Unveiling Encrypted Antimicrobial Peptides from Cephalopods’ Salivary Glands: A Proteolysis-Driven Virtual Approach

Antimicrobial peptides (AMPs) have potential against antimicrobial resistance and serve as templates for novel therapeutic agents. While most AMP databases focus on terrestrial eukaryotes, marine cephalopods represent a promising yet underexplored source. This study reveals the putative reservoir of AMPs encrypted within the proteomes of cephalopod salivary glands via in silico proteolysis. A composite protein database comprising 5,412,039 canonical and noncanonical proteins from salivary apparatus of 14 cephalopod species was subjected to digestion by 5 proteases under three protocols, yielding over 9 million of nonredundant peptides. These peptides were effectively screened by a selection of 8 prediction and sequence comparative tools, including machine learning, deep learning, multiquery similarity-based models, and complex networks. The screening prioritized the antimicrobial activity while ensuring the absence of hemolytic and toxic properties, and structural uniqueness compared to known AMPs. Five relevant AMP datasets were released, ranging from a comprehensive collection of 542,485 AMPs to a refined dataset of 68,694 nonhemolytic and nontoxic AMPs. Further comparative analyses and application of network science principles helped identify 5466 unique and 808 representative nonhemolytic and nontoxic AMPs. These datasets, along with the selected mining tools, provide valuable resources for peptide drug developers.

抗菌肽（Antimicrobial peptides, AMPs）可对抗抗菌药物耐药性，同时可作为新型治疗制剂的开发模板。目前多数抗菌肽数据库以陆生真核生物为核心研究对象，而海洋头足类是一类极具应用前景但尚未被充分挖掘的抗菌肽来源。本研究通过计算机模拟蛋白水解技术，从头足类唾液腺的蛋白质组中识别出隐藏其中的潜在抗菌肽储备库。研究构建了涵盖14种头足类唾液器官的5412039条经典与非经典蛋白质的复合蛋白质数据库，随后采用五种蛋白酶、遵循三种实验方案对该数据库进行酶解，最终得到超过900万条非冗余肽段。研究采用机器学习、深度学习、多序列相似性比对模型以及复杂网络分析等8种预测与序列比对工具，对上述肽段开展高效筛选。本次筛选以抗菌活性为核心优先级，同时确保候选肽段不具备溶血活性与毒性，且相较于已知抗菌肽具有结构独特性。本次研究共发布5组相关抗菌肽数据集，涵盖从包含542485条抗菌肽的全量集合到经过提纯的68694条无溶血、无毒性抗菌肽数据集的多个层级。通过进一步的比较分析与网络科学方法应用，研究最终筛选得到5466条独特型抗菌肽与808条代表性无溶血无毒性抗菌肽。上述数据集与所选用的挖掘工具，可为肽类药物研发人员提供极具价值的研究资源。