 3D structures via structure alignment. This skill contains the workflows needed to execute each type of analysis correctly.

Code templates are in the reference files — read them when you need exact code:

• references/score-guide.md — TM-score interpretation and standard output block
• references/code-examples.md — residue correspondence, batch, superposition, sub-optimal
• references/symmetry-analysis.md — self-comparison, rotation matrix, cyclic permutation, domain detection
• references/conformational-analysis.md — per-residue Cα displacement

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Setup

First-time setup

If pymican is not yet installed, run the bundled setup script first:

python .claude/skills/mican-analysis/scripts/setup_env.py

This checks for pymican, installs it via pip install pymican if missing, and verifies the MICAN binary. A C compiler (gcc) is required to build the binary. If the script reports a missing compiler, install gcc before proceeding:

• macOS: xcode-select --install or brew install gcc
• Ubuntu: sudo apt install gcc

Usage

mican is a class. Always instantiate it first:

from pymican import mican
m = mican()
aln = m.align(pdb1='a.pdb', pdb2='b.pdb', options='-w')

aln has these key attributes:

• TMscore, TMscore1, TMscore2 — TM-scores (normalized by min/pdb1/pdb2 length)
• coverage1, coverage2 — alignment coverage in % (0–100, not 0–1)
• rmsd, nalign, size1, size2, seq_identity
• alignment — pandas DataFrame; columns: residue1, residue2 (str), chain1, chain2, aatype1, aatype2, distance (Cα in Å)

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Workflow A: Compare Two Proteins

Step 1 — Choose alignment mode (if not specified, use RW and announce it):

Situation	Mode	Option
Same protein, different states	Sequential (SQ)	-s
Unknown relationship (default)	General-purpose (RW)	-w
Want reverse/antiparallel SSEs too	Exhaustive (RR)	-r
Large-scale screening	add Fast	-f

Step 2 — Run and report all TM-score variants (use Standard Output Block in score-guide.md):

aln = m.align(pdb1='a.pdb', pdb2='b.pdb', options='-w')
tm_mean = (aln.TMscore1 + aln.TMscore2) / 2
# Print TMscore, TMscore1+coverage1, TMscore2+coverage2, TM mean, RMSD, nalign, seq_identity
# Size warning if max(size1,size2)/min(size1,size2) >= 1.5

Step 3 — Interpret results:

TM-score	Meaning
> 0.9	Highly similar
> 0.7	Similar, notable differences (distantly related)
> 0.5	Same fold class
≤ 0.5	Low similarity — likely different folds

• Size ratio ≥ 1.5: TMscore (shorter-normalized) is inflated. Use TMscore1 or TMscore2 instead, or TM mean for a symmetric view.
• Low coverage: only part of one protein aligns — possible domain match, not whole-protein similarity.
• seq_identity and evolutionary relationship: structural comparison alone cannot determine evolutionary relationship. See score-guide.md §seq_identity for correct interpretation — avoid asserting "structural analog" or "no evolutionary relationship" from scores alone.

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Workflow B: Residue Correspondence / Active-Site Mapping

Step 1 — Run alignment (RW recommended).

Step 2 — Query aln.alignment. Key trap: residue1 and residue2 are strings — use str(N) for lookup.

Step 3 — Flag unreliable pairs: Cα distance > 4.5 Å means the aligned residues are spatially distant and should not be treated as functionally corresponding. Add a note column: '⚠ uncertain (>4.5 Å)' or 'OK'.

Step 4 — Print table and warn if any pairs exceed 4.5 Å. Save to CSV if requested.

Use code from references/code-examples.md — Residue Correspondence.

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Workflow C: Internal Symmetry and Rotational Symmetry

This is the most complex workflow. Follow every step in order.

Step 1: Self-comparison (always start here)

Run the protein against itself with large -g and -n:

# Use subprocess, not pymican.mican() — need raw output including rotation matrices
result = subprocess.run(
    [BINFILEPATH, pdb, pdb, '-w', '-g', '1000', '-n', '20'],
    capture_output=True, text=True
)

Read the "Brief description" table from stdout. DALI Z > 2 in Rank 2+ means statistically significant internal symmetry.

Step 2: Classify sub-optimal solutions — True vs False Positives

Before any further analysis, separate the solutions:

True symmetry operations (analyze these):

• Rotation angle > 30°
• Residue correspondence between different repeat units (u0→u1, u0→u2, etc.)
• Solutions appear in pairs (forward + reverse direction)

False positives (discard these):

• Rotation angle < 20° — nearly identity transformation; MICAN found spatially nearby regions, not a symmetry operation
• Correspondence within the same unit (u0→u0)

Pseudo higher-order candidates (examine after confirming true Cn):

• Same rotation axis as true Cn ranks (within ~1°)
• t∥ ≈ 0 (pure rotation, no screw component)
• DALI Z > 2
• Angle close to k × (360°/2n) within ~10°, but NOT matching true Cn within ~2°
• Example: C3 confirmed → ranks showing angles halfway between the 120° Cn steps (near odd multiples of 360°/2n = 60°) → pseudo C6 candidates

To get rotation angle for each rank, use -z -i RANK and parse rotation matrix lines starting with 1 , 2 , 3 . Then: cos θ = (trace(R) − 1) / 2.

