HD-AIP: a heterogeneous dual-stream alignment-free framework for anti-inflammatory peptide prediction based on language models and CT-Net.

Abstract

Motivation: Anti-inflammatory peptides (AIPs) show therapeutic potential for treating chronic and autoimmune diseases. Computational screening of these peptides remains challenging because their typically short sequences limit traditional feature extraction effectiveness, while homology-based methods incur high computational costs.

Results: This study proposes HD-AIP, an alignment-free heterogeneous dual-stream prediction architecture. This framework extracts peptide features in parallel from both macroscopic and microscopic perspectives. Macroscopically, HD-AIP integrates global semantic features extracted by two large protein language models, ProtT5 and ESM-2 3B, with sequence-level physicochemical properties, followed by feature selection and LightGBM classification. Microscopically, an asymmetric parallel network named CT-Net utilizes BioVec embeddings and residue-level physicochemical features, using a CNN branch to capture local motifs and a Transformer branch to model long-range dependencies. The two streams are adaptively fused via a dynamic soft ensemble strategy. On an independent test set, HD-AIP outperforms baseline models across multiple metrics including accuracy, area under the receiver operating characteristic curve, and the Matthews correlation coefficient. These results indicate that HD-AIP improves AIP prediction performance without sequence alignment. This architecture serves as an effective computational tool for the high-throughput virtual screening and candidate discovery of AIPs.

Availability and implementation: You can find the source code and dataset needed on GitHub (https://github.com/Zerofly0/HD-AIP). The required package files have been listed in the readme.