Fig 1.
Overview of the proposed workflow.
(A) Multi-ACPNet architecture overview. First, ESM C features are extracted and fed into the (B) Sequence Multi-Scale Network to capture multi-scale sequence features. Then, graphs are constructed using trRosetta, with domain knowledge features and sequence-based learned features as node features. (C) The Graph Multi-Scale Network performs multi-scale structural feature learning on both the full graph and key subgraphs. The Classifier outputs the final prediction results.
Fig 2.
ROC curves of the optimal model through 5-fold cross-validation on AntiCP 2.0 training datasets.
(A) Cross-validation result on the AntiCP 2.0_Main training dataset. (B) Cross-validation result on the AntiCP 2.0_Alternate training dataset.
Table 1.
Performance comparison between the proposed method and existing methods on the ACP-Mixed-80 dataseta.
Table 2.
Performance comparison between the proposed method and existing methods on the AntiCP 2.0 independent test dataseta.
Table 3.
Performance comparison between the proposed method and existing methods for ACP functional activity predictiona.
Fig 3.
Performance comparison between the proposed method and existing methods across seven cancer types.
Fig 4.
Performance comparison of sequence-only and structure-only models on the AntiCP 2.0_Main test set.
Fig 5.
t-SNE visualization of feature representations from the penultimate layer on AntiCP 2.0_Main test set.
(A) ACPNet_no_Graph feature distribution. (B) ACPNet_no_Seq feature distribution. (C) Multi-ACPNet feature distribution.
Fig 6.
Agglomerative clustering of feature vectors from the penultimate network layer on ACP-Mixed-80 test set.
Fig 7.
Comparative performance on the ACP toxicity test set ((s2).
(A) Confusion matrix for ToxGIN. (B) Confusion matrix for Multi-ACPNet.