---
name: coot-model-building-e8825dce8508
description: "Best Practices for Model-Building Tools and Refinement"
---
# Key Lessons from Chain A Refinement Session
## Workflow Checklist - Follow This for Model-Building and Refinement
Best practices for fixing any issue:
1. ☐ **ALWAYS recentre first** — every time you move to a new residue or problem, call this BEFORE anything else:
`coot.set_go_to_atom_chain_residue_atom_name(chain, resno, "CA")`
or `coot.set_rotation_centre(x, y, z)` for a map position.
The user is watching the screen. If you switch targets without recentring, they will be looking at
the wrong place while you work elsewhere. This is confusing and unacceptable.
2. ☐ Check current metrics (Rama/correlation/overlaps)
3. ☐ Make checkpoint if trying something experimental
4. ☐ Apply fix
5. ☐ Re-check metrics to confirm improvement
6. ☐ If worse, restore checkpoint
## Jiggle Fitting a Whole Chain
### When to use jiggle fitting
Use `fit_chain_to_map_by_random_jiggle_and_blur()` when a whole chain needs to be
placed or re-placed in the map — before any residue-level refinement.
Note, however, that the fit is only local. If the whole chain is (say) 10 or more Angstroms
away from the real position, then `fit_chain_to_map_by_random_jiggle_and_blur()` will not work.
The per-residue density correlation graph is the key diagnostic: if the mean correlation across
the whole chain is near zero with high variance (many residues below 0.4, many negative),
the chain is in the wrong place and jiggle fitting is the right first step.
**Do not attempt residue-level fixes (rotamers, pepflips, refinement) until the chain
is correctly placed.** Those tools cannot recover a globally misplaced chain.
### Diagnosing misplacement from the correlation graph
Before jiggling, always get a fresh per-residue correlation to use as a true baseline:
```python
stats = coot.map_to_model_correlation_stats_per_residue_range_py(
imol, chain_id, imol_map, 1, 0)
corrs = [entry[1][1] for entry in stats[0]]
mean_corr = sum(corrs) / len(corrs)
n_below = sum(1 for c in corrs if c < 0.4)
print(f"Mean correlation: {mean_corr:.3f}, residues below 0.4: {n_below}/{len(corrs)}")
```
**Interpretation:**
- Mean > 0.7, few residues below 0.4 → chain is well-placed, proceed to residue-level work
- Mean 0.4–0.7, moderate number below 0.4 → chain placed but needs refinement
- Mean near zero, high variance, many negative → **chain is in the wrong place**, jiggle fit first
**Why negative correlations occur in cryo-EM maps:** cryo-EM maps are Fourier-based
(no F000 term), so the mean density is zero. Negative density exists between atomic
features as a mathematical consequence of the reconstruction — not a physical reality.
A misplaced chain within the molecular envelope samples this real structured signal
(both positive peaks and negative troughs) from the wrong part of the map, producing
scattered correlations around zero. This is distinct from sitting in solvent, which
gives low-magnitude, low-variance correlations.
### Running the jiggle fit
Set the refinement map first, then use the blur variant for cryo-EM:
```python
coot.set_imol_refinement_map(imol_map)
result = coot.fit_chain_to_map_by_random_jiggle_and_blur(
imol, # model molecule
chain_id, # e.g. "N"
n_trials, # 200-500; more trials = better chance of finding global optimum
jiggle_scale, # 1.0-2.0; scale of random perturbations
blur_factor # 100-200; Fourier filtering makes the map smoother, helping
# the fit escape local minima and find the correct placement
)
print(f"Jiggle fit score: {result:.3f}")
```
**Return values:** the function returns a score (higher = better fit) or a large
negative number (e.g. −100, −999) when it cannot improve on the current placement.
These large negative returns are not errors — they mean the current position is already
a local optimum, or the function had difficulty scoring. Do not treat them as failures
without checking the actual correlation afterwards.
**Blur factor guidance:** the blur smooths the map by a something like a Fourier filter, effectively
low-pass filtering it. A higher blur factor produces a smoother map with broader
features, which helps the chain find the correct region before fine-grained fitting.
For cryo-EM maps, blur factors of 100–200 are typical starting points.
### Confirming the result
Always re-run the per-residue correlation after jiggling and compare to the baseline:
```python
stats_after = coot.map_to_model_correlation_stats_per_residue_range_py(
imol, chain_id, imol_map, 1, 0)
corrs_after = [entry[1][1] for entry in stats_after[0]]
mean_after = sum(corrs_after) / len(corrs_after)
n_below_after = sum(1 for c in corrs_after if c < 0.4)
print(f"AFTER — mean: {mean_after:.3f}, below 0.4: {n_below_after}")
```
A successful jiggle fit for a correctly placed chain should bring the mean correlation
above 0.6, with most residues above 0.7. Remaining poor-fitting residues in loop
regions are expected and addressed by subsequent residue-level refinement.
### Visualising the before/after result
Use the `coot-inline-graphs` skill to render a before/after bar chart with secondary
structure overlays. Fetch secondary structure from PDBe
(`/api/pdb/entry/secondary_structure/{pdb_id}`) rather than relying solely on Coot's
header secondary structure detection, which may miss strands — particularly single-
residue assignments and short strands in nanobody/VHH-type folds.
