Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning

• Published in: Marine drugs 2024-11, Vol.22 (11), p.501
• Main Authors: Zhang, Tingrui, Sun, Geyao, Cheng, Xueyu, Cao, Cheng, Cai, Zhonghua, Zhou, Jin
• Format: Article
• Language: English
• Subjects: Algae; Algorithms; Amides; Amino acids; anti-viral compounds; Antibiotics; Antiviral activity; Antiviral agents; Antiviral Agents - chemistry; Antiviral Agents - pharmacology; Antiviral drugs; application potential; Aquatic plants; Artificial intelligence; Biological Products - chemistry; Biological Products - pharmacology; Cyanobacteria; Cyanobacteria - chemistry; cyanobacterial secondary metabolites; Cytotoxicity; DNA polymerases; Drug development; Drug Evaluation, Preclinical - methods; Ebola virus; Effectiveness; Epidemics; Freshwater; HIV; HIV (Viruses); HIV-1 - drug effects; Human immunodeficiency virus; Humans; Hydrogen bonds; Inland water environment; Learning algorithms; Machine Learning; Message passing; Metabolites; Molecular docking; Molecular Docking Simulation; Natural products; Neural networks; Pentagastrin; Plant metabolites; Ribonuclease; Ribonuclease H; RNA-directed DNA polymerase; Screening; Seawater; Secondary metabolites; Severe acute respiratory syndrome coronavirus 2; Support vector machines; Virtual networks; Viruses
• ISSN: 1660-3397; 1660-3397
• DOI: 10.3390/md22110501

The secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are lacking. Advanced virtual screening techniques can significantly reduce the time and cost of novel antiviral drug identification. In this study, we used a cyanobacterial secondary metabolite library as an example and trained three models to identify compounds with potential antiviral activity using a machine learning method based on message-passing neural networks. Using this method, 364 potential antiviral compounds were screened from >2000 cyanobacterial secondary metabolites, with amides predominating (area under the receiver operating characteristic curve value: 0.98). To verify the actual effectiveness of the candidate antiviral compounds, HIV virus reverse transcriptase (HIV-1 RT) was selected as a target to evaluate their antiviral potential. Molecular docking experiments demonstrated that candidate compounds, including kororamide, mollamide E, nostopeptolide A3, anachelin-H, and kasumigamide, produced relatively robust non-covalent bonding interactions with the RNase H active site on HIV-1 RT, supporting the effectiveness of the proposed screening model. Our data demonstrate that artificial intelligence-based screening methods are effective tools for mining potential antiviral compounds, which can facilitate the exploration of various natural product libraries.