Department of Biological Science and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
Department of Immunology, Oslo University Hospital Rikshospitalet and University of Oslo, Norway.
Comput Biol Chem. 2023 Feb;102:107800. doi: 10.1016/j.compbiolchem.2022.107800. Epub 2022 Dec 5.
Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to antimicrobial agents and the lack of new commercially available antibiotics have made in silico discovery of AMPs increasingly important. Machine learning (ML) has improved the speed and efficiency of AMP discovery while reducing the cost of experimental approaches. Despite various ML platforms developed, there is still a lack of integrative use of ML platforms for AMP discovery from publicly available protein databases. Therefore, our study aims to screen potential AMPs with antibiofilm properties from databases using ML platforms, followed by protein-peptide molecular docking analysis and molecular dynamics (MD) simulations. A total of 5850 peptides classified as non-AMP were screened from UniProtKB and analyzed using various online ML platforms (e.g., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). Eight potential AMP peptides against Klebsiella pneumoniae with antibiofilm, non-toxic and non-hemolytic properties were then docked to MrkH, a transcriptional regulator of type 3 fimbriae involved in biofilm formation. Five of eight peptides bound more strongly than the native MrkH ligand when analyzed using HADDOCK and HPEPDOCK. Following the docking studies, our MD simulated that a Neuropeptide B (Peptide 3) bind strongly to the MrkH active sites. The discovery of putative AMPs that exceed the binding energies of the native ligand underscores the utility of the combined ML and molecular simulation strategies for discovering novel AMPs with antibiofilm properties.
抗菌肽 (AMPs) 是具有广谱抗菌活性的短肽。它们在许多生物体的宿主先天免疫中发挥着关键作用。由于对抗生素有抗药性的微生物不断增加，以及新的商业上可用的抗生素缺乏，因此越来越需要通过计算机进行抗菌肽的发现。机器学习 (ML) 提高了 AMP 发现的速度和效率，同时降低了实验方法的成本。尽管已经开发了各种 ML 平台，但仍然缺乏综合利用这些平台从公开的蛋白质数据库中发现 AMP。因此，我们的研究旨在使用 ML 平台从数据库中筛选具有抗生物膜特性的潜在 AMP，然后进行蛋白质 - 肽分子对接分析和分子动力学 (MD) 模拟。从 UniProtKB 筛选了 5850 种被分类为非 AMP 的肽，并使用各种在线 ML 平台（例如 CAMPr3、DBAASP、dPABBs、Hemopred 和 ToxinPred）进行了分析。然后，将针对肺炎克雷伯氏菌的 8 种具有抗生物膜、无毒和非溶血特性的潜在 AMP 肽与 MrkH 对接，MrkH 是参与生物膜形成的 III 型菌毛的转录调节剂。在使用 HADDOCK 和 HPEPDOCK 进行分析时，这 8 种肽中的 5 种与天然 MrkH 配体的结合力更强。在对接研究之后，我们的 MD 模拟表明，神经肽 B (肽 3) 与 MrkH 的活性位点结合紧密。发现的假定 AMP 超过了天然配体的结合能，这突出了将 ML 和分子模拟策略相结合用于发现具有抗生物膜特性的新型 AMP 的实用性。