### Complete workflow summary
```python
# 1. Get fresh baseline correlation (after any manual moves, before jiggle)
stats_before = coot.map_to_model_correlation_stats_per_residue_range_py(
imol, chain_id, imol_map, 1, 0)
corrs_before = [e[1][1] for e in stats_before[0]]
print(f"BEFORE — mean: {sum(corrs_before)/len(corrs_before):.3f}")
# 2. Set refinement map and jiggle fit
coot.set_imol_refinement_map(imol_map)
result = coot.fit_chain_to_map_by_random_jiggle_and_blur(imol, chain_id, 500, 1.0, 200.0)
print(f"Jiggle score: {result:.3f}") # large negative = ignore, check correlation anyway
# 3. Get after correlation and compare
stats_after = coot.map_to_model_correlation_stats_per_residue_range_py(
imol, chain_id, imol_map, 1, 0)
corrs_after = [e[1][1] for e in stats_after[0]]
print(f"AFTER — mean: {sum(corrs_after)/len(corrs_after):.3f}")
# 4. Only proceed to residue-level work once mean > 0.6
```
## Refinement Best Practices
### 1. Make Checkpoints Before Model Changes
**CRITICAL: Always create a checkpoint before making significant model changes.**
Use `make_backup_checkpoint()` before any operation that might need to be reverted:
- Adding/deleting residues
- Adding ligands or waters
- Major refinement operations
- Any experimental model building
```python
# Create a named checkpoint before risky operation
checkpoint_idx = coot.make_backup_checkpoint(0, "before adding OXT")
# Try the operation
coot.add_OXT_to_residue(0, "A", 93, "")
result = coot.refine_residues_py(0, [["A", 93, ""]])
# Check if it worked - if not, restore
if result_is_bad:
coot.restore_to_backup_checkpoint(0, checkpoint_idx)
```
**Why checkpoints are better than undo:**
- `apply_undo()` only steps back one operation at a time
- Checkpoints let you jump back to a specific point
- Named checkpoints are self-documenting
- Multiple checkpoints allow comparing different approaches
```python
# Compare two different approaches
checkpoint_before = coot.make_backup_checkpoint(0, "original state")
# Try approach 1
coot.auto_fit_best_rotamer(0, "A", 42, "", "", 1, 1, 0.01)
results = coot.refine_residues_py(0, [["A", 41, ""], ["A", 42, ""], ["A", 43, ""]])
score_approach1 = check_correlation(0, "A", 42)
checkpoint_approach1 = coot.make_backup_checkpoint(0, "after approach 1")
# Restore and try approach 2
coot.restore_to_backup_checkpoint(0, checkpoint_before)
coot.pepflip(0, "A", 42, "", "")
results = coot.refine_residues_py(0, [["A", 41, ""], ["A", 42, ""], ["A", 43, ""]])
score_approach2 = check_correlation(0, "A", 42)
# Keep the better result
if score_approach1 > score_approach2:
coot.restore_to_backup_checkpoint(0, checkpoint_approach1)
```
## MANDATORY: Complete Validation Workflow
**CRITICAL: You MUST check ALL validation metrics before AND after EVERY fix.**
Fixing only one problem (e.g., Ramachandran) while leaving others (rotamer, density fit) is a FAILED fix. A residue is only "fixed" when ALL metrics are acceptable.
### Before fixing ANY residue:
1. **ALWAYS** center on it: `coot.set_go_to_atom_chain_residue_atom_name(chain, resno, "CA")`
2. **ALWAYS** check ALL of these:
- Ramachandran probability (from `all_molecule_ramachandran_score_py`)
- Rotamer score (from `rotamer_graphs_py`)
- Density correlation - both all-atom and side-chain (from `map_to_model_correlation_stats_per_residue_range_py`)
- Atom overlaps involving this residue (from `molecule_atom_overlaps_py`)
### After fixing ANY residue:
3. **ALWAYS** re-check ALL the same metrics, Sometime residues/issues are just
not fixable (that's what makes refinement and model-building tricky).
4. Try to make the following true (but knowing that it might not actually be possible)
- Ramachandran probability > 0.02 (preferably > 0.1)
- Rotamer score > 1.0% (preferably > 5%)
- Density correlation > 0.7 (all-atom and side-chain, preferably > 0.8)
- No severe clashes (< 2.0 Å cubed overlap volume)
### If ANY metric is still bad after your first fix:
5. **MUST** try additional fixes:
- Bad rotamer → `auto_fit_best_rotamer()`, and try experiment with following that up
with refine_residues_py() for that residue and its upstream and downstream
neighbours (if any).
- Poor density fit → try alternative rotamers, check for missing atoms
- Persistent clashes → refine with the addition of spatial neighbours using
`residues_near_residue_py()`
6. **NEVER** declare a residue "fixed" based on only one metric improving
7. **ALWAYS** re-validate after each additional fix
### Acceptable Reasons to Stop (without perfect metrics):
- You've tried multiple approaches and documented them
- The best achievable metrics are still recorded
- You've created checkpoints to compare approaches
- You explain why the residue remains problematic (e.g., poor density, crystal contact)
## Example of CORRECT Workflow
```python
# 1. ALWAYS center on problem residue first
coot.set_go_to_atom_chain_residue_atom_name("A", 41, "CA")
# 2. Get ALL metrics BEFORE
rama_data = [r for r in coot.all_molecule_ramachandran_score_py(0)[5:]
if r[1] == ['A', 41, '']][0]
rama_prob_before = rama_data[2]
rotamer_data = [r for r in coot.rotamer_graphs_py(0)
if r[0] == 'A' and r[1] == 41][0]
rotamer_score_before = rotamer_data[3]
corr_data = [s for s in coot.map_to_model_correlation_stats_per_residue_range_py(0, "A", 1, 1, 0)[0]
if s[0][1] == 41][0]
correlation_before = corr_data[1][1]
overlaps_before = [o for o in coot.molecule_atom_overlaps_py(0, 30)
if (o['atom-1-spec'][1:3] == ['A', 41] or
o['atom-2-spec'][1:3] == ['A', 41])]
print(f"BEFORE: Rama={rama_prob_before:.4f}, Rotamer={rotamer_score_before:.2f}%, Corr={correlation_before:.3f}, Clashes={len(overlaps_before)}")
# 3. Apply first fix (e.g., pepflip for backbone)
coot.pepflip(0, "A", 41, "", "")
coot.refine_residues_py(0, [["A", 40, ""], ["A", 41, ""], ["A", 42, ""], ["A", 43, ""]])
# 4. Check ALL metrics AFTER first fix
rama_prob_after = [r for r in coot.all_molecule_ramachandran_score_py(0)[5:]
if r[1] == ['A', 41, '']][0][2]
rotamer_score_after = [r for r in coot.rotamer_graphs_py(0)
if r[0] == 'A' and r[1] == 41][0][3]
correlation_after = [s for s in coot.map_to_model_correlation_stats_per_residue_range_py(0, "A", 1, 1, 0)[0]
if s[0][1] == 41][0][1][1]
print(f"AFTER: Rama={rama_prob_after:.4f}, Rotamer={rotamer_score_after:.2f}%, Corr={correlation_after:.3f}")
# 5. If rotamer or correlation still bad, DON'T STOP - fix them!
if rotamer_score_after < 1.0:
print("Rotamer still bad - trying auto_fit_best_rotamer")
coot.auto_fit_best_rotamer(0, "A", 41, "", "", 1, 1, 0.01)
coot.refine_residues_py(0, [["A", 40, ""], ["A", 41, ""], ["A", 42, ""]])
# 6. ALWAYS re-check after additional fixes
rotamer_score_final = [r for r in coot.rotamer_graphs_py(0)
if r[0] == 'A' and r[1] == 41][0][3]
correlation_final = [s for s in coot.map_to_model_correlation_stats_per_residue_range_py(0, "A", 1, 1, 0)[0]
if s[0][1] == 41][0][1][1]
print(f"FINAL: Rotamer={rotamer_score_final:.2f}%, Corr={correlation_final:.3f}")
# 7. Only NOW can you move to the next residue
```
## Example of WRONG Workflow (DO NOT DO THIS)
```python
# ❌ WRONG: Checking only Ramachandran
coot.pepflip(0, "A", 41, "", "")
coot.refine_residues_py(0, [["A", 40, ""], ["A", 41, ""], ["A", 42, ""]])
rama_after = coot.all_molecule_ramachandran_score_py(0)[5][39][2]
print(f"Ramachandran improved to {rama_after}")
# MOVES ON without checking rotamer or density fit - WRONG!
# ❌ WRONG: Not centering on residue
# Goes straight to fix without set_go_to_atom_chain_residue_atom_name()
# ❌ WRONG: Not checking metrics before the fix
# How do you know if it improved if you don't know what it was before?
# ❌ WRONG: Declaring success with bad rotamer
rama = 0.30 # Good!
rotamer = 0.0001 # TERRIBLE!
correlation = 0.59 # POOR!
print("Residue fixed!") # NO IT ISN'T!
