Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database. (February 2023)

• Record Type: Journal Article
• Authors: Al-Khdhairawi, Ahmad; Sanuri, Danish; Akbar, Rahmad; Lam, Su Datt; Sugumar, Shobana; Ibrahim, Nazlina; Chieng, Sylvia; Sairi, Fareed
• Is Part Of: Computational biology and chemistry. Volume 102(2023)
• Publication Date: 2023-02
• Subjects: AMP Antimicrobial Peptide -- ANOVA Analysis of Variant -- APD Antimicrobial Peptide Database -- CAMPR3 Collection of Antimicrobial Peptides -- CDC Centre of Disease Control and Prevention -- CSV Comma Separated Value -- DA Discriminant Analysis -- RF Random Forest -- SVM Support Vector Machine; Antibiofilm peptide -- Klebsiella pneumoniae -- Molecular docking -- MrkH -- Type 3 Fimbriae -- Peptide-protein interaction; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107800
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: 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. Graphical Abstract: Overview strategies to ascertain antimicrobial peptide (AMP) from database using machine learning tools, in silico approach and molecular dynamic simulation. ga1 Highlights: Discovery of eight potential antimicrobial peptide (AMP) in UniProtKB using machine-learning tools. Docking analysis showed Eight potential AMP with high binding affinity towards the transcriptional activator, MrkH in K. pneumoniae. Molecular dynamic simulation ascertains Neuropeptide B as potential inhibitor against K. pneumomiae biofilm synthesis.