As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. A new in scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.

中文翻译：

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基于机器学习和虚拟消化的优化天然生物活性肽酶促采集的计算机方案

天然生物活性肽（NBAPs）作为一种潜在的天然活性物质，近年来越来越受到人们的关注。获得有效 NBAP 的传统蛋白水解方法相当麻烦，特别是因为可以使用多种蛋白酶，这阻碍了对可用 NBAP 的探索。尽管虚拟消化的发展带来了一定程度的便利，但所得肽的活性仍不清楚，这仍然无法有效地获取NBAP。有必要制定高效、准确的 NBAP 获取策略。提出了一种名为 SSA-LSTM-VD 的新方案，该方案结合了麻雀搜索算法-长短期记忆（SSA-LSTM）深度学习和虚拟消化，以优化 NBAP 的蛋白水解采集。其中，SSA-LSTM相对于传统机器学习算法和基本LSTM算法达到了最高效率值，达到98.00%。SSA-LSTM经过训练来预测蛋白质虚拟消化结果中肽的活性，获得目标活性肽的百分比，并为实际实验选择合适的蛋白酶。作为应用，采用SSA-LSTM来预测核桃蛋白虚拟消化结果中神经保护肽的百分比，最终发现胰蛋白酶具有最高值（85.29%）。用胰蛋白酶（WPTrH）消化核桃蛋白，分析得到的肽序列与理论神经保护肽非常吻合。更重要的是，WPTrH 的神经保护作用已在神经损伤小鼠模型中得到证实。本文提出的SSA-LSTM-VD使得NBAP的获取高效且准确。该方法结合了深度学习和虚拟消化技术。利用基于 SSA-LSTM-VD 的策略有望发现和开发具有神经保护特性或其他所需生物活性的肽。