```
## Why This Matters
A residue with:
- ✅ Good Ramachandran (0.30)
- ❌ Terrible rotamer (0.01%)
- ❌ Poor density fit (0.59)
is NOT fixed. The side chain is clearly wrong. The backbone geometry might be OK, but the model is still incorrect.
**ALL metrics must be acceptable before moving on.**
### 2. Extend Selection Around Problem Residues
**Don't refine problem residues in isolation** - include neighboring residues for context.
- ❌ **Bad:** `refine_residues_py(0, [["A", 41, ""]])` - Often fails to correct the model
- ✅ **Good:** `refine_residues_py(0, [["A", 40, ""], ["A", 41, ""], ["A", 42, ""], ["A", 43, ""]])`
**Recommended approach:**
- For single problem residue: include ±1 or ±2 neighbors
- For consecutive problem residues: include ±1 neighbor on each end
- Larger regions (±3-4 residues) can sometimes help severe issues
**Example from session:**
- Residues 41-42 had severe Ramachandran outliers
- Refining just 41-42 failed
- Refining 40-43 succeeded: Residue 41 improved from p=0.00004 to p=0.308
**Neighboring Residues:**
- You can use coot.residues_near_residue_py() to find residues that are close in space
but distant in sequence, so that they can be added to the residue selection for
refinement.
### 3. Include Spatial Neighbours, Not Just Sequence Neighbours
**Critical insight:** Residues that are close in 3D space affect each other during refinement, even if they're far apart in sequence.
Coot's refinement includes spatially neighbouring atoms in the non-bonded contact interactions, but only the selected residues can move during minimization. If a nearby (but unselected) residue is in the wrong position, it will "push" your selected residues away via non-bonded contact penalties - potentially pushing them out of correct density to avoid the clash with the incorrectly-placed neighbour.
**Diagnostic workflow:**
1. **Check for clashes** after refinement:
```python
overlaps = coot.molecule_atom_overlaps_py(0, 50)
for o in overlaps:
spec1, spec2 = o['atom-1-spec'], o['atom-2-spec']
vol = o['overlap-volume']
if vol > 0.5: # Significant clash
print(f"{spec1[1]}/{spec1[2]} {spec1[4]} - {spec2[1]}/{spec2[2]} {spec2[4]}: {vol:.2f}")
```
2. **If a problem residue clashes with a distant residue**, fix the distant residue first:
```python
# Example: A/2 has poor correlation (0.13) and clashes with A/89
# First fix A/89:
coot.auto_fit_best_rotamer(0, "A", 89, "", "", 1, 1, 0.01)
results = coot.refine_residues_py(0, [["A", 88, ""], ["A", 89, ""], ["A", 90, ""]])
# Then re-refine A/2 INCLUDING A/89 as a spatial neighbour:
results = coot.refine_residues_py(0, [["A", 1, ""], ["A", 2, ""], ["A", 3, ""], ["A", 89, ""]])
# A/2 correlation improved: 0.13 → 0.81
```
**Why this matters:**
- Coot's refinement "feels" spatial neighbours via non-bonded contact terms
- But only selected residues can move during minimization
- A/89 was in a wrong position (correlation 0.050) and pushing A/2 away
- A/2 moved out of its correct density to reduce the non-bonded penalty with A/89
- Fixing A/89 first put it in the right place, so it no longer pushed A/2 incorrectly
**Real example:**
```
Before: A/2 correlation = 0.131, A/89 correlation = 0.050
A/2 CA ↔ A/89 CZ clash: 1.06 Ų
After fixing A/89 first, then refining together:
A/2 correlation = 0.805, A/89 correlation = 0.928
No clash
```
### 4. Iterative Refinement Strategy
Sometimes multiple rounds of refinement with different selections help:
1. **First pass:** Refine larger region to establish general geometry
2. **Second pass:** Refine smaller region to fine-tune specific problem
3. **Check validation** after each step
4. **Restore checkpoint** if results get worse
**Example workflow:**
```python
# Create checkpoint first!
checkpoint = coot.make_backup_checkpoint(0, "before iterative refinement")
# First: larger region
results_1 = coot.refine_residues_py(0, [["A", i, ""] for i in range(40, 44)])
check_validation() # Did it help?
# Second: targeted refinement
results_2 = coot.refine_residues_py(0, [["A", 41, ""], ["A", 42, ""], ["A", 43, ""]])
check_validation() # Better or worse?
# If worse:
coot.restore_to_backup_checkpoint(0, checkpoint)
```
### 5. Measure Before and After
**Always validate changes objectively** using:
- Ramachandran probabilities
- Density correlation (all-atom and side-chain)
- Geometry statistics
```python
def check_residue_validation(imol, chain_id, resno):
"""Check both Ramachandran and density correlation"""
# Get Ramachandran
rama_data = coot.all_molecule_ramachandran_score_py(imol)
residue_data = rama_data[5]
rama_score = None
for r in residue_data:
if r[1][0] == chain_id and r[1][1] == resno:
rama_score = r[2]
break
# Get density correlation
corr_data = coot.map_to_model_correlation_stats_per_residue_range_py(
imol, chain_id, 1, 1, 1
)
all_atom_corr = None
sidechain_corr = None
for r in corr_data[0]:
if r[0][1] == resno:
all_atom_corr = r[1][1]
break
for r in corr_data[1]:
if r[0][1] == resno:
sidechain_corr = r[1][1]
break
return {
'residue': resno,
'rama_prob': rama_score,
'all_atom_corr': all_atom_corr,
'sidechain_corr': sidechain_corr
}
# Usage with checkpoint
checkpoint = coot.make_backup_checkpoint(0, "before refinement test")
before = check_residue_validation(0, "A", 41)
coot.refine_residues_py(0, [["A", 40, ""], ["A", 41, ""], ["A", 42, ""], ["A", 43, ""]])
coot.accept_moving_atoms_py()
after = check_residue_validation(0, "A", 41)
# Compare and decide
if after['all_atom_corr'] > before['all_atom_corr']:
