Quantifying the hemolytic activity of peptides is a crucial step in the discovery of novel therapeutic peptides. Computational methods are attractive in this domain due to their ability to guide wet-lab experimental discovery or screening of peptides based on their hemolytic activity. However, existing methods are unable to accurately model various important aspects of this predictive problem such as the role of N/C-terminal modifications, D- and L- amino acids, etc. In this work, we have developed a novel neural network-based approach called HemoNet for predicting the hemolytic activity of peptides. The proposed method captures the contextual importance of different amino acids in a given peptide sequence using a specialized feature embedding in conjunction with SMILES-based fingerprint representation of N/C-terminal modifications. We have analyzed the predictive performance of the proposed method using stratified cross-validation in comparison with previous methods, non-redundant cross-validation as well as validation on external peptides and clinical antimicrobial peptides. Our analysis shows the proposed approach achieves significantly better predictive performance (AUC-ROC of 88%) in comparison to previous approaches (HemoPI and HemoPred with AUC-ROC of 73%). HemoNet can be a useful tool in the search for novel therapeutic peptides. The python implementation of the proposed method is available at the URL: https://github.com/adibayaseen/HemoNet.

中文翻译：

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HemoNet：通过集成特征学习预测肽的溶血活性

量化肽的溶血活性是发现新型治疗性肽的关键步骤。计算方法在该领域很有吸引力，因为它们能够根据溶血活性指导湿实验室实验发现或肽筛选。然而，现有的方法无法准确地模拟这一预测问题的各个重要方面，例如 N/C 末端修饰、D 和 L 氨基酸等的作用。在这项工作中，我们开发了一种新的神经网络——基于称为 HemoNet 的方法，用于预测肽的溶血活性。所提出的方法使用嵌入的特殊特征结合基于 SMILES 的 N/C 末端修饰指纹表示来捕获给定肽序列中不同氨基酸的上下文重要性。我们使用分层交叉验证与以前的方法、非冗余交叉验证以及外部肽和临床抗菌肽的验证相比，分析了所提出方法的预测性能。我们的分析表明，与以前的方法（HemoPI 和 HemoPred 的 AUC-ROC 为 73%）相比，所提出的方法实现了显着更好的预测性能（AUC-ROC 为 88%）。HemoNet 可以成为寻找新型治疗性肽的有用工具。建议方法的 python 实现可在以下 URL 获得：我们的分析表明，与以前的方法（HemoPI 和 HemoPred 的 AUC-ROC 为 73%）相比，所提出的方法实现了显着更好的预测性能（AUC-ROC 为 88%）。HemoNet 可以成为寻找新型治疗性肽的有用工具。建议方法的 python 实现可在以下 URL 获得：我们的分析表明，与以前的方法（HemoPI 和 HemoPred 的 AUC-ROC 为 73%）相比，所提出的方法实现了显着更好的预测性能（AUC-ROC 为 88%）。HemoNet 可以成为寻找新型治疗性肽的有用工具。建议方法的 python 实现可在以下 URL 获得：https://github.com/adibayaseen/HemoNet.