PAO1 Anti-virulence DiscoveryPAO1 抗毒力候选物优先级排序
Can computational prioritization across multiple virulence-associated proteins identify credible anti-virulence candidates for PAO1 without relying on bactericidal activity?在不把杀菌活性作为前提的情况下，多靶点计算优先级排序能否为 PAO1 筛出值得进一步验证的抗毒力候选物？
Why this project exists为什么开展这个项目
Evidence-stratified computational prioritization of natural-product candidates across virulence-associated PAO1 targets.针对 PAO1 毒力相关靶点，对天然产物候选物进行分层证据支持的计算优先级排序。
The falsifiable question可证伪的问题
Can computational prioritization across multiple virulence-associated proteins identify credible anti-virulence candidates for PAO1 without relying on bactericidal activity?在不把杀菌活性作为前提的情况下，多靶点计算优先级排序能否为 PAO1 筛出值得进一步验证的抗毒力候选物？
How the question is tested如何检验这个问题
- Molecular docking (Vina / GNINA funnel)分子对接（Vina / GNINA 漏斗）
- Multi-target virulence screening多靶点毒力相关筛选
- Exposure-aware reranking for Gram-negative targets面向革兰氏阴性菌暴露能力的重排序
- Computational prioritization with explicit claim boundaries带明确论断边界的计算优先级排序
What the current evidence supports现有证据支持什么
- Multi-criteria ranking identified natural-product candidates for higher-priority experimental follow-up.多指标综合排序得到了一批应优先开展实验验证的天然产物候选物。
- Molecular dynamics simulations were used as an additional computational check for selected candidate–target complexes.对选定候选物—靶点复合物使用分子动力学模拟进行了额外的计算检查。
Where the claim stops论断止于何处
- All hits are computational predictions; no wet-lab validation is claimed.所有候选物都来自计算预测；本项目不声称已经过湿实验验证。
- Docking scores are used for ranking, not as affinity estimates.对接分数只用于排序，不作为结合亲和力估计。
What comes next接下来做什么
- Respond to peer review and prioritize experimental validation of the computational shortlist.根据同行评审意见修订，并优先开展计算候选清单的实验验证。