六肽中的简明淀粉样蛋白和非淀粉样蛋白模式
Succinct Amyloid and Non-Amyloid Patterns in Hexapeptides
摘要：
六肽被广泛用作研究多肽（包括蛋白质）的淀粉样蛋白形成特性的模型系统。最近，大的实验数据库已成为公开可用的淀粉样蛋白标记。使用这些数据集进行训练和测试，可以构建基于人工智能（AI）的分类器，用于预测肽的淀粉样蛋白状态。在我们之前的工作中（Biomolecules，11（4）500，（2021）），我们描述了基于支持向量机（SVM）的布达佩斯淀粉样蛋白预测器（\url{https://pitgroup.org/bap}）。在这里，我们应用布达佩斯淀粉样蛋白预测发现许多淀粉样蛋白和非淀粉样蛋白六肽模式的准确性在80\%和84\%之间，作为进一步理解肽的淀粉样蛋白状态的令人惊讶和简洁的新规则。例如，我们已经表明，对于任何独立突变的残基（位置标记为“x”），预测模式CxFLWx、FxFLFx或xxIVIV是淀粉样蛋白生成的，而PxDxxx、xxKxEx和xxPQxx的模式根本不是淀粉样蛋白生成的。我们注意到每个具有两个X的淀粉样蛋白生成模式（例如，CxFLWx）简洁地描述了$20^2=400$六肽，而非淀粉样蛋白生成模式包括四个点突变（例如，PxDxxx）总共得到$20^4=160,000$六肽。据我们所知，在目前的工作之前，没有类似的人工智能工具或简洁的淀粉样蛋白模式的应用。
英文摘要：
Hexapeptides are widely applied as a model system for studying amyloid-forming properties of polypeptides, including proteins. Recently, large experimental databases have become publicly available with amyloidogenic labels. Using these datasets for training and testing purposes, one may build artificial intelligence (AI)-based classifiers for predicting the amyloid state of peptides. In our previous work (Biomolecules, 11(4) 500, (2021)) we described the Support Vector Machine (SVM)-based Budapest Amyloid Predictor (\url{https://pitgroup.org/bap}). Here we apply the Budapest Amyloid Predictor for discovering numerous amyloidogenic and non-amyloidogenic hexapeptide patterns with accuracy between 80\% and 84\%, as surprising and succinct novel rules for further understanding the amyloid state of peptides. For example, we have shown that for any independently mutated residue (position marked by ``x''), the patterns CxFLWx, FxFLFx, or xxIVIV are predicted to be amyloidogenic, while those of PxDxxx, xxKxEx, and xxPQxx non-amyloidogenic at all. We note that each amyloidogenic pattern with two x's (e.g.,CxFLWx) describes succinctly $20^2=400$ hexapeptides, while the non-amyloidogenic patterns comprising four point mutations (e.g.,PxDxxx) gives $20^4=160,000$ hexapeptides in total. To our knowledge, no similar applications of artificial intelligence tools or succinct amyloid patterns were described before the present work.
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