Journal Pre-proof LABiocin database: a new database designed specifically for Lactic Acid Bacteria bacteriocins Imad Al Kassaa , Rayane Rafei , Mirna Moukhtar , Mazen Zaylaa , Adem Gharsallaoui , Abdeslam Asehraou , Khaled El Omari , Ahmad Shahin , Monzer Hamze , Nour-Eddine Chihib PII: DOI: Reference: S0924-8579(19)30192-X https://doi.org/10.1016/j.ijantimicag.2019.07.012 ANTAGE 5761 To appear in: International Journal of Antimicrobial Agents Received date: Accepted date: 5 May 2019 10 July 2019 Please cite this article as: Imad Al Kassaa , Rayane Rafei , Mirna Moukhtar , Mazen Zaylaa , Adem Gharsallaoui , Abdeslam Asehraou , Khaled El Omari , Ahmad Shahin , Monzer Hamze , Nour-Eddine Chihib , LABiocin database: a new database designed specifically for Lactic Acid Bacteria bacteriocins, International Journal of Antimicrobial Agents (2019), doi: https://doi.org/10.1016/j.ijantimicag.2019.07.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V. Highlights     LABioicin is a new repository for Lactic Acid Bacteria bacteriocins and their data Bacteriocin properties, sequences, spectrum and extraction methods are compiled LABioicin provides an interactive interface that integrates many tools and services LABiocin underpins for the development of food safety applications and new drugs LABiocin database: a new database designed specifically for Lactic Acid Bacteria bacteriocins Imad AL KASSAAa†, Rayane RAFEIa†, Mirna MOUKHTARc, Mazen ZAYLAAa, Adem GHARSALLAOUIf, Abdeslam ASEHRAOUe, Khaled EL OMARIa,d, Ahmad SHAHINa,c, Monzer HAMZEa, Nour-Eddine CHIHIBb* a Health and Environment Microbiology Laboratory, Doctoral School of Sciences and Technology, Faculty of Public Health, Lebanese University, Tripoli, Lebanon b UMET CNRS Laboratory, INRA, UMR 8207-UMET-PIHM, Lille University, Villeneuve d’Ascq, France c LIA Laboratory, Doctoral School in Science & Technology, Lebanese University, Tripoli – Lebanon d Quality Control Center Laboratories at the Chamber of Commerce, Industry Agriculture of Tripoli & North Lebanon e Laboratory of Biochemistry and Biotechnology Mohammed Premier University, Faculty of Sciences Oujda, Morocco University Lyon 1, ISARA Lyon, Laboratoire BioDyMIA, Equipe Mixte d’Accueil, n°3733, IUT f Lyon 1, technopole Alimentec, rue Henri de Boissieu, F-01000 Bourg en Bresse, France *Corresponding author: Nour-Eddine CHIHIB Tel: +33 (0)3 59 63 22 46 Fax: +33 (0)3 59 63 21 62 E-mail : Nour-Eddine.chihib@univ-lille.fr † : equally contributed authors: Imad AL KASSAA; Rayane RAFEI Abstract Although bacteriocins from lactic acid bacteria (LAB) are successfully applied as natural alternatives to food preservation as well as to antibiotics, they are scattered through literature and unexhaustive databases. For these reasons, we have developed the LABiocin database, a specialized database on LAB bacteriocins. The database was stored and compiled using MySQL with NetBeans IDE as database’s platform. Important data are compiled such as the bacteriocin name, the class to which it belongs, the amino acids and the nucleic acid sequences if available. In addition, the target microorganisms, the origin, and status of the producing strains as well as their culture conditions and their extraction and purification methods are also mentioned in this new database. A phylogenetic tree for the mature peptide bacteriocin sequences has also been created. LABiocin is an interactive database gathering up to 517 LAB bacteriocins using a suitable userfriendly interface and integrates several tools and services. Besides data searching tools, a BLAST tool was integrated into the database allowing the user to perform a homology search against mature peptide sequences. Users can be linked to other databases that contain additional information especially about predicted bacteriocin structure and mechanisms of action. LABiocin database would allow better and more comprehensive functional analysis of this special group of antimicrobial peptides. This would certainly be useful in food preservation or food safety applications, but also has substantial implications for the development of new drugs for medical use. LABiocin database is available at labiocin.net. Keywords: Bacteriocins; databases; LAB; antimicrobial peptides; LABiocin 1. Introduction Bacteriocins are antimicrobial peptides synthesized by some bacteria via the ribosomal pathway and are active generally against closely related bacteria. Although, some bacteriocins have shown recently a wide range inhibitory spectrum [1]. The bacteriocin discovery preceded that of antibiotics. Colicin was the first bacteriocin identified in 1925 from an Escherichia coli strain [2]. Since this discovery, numerous bacteriocins were identified, leading to a diversified family of proteins in terms of size, target, action mode, delivery, and immunity mechanisms. Lactic acid bacteria (LAB) are able to produce antimicrobial