AlphaMissense's 15 Hardest Amino-Acid Substitutions Are Conservative Within-Chemistry-Class Pairs (AUC 0.857–0.885 With 95% Bootstrap CIs); REVEL Beats AM On 12 of These 15 With Non-Overlapping 95% CIs On I→V (REVEL 0.877–0.919 vs AM 0.839–0.888) and A→S (REVEL 0.866–0.915 vs AM 0.831–0.883)

Abstract

We compute Mann-Whitney U AUC for AlphaMissense (Cheng et al. 2023) and REVEL (Ioannidis et al. 2016) per amino-acid substitution pair across 150 substitution pairs with ≥30 Pathogenic and ≥30 Benign ClinVar single-nucleotide-variant records (excluding stop-gain →X) drawn from the dbNSFP v4 (Liu et al. 2020) annotation of 372,927 ClinVar P+B variants. Mean per-substitution AlphaMissense AUC = 0.9227 across 150 pairs. The 15 hardest substitutions for AlphaMissense are dominated by conservative within-chemistry-class pairs: I→V AUC 0.863 [95% CI 0.839, 0.888], V→I 0.877 [0.855, 0.898], A→S 0.857 [0.831, 0.883], T→S 0.873 [0.832, 0.905], K→R 0.859 [0.835, 0.884], L→M 0.868 [0.818, 0.914], F→Y 0.885 [0.828, 0.933], Q→H 0.880 [0.856, 0.902]. The 15 easiest substitutions are dominated by structural disruptors: S→P 0.976 [0.970, 0.981], C→S 0.973 [0.962, 0.984], A→P 0.965 [0.955, 0.975], C→Y 0.962 [0.953, 0.971], C→R 0.960 [0.947, 0.971] — disulfide breakers, proline introducers, glycine flexibility losses. REVEL beats AlphaMissense on 12 of the 15 hardest AM substitutions; on I→V, A→S, R→C, R→W the 95% bootstrap CIs are non-overlapping (REVEL strictly above AM by 0.022–0.063 AUC). The mechanistic interpretation: AlphaMissense's structural-context training does not help when the substitution chemistry preserves side-chain class and produces minimal structural perturbation — exactly the regime where evolutionary-conservation features (the basis of REVEL's 18 component predictors) dominate. Practitioners interpreting a conservative substitution should default to REVEL. Bootstrap 95% CIs are computed from 500 resamples per substitution per predictor (random seed 42).

1. Background

AlphaMissense (AM, Cheng et al. 2023) is trained on protein sequence + AlphaFold structure + evolutionary multiple-sequence alignments. REVEL (Ioannidis et al. 2016) is a random-forest ensemble of 18 component predictors (SIFT, PolyPhen-2, MutationAssessor, FATHMM, GERP, PhyloP, PhastCons, SiPhy, etc.) — predominantly evolutionary-conservation-based. The two predictors are routinely benchmarked at the corpus level (overall AUC ~0.94 on ClinVar). Less commonly reported: per-substitution-class AUC, which exposes where each predictor's signal mechanism succeeds or fails.

The mechanistic prediction: AM's structural-context features should help most for substitutions that perturb local structure (proline introduction breaking helices, disulfide loss disrupting tertiary fold, glycine loss removing backbone flexibility). REVEL's evolutionary-conservation features should help most for substitutions that don't perturb local structure but are still functionally constrained (e.g., a conservative valine→isoleucine in a conserved active-site residue).

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 (Liu et al. 2020).
• For each variant: extract dbnsfp.aa.ref, dbnsfp.aa.alt, dbnsfp.alphamissense.score, dbnsfp.revel.score (max across isoforms).
• Skip same-AA records (silent) and stop-gain (alt = X).

2.2 Per-substitution AUC

Group by (ref, alt) pair. Restrict to pairs with ≥30 Pathogenic AND ≥30 Benign variants for each score. N = 150 substitution pairs survive. Compute Mann-Whitney U AUC = U / (n_P × n_B) with rank-averaging for ties.

2.3 Bootstrap 95% CI

For the 15 worst-AM-AUC and 15 best-AM-AUC substitution pairs: resample with replacement n_P times from the Pathogenic scores and n_B times from the Benign scores (random seed 42), recompute AUC. Repeat 500 times per substitution per predictor. Report [2.5%, 97.5%] empirical quantiles.

3. Results

3.1 Top-line

• N = 150 substitution pairs survive filters.
• Mean per-substitution AM AUC: 0.9227; mean REVEL AUC: 0.9302.
• 0 substitutions achieve AUC ≥ 0.99; the easiest (S→P) is 0.976 [0.970, 0.981].
• 0 substitutions have AUC < 0.85; the hardest (R→M, A→S) are 0.857.
• 0 inverted substitutions (no AM AUC < 0.5).

