TriTower-m6Am：融合语义、序列与结构信息的三塔异构深度学习架构用于 mRNA m6Am 位点预测
TriTower-m6Am: A triple-tower heterogeneous deep learning architecture integrating semantic, sequential, and structural information for mRNA N6,2′-O-dimethyladenosine site prediction
TriTower-m6Am 用语义、序列与结构三塔异构架构预测 mRNA 的 m6Am 位点，在 640 条序列的独立测试集上取得 AUC 0.776、MCC 0.425，优于 DTC-m6Am 的 AUC 0.765、MCC 0.411，且可训练参数少 4.4 倍。三塔分别采用预训练 RNA 基础模型 RNA-FM 配合 BellPooling、One-Hot BiLSTM 和基于 41-nt 窗口的关系图卷积网络 RGCN。在 0.40 至 0.70 的每个匹配特异度下，它从 320 个真实位点中多召回 1–7 个。
来源：PLOS Computational Biology · journals.plos.org
by Kaifeng Xiong, Jianhua Jia
Background
N6,2′-O-dimethyladenosine (m6Am) is a cap-proximal mRNA modification deposited by PCIF1 (phosphorylated C-terminal domain (CTD)-interacting factor 1) at the first transcribed nucleotide of eukaryotic mRNAs. Knowing where m6Am sites sit would help explain how cells tune mRNA stability and translation, but current predictors typically depend on a single representation and do not jointly model multiple views of the same RNA sequence.
Results
We present TriTower-m6Am, combining semantic (RNA-FM, a pretrained RNA foundation model, with BellPooling), sequential (One-Hot BiLSTM, bidirectional long short-term memory), and structural (RGCN, a relational graph convolutional network, over the 41-nt window) representations of each RNA window. On an independent test set of 640 sequences, TriTower-m6Am reaches an area under the ROC curve (AUC) of 0.776 and a Matthews correlation coefficient (MCC) of 0.425, against DTC-m6Am’s AUC = 0.765 and MCC = 0.411, using 4.4 times fewer trainable parameters; at every matched specificity between 0.40 and 0.70 it recovers 1–7 more true sites out of 320 than DTC-m6Am. The AUC-weighted voting keeps every tower’s contribution readable for any individual prediction. Analyses of the graph tower delimit its benefit to the relational aggregation of local backbone adjacency over sequence-derived node features: the edge-type ablation, the node-feature ablation, and a structure-permutation control agree that the specific RNAfold-predicted fold carries no attributable signal, and a probing analysis shows that per-base base-pairing status is largely recoverable from the sequence-derived RNA-FM embeddings (AUC = 0.72 vs. 0.65 from the raw sequence), so the fold annotation is substantially redundant with the semantic representation the ensemble already receives.
Conclusions
Combining semantic, sequential, and structural views of the same RNA sequence improves m6Am prediction beyond any single representation, and the ensemble’s voting weights make each prediction auditable without a post-hoc explainer. Whether the triple-tower pattern transfers to other RNA modification prediction tasks remains to be tested in future work.