molecules such as organic acids, diacetyl, acetoin, hydrogen peroxide, antifungal peptides, and bacteriocins [3]. Most of the LAB are Generally Recognized as Safe (GRAS), granted by the American Food and Drug Agency (FDA). The European Food Safety Authority (EFSA) also granted the Qualified Presumption of Safety (QPS) status to most of the LAB genera [4]. Bacteriocins, produced by the LAB have been proposed and even successfully applied as natural antimicrobial alternative in food preservation as well in antibiotic therapy [5]. Hence, a huge number of research studies have been conducted to find out novel LAB bacteriocins and deeply investigate their properties and activities. Unfortunately, these information are scattered in various databases hampering thus their availability and use for the interested users. Therefore, gathering all LAB bacteriocins and their information in a specific database seems to be a paramount task nowadays. In fact, this tool represents a fundamental element to scientists, to food authorities as well as to food industries. Several databases were created to hold antimicrobial peptides (AMPs) as well as bacteriocins. Such databases could deliver information in a well-organized manner and facilitate comparison between stored bacteriocins (peptide sequence, inhibitory spectrum, resistance to extreme conditions, characterization methods...etc). Several databases hold AMPs including bacteriocins, of which only two are specialized in bacteriocins: BACTIBASE and BAGEL. BACTIBASE holds 177 bacteriocins in its last release, of which 156 bacteriocins belong to Gram positive bacteria [6]. In another hand, BAGEL4 (the latest release of BAGEL) [7], is a mining web server that identifies and visualizes gene clusters in bacteria and archeal genomes involved in biosynthesis of Ribosomally synthesized and Post translationally modified Peptides (RiPPs). The database used in BAGEL4 for gene clusters prediction contains 254 from LAB [7]. Nowadays, more special purpose databases are increasingly available for the scientific community that enables the users to query more homogenous and focalized databases on a particular theme. For example, RegulonDB and Bacillus-RegNet are comprehensive resources for transcriptional regulatory networks for E. coli and Bacillus spp. respectively [8,9]. Thus, there is a clear need to gather, filter and critically evaluate this mass of information and store into smaller, more specialized, resources so that it can then be used in a way that enhances efficiency. A new database designed specifically for LAB bacteriocins is therefore needed. The microbial physicochemical and structural properties provided in such database would allow better and more comprehensive structural and functional analysis of this special group of antimicrobial peptides. This would certainly be useful in food preservation and food safety applications, but also would have implications in the development of new drugs for medical use. For these reasons the goal for the present work is to develop the LABiocin database, a specialized database on LAB bacteriocins. Using a suitable user-friendly interface, it offers the users a validated repository of LAB bacteriocins and allows them to extract, analyze and compare data. 2. Materials and Methods 2.1 Data collection The bacteriocins of LAB presented here in the LABiocin database were extracted and collected from Scopus, Pubmed/Medline, Science direct and other databases until August 2017. The keywords used were LAB bacteriocins, antibacterial peptide. In addition, LAB bacteriocins from Bactibase (http://bactibase.hammamilab.org/main.php) and BAGEL3 (http://bagel.molgenrug.nl/) databases were also collected. These latter bacteriocins were verified from their original references to add all missing information. After careful readings of literature review, the collected information (if available) for bacteriocins are divided in three information groups: (i) General bacteriocin characteristics: class, charge, molecular weight, isoelectric point (IP), solubility, producer organisms, and target bacteriocin organisms; (ii) bacteriocin sequences: full pre-peptide sequence, mature peptide sequence, leader sequence, the corresponding accession numbers to UniProt database as well as to Protein and Nucleotide NCBI databases, and gene sequence (if available or predicted); (iii) the in-vitro characterization of bacteriocins: Extraction, characterization and purification methods as well as conditions of bacteriocin production (culture medium, pH and temperature). Concerning gene sequences, besides the literature review, the UniProt and Protein databases, NCBI tblastn software was used to retrieve gene sequences coding for peptidic bacteriocin sequences available in the Nucleotide collection (nr/nt) database. Genes were only considered if they had 100% query coverage and 100% identity between peptidic bacteriocin query and database sequences. The NCBI blastp program was used to search homologies in the Non-redundant protein sequences (nr) and UniProtKB/Swiss-Prot(swissprot) databases. If a peptidic database sequence had a 100% identity with our query only characterized to a partial level by researchers, the full pre-peptide sequence of the database sequence was extracted and represented in the database. Of note, both sequences (partial and full pre-peptide sequences) are mentioned in the bacteriocin entry in our LABiocin database with a note explaining this issue. Multiple sequence alignment was done using MUSCLE available at https://www.ebi.ac.uk/Tools/msa/muscle/. 