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AI 辅助表位筛选 - 版本历史
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<p>建立内容为“<div style="padding: 0 4%; line-height: 1.8; color: #1e293b; font-family: 'Helvetica Neue', Helvetica, 'PingFang SC', Arial, sans-serif; background-color: #ffffff…”的新页面</p>
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<strong>[[AI 辅助表位筛选]] (AI-Assisted Epitope Screening)</strong> 是计算生物学与现代免疫学的交叉前沿领域，利用 <strong>[[深度学习]]</strong> 和 <strong>[[结构生物学]]</strong> 算法从海量抗原数据中快速识别能够诱导强效免疫应答的特异性片段。相比传统实验筛选，AI 能在数秒内预测数百万个候选肽段与 <strong>[[MHC 分子]]</strong> 或 <strong>[[抗体]]</strong> 的结合亲和力，极大缩短了 <strong>[[广谱疫苗]]</strong> 和个体化肿瘤 <strong>[[新抗原 (Neoantigen)]]</strong> 疫苗的开发周期。<br />
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<div style="font-size: 1.25em; font-weight: bold; letter-spacing: 1.2px;">AI 表位筛选档案</div><br />
<div style="font-size: 0.7em; opacity: 0.85; margin-top: 4px; white-space: nowrap;">In silico Epitope Prediction · 核心档案</div><br />
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<div style="font-size: 0.8em; color: #64748b; margin-top: 10px; font-weight: 600;">核心任务：精准预测分子相互作用</div><br />
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<td colspan="2" style="padding: 6px; border-bottom: 1px solid #e2e8f0;">核心算法模型</td><br />
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<th style="text-align: left; padding: 6px 10px; background-color: #f8fafc; border-bottom: 1px solid #f1f5f9; width: 40%;">预测对象</th><br />
<td style="padding: 6px 10px; border-bottom: 1px solid #f1f5f9; color: #0f172a;">T 细胞表位 / B 细胞表位</td><br />
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<th style="text-align: left; padding: 6px 10px; background-color: #f8fafc; border-bottom: 1px solid #f1f5f9;">底层技术</th><br />
<td style="padding: 6px 10px; border-bottom: 1px solid #f1f5f9; color: #1e40af;">[[Transformer]], [[CNN]]</td><br />
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<th style="text-align: left; padding: 6px 10px; background-color: #f8fafc; border-bottom: 1px solid #f1f5f9;">主要工具</th><br />
<td style="padding: 6px 10px; border-bottom: 1px solid #f1f5f9; color: #0f172a;">NetMHCpan, AlphaFold-Multimer</td><br />
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<td colspan="2" style="padding: 6px; border-bottom: 1px solid #e2e8f0;">应用场景</td><br />
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<th style="text-align: left; padding: 6px 10px; background-color: #f8fafc; border-bottom: 1px solid #f1f5f9;">肿瘤免疫</th><br />
<td style="padding: 6px 10px; border-bottom: 1px solid #f1f5f9; color: #0f172a;">[[新抗原]] 预测与定制</td><br />
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<th style="text-align: left; padding: 6px 10px; background-color: #f8fafc; border-bottom: 1px solid #f1f5f9;">传染病防御</th><br />
<td style="padding: 6px 10px; border-bottom: 1px solid #f1f5f9; color: #0f172a;">[[广谱疫苗]] 保守表位筛选</td><br />
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<th style="text-align: left; padding: 6px 10px; background-color: #f8fafc;">关键指标</th><br />
<td style="padding: 6px 10px; color: #0f172a;">结合亲和力 ($IC_{50}$), 免疫原性</td><br />
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AI 表位筛选经历了从基于序列的简单预测到基于 3D 结构的复杂建模的进化：<br />
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<li style="margin-bottom: 12px;"><strong>MHC 结合预测 (T 细胞表位)：</strong> 利用 <strong>[[Transformer]]</strong> 模型，学习 <strong>[[MHC-I]]</strong> 或 <strong>[[MHC-II]]</strong> 结合槽的偏好规律，预测肽段被呈递在细胞表面的概率。</li><br />
<li style="margin-bottom: 12px;"><strong>三维构象匹配 (B 细胞表位)：</strong> 利用 <strong>[[AlphaFold]]</strong> 等预测技术，识别抗原表面的构象型表位，预测抗体与之结合的稳定性。</li><br />
