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Shoutao Zhang

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Open access Jul 2026

A receptor-Fc based SFV replicon Trim-Away platform for targeted viral protein degradation.

BACKGROUND Developing effective antiviral strategies is urgently needed during global viral pandemics. Traditional approaches, including small-molecule inhibitors, neutralizing antibodies, and RNA interference (RNAi), often face challenges such as drug resistance, limited specificity, and inefficient delivery. These limitations highlight the pressing need for innovative strategies focused on the targeted degradation of viral proteins. METHODS We developed an optimized Trim-Away system employing a receptor-Fc fusion protein strategy. This system integrates the E3 ubiquitin ligase TRIM21 with engineered receptor-Fc proteins to ensure highly specific recognition and intracellular degradation. A key innovation is the use of the Semliki Forest virus (SFV) self-amplifying replicon (pSFV). This platform enables sustained and robust expression of the Trim-Away components. Furthermore, this plasmid-based delivery eliminates the need for protein purification, thereby streamlining the process and improving delivery efficiency. RESULTS The system effectively degrades diverse viral targets. Specifically, it successfully degraded the spike proteins of both wild-type SARS-CoV-2 and its Omicron variant. It also targeted adeno-associated virus (AAV) capsid proteins. In vivo assays further confirmed that the self-amplifying replicon markedly reduces AAV-encoded luciferase expression. These data demonstrate that the system maintains high potency even at low dosages. CONCLUSIONS Our findings demonstrate that the pSFV-driven Trim-Away system is a powerful tool for viral protein degradation. The receptor-Fc strategy provides a significant advantage against rapidly mutating viruses. This study establishes a versatile and adaptable platform for future antiviral intervention.

Yiming Han, Qisheng Dong, Zhen Tian et al. · 0 citations
Open access Jul 2026

Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction

Characterizing protein-protein interactions (PPIs) is essential for deciphering core biological processes, including signal transduction, metabolic pathway regulation, immune recognition, and cell cycle control. However, experimental PPI determination remains time-consuming and expensive, driving the adoption of deep learning as an efficient and accurate computational approach. Current deep-learning-based PPI prediction models typically process both intra- and inter-protein as isolated units in feature extraction, thereby ignoring mutual information transfer within a single protein and the interacting pair. To address this limitation, we propose DCAPPI (Dual Cross-Attention network for Protein-Protein Interaction prediction), a novel framework leveraging dual cross-attention modules for hierarchical feature fusion at both intra- and inter-protein levels. First, the Channel Cross-Attention module processes protein sequence and structure as distinct input channels. It generates deep intra-protein representations by performing cross-attention between sequence-derived and structure-derived tokens, achieving multimodal feature integration. Second, the Partner Cross-Attention module models the target protein and its interacting partner as a pair of correlative units. By performing cross-attention operations across these units, it enables collaborative feature fusion and constructs context-aware inter-protein interaction features. Evaluation results indicate that DCAPPI achieves superior performance over state-of-the-art methods on benchmark datasets.

Shuai Lu, Yuguang Li, Zhen Tian et al. · 0 citations