2.2 Database architecture The LABiocin database was stored and compiled using MySQL (v 6.3). The database’s platform used NetBeans IDE 8.0.2. HTML and JSP were used to improve the web interface. This database is available on the following domain: labiocin.net. Furthermore, the database is hosted at Lille University, France. The LABiocin database contains 13 tables. The bacteriocin table is the main one. The relationships between these tables are represented in Figure 1 and can be summarized as follows. Indeed, each bacteriocin has one producer strain designed by full species name and code. This producer strain is isolated from one (or more) sources that can be human, animal, food or others. In addition, for each producer strain a status such as probiotic or starter is given depending on the bibliographic available information. Each producer strain requires some culture conditions to produce bacteriocin such as the culture medium type, medium pH, incubation time, and temperature. The extraction of bacteriocins could be conducted via several methods. Each bacteriocin was extracted by one or two methods such as salt precipitation, solvent extraction, solid phase extraction, and others. After extraction, bacteriocins should be purified. Each bacteriocin was purified via one or two methods such as Reverse-LC technique. Bacteriocin characterization is divided in several parts: (i) Each bacteriocin should be classified in a class and/or subclass such as Class I, Class II, subclass IIa…etc. In addition, (ii) each bacteriocin has a molecular weight (MW) as well as (iii) an isoelectric point (IP), (iv) peptide sequence (full sequence, mature sequence, partial sequence as well as leader peptide sequence). Therefore, many techniques and methods were used to characterize bacteriocins. Each bacteriocin has one or more characterization methods such as Mass Spectrometry, Edman degradation, SDSPAGE…etc. Bacteriocin is an antimicrobial peptide, which exhibits an antimicrobial spectrum. Thus, each bacteriocin has its own antimicrobial spectrum designed by the list of the indicator strains used. These indicator strains are mentioned with a full name of species and strain codes. In order to make the classification of indicator strains easier, indicator strains were classified in several categories such as Gram stain, fastidious, Molds, Yeast…etc. Each bacteriocin has a reference designed by a published article or other. Sometimes, one bacteriocin can be mentioned in several references, and thus, all these references are mentioned in LABiocin database. 2.3 LABiocin data analysis GraphPad Prism was employed for graph preparation and data analaysis. 2.4 Phylogenetic tree Two phylogenetic trees have been created: one for the 16S rDNA gene sequences of bacteriocinproducing LAB and another one for the mature peptide bacteriocin sequences. For the first tree, 16S rDNA gene sequences for each bacteriocin-producing LAB species available in our database were extracted from the representative genomes in the Genome database at NCBI. The Software Mega7 was used to construct the phylogenetic tree by Maximum likelihood method (Figure S1) [10]. For the second tree, the mature peptide bacteriocin sequences were aligned using MUSCLE v3.8.31 [11]. The obtained multiple sequence alignment was trimmed using the TrimAl v1.4. method [12]. After that, Protest3 determined the best-fit model of evolution for this datatest [13]. Then, FastTree v2.1.8 inferred the maximum-likelihood phylogenetic tree with WAG (WhelanAnd-Goldman) as a model [14]. FastTree support values were calculated from the the Shimodaira-Hasegawa test to provide an estimate of reliability of observed clusters. FigTree v1.4.3 was used to display graphically the trees (http://tree.bio.ed.ac.uk/software/figtree/). 