aBiofilm: a resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance

• Published in: Nucleic acids research Vol. 46; no. D1; pp. D894 - D900
• Main Authors: Rajput, Akanksha, Thakur, Anamika, Sharma, Shivangi, Kumar, Manoj
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 04.01.2018
• Subjects: Anti-Bacterial Agents - pharmacology; Anti-Bacterial Agents - therapeutic use; Anti-Infective Agents - pharmacology; Anti-Infective Agents - therapeutic use; Antifungal Agents - pharmacology; Antifungal Agents - therapeutic use; Bacteria - drug effects; Biofilms - drug effects; Data Curation; Data Display; Database Issue; Databases, Chemical; Drug Delivery Systems; Drug Discovery; Drug Resistance, Microbial; Fungi - drug effects; Information Storage and Retrieval; Microbiota - drug effects; Quantitative Structure-Activity Relationship; Quorum Sensing
• DOI: 10.1093/nar/gkx1157
• ISSN: 0305-1048

Abstract Biofilms play an important role in the antibiotic drug resistance, which is threatening public health globally. Almost, all microbes mimic multicellular lifestyle to form biofilm by undergoing phenotypic changes to adapt adverse environmental conditions. Many anti-biofilm agents have been experimentally validated to disrupt the biofilms during last three decades. To organize this data, we developed the 'aBiofilm' resource (http://bioinfo.imtech.res.in/manojk/abiofilm/) that harbors a database, a predictor, and the data visualization modules. The database contains biological, chemical, and structural details of 5027 anti-biofilm agents (1720 unique) reported from 1988-2017. These agents target over 140 organisms including Gram-negative, Gram-positive bacteria, and fungus. They are mainly chemicals, peptides, phages, secondary metabolites, antibodies, nanoparticles and extracts. They show the diverse mode of actions by attacking mainly signaling molecules, biofilm matrix, genes, extracellular polymeric substances, and many more. The QSAR based predictor identifies the anti-biofilm potential of an unknown chemical with an accuracy of ∼80.00%. The data visualization section summarized the biofilm stages targeted (Circos plot); interaction maps (Cytoscape) and chemicals diversification (CheS-Mapper) of the agents. This comprehensive platform would help the researchers to understand the multilevel communication in the microbial consortium. It may aid in developing anti-biofilm therapeutics to deal with antibiotic drug resistance menace.