Xia Xiaoqiong, Torres Marcelo D T, de la Fuente-Nunez Cesar
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
bioRxiv. 2025 Jun 3:2025.03.17.643752. doi: 10.1101/2025.03.17.643752.
Recent computational discoveries have identified numerous bioactive peptides within the human proteome, as well as across the broader tree of life, which were previously unrecognized for their roles in host immunity. These findings have led us to propose the "cross-talk hypothesis", suggesting that many molecules, such as proteins and peptides, traditionally viewed as extraneous to immune function may in fact actively contribute to immunity. Building on our earlier studies, which uncovered proteasome-derived peptides with putative antimicrobial activity in the human proteome, here we systematically interrogated the proteasome, a large protein complex responsible for degrading and recycling damaged or surplus proteins, for additional antimicrobial peptides. Using deep learning, we systematically mined ProteasomeDB, a curated repository of proteasomal cleavage and splicing events, to predict antibiotic activity against 11 clinically relevant pathogens. This deep learning approach uncovered 59 candidate peptides ("proteasomins") with a median minimum inhibitory concentration (MIC) of ≤64 μmol L . Refinement yielded 21 sequence-diverse proteasomins, which were characterized for their physicochemical properties. These peptides were enriched in cationic residues and exhibit enhanced amphiphilicity, key attributes for disrupting microbial membranes. Dimensionality reduction via UMAP further showed that proteasomins are sequence-distinct from known antimicrobial peptides, underscoring their novelty and potential for unique mechanisms of action. Moreover, comparative analyses revealed that cis- and trans-spliced proteasomins exhibit similar predicted antimicrobial activities, suggesting that critical structural determinants remain conserved irrespective of splicing modality. Collectively, these findings expand our understanding of the proteasome, underscore the extensive, previously unrecognized repertoire of innate immune peptides, and provide a promising foundation for developing innovative therapeutics to combat multidrug-resistant pathogens.
最近的计算发现已在人类蛋白质组以及更广泛的生命之树中鉴定出许多生物活性肽，这些肽以前未因其在宿主免疫中的作用而被识别。这些发现促使我们提出“串扰假说”，表明许多传统上被视为与免疫功能无关的分子，如蛋白质和肽，实际上可能对免疫有积极贡献。基于我们早期的研究，该研究在人类蛋白质组中发现了具有假定抗菌活性的蛋白酶体衍生肽，在此我们系统地研究了蛋白酶体（一种负责降解和回收受损或多余蛋白质的大型蛋白质复合物），以寻找更多的抗菌肽。我们使用深度学习系统地挖掘了ProteasomeDB（一个经过整理的蛋白酶体切割和剪接事件库），以预测对11种临床相关病原体的抗生素活性。这种深度学习方法发现了59种候选肽（“蛋白酶体素”），其最小抑菌浓度（MIC）中位数≤64 μmol/L。经过优化得到了21种序列不同的蛋白酶体素，并对其理化性质进行了表征。这些肽富含阳离子残基并表现出增强的两亲性，这是破坏微生物膜的关键属性。通过UMAP进行降维进一步表明，蛋白酶体素在序列上与已知的抗菌肽不同，突出了它们的新颖性和独特作用机制的潜力。此外，比较分析表明，顺式和反式剪接的蛋白酶体素表现出相似的预测抗菌活性，这表明关键的结构决定因素无论剪接方式如何都保持保守。这些发现共同扩展了我们对蛋白酶体的理解，强调了先天免疫肽广泛的、以前未被认识的库，并为开发对抗多重耐药病原体的创新疗法提供了有希望的基础。