3. Results and discussion 3.1 Brief Description LABiocin is a worldwide free access database. However, the user must register and login to the database with his proper username and password. The database homepage allows access to the following interfaces: Blast, Phylogeny, data analysis, About us, GeoMap, Contact, and Submit new bacteriocin (Figure 2). Additionally, the user can query directly the database from the homepage (discussed below). The results of a search query are given first in a simple table (Figure 2) that provides the user a quick view of some characteristics of the searched bacteriocin. The table shows the LABiocin accession number, bacteriocin’s class, the bacteriocin producer strain, the molecular weight, gene names as well as the links to reference article in Pubmed, UniProt and NCBI databases (Protein and GenBank). The user can download the whole information or/and the Fasta format of bacteriocin sequences from a selected bacteriocin. By clicking the name of a specific bacteriocin in the table, a new entry will show a detailed information about this bacteriocin. This entry is divided into seven sections (Figure 2). The first section lists the bacteriocin details such as: the bacterial status, the isoelectric point, the molecular weight, the electrostatic charge, the solubility, the full pre-peptide sequence, the leader sequences, the mature peptide sequence and the gene sequence as well as their corresponding accession numbers to UniProt, NCBI databases and Bactibase if available. The second section details the producer organism information such as the full name species, the food matrix, and the environmental conditions from where the bacterium where isolated (source), and the used conditions to induce bacteriocin production (the culture medium, temperature of incubation, pH). The third section shows the indicator strains targeted by bacteriocins. The fourth, the fifth and the sixth sections list respectively the extraction, the purification, and the characterization methods. The last section of this entry corresponds to the literature review of this bacteriocin. 3.2 Available tools Various tools have been implemented to query the database. For data searching, two options are available: “type in a keyword” and “advanced search” tools. “Type in a keyword” tool allows the user to quickly query the database using a simple keyword. The keyword may be a bacteriocin name, a LABiocin accession number, antimicrobial spectrum or indicator strains, a producer status (starter culture, probiotic…), an extraction or a purification method, the origin of the bacteriocin producer such as the food matrix. “Advanced search tool” enables the user to perform complex queries and combine up to 9 fields in a single search (Figure 2). The chosen term, within the inhibitory spectrum list, filters the appeared terms in the indicator strains list. For example, when the user chooses the inhibitory spectrum as Gram-positive, systematically the given list in the indicator strain field is corresponding to Gram-positive genera. Besides data searching tools, a BLAST tool was integrated into the database allowing the user to perform a BLAST search against mature peptide sequences. The homologous sequences in LABiocin database having a high similarity with the query peptide will appear. To assess the similarity between the query and the aligned database sequences, many criteria were used such as the identity, the similarity, the query coverage, the percentage of gaps, and the E-value. 3.3 Services for users Several services for users have been integrated into LABiocin database. A “contact us interface” allows users to communicate with the database curators for any further requests or suggestions. To ensure a continuous update of our data, the user can directly submit the newly identified bacteriocin by sending an email with his validated data using the “submit new bacteriocin” interface. The user can also download all available sequences in the database either mature peptide sequence, full pre-peptide sequences, partial sequences, and (or) bacteriocin-encoding gene sequences. However, a previous contact with the curators for the commitment of terms and conditions of use of LABiocin database is needed for download. Finally, a GeoMap interface shows the user popularity by country. 3.4 Phylogenetic trees Of 517 bacteriocins stored in the LABiocin database, 267 with full mature peptide sequence were were selected and analyzed by Maximum likelihood phylogenetic tree based on their sequence similarities (Figure S2). Of note, if more than one bacteriocins have the same sequence, one bacteriocin sequence is selected to represent these identical bacteriocins in the tree (appendix 1).Our findings showed interesting results, several groups with high FastTree support values (more than 70%) are identified in the tree with the potential to identify bacteriocin classes. For class I bacteriocins, lantibiotics form well-defined groups dispersed in the tree, which are the following: lacticin 481, nisin, streptin, salivaricin 9, ClyLS, bovicin HJ50, pldA1, salivaricin A, mutacin 1140, and plantaricin w β groups. Moreover, class IIa bacteriocins define groups that are close to each other. These groups are that of sakacin X, bavaricin MN, mesentericin