Step 3: Rotation matrix analysis — identify Cn symmetry

For each true-symmetry rank:

1. Shared axis? If all ranks have rotation axes within ~1° of each other → single true symmetry axis exists.
2. Cn match? Check if rotation angles equal k/n × 360° (within ~1°) for some integer n.
3. Pure rotation or screw? Compute t∥ = t · axis. If ≈ 0 → pure Cn rotation. If ≠ 0 → screw symmetry.
4. Pseudo higher-order symmetry? After confirming true Cn, check remaining significant ranks (same axis ±1°, DALI Z > 2, t∥ ≈ 0) whose angles do NOT match any Cn multiple. Test whether those angles match k × (360°/2n) within ~10°:
   • If yes → pseudo C(2n) character. Always state both in the conclusion: "True symmetry: Cn. Visually pseudo C(2n) — repeat units are structurally similar but not strictly equivalent (Δ~X° from exact C(2n) angles)."
   • If no → note the unexplained rank(s) and consider domain effects.
   • Example: C3 true → additional ranks near halfway-between angles (odd multiples of 360°/2n, within ~10°) → pseudo C6.
   • Use detection code from references/symmetry-analysis.md §2.
   • Do not assert fold class (e.g., β-propeller) from angles alone.

Use code from references/symmetry-analysis.md §2.

Step 4: Cyclic permutation — algebraic proof of Cn

Map each residue to its repeat unit index. Check whether the unit-level mapping across ranks forms all cyclic shifts {0, 1, ..., n−1}. If yes → Cn is algebraically proven.

Estimate repeat unit size from alignment shifts — use Cn-matched ranks only, then pick the smallest dominant shift:

• First, filter to Cn-matched ranks only: those whose angle matches k/n × 360° within ~2°. Do NOT include pseudo-symmetric ranks (e.g., ranks at ~38.5° in a C4 protein whose shifts reflect intra-unit partial alignments, not full inter-unit steps).
• Among Cn-matched ranks, find the most common (dominant) non-zero shift per rank.
• Pick the rank whose dominant shift is smallest — that is the 1-unit rank (shift = 1×unit).
• Compute GCD using only the dominant shift values (filter out minority shifts first). Do NOT compute GCD over all shifts — even one outlier shift (e.g., 44 when dominant is 42) will collapse GCD(42, 44) = 2, giving a wrong result.
• Example: in C3, Rank 2 (120°) dominant shift = 50 (= 1×unit). GCD({50, 50, ...}) = 50. ✓

Use code from references/symmetry-analysis.md §4.

Step 5: Check for multiple domains first (do this before Step 3 if uncertain)

Signs that the protein has multiple structural domains:

• High-scoring ranks (Rank 2–K) cover only part of the chain (res1 max ≤ X)
• Lower ranks suddenly expand to the full chain (res1 max jumps)
• Some ranks show angles that don't fit any Cn hypothesis → those ranks span a domain boundary

If domain signals appear:

1. Use extract_domain() to split the PDB at the estimated boundary.
2. Restart from Step 1 for each domain independently.
3. For the symmetric domain, also estimate the repeat unit count using estimate_unit_size() (§4) on its sub-optimal alignments. Report this count explicitly (e.g., "4 repeat units of ~60 residues each").

Use code from references/symmetry-analysis.md §4.

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Workflow D: Conformational Change (Apo/Holo)

Step 0 — Check for internal symmetry first. If the protein has tandem repeats or Cn symmetry (check with Workflow C), do NOT analyze the whole chain at once. Instead: identify repeat units → extract each unit from both states → run Steps 1–2 on each unit independently. See references/conformational-analysis.md — Workflow for repeat proteins.

Step 1 — Align on a conserved core using -q threshold:

• Use SQ mode (-s) — same protein, sequence order is conserved
• -q 3.0 is recommended for most conformational change analyses
• Tighten to -q 2.0 only when the change is small and you need a stricter anchor
• The core = residues that stay fixed between states (the superposition anchor)

Step 2 — Apply the rotation matrix to ALL residues (not just aligned ones), then measure Cα displacement:

• in_core=True residues: small displacement — structurally invariant anchor
• in_core=False with large displacement (> 5 Å): moved regions — conformational change
• Large jump between core RMSD (~2 Å) and non-core RMSD (>> 5 Å) → rigid-body domain movement

Use analyze_conformational_change() from references/conformational-analysis.md.

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Key CLI Options

Option	Default	Notes
-n INT	5	Solutions to return; use 20 for symmetry analysis
-g INT	50	GH candidates; use 1000 for symmetry/sub-optimal analysis
-q FLOAT	—	Cα distance cutoff (tighter = more precise core)
-i INT	—	Retrieve rank N solution (requires -z)
-z	off	Machine-readable output; required for -i and rotation matrix parsing
-c1 ID / -c2 ID	—	Chain ID for pdb1/pdb2
-l INT	3	Minimum segment length