Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identity alone. Protein language models and emission-band averages capture global trends, but do not model how local physical signals act on specific chromophore regions. We present a chromophore-centred mechanism graph algorithm for QY prediction. Each PDB structure is converted into a typed 3D residue graph, registered to a mature-CRO state, partitioned into phenolate, bridge and imidazolinone regions, and transformed by channel-signal-region propagation. The representation contains 121 enrichment features; after removing identity shortcuts, 52 non-identity features are used for band-specific ExtraTrees regression. Because each feature encodes a contact channel, seed signal and target CRO region, interpretation is intrinsic rather than post hoc. On a 531-protein benchmark, the method achieved the best random-CV performance among model-based baselines (R = 0.772 +/- 0.008, MAE = 0.131 +/- 0.002), exceeding Band mean (R = 0.632), ESM-C (R = 0.734) and SaProt (R = 0.731), and ranked first in bright screening (Bright P@5 = 0.704). Under homology control, the advantage was clearest in the remote bucket (<50% similarity; R = 0.697 versus 0.633, 0.575 and 0.408), with the strongest overall bright/dark Top-K screening. Stable selected features recovered band-specific mechanisms: aromatic packing and clamp asymmetry in GFP-like proteins, charge/clamp balance in Red proteins, and flexibility-risk/bulky-contact features in Far-red proteins. Source code, feature tables and evaluation scripts are available from the first author upon request. Contact: [email protected]

翻译：荧光蛋白量子产率（QY）由成熟发色团及其三维微环境共同决定，而非仅由序列同源性决定。蛋白质语言模型和发射波段平均值能捕捉全局趋势，但无法建模局部物理信号如何在特定发色团区域发挥作用。我们提出一种以发色团为中心的机制图算法用于QY预测。每个PDB结构被转化为带类型的3D残基图，配准到成熟发色团（CRO）状态，分为酚盐、桥部和咪唑啉酮三个区域，并通过通道-信号-区域传播进行转换。该表示包含121个富集特征；去除恒等值捷径后，使用52个非恒等值特征进行波段特异性的ExtraTrees回归。由于每个特征编码一个接触通道、种子信号和目标CRO区域，解释是内在的而非事后分析。在包含531种蛋白质的基准数据集上，该方法在基于模型的基线方法中取得最佳随机交叉验证性能（R=0.772±0.008，MAE=0.131±0.002），优于波段均值（R=0.632）、ESM-C（R=0.734）和SaProt（R=0.731），并在明亮筛选任务中排名第一（Bright P@5=0.704）。在同源控制条件下，该方法在远亲类别（<50%相似性；R=0.697 vs 0.633、0.575和0.408）中优势最明显，整体明/暗Top-K筛选效果最强。稳定的选定特征恢复了波段特异性机制：GFP类蛋白中的芳香堆积与钳夹不对称性、Red蛋白中的电荷/钳夹平衡、以及Far-red蛋白中的柔性风险/笨重接触特征。源代码、特征表和评估脚本可向第一作者索取。联系方式：[email protected]