<li style="margin-bottom: 12px;"><strong>免疫原性评分：</strong> AI 通过学习 <strong>[[免疫组库]]</strong> 数据，判断结合肽段是否真正能够激活 T/B 细胞，而非导致免疫耐受。</li><br />
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<h2 style="background: #f1f5f9; color: #0f172a; padding: 10px 18px; border-radius: 0 6px 6px 0; font-size: 1.25em; margin-top: 40px; border-left: 6px solid #0f172a; font-weight: bold;">AI 筛选在广谱疫苗研发中的价值</h2><br />
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<th style="padding: 12px; border: 1px solid #cbd5e1; color: #0f172a; width: 25%;">任务目标</th><br />
<th style="padding: 12px; border: 1px solid #cbd5e1; color: #475569;">AI 介入方式</th><br />
<th style="padding: 12px; border: 1px solid #cbd5e1; color: #1e40af;">研究收益</th><br />
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<td style="padding: 10px; border: 1px solid #cbd5e1; font-weight: 600;">识别保守表位</td><br />
<td style="padding: 10px; border: 1px solid #cbd5e1;">对比数千种病毒变异株，筛选结合最稳定的核心肽段。</td><br />
<td style="padding: 10px; border: 1px solid #cbd5e1;">实现跨亚型保护，对抗抗原漂移。</td><br />
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<td style="padding: 10px; border: 1px solid #cbd5e1; font-weight: 600;">抗原性屏蔽设计</td><br />
<td style="padding: 10px; border: 1px solid #cbd5e1;">通过模拟计算寻找最佳的糖基化位点以掩盖非目标表位。</td><br />
<td style="padding: 10px; border: 1px solid #cbd5e1; color: #1e40af;">引导免疫系统攻击 <strong>[[保守表位]]</strong>。</td><br />
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<h2 style="background: #f1f5f9; color: #0f172a; padding: 10px 18px; border-radius: 0 6px 6px 0; font-size: 1.25em; margin-top: 40px; border-left: 6px solid #0f172a; font-weight: bold;">权威参考文献与专家点评</h2><br />
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[1] <strong>Jurtz, V., et al. (2017).</strong> <em>NetMHCpan-4.0: Improved prediction of MHC antigen presentation.</em> <strong>Journal of Immunology</strong>, 199(9), 3360-3368.<br><br />
<span style="color: #475569;">[专家点评]：目前表位预测领域的工业标准模型，证明了集成实验质谱数据对提升预测精度的关键价值。</span><br />
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[2] <strong>Bulik-Sullivan, B., et al. (2018).</strong> <em>Deep learning improves neoantigen identification.</em> <strong>Nature Biotechnology</strong>, 36(1), 55-63.<br><br />
<span style="color: #475569;">[专家点评]：将深度学习应用于新抗原识别，将筛选准确率提升了近 10 倍。</span><br />
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<li style="margin-bottom: 8px;"><strong>[[MHC-I/抗原肽]]：</strong> AI 预测 T 细胞应答的最核心物理靶点。</li><br />
<li style="margin-bottom: 8px;"><strong>[[新抗原]] (Neoantigen)：</strong> 癌细胞突变产生的特异性肽段。</li><br />
<li style="margin-bottom: 8px;"><strong>[[反向免疫学]]：</strong> 从基因组出发，利用计算手段筛选疫苗靶点的学科。</li><br />
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AI 辅助表位筛选 · 知识图谱<br />
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<td style="width: 110px; background-color: #f8fafc; color: #334155; font-weight: 600; padding: 10px 12px; text-align: right; vertical-align: middle; white-space: nowrap;">核心模型</td><br />
<td style="padding: 10px 15px; color: #334155;">[[NetMHCpan]]•[[AlphaFold]]•[[MHCflurry]]</td><br />
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<td style="width: 110px; background-color: #f8fafc; color: #334155; font-weight: 600; padding: 10px 12px; text-align: right; vertical-align: middle; white-space: nowrap;">数据来源</td><br />
<td style="padding: 10px 15px; color: #334155;">[[IEDB 数据库]]•[[质谱免疫肽组学]]</td><br />
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<td style="width: 110px; background-color: #f8fafc; color: #334155; font-weight: 600; padding: 10px 12px; text-align: right; vertical-align: middle; white-space: nowrap;">应用领域</td><br />
<td style="padding: 10px 15px; color: #334155;">[[广谱疫苗研究]]•[[肿瘤个体化治疗]]</td><br />
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