Antioxidant proteins perform significant functions in disease control and delaying aging which can prevent free radicals from damaging organisms. Accurate identification of antioxidant proteins has important implications for the development of new drugs and the treatment of related diseases, as they play a critical role in the control or prevention of cancer and aging-related conditions. Since experimental identification techniques are time-consuming and expensive, many computational methods have been proposed to identify antioxidant proteins. Although the accuracy of these methods is acceptable, there are still some challenges. In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.

中文翻译：

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ANPrAod：通过融合氨基酸聚类策略和肽组合识别抗氧化蛋白质

抗氧化蛋白在疾病控制和延缓衰老方面发挥着重要作用，可以防止自由基破坏生物体。抗氧化蛋白的准确鉴定对于新药开发和相关疾病的治疗具有重要意义，因为它们在控制或预防癌症和衰老相关疾病方面发挥着关键作用。由于实验鉴定技术耗时且昂贵，因此已经提出了许多计算方法来鉴定抗氧化蛋白质。虽然这些方法的准确性是可以接受的，但仍然存在一些挑战。在这项研究中，我们开发了一个名为 ANPrAod 的计算模型，以基于支持向量机识别抗氧化蛋白质。为了消除潜在的冗余特征，提高预测精度，我们计算了 673 个氨基酸减少字母表以找到最佳特征表示方案。最终模型在五折交叉验证中可以产生 87.53% 的总体准确率，ROC 为 0.7266，优于现有方法。独立数据集的结果也证明了 ANPrAod 出色的稳健性和可靠性，这可能是一种很有前途的抗氧化蛋白鉴定工具，并有助于假设驱动的实验设计。