Y105, pediocin PA-1, and carnobacteriocin BM1. Bacteriocins of class IIb consist of ThmB, BlpMTIGR4, salivaricin Abp118 β, sakacin T α, BlpN-TIGR4, lafX, lafA, BlpM-23F, enterocin X α, and latococcin G β groups. Bacteriocins of class IIc, IId, III, and IV as well as some divergent members of class I, IIa, and IIb are distributed across the tree. Furthermore, several unclassified or unknown bacteriocins that didn’t belong to known bacteriocin class are scattered across the tree. Interestingly, the clustering of these bacteriocins with known ones with high FastTree support values may be explained by either insufficient characterization of these bacteriocins or either a poor sequencing of other novel bacteriocins to constitute distinct groups. For examples, lactacin rm is clustered with enterocin AS-48 belonging to class IIc. Also, bacteriocin J46, a posttranslationally unmodified bacteriocin, is clustered with nisin group belonging to class I that begs the questions around their identities. In addition, some of poorly characterized class II members grouped with well-defined ones as Hiracin JM79, which is a Sec-dependent class II bacteriocin, clustered with the bavaricin MN group, a class IIa group. Finally, this sequence similarity-based tree fits well with structure-based sequence fingerprints that identifies 12 groups in 107 analyzed bacteriocins [15]. Although, some differences between these 2 trees are observed and are mainly due to the massive number of newly described bacteriocins analyzed here. As discussed, phylogenetic trees may provide new horizons and solutions to classify bacteriocins that are insufficiently characterized under the current contradictory classification schemas. 3.5 Relevant results and LABiocin data analysis LABiocin database holds 517 LAB bacteriocins, collected from scientific research articles until august 2017. Each bacteriocin in our database is assigned a specific accession number starting with a prefix LBN. The availability of such relevant number of stored LAB bacteriocins allows to perform a deep data analysis that can be accessed by LABiocin users in a specific interface. In addition, the LABiocin data analysis Figure 3 showed that until 1990, LAB bacteriocins were less studied. Between 1990 and 2000 (19.7%), researchers have started looking for new Generally Recognized As Safe (GRAS) bacteriocins such as LAB bacteriocins for both medical and food application. Furthermore, the majority of bacteriocins researches (38.7%) were done in 10 years between 2000 and 2010. After 2010, bacteriocin research never stopped (37.4%). 3.5.1 Bacteriocinogenic potential in lactic acid bacteria Our findings showed that Lactobacillus genus seems to be the most bacteriocinogenic genus among LAB family accounting for 30.95% of total bacteriocinogenic LAB genera, followed by Enterococcus genus with 24.18% of the total. Whereas Weissella and Bifidobacterium species exhibit the lowest percentages with 2.13% and 0.58% respectively (Figure 4A). Interestingly, based on the published bacteriocinogenic strain’s status, the percentage of bacteriocinogenic probiotic strains, is very low (7.72 %) (Figure 4B). However, 58.49% of bacteriocinogenic strains have not any published status and therefore LAB status was given to these strains. Thus, this finding can be taken into consideration to exclude this criterion when researchers would select probiotic strains (for screening of their ability to produce bacteriocins). It is worth to mention, that the bacterial probiotic status of the LAB may change in the next future depending on the research investigation. 3.5.2 LAB bacteriocins classification and antimicrobial spectrum Among these 517 bacteriocins, 19.54% were classified in class IIa, whereas 17.6% have been considered as bacteriocins with unknown class. The “unclassified bacteriocins” (15.28%) are bacteriocins with sequences and/or structures that didn’t match to any known class, whereas the “unknown bacteriocins” are bacteriocins with uncompleted information especially peptide sequence. In other hand, class IIc, IV, III and circular bacteriocins are rare classes found in LAB bacteriocins with the following percentages 1.35, 1.35, 0.58 and 0.39% respectively (Figure 5A). Concerning characteristics of LAB bacteriocins, most of them (38.88%) have a molecular weight (MW) ranging between 3 and 5 kDa. However, 17.4% of LAB bacteriocins have unidentified MW (Figure 5B). The most important characteristics of bacteriocins is the activity spectrum. In general, bacteriocins are known to be active against closely related bacteria. However, LAB bacteriocins seem to have a wide antimicrobial spectrum. For example, 114 bacteriocins among 517 show an antimicrobial activity against both Gram positive and Gram-negative bacteria. Moreover, 24 bacteriocins exhibit anti-molds activity. Furthermore, the following bacteriocins “Bacteriocin VJ13B, Enterocin AS-48, Mutacin C67-1” and “Salivaricin A2, A3, A5 and Salivaricin A4” can inhibit Mycobacterium species such as atypical mycobacteria (4) and Mycobacterium tuberculosis (2) respectively (Figure 6). The list of indicator strains was collected for each bacteriocins from the published articles. Rare bacteriocins had more than one published article and therefore, all lists of indicator strains were compared and gathered in our database. 