通过多层次结构建模提升蛋白质相互作用预测精度
Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding
- 从残基到基序再到蛋白，分三层融合序列、结构与功能信息
- 在多个数据集上超越现有模型，尤其在小样本和复杂划分下表现突出
- 适用于生物医学研究中需要高精度PPI预测的场景
蛋白质-蛋白质相互作用（PPI）在多种生物过程中至关重要。然而，现有预测方法存在两大缺陷：忽视蛋白质的层级结构，尤其是调控PPI的介观尺度基序；未能有效整合序列、结构和功能多模态信息。为此，我们提出MMM-PPI，一种基于层次基序的多模态蛋白编码器，采用自底向上的多模态方式构建PPI嵌入。在微观尺度，编码三种残基特征；在介观尺度，设计新颖的多模态基序编码器，将残基聚合为空间感知的基序嵌入；在宏观尺度，多模态蛋白编码器通过联合建模基序重要性与模态间相关性，将基序整合为蛋白嵌入。预训练编码器可直接用于大规模PPI预测。在多个PPI数据集上的实验表明，MMM-PPI优于当前最先进的多标签PPI预测模型，尤其在挑战性数据划分和有限数据场景下表现更优。代码见https://github.com/yzf-code/MMM-PPI。
原文摘要 · Abstract (English)
Protein-protein interactions (PPIs) are essential for many biological processes. However, existing PPI prediction approaches suffer from two major limitations: they overlook the hierarchical organization of proteins, particularly meso-scale motifs that critically regulate PPIs, and fail to effectively integrate sequence, structure, and function modalities. To address these limitations, we propose MMM-PPI, a Hierarchical Motif-based Multi-Modal protein Encoder for PPI Prediction that constructs PPI embeddings in a bottom-up multi-modal manner across three scales. At the micro-scale, we encode three modal residue features; at the meso-scale, a novel multimodal motif encoder aggregates residues into spatially-informed motif embeddings; at the macro-scale, a multimodal protein encoder integrates motifs into protein embeddings by jointly modeling motif importance and inter-modal correlations. The pre-trained encoder can be used off-the-shelf for large-scale PPI prediction. Extensive experiments on multiple PPI datasets show that MMM-PPI outperforms state-of-the-art multi-label PPI prediction models, particularly under challenging data partitions and limited data scenarios. Codes are in https://github.com/yzf-code/MMM-PPI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。