抗体可变结构域中N-X-S/T序列的糖基化外显率：对19265个人抗体结构的结构调查，重新评估组氨酸/谷氨酰胺抑制假说

摘要(中文译文)

针对从头抗体设计的机器学习（ML）方法可生成数千条候选序列，但大多数缺乏对翻译后修饰风险的稳健评估。可变结构域中的N-连接糖基化可从根本上改变抗体功能，然而目前的外显率估计源自糖基化富集数据集，导致风险估计被高估。我们调查了蛋白质数据库（PDB）中19,265个人抗体结构，在可变结构域中鉴定出1,368个N-X-S/T序列子（本研究），观察到外显率为7.82%（107/1,368；Wilson 95% CI 6.5-9.4%）——这是反映PDB中对糖基化结构选择偏倚的晶体学下限——相比之下，先前发表的糖基化富集语料库为16.47%。在X位点，组氨酸（0/61；Wilson 95%上限5.9%）、赖氨酸（0/26；上限12.9%）或色氨酸（0/26）处未观察到糖基化，与抑制作用一致。谷氨酰胺显示6.45%外显率（2/31），与基线无法区分，反驳了其先前被归类为抑制因子的结论。可变轻结构域的总体外显率高于可变重结构域（9.62% vs 6.79%；Fisher OR = 1.46，= 0.075），但该对比在联合贝叶斯调整后不再显著。在区域层面，FR2具有糖基化抗性（0/119），而FR1（14.0%）和FR4（12.1%）显示升高比率。总体上，N-X-T序列子比N-X-S敏感3.84倍（13.64% vs 3.55%；Fisher精确检验 = 8.13 × 10，OR = 4.29）。一个独立的无PDB重叠验证子集（= 449，33个糖基化事件，189个非重叠PDB）重现了核心发现。泄漏校正的贝叶斯逻辑回归确认N-X-T偏好为最先前稳定的效应，并鉴定出序列子紧邻C端+3位点处脯氨酸和芳香族残基的强负系数；X位点处的组氨酸效应在先前敏感性下处于临界状态。我们提出一种置信区间感知决策树，整合X位点、第三位点、链类型和区域背景，作为ML设计抗体候选的仅序列分诊工具。

Abstract

Machine learning (ML) approaches for de novo antibody design generate thousands of candidate sequences, but most lack a robust assessment of post-translational modification liabilities. N-linked glycosylation in variable domains can fundamentally alter antibody function, yet current penetrance estimates are derived from glycosylation-enriched datasets inflating risk estimates. We surveyed 19,265 human antibody structures from the Protein Data Bank (PDB), identifying 1,368 N-X-S/T sequons in variable domains (this study) with 7.82% observed penetrance (107/1,368; Wilson 95% CI 6.5-9.4%) - a crystallographic lower bound reflecting selection against glycosylated structures in the PDB - compared with 16.47% from a previously published glycosylation-enriched corpus. At the X-position, no glycosylation was observed at histidine (0/61; Wilson 95% upper bound 5.9%), lysine (0/26; upper bound 12.9%), or tryptophan (0/26), consistent with suppression. Glutamine showed 6.45% penetrance (2/31), indistinguishable from baseline and refuting its prior classification as a suppressor. Variable light domains showed higher aggregate penetrance than variable heavy (9.62% vs 6.79%; Fisher OR = 1.46, = 0.075), though this contrast did not survive joint Bayesian adjustment. Regionally, FR2 was glycosylation-resistant (0/119) while FR1 (14.0%) and FR4 (12.1%) showed elevated rates. N-X-T sequons were 3.84-fold more susceptible than N-X-S in aggregate (13.64% vs 3.55%; Fisher exact = 8.13 × 10, OR = 4.29). An independent PDB-overlap-free validation subset ( = 449, 33 glycosylation events, 189 nonoverlapping PDBs) reproduced the central findings. Leakage-corrected Bayesian logistic regression confirmed the N-X-T preference as the most prior-stable effect and identified strong negative coefficients for proline and aromatic residues at the +3 position immediately C-terminal to the sequon; the histidine effect at the X-position was borderline under prior sensitivity. We present a confidence-interval-aware decision tree that integrates X-position, third-position, chain type, and regional context as a sequence-only triage instrument for ML-designed antibody candidates.

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