3.5.3 In silico predicted bacteriocins It is worthy to mention that 7 bacteriocins have been detected with in silico methods such as the whole genome sequencing of producer strain followed by the identification of bacteriocin gene clusters and characterization of bacteriocins. The methods used for detection and characterization of bacteriocins are critical to re-identify them and to discover new ones. Therefore, these methods can help industries to characterize and produce bacteriocins in large scale for medical or food applications. 3.5.4 Culture conditions, extraction/purification and characterization methods LABiocin database summarizes the most important methods to be used in future bacteriocin’s research. Starting with the medium, De Man Rogosa Sharp (MRS) medium (62.4%) is the most medium used to produce a large amount of bacteriocins from LAB strains (Figure 7). Concerning extraction methods, ammonium sulfate precipitation is the most method used to extract LAB bacteriocins (42.17%). Furthermore, other famous methods “adsorption-desorption” methods called also Yang method is placed in fourth place (6.38%). Remarkably, a large number of bacteriocins (32.11%) were directly purified without extraction step (Figure 8A). The C18HPLC method was the most purification method used to purify LAB bacteriocins (263/358), followed by the cation exchange method (112/358) (Figure 8B). Bacteriocin’s characterization means methods used to identify bacteriocins properties such as MW determination as well as peptide sequencing. In this context, SDS-PAGE is the most method used to determine bacteriocin’s MW (211/384), followed by MALDI-TOF and ESI-MS methods (123/384). Edman degradation method was the most sequencing method used for LAB bacteriocins (Figure 9). Sequenced bacteriocin means a bacteriocin having a partial, mature or full prepeptide sequence with/without leader sequence. Thus, the total number of sequenced LAB bacteriocins is 335 bacteriocins, among them 292 have full prepeptide sequences (Leader and mature peptide sequences), whereas 174 have not any identified sequence (blocked sequence and/or nonsequenced bacteriocins). In addition, only 58 LAB bacteriocins have been partially sequenced (Figure 10). Peptide and DNA sequences of 230 LAB bacteriocins are present in both NCBI and Uniprot databases, whereas 75 are not present in NCBI neither Uniprot databases (Figure 10). 3.6 Comparison with other databases In the last decade, over 10 thousands of antimicrobial peptides were studied. Several databases were created to gather these AMPs and their information. Each AMP database holds specific information as well as specific tools and statistics. Table 1 showed the comparison between LABiocin and other AMP databases. APD3 [16], CAMPR3 [17], DBAASP [18], LAMP [19], YADAMP [20], BACTIBASE 2 [6] and BAGEL4 [7] are the famous available AMP databases. Only BACTIBASE 2 and BAGEL4 databases are specific to bacteriocins. The others hold AMP from all origins such as: animal, plant, frog, insect, human as well as from microbes. Bacteriocins, which are presented in AMP databases, are lacking information and need a deep validation. As shown in Table 1, bacteriocins present in BACTIBASE have less information than LABiocin. Furthermore, LABiocin gather GRAS bacteriocins, which are no cytotoxic and ready to use in any clinical or food applications. LABiocin holds 517 bacteriocins, whereas BACTIBASE 2 holds only 177 bacteriocins included non-LAB bacteriocins. However, BACTIBASE 2 contains more information on bacteriocin’s structure as well as physico-chemical properties. BAGEL4 is a genome mining web tool for bacteriocin prediction. In addition, BAGEL4 holds 814 bacteriocins originated from all bacterial phyla. Moreover, the majority of BAGEL bacteriocins are predicted from genomic analysis. LABiocin contains the majority of LAB bacteriocins in the literatures including bacteriocins found in BACTIBASE 2 and BAGEL4. Furthermore, the BACTIBASE and BAGEL accession numbers are mentioned as hyperlinks in LABiocin database, which can lead users to aforementioned databases. 