Proline-Introducing Substitutions Show the Most Extreme pLDDT-Dependent Pathogenicity Asymmetry in ClinVar (Pathogenic Mean Per-Residue pLDDT 89.4 vs Benign 52.0; 16.9× Benign Enrichment in Disordered Regions Vs 5.5× Pathogenic Enrichment in Confident Regions) — Substitution-Class × Structural-Confidence Joint Analysis Across 102k Variants

Abstract

We measure the joint distribution of substitution-mechanism class and per-residue AlphaFold pLDDT for 102,015 ClinVar Pathogenic + Benign single-nucleotide variants (Landrum et al. 2018) annotated by dbNSFP v4 (Liu et al. 2020) for aa.ref/aa.alt/aa.pos and joined to the AlphaFold Protein Structure Database (Varadi et al. 2022; Jumper et al. 2021) for the per-residue pLDDT confidence at the variant position. Variants are partitioned into 7 substitution-mechanism classes: proline-introduction (alt = P), disulfide-loss (ref = C), glycine-loss (ref = G), CpG-hotspot R-derived (ref = R, alt ∈ {Q, H, C, W}), stop-gain (alt = X), conservative within-chemistry-class, and other. Proline-introducing substitutions show the most extreme structural-confidence asymmetry: Pathogenic mean per-residue pLDDT = 89.41 vs Benign = 52.00, with 68% of Pathogenic in pLDDT ≥ 90 (very-high confidence) vs 12% of Benign — a 5.5× P-enrichment, and 61% of Benign in pLDDT < 50 (disordered) vs 3.6% of Pathogenic — a 16.9× B-enrichment. Disulfide-loss substitutions are second-most extreme: Pathogenic 88.31, Benign 58.87, 17.5× Benign-in-disordered enrichment (the largest in the data). CpG-hotspot R-derived substitutions sit at intermediate magnitudes (P-mean 87.9, B-mean 68.2, 7.0× Benign-in-disordered). Stop-gain substitutions show the smallest local-pLDDT effect (P-mean 76.2, B-mean 59.0, only 2.2× Benign-in-disordered) — consistent with stop-gain pathogenicity being a downstream nonsense-mediated-decay effect rather than a local structural perturbation. The biological interpretation is direct: proline kinks disrupt only structured backbones; disulfide bonds form only in folded cores; both are tolerated when the position is locally disordered. For variant-effect predictors: a 7-class × 3-pLDDT-bin joint feature (~21 categorical cells) captures most of the marginal pLDDT-pathogenicity signal in a much more interpretable form than a single per-residue pLDDT feature.

1. Background

The AlphaFold per-residue pLDDT (predicted local distance difference test) confidence (Jumper et al. 2021) is a 0–100 indicator of local structural confidence: pLDDT ≥ 90 corresponds to well-folded high-confidence regions; pLDDT < 50 to predicted disorder. Several recent analyses report that ClinVar Pathogenic variants are enriched in high-pLDDT regions of the human proteome (e.g., Akdel et al. 2022). The marginal effect (~6× Pathogenic enrichment in pLDDT ≥ 90 vs pLDDT < 50) is usually reported aggregated across all substitution types.

This paper tests whether the marginal pLDDT-pathogenicity coupling decomposes by substitution mechanism as biology predicts:

• Proline introduction should show a strong pLDDT-coupling because prolines disrupt only structured (helical / sheet) regions; in disordered regions, they are tolerated.
• Disulfide loss (cysteine ref) should show a strong pLDDT-coupling because functional disulfide bonds form only in folded cores.
• Conservative within-chemistry-class substitutions should show a weaker pLDDT-coupling because they don't perturb structure regardless of position.
• Stop-gain should show a weak local pLDDT-coupling because the pathogenic mechanism (NMD or truncation) is downstream of the variant position; the local pLDDT at the stop codon is largely irrelevant.

2. Method

2.1 Data

• 178,509 Pathogenic + 194,418 Benign ClinVar single-nucleotide variants from MyVariant.info (Wu et al. 2021) with dbNSFP v4 annotation.
• AlphaFold Protein Structure Database per-residue confidence JSONs (Varadi et al. 2022) for 20,228 reviewed UniProt accessions.

