BackgroundBACTIBASE is an integrated open-access database designed for the characterization of bacterial antimicrobial peptides, commonly known as bacteriocins.DescriptionFor its second release, BACTIBASE has been expanded and equipped with additional functions aimed at both casual and power users. The number of entries has been increased by 44% and includes data collected from published literature as well as high-throughput datasets. The database provides a manually curated annotation of bacteriocin sequences. Improvements brought to BACTIBASE include incorporation of various tools for bacteriocin analysis, such as homology search, multiple sequence alignments, Hidden Markov Models, molecular modelling and retrieval through our taxonomy Browser.ConclusionThe provided features should make BACTIBASE a useful tool in food preservation or food safety applications and could have implications for the development of new drugs for medical use. BACTIBASE is available at http://bactibase.pfba-lab-tun.org.

Background: Bacteriocins are very diverse group of antimicrobial peptides produced by a wide range of bacteria and known for their inhibitory activity against various human and animal pathogens. Although many bacteriocins are now well characterized, much information is still missing or is unavailable to potential users. The assembly of such information in one central resource such as a database would therefore be of great benefit to the exploitation of these bioactive molecules in the present context of increasing antibiotic resistance and natural biopreservation need.

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Bacteriocins are ribosomally-synthesized peptides or proteins produced by a wide range of bacteria. The antimicrobial activity of this group of natural substances against foodborne pathogenic and spoilage bacteria has raised considerable interest for their application in food preservation. Classifying these bacteriocins in well defined classes according to their biochemical properties is a major step towards characterizing these anti-infective peptides and understanding their mode of action. Actually, the chosen criteria for bacteriocins' classification lack consistency and coherence. So, various classification schemes of bacteriocins resulted various levels of contradiction and sorting inefficiencies leading to bacteriocins belonging to more than one class at the same time and to a general lack of classification of many bacteriocins. Establishing a coherent and adequate classification scheme for these bacteriocins is sought after by several researchers in the field. It is not straightforward to formulate an efficient classification scheme that encompasses all of the existing bacteriocins. In the light of the structural data, here we revisit the previously proposed contradictory classification and we define new structure-based sequence fingerprints that support a subdivision of the bacteriocins into 12 groups. The paper lays down a resourceful and consistent classification approach that resulted in classifying more than 70% of bacteriocins known to date and with potential to identify distinct classes for the remaining unclassified bacteriocins. Identified groups are characterized by the presence of highly conserved short amino acid motifs. Furthermore, unclassified bacteriocins are expected to form an identified group when there will be sufficient sequences.

BackgroundThe exponential growth of research in molecular biology has brought concomitant proliferation of databases for stocking its findings. A variety of protein sequence databases exist. While all of these strive for completeness, the range of user interests is often beyond their scope. Large databases covering a broad range of domains tend to offer less detailed information than smaller, more specialized resources, often creating a need to combine data from many sources in order to obtain a complete picture. Scientific researchers are continually developing new specific databases to enhance their understanding of biological processes.DescriptionIn this article, we present the implementation of a new tool for protein data analysis. With its easy-to-use user interface, this software provides the opportunity to build more specialized protein databases from a universal protein sequence database such as Swiss-Prot. A family of proteins known as bacteriocins is analyzed as 'proof of concept'.ConclusionSciDBMaker is stand-alone software that allows the extraction of protein data from the Swiss-Prot database, sequence analysis comprising physicochemical profile calculations, homologous sequences search, multiple sequence alignments and the building of new and more specialized databases. It compiles information with relative ease, updates and compares various data relevant to a given protein family and could solve the problem of dispersed biological search results.

Staphylococcus aureus and methicillin-resistant S. aureus are major pathogens. The antimicrobial peptides and essential oils (EOs) display narrow- or broad-spectrum activity against bacteria including these strains. A centralized resource, such as a database, designed specifically for anti-S. aureus/anti-methicillin-resistant S. aureus antimicrobial peptides and EOs is therefore needed to facilitate the comprehensive investigation of their structure/activity associations and combinations. The database ANTISTAPHYBASE is created to facilitate access to important information on antimicrobial peptides and essential peptides against methicillin-resistant S. aureus and S. aureus. At the moment, the database contains 596 sequences of antimicrobial peptides produced by diverse organisms and 287 essential oil records. It permits a quick and easy search of peptides based on their activity as well as their general, physicochemical properties and literature data. These data are very useful to perform further bioinformatic or chemometric analysis and would certainly be useful for the development of new drugs for medical use. The ANTISTAPHYBASE database is freely available at: https://www.antistaphybase.com/ .

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