提出新模型精准预测蛋白质相互作用的正负关系。
The Signed Two-Space Proximity Model for Learning Representations in Protein-Protein Interaction Networks
- 用两个独立隐空间区分正负相互作用，通过距离表示蛋白相似性。
- 在链接预测任务中优于现有方法，能准确识别交互存在与符号。
- 发现具有生物学意义的极端蛋白特征，适合生物网络分析者使用。
准确预测复杂的蛋白质-蛋白质相互作用（PPI）对解析细胞功能乃至疾病机制至关重要，但实验测定代价高昂。因此，机器学习方法受到关注。然而，针对带有正负标签的PPI网络（SPPI）的研究仍不足，这类网络能捕捉激活（正）与抑制（负）作用。为此，我们提出签名双空间邻近模型（S2-SPM），显式建模两类交互，反映生物系统中的复杂调控机制。该模型利用两个独立的隐空间区分正负互作，并通过空间中的接近度表示蛋白相似性。同时，可识别代表极端蛋白特征的原型。在链接预测任务中，S2-SPM在预测交互存在性及符号方面均优于基线方法。基因本体（GO）富集分析证实这些原型具有生物学意义，不同功能任务对应于由两类交互构成的原型群。研究还通过统计显著性与敏感性分析验证了结果可靠性。最后，基于贝叶斯归一化互信息（BNMI）指标，确认了原型结构的鲁棒性与一致性，证明模型能有效捕捉有意义的SPPI模式。
原文摘要 · Abstract (English)
Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns.
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