# Keep it!
pass
else:
# Revert to checkpoint
coot.restore_to_backup_checkpoint(0, checkpoint)
```
### 6. Use Checkpoints and Undo Liberally
**Don't be afraid to revert changes:**
- `make_backup_checkpoint()` / `restore_to_backup_checkpoint()` - for jumping back to a specific state
- `apply_undo()` - for stepping back one operation at a time
Use checkpoints when:
- Starting a new model-building task
- About to try something experimental
- Before a series of related operations
Use undo when:
- The last single operation made things worse
- Quick single-step revert needed
### 7. Auto-fit Rotamer for Side-chain Issues
**For poor side-chain density correlation**, try `auto_fit_best_rotamer()` first:
```python
# Create checkpoint first
checkpoint = coot.make_backup_checkpoint(0, "before rotamer fitting")
# Check if it's a side-chain problem
validation = check_residue_validation(0, "A", 89)
if validation['sidechain_corr'] < 0.5:
# Try auto-fit rotamer
score = coot.auto_fit_best_rotamer(0, "A", 89, "", "", 1, 1, 0.01)
if score > 0: # Positive score is good
# Check improvement
after = check_residue_validation(0, "A", 89)
if after['sidechain_corr'] > validation['sidechain_corr']:
# Success! (e.g., 0.034 → 0.900)
pass
else:
coot.restore_to_backup_checkpoint(0, checkpoint)
else:
# Negative score means failure
coot.restore_to_backup_checkpoint(0, checkpoint)
```
### 8. Set Refinement to Synchronous Mode
**Always call this at the start** to make refinement complete immediately:
```python
coot.set_refinement_immediate_replacement(1)
```
Without this, refinement may be asynchronous and difficult to control programmatically.
### 9. Navigate to Residue Before Working
**Bring residue to screen center** so you can watch the refinement:
```python
coot.set_go_to_atom_molecule(0)
coot.set_go_to_atom_chain_residue_atom_name("A", 41, "CA")
```
This helps with:
- Visual inspection of the problem
- Seeing the refinement in real-time
- Verifying the result makes geometric sense
### 10. Flipping peptides
If the Ramachandran Plot is poor, try using `coot.pepflip(imol, chain_id, res_no, ins_code, alt_conf)` followed by a refinement of the residues in the extended region.
### 11. Flipping side-chains terminal Chi-angle
If the Rotamer score is poor, try using `coot.do_180_degree_side_chain_flip()` to improve the Rotamer score. It is occasionally useful.
### 12. Delete Waters Displaced by Newly Built Residues
**CRITICAL: After adding any new residues, always check for and delete water molecules that are now too close to the new protein atoms.**
Waters are placed by earlier refinement cycles that had no knowledge of atoms you are about to build. Once new residues are placed, any water within ~3.5 Å is almost certainly spurious — it was filling density that now belongs to the protein model.
**Always do this immediately after building new residues, before refinement.**
```python
# After building new residues (e.g. A21-A25), check each for clashing waters
newly_built = [21, 22, 23, 24, 25]
waters_to_delete = set()
for resno in newly_built:
neighbours = coot.residues_near_residue_py(0, ["A", resno, ""], 3.5)
for n in neighbours:
chain, nresno, inscode = n[0], n[1], n[2]
name = coot.residue_name_py(0, chain, nresno, inscode)
if name == "HOH":
waters_to_delete.add((chain, nresno, inscode))
print(f"Water {chain}{nresno} within 3.5A of new A{resno} - will delete")
for chain, resno, inscode in waters_to_delete:
coot.delete_residue(0, chain, resno, inscode)
print(f"Deleted HOH {chain}{resno}")
```
**Why 3.5 Å?** This is a generous hydrogen-bond distance. Any water oxygen closer than this to a protein heavy atom is physically incompatible with the new model.
**Don't just check for hard clashes** — even waters that don't show up in `molecule_atom_overlaps_py()` should be deleted if they are within 3.5 Å, because the overlap check uses van der Waals radii and may miss close-but-not-overlapping waters that are nonetheless chemically unreasonable.
### 13. Building N-terminal Extensions with Enforced Secondary Structure
When building backwards (toward the N-terminus) into a known secondary structure element, use `add_terminal_residue_using_phi_psi()` with the target φ/ψ angles and enforce the conformation further by refining with `set_secondary_structure_restraints_type()`.
**α-helix:** φ = −57°, ψ = −47°
**β-strand:** φ = −120°, ψ = +120°
**Complete workflow for N-terminal helix extension:**
```python
# Step 1: Checkpoint
cp = coot.make_backup_checkpoint(0, "before N-terminal extension")
# Step 2: Build residues backwards one at a time with helix phi/psi
phi, psi = -57.0, -47.0
current_nterm = 26 # existing N-terminal residue of fragment
for i in range(4): # build 4 residues: 25, 24, 23, 22
r = coot.add_terminal_residue_using_phi_psi(0, "A", current_nterm, "auto", phi, psi)
current_nterm -= 1
# Step 3: IMMEDIATELY check for and delete displaced waters
newly_built = list(range(current_nterm + 1, 26))
waters_to_delete = set()
for resno in newly_built:
for n in coot.residues_near_residue_py(0, ["A", resno, ""], 3.5):
if coot.residue_name_py(0, n[0], n[1], n[2]) == "HOH":
waters_to_delete.add((n[0], n[1], n[2]))
for chain, resno, inscode in waters_to_delete:
coot.delete_residue(0, chain, resno, inscode)
print(f"Deleted displaced water {chain}{resno}")
# Step 4: Mutate from ALA placeholders to correct sequence
coot.mutate_residue_range(0, "A", 22, 25, "NLLND") # sequence from UniProt etc.
# Step 5: Autofit rotamers for mutated residues
for resno in newly_built:
coot.auto_fit_best_rotamer(0, "A", resno, "", "", 1, 1, 0.01)
# Step 6: Refine with helix restraints - always turn off afterwards
coot.set_secondary_structure_restraints_type(1)
coot.refine_residues_py(0, [["A", r, ""] for r in range(22, 28)])
coot.accept_moving_atoms_py()
coot.set_secondary_structure_restraints_type(0) # ALWAYS turn off when done
```
**Key rules for secondary structure restraints:**
- Always call `set_secondary_structure_restraints_type(0)` after refinement to clear them
- Extend the refinement selection 1-2 residues beyond the newly built region into the existing chain, to provide proper geometric context at the junction
- Helix restraints (type 1), strand restraints (type 2), no restraints (type 0)
```python
coot.set_secondary_structure_restraints_type(1) # alpha helix
coot.set_secondary_structure_restraints_type(2) # beta strand
coot.set_secondary_structure_restraints_type(0) # off - ALWAYS restore to this
```
### 14. Rebuilding a Residue with a Missing Main-chain N Atom
If a residue is missing a **main-chain** atom — most commonly the amide **N** — the ordinary atom/side-chain filling tools **silently do nothing**. They rebuild atoms onto an existing N–CA–C frame, so a residue with no N gives them nothing to build from. Do **not** infer "unfixable" from these no-ops; they are the wrong tools for a missing main-chain atom.
Observed no-ops on a CYS that had only `CA, CB, C, O` (no N, no SG):
- `fill_partial_residues(imol)` — whole-molecule fill; skips the residue
- `fill_partial_residue(imol, chain, resno, ins)` — single-residue fill; skips it
- `mutate_and_autofit_residue_range(...)` — cannot seat a side chain without the N frame
- `spin_N_py(imol, spec, angle)` — **repositions an existing N** (it interchanges the N and CB positions); it does **not create** one, so it is a no-op when no N exists. Its docstring ("on N-terminal addition the N ends up pointing the wrong way") is the signpost: the N is *created* by terminal-residue building, and `spin_N_py` is only the follow-up that orients it.
**The fix: delete the broken residue and rebuild it with `add_terminal_residue()`**, which regenerates a complete main-chain (N included) by fitting to the map. It comes back as an ALA stub; then restore the correct residue type and side chain.
```python
imol, imap = 0, 1
coot.set_refinement_immediate_replacement(1)
cp = coot.make_backup_checkpoint(imol, "before A72 mainchain-N rebuild")
# 1. Delete the residue that is missing its main-chain N
coot.delete_residue(imol, "A", 72, "")
# 2. Rebuild from the neighbour. Building forward from 71 (or backward from
# 73) regenerates a full main-chain, CREATING the N atom. add_terminal_residue
# returns 1 on success, 0 on failure. The residue returns as an ALA stub
# (N, CA, C, O, CB).
ok = coot.add_terminal_residue(imol, "A", 71, "auto", 1)
# 3. Confirm the N now exists before proceeding
atoms = [a[0][0].strip() for a in coot.residue_info_py(imol, "A", 72, "")]
assert "N" in atoms, "add_terminal_residue did not create the N atom"
# 4. Restore the real residue type and seat its side chain
coot.mutate_and_autofit_residue_range(imol, "A", 72, 72, "C") # "C" = CYS
# 5. Only if the newly built N points the wrong way, spin it into place
# (spin_N works now that an N exists; 120 deg swaps N and CB)
# coot.spin_N_py(imol, ["A", 72, ""], 120.0)
# 6. Refine with neighbours to seat the new backbone into density
coot.refine_residues_py(imol, [["A", 71, ""], ["A", 72, ""], ["A", 73, ""]])
```
**Verify main-chain geometry** (not just the validation metrics) — a correctly rebuilt N gives an ideal peptide bond and Cα–N distance:
```python
import math
def atom_dist(imol, c1, r1, a1, c2, r2, a2):
def xyz(c, r, a):
for at in coot.residue_info_py(imol, c, r, ""):
if at[0][0].strip() == a:
return at[2]
return None
p, q = xyz(c1, r1, a1), xyz(c2, r2, a2)
return math.dist(p, q) if p and q else None
# targets: C(i-1)-N(i) ~1.33 A ; N-CA ~1.46 A
print(atom_dist(0, "A", 71, "C", "A", 72, "N"))
print(atom_dist(0, "A", 72, "N", "A", 72, "CA"))
```
**Notes:**
- A rebuilt *complete* residue often shows a **lower** density correlation than the broken fragment did, because the fragment's correlation was computed over fewer atoms. Compare like with like: a complete residue at 0.62 in a mobile loop is a better model than a 4-atom stub scoring 0.68.
- This is the general recipe for **any** missing main-chain atom, not just N: delete and rebuild rather than trying to patch a single atom into an incomplete frame.
## NCS-Related Chains: Always Use Copy, Never Rebuild
**CRITICAL RULE: When the user asks you to fix a region in one chain using a corrected region
in another chain, ALWAYS use `copy_residue_range_from_ncs_master_to_others()` first.
Never attempt to rebuild the target chain manually.**
### Trigger Phrases That Must Invoke This Approach
Any of these mean: use the NCS copy tool immediately:
- *"Use the fixed B chain to fix chain A"*
- *"Apply the same fix to the other chain"*
- *"The equivalent region in chain X needs fixing"*
- *"Propagate this fix to the NCS-related chain"*
- *"Now do the same for chain A/B/C..."*
- Any mention of NCS, related chains, or symmetry-equivalent regions after a fix
### NCS Copy Workflow
```python
# 1. Check NCS is detected — do this first
ncs_chains = coot.ncs_chain_ids_py(imol)
print(ncs_chains) # e.g. [['A', 'B']] — master chain listed first
# 2. Set the fixed chain as NCS master
coot.ncs_control_change_ncs_master_to_chain_id(imol, "B") # B is the fixed chain
# 3. Copy the fixed region to all NCS-related chains using the NCS operator
coot.copy_residue_range_from_ncs_master_to_others(imol, "B", start_resno, end_resno)
# 4. Refine the target chain in its own density to let it settle
specs = [["A", r, ""] for r in range(start_resno - 1, end_resno + 2)
if coot.residue_name_py(imol, "A", r, "") not in [None, "False", ""]]
coot.refine_residues_py(imol, specs)
```
**Why this is always better than manual rebuilding:**
- The NCS operator correctly handles the rotation and translation between chains —
NCS-related chains are in different parts of the unit cell, so you cannot simply
copy coordinates without applying the NCS transformation
- It propagates correct backbone geometry that is already known to be good
- It is fast — one function call instead of dozens of pepflips and refinement cycles
- Refinement against the target chain's own density then handles the small differences
between NCS-related chains
**Only fall back to manual rebuilding if:**
- `ncs_chain_ids_py()` returns `False` (no NCS detected)
- The chains have genuinely different sequences in the region of interest
- The NCS copy produces clearly wrong geometry (check correlations immediately after)
---
## Per-Atom Density Probing: The Most Powerful Diagnostic
**Use `density_at_point()` per backbone atom to diagnose backbone problems before
attempting any fix. This is more informative than per-residue correlation alone.**
```python
def probe_backbone(imol, imol_map, imol_diff, chain, resnos):
sigma = coot.map_sigma_py(imol_map)
sigma_diff = coot.map_sigma_py(imol_diff)
backbone = [" N ", " CA ", " C ", " O "]
for resno in resnos:
atoms = coot.residue_info_py(imol, chain, resno, "")
if not isinstance(atoms, list): continue
name = coot.residue_name_py(imol, chain, resno, "")
coords = {a[0][0]: a[2] for a in atoms}
for atname in backbone:
xyz = coords.get(atname)
if xyz is None: continue
x, y, z = xyz
sig_main = coot.density_at_point(imol_map, x, y, z) / sigma
sig_diff = coot.density_at_point(imol_diff, x, y, z) / sigma_diff
flag = " << LOW" if sig_main < 0.8 else ""
if sig_main < 0.0: flag = " << NEGATIVE"
print(f" {chain}/{resno} {name} {atname.strip():4s} "
f"{sig_main:.2f}σ diff={sig_diff:+.2f}σ{flag}")
```
### Interpreting Per-Atom Density Values
| Pattern | Diagnosis | Fix |
|---|---|---|
| O near 0σ, N/CA/C good | Peptide bond flipped — carbonyl O in empty space | `pepflip()` at this residue |
| CA near 0σ or negative | Atom genuinely in wrong position | Register shift or delete/rebuild |
| Large negative diff at CA | Atom displaced — strong negative difference density | Delete and rebuild or NCS copy |
| Large positive diff nearby | Missing atoms or unmodelled density | Build missing residues |
| All atoms < 1σ | Entire residue misplaced | Delete and rebuild |
**The pepflip signature:** near-zero or negative carbonyl O with good N, CA, C.
This is the most common backbone error and the fastest to fix.
```python
# If O < 0.5σ but CA > 2σ — it's a pepflip
coot.pepflip(imol, chain_id, resno, "", "")
specs = [[chain_id, r, ""] for r in range(resno-2, resno+3)
if coot.residue_name_py(imol, chain_id, r, "") not in [None, "False", ""]]
coot.refine_residues_py(imol, specs)
# Re-probe to confirm O has moved into density
```
---
## Mainchain vs Sidechain Correlation: Distinguish Before Acting
**Always check mainchain and sidechain correlation separately before deciding on a fix.**
```python
# atom_mask_mode: 0=all, 1=mainchain only, 2=sidechain only
residue_specs = [[chain_id, resno, ""]]
mc = coot.map_to_model_correlation_py(imol, residue_specs, [], 1, imol_map)
sc = coot.map_to_model_correlation_py(imol, residue_specs, [], 2, imol_map)
aa = coot.map_to_model_correlation_py(imol, residue_specs, [], 0, imol_map)
print(f"All-atom: {aa:.3f} Mainchain: {mc:.3f} Sidechain: {sc:.3f}")
```
| Result | Conclusion | Fix |
|---|---|---|
| MC poor, SC good | Backbone error (pepflip, register) | Per-atom density probe, then pepflip |
| SC poor, MC good | Sidechain rotamer wrong | `auto_fit_best_rotamer()` |
| Both poor | Whole residue misplaced | Delete/rebuild or NCS copy |
---
## Register Shift Diagnosis
When a stretch of residues has consistently poor correlations flanked by good residues,
**always test for a register shift before attempting pepflips**.
A sequence of consecutive Ramachandran outliers with poor density for the sidechains, but
acceptable or even good coorelations for the mainchain atoms, is the hallmark of a
register error, not individual residue problems.
### How to Test for a Register Shift
```python
# Test if B[n] matches A[n-1] (chain B one residue ahead of A)
for n in range(start, end):
nb = coot.residue_name_py(imol, "B", n, "")
na = coot.residue_name_py(imol, "A", n-1, "")
match = "==" if nb == na else "!="
print(f"B/{n} {nb} vs A/{n-1} {na} {match}")
# Also compare phi/psi angles — a run of 3+ matching phi/psi confirms the shift
rama = coot.all_molecule_ramachandran_score_py(imol)
per_res = [r for r in rama[5] if r != -1]
rama_b = {r[1][1]: (r[0][0], r[0][1]) for r in per_res if r[1][0] == "B"}
rama_a = {r[1][1]: (r[0][0], r[0][1]) for r in per_res if r[1][0] == "A"}
for n in range(start, end):
phi_b, psi_b = rama_b.get(n, (0, 0))
phi_a, psi_a = rama_a.get(n-1, (0, 0))
dphi = min(abs(phi_b-phi_a), 360-abs(phi_b-phi_a))
dpsi = min(abs(psi_b-psi_a), 360-abs(psi_b-psi_a))
match = "GOOD" if dphi < 30 and dpsi < 30 else "bad"
print(f"B/{n} vs A/{n-1}: dphi={dphi:.0f} dpsi={dpsi:.0f} {match}")
```
### Also Verify Chain Geometry
```python
import math
def dist(a, b): return math.sqrt(sum((a[i]-b[i])**2 for i in range(3)))
gap = dist(c_prev, n_next)
# ~1.33 A = direct peptide bond (no gap)
# ~3.8 A = one residue missing
# ~9.6 A = two residues missing
```
---
## Spurious Inserted Residues: Delete and Renumber
A residue with near-zero or negative correlation, missing sidechain atoms, and a
zero Ramachandran score, and highly distorted bonds and angles is almost certainly
a phantom insertion placed to compensate for a genuine deletion elsewhere in the loop.
This is called an "out of register" error - at some point in the model there will
be a residue squeezed in to where it should not be and elsewhere there will be a residue
that is stretched across where 2 residues should be.
**Signs of a spurious residue:**
- Correlation < 0.2 or negative
- Ramachandran score = 0.00000
- Missing sidechain atoms
- Flanking residues fit well
- CA→CA distance to neighbour is too short (~3.2 Å instead of ~3.8 Å)
**The correct fix is delete + renumber, NOT pepflip + refine:**
```python
coot.make_backup_checkpoint(imol, "before delete spurious residue")
coot.delete_residue(imol, chain_id, resno, "")
coot.renumber_residue_range(imol, chain_id, resno+1, last_resno, -1)
# Fix residue types if sequence labels are now wrong
for n, target_restype in corrections.items():
coot.mutate(imol, chain_id, n, "", target_restype)
for n in affected_resnos:
coot.auto_fit_best_rotamer(imol, chain_id, n, "", "", imol_map, 1, 0.01)
specs = [[chain_id, r, ""] for r in range(resno-2, last_affected+2)
if coot.residue_name_py(imol, chain_id, r, "") not in [None, "False", ""]]
coot.refine_residues_py(imol, specs)
```
**After renumbering, always check:**
1. Bond geometry: C→N ~1.33 Å, CA→CA ~3.8 Å
2. Sequence alignment vs the other NCS chain
3. Correlations throughout the corrected region
---
## Anisotropic B-Factors: Always Handle Safely
B-factor fields can be lists (anisotropic) not floats (isotropic).
Always use this helper — failing to do so causes a TypeError:
```python
def get_b(atom):
b = atom[1][1]
return b[0] if isinstance(b, list) else b
```
---
## Chain Break Geometry: Measure Before Building
```python
import math
def dist(a, b): return math.sqrt(sum((a[i]-b[i])**2 for i in range(3)))
c_prev = get_coords(imol, chain, resno_before)['C']
n_next = get_coords(imol, chain, resno_after)['N']
cn_dist = dist(c_prev, n_next)
print(f"C->N: {cn_dist:.2f} A")
# ~1.33 A: direct bond — no residue missing, do not attempt to build
# ~3.8 A: one residue missing
# ~9.6 A: two residues missing
```
If C→N ≈ 1.33 Å, there is no room — the deletion is genuine, the model is correct.
---
## Know When to Stop Automated Methods
When pepflips and rotamer fitting cycle without convergence after 2-3 attempts, stop.
Use the per-atom density probe to diagnose the true problem.
Oscillation (O good → CA bad → O bad → CA good) means the residue is genuinely
misplaced and automated tools cannot find the correct basin. Options:
- Use `protein_db_loops_py()` for proper fragment-database loop fitting
- Use NCS copy if a related chain is already correct (always prefer this)
**Do not use `multi_residue_torsion_fit_py` as a substitute for proper loop fitting.**
It is useful for initial placement of manually built placeholder atoms only.
---
## Loop Fitting with protein_db_loops_py
Use only when NCS copy is not available and the loop is genuinely missing (not a
register error).
```python
# Anchors must exist; missing residues need placeholder atoms first
specs = [
[chain_id, anchor_start, ""], # last good residue before gap
[chain_id, anchor_start+1, ""], # last good residue before gap
[chain_id, missing_1, ""], # built with placeholder atoms
[chain_id, missing_2, ""], # built with placeholder atoms
[chain_id, anchor_end, ""], # first good residue after gap
]
result = coot.protein_db_loops_py(
imol, specs, imol_map,
20, # nfrags — 20 is thorough
True # preserve_residue_names
)
```
---
## MANDATORY: Complete Validation Workflow
**Check ALL metrics before AND after EVERY fix.**
### Before fixing ANY residue:
1. Center on it
2. Check Ramachandran, rotamer, all-atom corr, sidechain corr, overlaps
3. For backbone problems: check per-atom density with `density_at_point()`
4. For NCS structures: check if the related chain has the correct conformation
### After fixing ANY residue:
- Ramachandran probability > 0.02 (preferably > 0.1)
- Rotamer score > 1.0% (preferably > 5%)
- Density correlation > 0.7 all-atom and sidechain (preferably > 0.8)
- No severe clashes (overlap volume < 2.0 ų)
- Backbone atoms all > 1σ in 2mFo-DFc map
### Acceptable Reasons to Stop Without Perfect Metrics:
- Per-atom probe shows atoms are in correct density despite low per-residue correlation
- Remaining issues are at gap boundaries (Ramachandran nan is expected there)
- Residue is genuinely disordered (high B-factors consistent with low correlation)
- Multiple approaches documented and best achievable state recorded
---
## Refinement Best Practices
### Make Checkpoints Before Changes
```python
checkpoint_idx = coot.make_backup_checkpoint(imol, "before pepflip A/262")
# ... make changes ...
# If worse: coot.restore_to_backup_checkpoint(imol, checkpoint_idx)
# If checkpoint fails: coot.set_undo_molecule(imol); coot.apply_undo()
```
### Extend Selection Around Problem Residues
```python
specs = [[chain_id, r, ""] for r in range(resno-2, resno+3)
if coot.residue_name_py(imol, chain_id, r, "") not in [None, "False", ""]]
coot.refine_residues_py(imol, specs)
```
### Sphere Refinement for Spatial Context
```python
central = [chain_id, resno, ""]
neighbours = coot.residues_near_residue_py(imol, central, 4.5)
coot.refine_residues_py(imol, neighbours + [central])
```
### Set Refinement to Synchronous Mode
```python
coot.set_refinement_immediate_replacement(1) # once at session start
coot.set_imol_refinement_map(imol_map)
```
### Delete Waters Displaced by Newly Built Residues
```python
for resno in newly_built_resnos:
for n in coot.residues_near_residue_py(imol, [chain, resno, ""], 3.5):
if coot.residue_name_py(imol, n[0], n[1], n[2]) == "HOH":
coot.delete_residue(imol, n[0], n[1], n[2])
```
### Secondary Structure Restraints
```python
coot.set_secondary_structure_restraints_type(1) # helix (phi=-57, psi=-47)
coot.set_secondary_structure_restraints_type(2) # strand (phi=-120, psi=+120)
coot.refine_residues_py(imol, specs)
coot.set_secondary_structure_restraints_type(0) # ALWAYS turn off afterwards
```
---
## Mainchain Geometry Diagnostic: Bond, Angle and Omega Z-scores
**Use this tool to identify distorted backbone geometry. It is fast, independent of the
refinement environment, and often reveals register errors and misplaced residues that
correlation and Ramachandran scores alone miss.**
The **omega torsion angle** (the C-CA-C-N dihedral across the peptide bond, ideal = 180°
for trans) is the most sensitive indicator. A wildly wrong omega (e.g. 9°, 60°, 123°)
is definitive proof that the backbone is misplaced — it cannot be a real conformation.
### Complete Diagnostic Function
```python
import math
def dist(a, b):
return math.sqrt(sum((a[i]-b[i])**2 for i in range(3)))
def angle_deg(a, b, c):
ba = tuple(a[i]-b[i] for i in range(3))
bc = tuple(c[i]-b[i] for i in range(3))
cos_a = sum(ba[i]*bc[i] for i in range(3)) / (
math.sqrt(sum(x*x for x in ba)) * math.sqrt(sum(x*x for x in bc)))
return math.degrees(math.acos(max(-1.0, min(1.0, cos_a))))
def omega_deg(ca1, c1, n2, ca2):
def cross(a, b):
return (a[1]*b[2]-a[2]*b[1], a[2]*b[0]-a[0]*b[2], a[0]*b[1]-a[1]*b[0])
def sub(a, b): return tuple(a[i]-b[i] for i in range(3))
def norm(v):
l = math.sqrt(sum(x*x for x in v))
return tuple(x/l for x in v)
def dot(a, b): return sum(a[i]*b[i] for i in range(3))
b1 = sub(c1, ca1); b2 = sub(n2, c1); b3 = sub(ca2, n2)
n1 = norm(cross(b1, b2)); n2v = norm(cross(b2, b3))
m1 = cross(n1, norm(b2))
return math.degrees(math.atan2(dot(m1, n2v), dot(n1, n2v)))
# Engh & Huber ideal values (sigma = expected standard deviation)
IDEAL = {
'N-CA': (1.458, 0.019),
'CA-C': (1.525, 0.021),
'C-N': (1.329, 0.014), # peptide bond
'C-O': (1.231, 0.020),
'N-CA-C': (111.2, 2.8),
'CA-C-N': (116.2, 2.0),
'C-N-CA': (121.7, 1.8),
'CA-C-O': (120.8, 1.7),
}
def get_coords(imol, chain, resno):
atoms = coot.residue_info_py(imol, chain, resno, "")
if not isinstance(atoms, list) or atoms is False:
return {}
return {a[0][0].strip(): a[2] for a in atoms}
def check_mainchain_geometry(imol, chain, res_start, res_end, z_threshold=3.0):
"""
Check mainchain bond lengths, angles and omega torsions against Engh & Huber ideals.
Returns list of (residue_tag, parameter, ideal, actual, z_score, kind) tuples,
sorted by |Z| descending.
"""
distortions = []
prev = None
for resno in range(res_start, res_end + 1):
name = coot.residue_name_py(imol, chain, resno, "")
if not name or name in ["False", "", False]:
prev = None
continue
c = get_coords(imol, chain, resno)
if not c or "CA" not in c:
prev = None
continue
tag = f"{chain}/{resno} {name}"
# Intra-residue bonds
for bond, (a1, a2) in [("N-CA",("N","CA")), ("CA-C",("CA","C")), ("C-O",("C","O"))]:
if a1 in c and a2 in c:
d = dist(c[a1], c[a2])
ideal, sigma = IDEAL[bond]
z = (d - ideal) / sigma
if abs(z) >= z_threshold:
distortions.append((tag, bond, ideal, d, z, "bond"))
# Intra-residue angles
for ang_name, (a1,a2,a3) in [("N-CA-C",("N","CA","C")), ("CA-C-O",("CA","C","O"))]:
if all(a in c for a in (a1,a2,a3)):
ang = angle_deg(c[a1], c[a2], c[a3])
ideal, sigma = IDEAL[ang_name]
z = (ang - ideal) / sigma
if abs(z) >= z_threshold:
distortions.append((tag, ang_name, ideal, ang, z, "angle"))
# Inter-residue geometry (peptide bond to previous residue)
if prev:
prev_name = coot.residue_name_py(imol, chain, resno-1, "")
ptag = f"{chain}/{resno-1} {prev_name}→{tag}"
if "C" in prev and "N" in c:
# Peptide C-N bond length
d = dist(prev["C"], c["N"])
ideal, sigma = IDEAL["C-N"]
z = (d - ideal) / sigma
if abs(z) >= z_threshold:
distortions.append((ptag, "C-N(pept)", ideal, d, z, "bond"))
# Angles across the peptide bond
if "CA" in prev:
ang = angle_deg(prev["CA"], prev["C"], c["N"])
ideal, sigma = IDEAL["CA-C-N"]
z = (ang - ideal) / sigma
if abs(z) >= z_threshold:
distortions.append((ptag, "CA-C-N", ideal, ang, z, "angle"))
if "CA" in c:
ang = angle_deg(prev["C"], c["N"], c["CA"])
ideal, sigma = IDEAL["C-N-CA"]
z = (ang - ideal) / sigma
if abs(z) >= z_threshold:
distortions.append((ptag, "C-N-CA", ideal, ang, z, "angle"))
# Omega torsion — flag deviation from 180° > 15°
if "CA" in prev:
om = omega_deg(prev["CA"], prev["C"], c["N"], c["CA"])
dev = abs(abs(om) - 180.0)
if dev > 15.0:
# Express as Z using sigma~5° for trans peptides
distortions.append((ptag, "omega", 180.0, om, dev/5.0, "torsion"))
prev = c
distortions.sort(key=lambda x: -abs(x[4]))
return distortions
```
### Usage
```python
# Check a region — results sorted worst first
dist_b = check_mainchain_geometry(imol, "B", 256, 268, z_threshold=3.0)
for res, param, ideal, actual, z, kind in dist_b:
unit = "Å" if kind == "bond" else "°"
flag = " ***" if abs(z) > 5 else ""
print(f" {res:35s} {param:12s} ideal={ideal:.3f}{unit} actual={actual:.3f}{unit} z={z:+.2f}{flag}")
```
### Interpreting the Results
| Distortion | Likely cause | Fix |
|---|---|---|
| omega far from 180° (> 20°) | Misplaced backbone / register error | Delete + renumber or NCS copy |
| omega near 0° (cis peptide) | May be genuine (rare) — check density | Visual inspection; pepflip if in empty density |
| N-CA-C angle severely wrong | Atom in completely wrong position | Per-atom density probe, then delete/rebuild |
| N-CA or CA-C bond short | Compressed geometry from register error | Delete + renumber |
| CA-C-N or C-N-CA angle wrong | Distorted peptide junction | Pepflip or rebuild |
**Omega outliers are the fastest route to finding register errors.** A run of 3+ consecutive
omega outliers in the same region is essentially diagnostic of a misplaced loop.
### Always render the results as an interactive SVG widget
After running `check_mainchain_geometry()`, render the results using `visualize:show_widget`
so each distorted residue is a clickable block that navigates to it or triggers a fix.
Use `c-red` for |Z| > 5 or omega deviation > 20°, `c-amber` for |Z| 3–5. See the
`coot-essential-api` skill for the widget rendering guidelines.
---
## Key Takeaways
**For NCS structures: always check for a related chain that is correctly built before
attempting manual rebuilding. One NCS copy call beats dozens of pepflips.**
**Use per-atom density probing before pepflipping.** The pattern of which backbone
atoms are in low density tells you exactly what is wrong and what fix to apply.
**Distinguish mainchain from sidechain problems** using `atom_mask_mode=1` and `2`
before deciding on a fix strategy.
**Measure C→N and CA→CA distances before building.** If C→N ≈ 1.33 Å there is no
gap and no room to build — the deletion is genuine.
**Context matters in refinement.** Include neighbouring residues, both sequence and
spatial neighbours, for best results.
**Know when to stop.** If automated methods cycle without convergence after 2-3
attempts, use the density probe to decide whether to delete, rebuild, or accept.
## Key Takeaway
**Context matters in refinement.** Including neighboring residues provides the geometric and density context needed for refinement algorithms to find better solutions, especially for severe outliers.
**Always checkpoint before changes.** Use `make_backup_checkpoint()` before any significant model modification so you can easily revert if needed.
**Delete displaced waters immediately** after building new residues — don't wait until refinement reveals clashes.
**Always turn off secondary structure restraints** (`coot.set_secondary_structure_restraints_type(0)`) after the refinement that uses them, so they don't inadvertently affect subsequent work.
---
Source attribution: pemsley
Original project: https://github.com/pemsley/coot/blob/ee9537dd4d6e014437f7fce149a7ff7ad19ce650/mcp/docs/skills/model-building/SKILL.md
License: GPL-3.0-only
License information: https://www.gnu.org/licenses/gpl-3.0.html
Original upstream instructions; downloaded from Mega AI Hub.
## Upstream license notice
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc.
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
Copyright (C)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
Copyright (C)
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
.
# Mega AI Hub resource metadata
Creator / publisher: pemsley
Original project: https://github.com/pemsley/coot/blob/ee9537dd4d6e014437f7fce149a7ff7ad19ce650/mcp/docs/skills/model-building/SKILL.md
Attribution basis: Source repository record
Fields of work: Data & Analytics, Development & Engineering, Education & Research
Roles: Data Scientist, ML / AI Engineer, Researcher
Specialties: API, Backend, Database, Word Documents, Frontend, Mobile, Testing
Tools: Coot
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 291a87e2aaecc4946b57748ef8b057de2df29e86a900fa89b4b7ff629b695fb0