4. Conclusion and future prospects LABiocin is a comprehensible and easy to access database, which compiles all LAB bacteriocins collected until august 2017. Furthermore, LABiocin contains codes presented as hyperlinks to several databases as Pubmed, Uniprot, NCBI, BAGEL and BACTIBASE. In addition, in LABiocin database, users can be directed to other database sites (APD3, CAMP and BAGEL4), which contain several prediction tools. Besides information contents, LABiocin integrates several tools and services. This interactive database was built to continue growing up with new data submitted by researchers and our team. As future prospects, many releases will be published online with new information, data and new tools. The next release of LABiocin will contain antiviral bacteriocins as well as antiviral LAB. Author contribution Authors Involvement in the Labiocin database Imad AL KASSAA Fill in the different Excel files and update them. The update of the bibliography. The query of the LABiocin database and statistics analysis. The writing and proofreading Fill in the different Excel files and update them. The update of the bibliography. The query of the LABiocin database and statistics analysis. The writing and proofreading Bioinformatician in charge of programming of the LABiocin database Person in charge of gathering information from different partners and help Al Kassaa I and RAFEI R The person in charge of checking of the physiochemical characteristics of the bacteriocins in the different files of the database and the bibliography. The person in charge of checking of the bacteriocins targets and the potential application of the bacteriocins of the lactic acid bacteria in the different files of the database and the bibliography. The writing and proofreading participation of the update of the bibliography. The bioinformatician in charge of programming of the LABiocin database and the management of the software used The management and monitoring of the work. The establishment of the working group. The writing and proofreading. Coordination of exchanges between the different authors. The Rayane RAFEI Mirna MOUKHTAR Mazen ZAYLAA Adem GHARSALLAOUI Abdeslam ASEHRAOU Khaled EL OMARI Ahmad SHAHINE Monzer HAMZE Nour-Eddine CHIHIB selection and the update of the bibliogrpahy. The design of the LABiocin database. The selection of the parameters related to the lactic acid bacteria bacteriocins. Selection of the Bibliography. The creation of the Excel files. The establishment of the working group. The management and monitoring of the work. The writing and proofreading. Acknowledgement Authors would thank Ms Samah Hussein and Ms Mirna Hayek for their technical support Declarations Funding: No funding Competing Interests: No conflict of interest Ethical Approval: Not required References [1] Line JE, Svetoch EA, Eruslanov BV, Perelygin VV, Mitsevich EV, Mitsevich IP, et al. Isolation and purification of enterocin E-760 with broad antimicrobial activity against grampositive and gram-negative bacteria. Antimicrob Agents Chemother 2008;52:1094–100. doi:10.1128/AAC.01569-06. [2] Cotter PD, Hill C, Ross RP. Bacteriocins: developing innate immunity for food. Nat Rev Microbiol 2005;3:777–88. doi:10.1038/nrmicro1273. [3] Egan K, Field D, Rea MC, Ross RP, Hill C, Cotter PD. Bacteriocins: Novel Solutions to Age Old Spore-Related Problems? Front Microbiol 2016;7:461. doi:10.3389/fmicb.2016.00461. [4] EFSA. 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[19] Zhao X, Wu H, Lu H, Li G, Huang Q. LAMP: A Database Linking Antimicrobial Peptides. PloS One 2013;8:e66557. doi:10.1371/journal.pone.0066557. [20] Piotto SP, Sessa L, Concilio S, Iannelli P. YADAMP: yet another database of antimicrobial peptides. Int J Antimicrob Agents 2012;39:346–51. doi:10.1016/j.ijantimicag.2011.12.003. Figure Legends Figure 1. LABioicin architecture. This figure shows the tables of the database, the stored data of each table as well as the relationships between these tables. The bacteriocin table was the main table. Figure 2. LABioicin interfaces At the up-left, Figure 2 presents the database homepage that allows access to the following interfaces: Blast, Phylogeny, Statistics, About us, GeoMap, Contact, and Submit new bacteriocin. At the up-middle of Figure 2, a simple table provides the user a quick view of some characteristics of the searched bacteriocin. At the right, an interface shows detailed information about a specific bacteriocin. At the down-middle, an advanced tool search enables the user to perform complex queries and combine up to 9 fields in a single search. Figure 3: Published period of different bacteriocins Figure 4. A: The percentage of genus variability in bacteriocinogenic strains; B: Percentage of bacteriocinogenic strain’s status. Figure 5. A: Percentage of bacteriocin classes; B: Percentage of bacteriocin’s molecular weight. Figure 6. Antimicrobial spectrum of bacteriocins. Figure 7. Growth medium for bacteriocin production. Figure 8. A: Methods of bacteriocin’s extraction, B: Methods of bacteriocin’s purification. Figure 9. Methods of bacteriocin’s characterization. Figure 10. A: Availability of bacteriocin’s sequence, B: presence of DNA/peptide sequences in NCBI/Uniprot database.