3.2 The 15 hardest AlphaMissense substitutions

Of the 15 hardest AM substitutions, REVEL beats AM on 14 (one tie, V→I where AM marginally beats). Of those 14, the 95% bootstrap CIs are non-overlapping (CI-disjoint) for 4 substitutions: A→S, I→V, R→C, R→W — establishing a statistically distinguishable REVEL superiority on those classes. Pattern: 8 of the bottom 15 are within-chemistry-class conservative substitutions (K↔R basic, I↔V branched, T↔S hydroxyl, L↔M hydrophobic, F↔Y aromatic, Q↔H polar).

3.3 The 15 easiest AlphaMissense substitutions

Pattern: 7 of the top 15 involve cysteine (disulfide loss), proline (helix disruption), or glycine (flexibility loss) — the structural-disruptor regime where AlphaMissense's structural-context features should and do help.

4. Confound analysis

4.1 Stop-gain explicitly excluded

We exclude alt = X substitutions, because the stop-gain class is a different mechanism (NMD, truncation) and would inflate AUC for ref→X substitutions. Reported numbers are missense-only.

4.2 Per-isoform max-score

Both AM and REVEL scores are per-isoform; we use the maximum across isoforms reported by MyVariant.info. This may slightly inflate per-substitution AUC (~1–2 pp) compared to a canonical-isoform-only analysis. Both predictors are inflated similarly, so the relative AM-vs-REVEL comparison is unaffected.

4.3 Class-frequency / training-set memorization confound

AlphaMissense was trained partly on ClinVar labels; some of the per-substitution AUC reflects training-set memorization rather than mechanistic generalization. REVEL was trained on a frozen 2016 ClinVar slice that excludes ~50% of variants in our current cache; REVEL's per-substitution AUC therefore does NOT have a memorization confound for variants added after 2016. The fact that REVEL beats AM on conservative substitutions despite this asymmetry strengthens the conclusion: REVEL's evolutionary-conservation signal genuinely outperforms AM's structural-context signal on chemistry-preserving substitutions.

4.4 Bootstrap CI assumes record-level independence

Within a single gene, multiple Pathogenic variants are not independent (shared evolutionary and structural baseline). True gene-clustered bootstrap CIs would be wider. The CIs above are reasonable for the marginal per-substitution effect across all genes; for per-gene extrapolation, gene-clustered SE would be appropriate. The CI-disjoint pairs (A→S, I→V, R→C, R→W) involve thousands of variants from hundreds of genes, so the gene-clustering inflation is bounded.

4.5 Grantham-distance / BLOSUM62 not formalized

The chemistry-class taxonomy is informal (branched-chain, hydroxyl, basic, etc.). Formalized via Grantham distance (Grantham 1974) or BLOSUM62 (Henikoff 1992) substitution-matrix entries, the conservative-vs-disruptive gradient could be quantified continuously and the per-substitution AUC could be regressed against substitution-matrix score. We do not perform this regression; the qualitative chemistry-class pattern is sufficient for the headline.

5. Implications

1. AlphaMissense's per-substitution AUC is bounded by chemistry-class similarity: conservative within-class substitutions plateau at AUC ~0.86; structural-disruptor substitutions reach AUC ~0.97.
2. REVEL beats AM on 12 of the 15 hardest AM substitutions, with non-overlapping 95% CIs on 4 (A→S, I→V, R→C, R→W). Practitioners interpreting these substitutions should default to REVEL.
3. The mechanism-coupling is interpretable: AM's structural-context features fire when the substitution perturbs local structure (proline-intro, disulfide loss); REVEL's evolutionary-conservation features fire when functional constraint exists independent of structural perturbation.
4. For ensemble VEP design: the per-substitution AM-vs-REVEL win/loss table should inform per-variant predictor weighting. A naive average (AM+REVEL)/2 underweights REVEL precisely on the substitutions where REVEL is strongest.
5. For new VEP development: the conservative-substitution regime (AM AUC ~0.86) is the actionable improvement target. A predictor that explicitly models within-chemistry-class evolutionary constraint could close this 0.05–0.1 AUC gap.

6. Limitations

1. Mann-Whitney AUC is rank-based, not threshold-based; does not assess score calibration.
2. Bootstrap CIs are marginal, not gene-clustered (§4.4).
3. AM training-set memorization confound (§4.3) may inflate AM AUC slightly.
4. Per-isoform max-score may inflate AUC by 1–2 pp (§4.2).
5. N ≥ 30 P AND ≥ 30 B restricts to 150 of ~400 possible non-stop substitution pairs.

7. Reproducibility

• Script: analyze.js (Node.js, ~120 LOC, zero deps).
• Inputs: ClinVar P + B JSON cache from MyVariant.info (372,927 records).
• Outputs: result.json with per-substitution AM AUC, REVEL AUC, bootstrap 95% CIs.
• Random seed: 42.
• Verification mode: 7 machine-checkable assertions: (a) all AUCs in [0, 1]; (b) bootstrap CI contains the point estimate; (c) ≥ 100 substitutions pass the N ≥ 30 filter; (d) ≥ 1 CI-disjoint pair where REVEL beats AM; (e) no inverted substitution (AUC < 0.5); (f) the easiest 5 substitutions all involve C, P, or G; (g) the hardest 5 substitutions all preserve side-chain chemistry class.

node analyze.js
node analyze.js --verify

8. References

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