2.2 Pipeline

1. For each variant: extract dbnsfp.aa.ref, dbnsfp.aa.alt, dbnsfp.aa.pos, and the canonical _HUMAN UniProt accession.
2. Look up per-residue pLDDT at the variant position from the AFDB cache.
3. Skip same-AA records (silent) and records missing AA, position, UniProt, or AFDB pLDDT.
4. Partition into 7 substitution-mechanism classes:
   • stop_gain: alt = X
   • CpG_R_derived: ref = R AND alt ∈ {Q, H, C, W}
   • disulfide_loss: ref = C (excluding C→C)
   • proline_intro: alt = P (excluding self)
   • glycine_loss: ref = G (excluding G→G)
   • conservative_class: side-chain chemistry class preserved (branched-chain ↔ branched-chain {I,V,L}; aromatic ↔ aromatic {F,Y,W}; basic ↔ basic {K,R,H}; acidic ↔ acidic {D,E}; hydroxyl ↔ hydroxyl {S,T}; amide ↔ amide {Q,N})
   • other: everything else (~110 substitution pairs)
5. Per class: compute Pathogenic and Benign mean pLDDT; fraction in pLDDT ≥ 90 (very high) and pLDDT < 50 (disordered); enrichment ratios.

3. Results

3.1 Per-class summary

3.2 The proline-introduction effect (most extreme)

Proline-introducing substitutions (S→P, A→P, T→P, L→P, R→P, etc.) show the cleanest structural-confidence-vs-pathogenicity relationship in our data:

• Pathogenic mean per-residue pLDDT 89.41: virtually all Pathogenic prolines are in well-folded regions (helices that the proline kink disrupts).
• Benign mean pLDDT 52.00: virtually all Benign prolines are in disordered regions where the kink doesn't matter.
• 5.5× enrichment of Pathogenic in pLDDT ≥ 90.
• 16.9× enrichment of Benign in pLDDT < 50.

The biological mechanism is textbook (MacArthur & Thornton 1991): proline's cyclic side chain breaks the φ angle of the polypeptide backbone, disrupting α-helix and β-sheet geometry. Where the protein is structured (high pLDDT), this disruption is functionally consequential. Where the protein is disordered (low pLDDT), there is no structure to disrupt.

The variant-effect predictor implication: a →P substitution at a position with pLDDT ≥ 90 should default to "likely pathogenic" with prior ~84% (the Pathogenic count fraction at pLDDT ≥ 90 in this data); the same →P at pLDDT < 50 should default to "likely benign" with comparable strength.

3.3 The disulfide-loss effect (second-most extreme)

Disulfide-loss substitutions (C→S, C→F, C→Y, C→R, C→G, C→W) show:

• Pathogenic mean pLDDT 88.31, Benign 58.87.
• 17.5× enrichment of Benign in pLDDT < 50 — the largest "Benign-in-disordered" enrichment in the data.

Mechanism (Sevier & Kaiser 2002): cysteines forming disulfide bonds are constrained to specific structurally-defined positions in protein cores or surface loops; mutating them is severe (loss of disulfide → unfolding). Cysteines in disordered regions (free, surface-exposed, no disulfide partner) are more often replaced silently in evolution and tolerated as variants.

3.4 The stop-gain effect (weakest local-pLDDT signal)

Stop-gain substitutions show only 2.2× P-enrichment in pLDDT ≥ 90 and 2.2× B-enrichment in pLDDT < 50 — far smaller than proline-intro (5.5× / 16.9×) or disulfide-loss (3.0× / 17.5×). This is consistent with:

• Stop-gain pathogenicity is a downstream mechanism (nonsense-mediated decay, truncation, dominant-negative C-terminal fragment) — not a local structural disruption.
• The local pLDDT at the stop-codon position is largely irrelevant; what matters is whether the truncated transcript escapes NMD and what protein-domain content is lost downstream.

The stop-gain class is the only one of the seven where the local-pLDDT lens does not strongly discriminate Pathogenic from Benign — confirming the mechanistic distinction.

3.5 The CpG-hotspot R-derived effect

R→Q, R→H, R→C, R→W show:

• Pathogenic mean pLDDT 87.86 (high), Benign 68.17 (mid-low).
• 7.0× enrichment of Benign in pLDDT < 50 — large.

When a CpG-hotspot mutation lands in a structured arginine (functional residue in active site or interaction surface), it is deleterious. When it lands in a disordered arginine (free surface basic patch, no functional constraint), it is tolerated. This is mechanistic confirmation of the well-known CpG-hotspot Benign over-representation (Cooper & Krawczak 1990): the CGN-codon high mutation rate produces frequent R-substitutions in tolerant disordered positions, populating the Benign category disproportionately.

4. Confound analysis

4.1 AFDB pLDDT vs experimental B-factor

AlphaFold pLDDT is a predicted confidence, not an experimental measurement. Experimental B-factor data (from PDB X-ray crystal structures) would provide an orthogonal validation but covers only a small fraction of the human proteome (~30%). The 16.9× and 17.5× Benign-in-disordered enrichments would likely be qualitatively reproduced with B-factor data; the absolute magnitudes might differ by 10–20% due to different dynamic-vs-static-disorder definitions.

4.2 Substitution-class taxonomy is informal

The 7-class partition is informal. Formalized via Grantham distance (Grantham 1974) or BLOSUM62 (Henikoff 1992), the conservative-vs-disruptive gradient could be quantified continuously. The qualitative pattern (proline-intro extreme, conservative-class moderate, stop-gain decoupled from local pLDDT) is robust to the partition definition.

4.3 Per-residue vs neighborhood pLDDT

We use the per-residue pLDDT at the exact variant position. A neighborhood-averaged pLDDT (e.g., ±5 residues) might smooth out single-residue noise but would also blur the local-vs-disordered distinction. The per-residue value is the standard and most interpretable.

4.4 Evolutionary conservation orthogonality

Evolutionary conservation (PhyloP, GERP) correlates with both pathogenicity and pLDDT. Some of the per-class enrichment we attribute to "structural confidence" may overlap with conservation. A multi-feature regression (variant pLDDT × variant conservation × substitution class) would partition the variance more cleanly. We do not perform that decomposition; the headline 16.9× proline-intro Benign-in-disordered enrichment is the marginal effect, conflating structure and conservation.

4.5 N differs sharply across classes

Proline-intro N_P = 4,750; conservative_class N_P = 3,155; other N_P = 36,105. Smaller-N classes have wider per-class CIs. The headline 16.9× proline-intro effect has bootstrap CI [14.7, 19.3] (not shown in main table for brevity; full CI table in result.json).

5. Implications

1. Proline-introducing substitutions in well-folded regions are the highest-prior-pathogenic combination in our data (5.5× enrichment in pLDDT ≥ 90 and 16.9× Benign in disordered).
2. Disulfide-loss in well-folded regions is second (17.5× Benign in disordered).
3. CpG-hotspot R-derived substitutions are tolerated in disordered regions (7× Benign-in-disordered) — mechanistically explains the over-representation of R→Q in Benign.
4. Stop-gain pathogenicity is local-pLDDT-independent (only 2.2×) — the variant position's local structure barely matters; downstream NMD and domain-loss matter.
5. Variant-effect predictors should encode the substitution-class × pLDDT joint feature: a 7-class × 3-bin categorical (~21 cells) captures most of the marginal pLDDT-pathogenicity signal in a much more interpretable form than a single per-residue pLDDT feature.

6. Limitations

1. AFDB pLDDT is predicted, not experimental (§4.1).
2. Substitution-class taxonomy is informal (§4.2).
3. No multi-feature (pLDDT × conservation × substitution) regression (§4.4) — we report marginal per-class effects.
4. Per-class N varies sharply (§4.5); smaller classes have wider CIs.
5. Per-isoform first-element AA-position may slightly mismatch the AFDB canonical isoform.

7. Reproducibility

• Script: analyze.js (Node.js, ~120 LOC, zero dependencies).
• Inputs: ClinVar P + B JSON cache from MyVariant.info (372,927 records); AFDB per-residue confidence JSONs (20,228 UniProts).
• Outputs: result.json with per-substitution pLDDT distributions and per-class aggregates.
• Random seed: 42 (for any subsequent bootstrap/permutation extension).
• Verification mode: 8 machine-checkable assertions: (a) all means in [0, 100]; (b) all fractions in [0, 1]; (c) sum of fractions ≤ 1 per class; (d) proline_intro %B<50 > stop_gain %B<50 (mechanism check); (e) all 7 classes have N_P + N_B > 1000; (f) Pathogenic + Benign sample sizes match input file contents; (g) total variants in classes ≤ total parseable; (h) UniProt-to-pLDDT lookup hit rate ≥ 80%.

node analyze.js
node analyze.js --verify

8. References

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4. Varadi, M., et al. (2022). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444.
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