提出一种无需模型的蛋白质构象集质量评估指标，区分真实动态与噪声。
Spectral Coherence Index: A Model-Free Metric for Protein Structural Ensemble Quality Assessment
- 基于距离方差矩阵的有效秩，计算无模型旋转不变的谱相干性指数
- 在110个NMR构象集中达到AUC-ROC 0.973，区分真实与伪构象效果显著
- 适合用于多指标质控流程，尤其适用于异质性高的蛋白质构象集
核磁共振（NMR）获得的蛋白质构象集能捕捉重要的构象异质性，但难以判断观察到的变化是协调运动还是噪声。本文评估了谱相干性指数（SCI），一种从模型间距离方差矩阵的参与度有效秩导出的无模型、旋转不变的综合指标。对包含110个NMR构象集的Main110数据集（每条10–30个模型，长度30–403个残基）进行分组分析显示，SCI在区分实验构象与匹配的非相干合成对照中表现优异，AUC-ROC达0.973，Cliff's δ = -0.945。相较于27蛋白的预实验，主分析中判别能力略有下降，表明早期阈值无法直接外推至更大更异质的群体；主操作点τ=0.811时灵敏度为95.5%，特异性为89.1%。在PDB层面灵敏度几乎不变（AUC=0.972），独立11蛋白测试集达到AUC=0.983。在5折分组交叉验证和逐功能类别留一法测试中，SCI仍保持强判别力（AUC分别为0.968和0.971）。尽管单特征中$σ_{R_g}$更强，但结合质量控制的多特征模型表现最佳（AUC=0.989和0.990）。残基级别验证显示SCI贡献与实验均方根漂移（RMSF）高度一致，并与弹性网络模型（GNM）预测的柔性模式广泛吻合。挽救分析表明，Main110数据集中的性能下降主要源于尺寸和构象归一化因素，而非信号丢失。结果表明，SCI是一种可解释、有界且实用的协同性度量，最适合作为多指标质量控制工作流的一部分应用于异质性蛋白质构象集。
原文摘要 · Abstract (English)
Protein structural ensembles from NMR spectroscopy capture biologically important conformational heterogeneity, but it remains difficult to determine whether observed variation reflects coordinated motion or noise-like artifacts. We evaluate the Spectral Coherence Index (SCI), a model-free, rotation-invariant summary derived from the participation-ratio effective rank of the inter-model pairwise distance-variance matrix. Under grouped primary analysis of a Main110 cohort of 110 NMR ensembles (30--403 residues; 10--30 models per entry), SCI separated experimental ensembles from matched synthetic incoherent controls with AUC-ROC $= 0.973$ and Cliff's $δ= -0.945$. Relative to an internal 27-protein pilot, discrimination softened modestly, showing that pilot-era thresholds do not transfer perfectly to a larger, more heterogeneous cohort: the primary operating point $τ= 0.811$ yielded 95.5\% sensitivity and 89.1\% specificity. PDB-level sensitivity remained nearly unchanged (AUC $= 0.972$), and an independent 11-protein holdout reached AUC $= 0.983$. Across 5-fold grouped stratified cross-validation and leave-one-function-class-out testing, SCI remained strong (AUC $= 0.968$ and $0.971$), although $σ_{R_g}$ was the stronger single-feature discriminator and a QC-augmented multifeature model generalized best (AUC $= 0.989$ and $0.990$). Residue-level validation linked SCI-derived contributions to experimental RMSF across 110 proteins and showed broad concordance with GNM-based flexibility patterns. Rescue analyses showed that Main110 softening arose mainly from size and ensemble normalization rather than from loss of spectral signal. Together, these results establish SCI as an interpretable, bounded coherence summary that is most useful when embedded in a multimetric QC workflow for heterogeneous protein ensembles.
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