Computers in Biology and Medicine 58 (2015) 146–153

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Computers in Biology and Medicine journal homepage: www.elsevier.com/locate/cbm

BtoxDB: A comprehensive database of protein structural data on toxin–antitoxin systems Luiz Carlos Bertucci Barbosa a,n,1, Saulo Santesso Garrido b,1, Reinaldo Marchetto b,1 a

UFT – Federal University of Tocantins, Department of Biotechnology, Caixa Postal 66, Gurupi 77402-970, Tocantins, Brazil UNESP – