---
name: bio-structural-biology-structure-validation
description: Judges whether a macromolecular model (or a region of it) is reliable enough to build on, using resolution, R-free, B-factors, MolProbity geometry, and predicted-model confidence with Bio.PDB. Use when deciding if a structure or a specific region is trustworthy before docking/mechanism/measurement; reading resolution, R-work vs R-free and the R-free-minus-R-work overfitting gap; sanity-checking per-residue and mean B-factors; flagging clashscore, Ramachandran and rotamer outliers and cis non-proline peptides; validating a PREDICTED (AlphaFold/ESMFold) model via pLDDT bands and PAE before docking or molecular replacement; and interpreting cryo-EM global-vs-local resolution (FSC 0.143 half-map vs 0.5 map-model) or an NMR ensemble spread. Keywords validation, resolution, R-free, B-factor, MolProbity, clashscore, Ramachandran, rotamer, pLDDT, PAE, wwPDB, cryo-EM local resolution.
tool_type: python
primary_tool: Bio.PDB
---

## Version Compatibility

Reference examples tested with: biopython 1.85+, numpy 1.26+

MolProbity, phenix (`phenix.molprobity`, `phenix.process_predicted_model`), and DSSP (`mkdssp`) are external CLI tools invoked via `subprocess`, not pip packages; install them separately and confirm they are on PATH before use.

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Structure Validation

**"Is this structure good enough to build on?"** -> Read the data-quality metadata, run geometry validation, and decide per-region rather than per-file.
- Python: `Bio.PDB.MMCIF2Dict` (resolution/R-free), `Bio.PDB.calc_dihedral` (phi/psi/omega), `subprocess` -> `phenix.molprobity` (clashscore, rotamer, Ramachandran).

## Governing Principle

A coordinate file is an INTERPRETED MODEL fit to experimental data, not the data and not ground truth, and its reliability varies PER-ATOM within one file. The single global resolution is a DATA CEILING on what the map can resolve, never a per-region quality certificate: a 1.5 Angstrom structure can still carry a guesswork surface loop, and a 3.2 Angstrom structure can have a rigid, locally excellent active site. The number that answers "can I trust THIS region" is the LOCAL signal - the per-residue B-factor and the real-space fit (RSRZ) for the residues actually used - not the headline resolution. Validate before any geometric interpretation: clashscore, Ramachandran and rotamer outliers, cis-peptides, and bond/angle deviations tell whether the model is even self-consistent before a distance or an angle computed on it means anything.

R-free, not R-work, is the cross-validation statistic. R-work is computed on the reflections used in refinement and can be driven down by adding parameters (waters, alt-confs, loose restraints) that fit noise; R-free is the same metric on a held-out test set that never entered refinement (Brunger 1992 *Nature* 355:472-475). The R-free-MINUS-R-work GAP is the overfitting flag - a gap much larger than expected for the resolution signals a model fitting its own noise, and a suspiciously small gap signals test-set leakage (Kleywegt & Brunger 1996 *Structure* 4:897-904). B-factors conflate genuine thermal motion, static disorder, and MODEL ERROR into one number, so they are a within-structure relative signal ("which atoms are least certain here"), NOT a portable cross-structure dynamics readout; normalize before comparing across structures and exclude high-B atoms (say >60-80 Angstrom^2 at moderate resolution) from precise geometric claims.

For a PREDICTED model the confidence scores bound SELF-CONSISTENCY, NOT correctness. pLDDT is per-residue and PAE is inter-residue confidence in the model's OWN frame (Jumper et al. 2021 *Nature* 596:583-589); a confident model can be confidently wrong when AlphaFold modeled a monomer/apo state where the truth is a complex/holo/alternative state. Trim low-pLDDT residues and split by PAE-defined domains before docking or molecular replacement (Oeffner et al. 2022 *Acta Crystallogr D* 78:1303-1314). Cryo-EM has GLOBAL vs LOCAL resolution - flexible peripheries in a big map are often docked hypotheses at 6-8 Angstrom local resolution inside a "2.5 Angstrom" map - and TWO different FSC thresholds: 0.143 on the gold-standard half-map FSC for the resolution claim, 0.5 for map-vs-model agreement (Rosenthal & Henderson 2003 *J Mol Biol* 333:721-745). An NMR deposit is an ENSEMBLE of models; validate per-model and report the per-residue spread - never average coordinates, which produces a physically impossible structure with distorted bonds and clashes (Montelione et al. 2013 *Structure* 21:1563-1570).

