Antimicrobial peptides (AMPs) provide a robust alternative to conventional antibiotics, combating escalating microbial resistance through their diverse functions and broad pathogen-targeting abilities. While current deep learning technologies enhance AMP generation, they face challenges in developing multifunctional AMPs due to intricate amino acid interdependencies and limited consideration of diverse functional activities. To overcome this challenge, we introduce a novel de novo multifunctional AMP design framework that enhances a Feedback Generative Adversarial Network (FBGAN) by integrating a global quantitative AMP activity regression module and a multifunctional-attribute integrated prediction module. This integrated approach not only facilitates the automated generation of potential AMP candidates, but also optimizes the network’s ability to assess their multifunctionality. Initially, by integrating an effective pre-trained regression and classification model with feedback-loop mechanisms, our model can not only identify potential valid AMP candidates, but also optimizes computational predictions of Minimum Inhibitory Concentration (MIC) values. Subsequently, we employ a combinatorial predictor to simultaneously identify and predict five multifunctional AMP bioactivities, enabling the generation of multifunctional AMPs. The experimental results demonstrate the efficiency of generating AMPs with multiple enhanced antimicrobial properties, indicating that our work can provide a valuable reference for combating multi-drug-resistant infections.

Nanobodies have emerged as highly valuable biotherapeutic and diagnostic reagents due to their high specificity, low immunogenicity, and superior tissue penetration. However, traditional nanobody discovery methods rely on camelid immunization and phage display techniques, which are time-consuming and labor-intensive. Meanwhile, existing computational prediction methods for Nanobody–Antigen Interaction (NAI) suffer from several limitations: first, general Protein–Protein Interaction (PPI) models cannot adapt to the specific binding patterns of NAI; second, sequence-based models struggle to capture critical binding features, resulting in unsatisfactory prediction accuracy. To address these challenges, we propose an NAI prediction method based on Protein Language Models (PLMs). Specifically, a structure-aware PLM is first fine-tuned on PPI datasets to learn universal protein binding patterns. This model performs joint encoding of amino acid sequences and protein local structure-related sequences. It can implicitly learn spatial structural priors solely from sequence inputs, which alleviates the limitation of conventional sequence-based models in capturing structural binding characteristics. Subsequently, we use mean, max and min pooling to extract complementary global sequence features that a single pooling method cannot fully capture. We then apply voting fusion to reduce prediction bias and improve model robustness under class imbalance and small-sample scenarios. Evaluated on the NAI benchmark dataset constructed from SAbDab-nano, the proposed model outperforms the best baseline methods in key metrics including Accuracy, Recall, F1-score, AUC-ROC, and AUPR. It exhibits robust performance under class imbalance and small-sample scenarios, validating the effectiveness of the framework.