## Decision: what the resolution buys (X-ray / cryo-EM global)

| Resolution (A) | Reliably resolvable | Do NOT over-read |
|---|---|---|
| < 1.2 (atomic) | Individual atoms, H atoms, anisotropic ADPs, alt-confs | - |
| 1.2-1.8 | Side-chain rotamers, ordered waters, alt-confs | Surface/loop atoms with high B still uncertain |
| 1.8-2.5 (typical) | Backbone plus most side chains; fold solid | Long polar rotamers, water networks, weak ligand density are model-dependent |
| 2.5-3.5 | Backbone trace, secondary structure, domain arrangement | Side-chain conformations, exact ligand pose, H-bond geometry are interpretive |
| > 3.5 | Overall fold, gross assembly | Individual side chains largely placed by geometry; treat atomic detail as hypothesis |

## Decision: R-free and the overfitting gap

| Resolution | Typical R-work | Typical R-free | Concern if gap (R-free - R-work) exceeds ~ |
|---|---|---|---|
| ~1.5 A | 0.13-0.17 | 0.15-0.19 | 0.03-0.04 |
| ~2.0 A | 0.16-0.20 | 0.19-0.24 | 0.04-0.05 |
| ~2.5-3.0 A | 0.18-0.24 | 0.23-0.29 | 0.05-0.06 |

Absolute R rises with resolution (weaker high-angle reflections are noisier); a gap wider than the row's threshold flags overfitting, a near-zero gap flags work/test contamination. These are heuristics - verify against contemporaneous PDB statistics for the resolution.

## Decision: geometry validation targets (MolProbity)

| Metric | Measures | Target (goal) | Concern | Source |
|---|---|---|---|---|
| Clashscore | Serious all-atom overlaps (>0.4 A) per 1000 atoms | low, high same-resolution percentile | high vs same-resolution peers | Chen 2010 |
| Ramachandran favored | % residues in favored phi/psi basins | > 98% | < 95% | Williams 2018 |
| Ramachandran outliers | % residues in disallowed phi/psi | < 0.2% (goal 0) | > 0.5% | Williams 2018 |
| Poor rotamers | % side chains in disallowed rotamers | < 0.3% | > 1.5% | Williams 2018 |
| RSRZ outliers (X-ray) | Residues poorly fitting local density (RSRZ > 2) | few, dispersed | many, or clustered in the region of interest | Gore 2017 |
| MolProbity score | Composite mapped to a resolution-equivalent | <= deposited resolution | >> deposited resolution | Chen 2010 |

The poor-rotamer target tightened from ~1.0% to 0.3% with the updated reference distributions (Williams 2018). Read the wwPDB report's PERCENTILE sliders alongside the raw numbers: they rank an entry against the whole archive AND against same-resolution entries (Gore et al. 2017 *Structure* 25:1916-1927).

## Decision: how to validate this model (experimental vs predicted fork)

| The model is | Trust question | What to read | Authoritative route |
|---|---|---|---|
| X-ray | Is the region well-determined? | resolution + local B + RSRZ; R-free and its gap | wwPDB report + `phenix.molprobity` |
| Cryo-EM | Is the region rigid or a docked guess? | LOCAL resolution map, not global; map-model FSC (0.5) | EMDB local-resolution + map-model FSC |
| NMR | How wide is the conformational spread? | per-residue RMSD across all models | validate each model; report spread |
| Predicted (AlphaFold/ESMFold) | Is it self-consistent AND in the right biological context? | pLDDT bands + PAE blocks; is it apo/monomer where truth is holo/complex? | `phenix.process_predicted_model` (trim + PAE split) |

## Read Validation Metadata From the mmCIF Header

**Goal:** Pull resolution, R-work, R-free, and method from a deposited entry and flag the overfitting gap - the numbers Bio.PDB's thin `structure.header` omits.

**Approach:** Read the raw mmCIF categories with `MMCIF2Dict` (which reaches anything in the file), cast the strings, and compare the R-free-minus-R-work gap against a resolution-scaled expectation.

```python
from Bio.PDB.MMCIF2Dict import MMCIF2Dict

def read_refinement_metadata(cif_path):
    meta = MMCIF2Dict(cif_path)
    def first(key):
        val = meta.get(key, ['NA'])[0]
        try:
            return float(val)
        except ValueError:
            return val
    method = meta.get('_exptl.method', ['NA'])[0]
    resolution = first('_refine.ls_d_res_high')
    r_work = first('_refine.ls_R_factor_R_work')
    r_free = first('_refine.ls_R_factor_R_free')
    gap = r_free - r_work if isinstance(r_free, float) and isinstance(r_work, float) else None
    # A gap wider than ~0.05 flags overfitting at typical (~2 A) resolution (Kleywegt & Brunger 1996).
    overfit_flag = gap is not None and gap > 0.05
    return {'method': method, 'resolution': resolution, 'r_work': r_work, 'r_free': r_free, 'gap': gap, 'overfit_flag': overfit_flag}
```

## Sanity-Check B-factors Within One Structure

**Goal:** Locate the least-certain atoms of THIS model so precise-distance claims avoid them - a relative, within-structure read, never a cross-structure comparison.

**Approach:** Collect per-residue mean B for a chain, then flag residues whose B sits far above the structure's own median as low-confidence.

```python
import numpy as np
from Bio.PDB import PDBParser

def bfactor_outliers(structure, chain_id, z_cut=2.0):
    residue_b = {}
    for residue in structure[0][chain_id]:
        if residue.id[0] != ' ':  # skip hetero/water; validate the polymer only
            continue
        residue_b[residue.id] = np.mean([a.get_bfactor() for a in residue])
    vals = np.array(list(residue_b.values()))
    median, mad = np.median(vals), np.median(np.abs(vals - np.median(vals))) + 1e-9
    # Robust z on B: |B - median| / (1.4826*MAD); high B marks disorder/model error, not portable dynamics.
    return {rid: b for rid, b in residue_b.items() if (b - median) / (1.4826 * mad) > z_cut}
```

## Flag Ramachandran Outliers and cis Non-Proline Peptides

**Goal:** Catch backbone geometry that is almost always a modeling error - residues in disallowed phi/psi and cis peptide bonds that are not proline.

**Approach:** Get per-residue phi/psi from `PPBuilder`, coarsely classify against the canonical basins, and compute omega (Ca-C-N-Ca) directly to find cis bonds; a cis assignment at a non-proline is a validation flag until proven by density.

```python
import numpy as np
from Bio.PDB import PDBParser, PPBuilder, calc_dihedral

# Coarse general-allowed basins (deg): alpha, beta/PPII, left-handed. Authoritative
# favored/allowed/outlier percentages need MolProbity's rama8000 contours (Williams 2018);
# this only screens gross outliers to decide whether to run MolProbity.
_BASINS = [(-160, -40, -80, 30), (-180, -40, 90, 180), (30, 90, -30, 90)]

def is_rama_allowed(phi, psi):
    d = np.degrees([phi, psi])
    return any(lo_p <= d[0] <= hi_p and lo_s <= d[1] <= hi_s for lo_p, hi_p, lo_s, hi_s in _BASINS)

def geometry_flags(structure):
    outliers, cis_nonpro = [], []
    ppb = PPBuilder()
    for pp in ppb.build_peptides(structure[0]):
        for residue, (phi, psi) in zip(pp, pp.get_phi_psi_list()):
            if phi is not None and psi is not None and not is_rama_allowed(phi, psi):
                outliers.append((residue.get_parent().id, residue.id[1], residue.resname))
        residues = list(pp)
        for prev, curr in zip(residues, residues[1:]):
            omega = calc_dihedral(prev['CA'].get_vector(), prev['C'].get_vector(), curr['N'].get_vector(), curr['CA'].get_vector())
            # omega ~180 = trans, ~0 = cis; |omega| < 30 deg is cis. cis at non-Pro is rare and usually an error.
            if abs(np.degrees(omega)) < 30 and curr.resname != 'PRO':
                cis_nonpro.append((curr.get_parent().id, curr.id[1], curr.resname))
    return {'rama_outliers': outliers, 'cis_nonproline': cis_nonpro}
```

## Run MolProbity for Authoritative Geometry (the real answer)

**Goal:** Get archive-calibrated clashscore, rotamer, and Ramachandran outlier percentages rather than the coarse Python screen above.

**Approach:** Shell out to `phenix.molprobity` (or the MolProbity web service / `molprobity.molprobity`), which carries the reference contour and rotamer distributions the Python screen cannot reproduce.

```python
import subprocess

def run_molprobity(model_path):
    # phenix.molprobity writes molprobity.out with clashscore, rotamer_outliers,
    # ramachandran_outliers, ramachandran_favored, molprobity_score. Parse that file.
    result = subprocess.run(['phenix.molprobity', model_path], capture_output=True, text=True)
    return result.stdout
```

The coarse Python screen decides WHETHER to run MolProbity; MolProbity (Chen et al. 2010 *Acta Crystallogr D* 66:12-21; Williams et al. 2018 *Protein Sci* 27:293-315) gives the numbers to report. For a deposited entry, prefer the pre-computed wwPDB validation report (`https://files.rcsb.org/pub/pdb/validation_reports/<xy>/<id>/<id>_validation.xml.gz`) - it already carries the percentile sliders and per-residue RSRZ.

## Validate a Predicted Model Before Docking or MR

**Goal:** Turn an AlphaFold/ESMFold model into a trustworthy input by trimming low-confidence residues and reading inter-domain confidence - because pLDDT/PAE bound self-consistency, not correctness.

**Approach:** Read pLDDT from the B-factor column into bands, read the PAE matrix for domain segmentation, then hand the raw file to `phenix.process_predicted_model` (which converts pLDDT to a pseudo-B, trims, and splits by PAE-defined domains).

```python
import json
import numpy as np
import subprocess
from Bio.PDB import MMCIFParser

_PLDDT_BANDS = [(90, 'very_high'), (70, 'confident'), (50, 'low'), (0, 'very_low')]

def plddt_bands(cif_path):
    # pLDDT rides in the B-factor column but is confidence (high = good), OPPOSITE polarity to a real B-factor.
    structure = MMCIFParser(QUIET=True).get_structure('pred', cif_path)
    counts = {label: 0 for _, label in _PLDDT_BANDS}
    for residue in structure[0].get_residues():
        if 'CA' not in residue:
            continue
        score = residue['CA'].get_bfactor()
        counts[next(label for cut, label in _PLDDT_BANDS if score >= cut)] += 1
    return counts  # a long very_low run usually flags an intrinsically disordered region, not an error

def pae_interdomain_confident(pae_json, block_a, block_b, cutoff=5.0):
    # Off-diagonal PAE (A) between two domain blocks; low = relative orientation trusted, high = independent guess.
    pae = np.array(json.load(open(pae_json))[0]['predicted_aligned_error'])
    return pae[np.ix_(block_a, block_b)].mean() < cutoff

def process_for_mr(model_path):
    # Trims below ~0.7 fractional pLDDT, converts pLDDT->pseudo-B, splits into PAE-defined domains (Oeffner 2022).
    return subprocess.run(['phenix.process_predicted_model', model_path], capture_output=True, text=True).stdout
```

## Report an NMR Ensemble Spread (never average coordinates)

**Goal:** Turn a multi-model NMR deposit into a per-residue uncertainty map instead of over-claiming precision from model 1.

**Approach:** Superpose all models on a reference and report per-residue Ca RMSD across the ensemble; wide spread marks flexible or under-restrained regions.

```python
import numpy as np
from Bio.PDB import PDBParser

def ensemble_ca_spread(structure, chain_id):
    coords = []
    for model in structure:
        coords.append(np.array([res['CA'].get_coord() for res in model[chain_id] if 'CA' in res]))
    stack = np.stack(coords)  # (n_models, n_residues, 3); assumes consistent residue set across models
    # Per-residue spread = mean distance of each model's Ca from the ensemble mean position.
    return np.linalg.norm(stack - stack.mean(axis=0), axis=2).mean(axis=0)
```

Secondary-structure validation (DSSP, `Bio.PDB.DSSP(model, path, dssp='mkdssp')`) needs backbone geometry to place the amide H and computes its own H-bond energy; it processes only the first model and its output drifts across the dssp->mkdssp v2->v4 rewrites, so name the version (Kabsch & Sander 1983 *Biopolymers* 22:2577-2637). See geometric-analysis for dihedral and DSSP mechanics.

## Common Errors

| Symptom | Cause | Fix |
|---|---|---|
| "It is a 1.5 A structure so every atom is accurate" | Global resolution read as per-region quality | Read the local B-factor and RSRZ for the specific residues used; resolution is a data ceiling |
| Low R-work reported as proof of a good model | R-work is fit on refinement reflections and rewards overfitting | Report R-free (held-out) and the R-free-minus-R-work gap; a wide gap flags overfitting |
| B-factors of two structures compared at face value | B conflates thermal motion, disorder, and model error and is not portable | Compare within one structure only; normalize (z/percentile) before any cross-structure claim |
| Coloring an AlphaFold model "by B-factor" to infer flexibility | pLDDT rides in the B-factor column with OPPOSITE polarity (high = confident) | Read the column as pLDDT bands; high value means high confidence, not high motion |
| Deleting all low-pLDDT residues as junk | Low pLDDT often marks a real intrinsically disordered region | Distinguish disorder (keep, annotate) from misfold; trim only for MR/geometry pipelines |
| Docking straight into an AlphaFold pocket | Pocket is apo, side-chain rotamers are the least reliable atoms, may be wrong state | Prefer an experimental holo structure; if using the model, ensemble/flexible-side-chain dock and treat hits as hypotheses |
| Confident predicted model trusted for a complex | pLDDT/PAE bound self-consistency, not biological correctness | Ask what context AF could not see (partner, ligand, PTM); a monomer/apo model can be confidently wrong |
| Cryo-EM peripheral domain trusted at the headline resolution | Global resolution hides a low-local-resolution flexible arm | Consult the EMDB local-resolution map; treat low-local-res regions as docked hypotheses |
| Quoting FSC 0.5 as the cryo-EM resolution | 0.143 is the half-map criterion; 0.5 is the map-vs-model curve | Use gold-standard half-map FSC at 0.143 for resolution; 0.5 for checking the model against the map |
| Averaging NMR model coordinates into one structure | The mean of two valid conformers is physically impossible (distorted bonds, clashes) | Compute on each model and report the spread, or pick a representative/medoid model |
| cis peptide flagged everywhere, or missed entirely | omega near 0 (cis) vs 180 (trans) not checked; cis-Pro is common but cis non-Pro is rare | Compute omega; treat cis non-proline as a validation flag pending density, cis-Pro as plausible |
| Python Ramachandran percentages reported as authoritative | Coarse basin boxes are not MolProbity's rama8000 contours | Use the screen to decide whether to run `phenix.molprobity`; report MolProbity's numbers |
| `resolution`/`R-free` come back `None` from `structure.header` | Bio.PDB's header dict is thin | Read `_refine.ls_d_res_high`, `_refine.ls_R_factor_R_free`, `_exptl.method` via `MMCIF2Dict` |

## Related Skills

- structure-io - Read resolution/R-free via MMCIF2Dict and fetch the biological assembly the validation applies to
- structure-navigation - Resolve altlocs, insertion codes, and multi-model NMR files before validating per-model
- geometric-analysis - Compute the dihedrals and DSSP secondary structure this skill validates; measure only after validation passes
- structure-modification - Trim low-pLDDT residues or edit B-factors once a predicted model is validated
- structure-preparation - Add hydrogens, protonation, and missing atoms after validation and before docking/MD
- alphafold-predictions - Download the AlphaFold model plus its PAE JSON that this skill reads for confidence
- modern-structure-prediction - Reconcile a re-run prediction with pLDDT/PAE/pTM when the AFDB entry is untrustworthy
- interface-analysis - Validate the assembly before interpreting an interface that only exists in it
- database-access/uniprot-access - Map validated residues back to a UniProt reference sequence

## References

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- Brunger AT (1992). Free R value: a novel statistical quantity for assessing the accuracy of crystal structures. *Nature* 355(6359):472-475. DOI 10.1038/355472a0.
- Kabsch W, Sander C (1983). Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. *Biopolymers* 22(12):2577-2637. DOI 10.1002/bip.360221211.
- Kleywegt GJ, Brunger AT (1996). Checking your imagination: applications of the free R value. *Structure* 4(8):897-904. DOI 10.1016/S0969-2126(96)00097-4.
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- Gore S, Sanz Garcia E, Hendrickx PMS, Gutmanas A, Westbrook JD, Yang H, Feng Z, Baskaran K, Berrisford JM, et al. (2017). Validation of structures in the Protein Data Bank. *Structure* 25(12):1916-1927. DOI 10.1016/j.str.2017.10.009.
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- Jumper J, Evans R, Pritzel A, et al. (2021). Highly accurate protein structure prediction with AlphaFold. *Nature* 596(7873):583-589. DOI 10.1038/s41586-021-03819-2.
- Oeffner RD, Croll TI, Millan C, Poon BK, Schlicksup CJ, Read RJ, Terwilliger TC (2022). Putting AlphaFold models to work with phenix.process_predicted_model and ISOLDE. *Acta Crystallogr D* 78(11):1303-1314. DOI 10.1107/S2059798322010026.
- Montelione GT, Nilges M, Bax A, et al. (2013). Recommendations of the wwPDB NMR Validation Task Force. *Structure* 21(9):1563-1570. DOI 10.1016/j.str.2013.07.021.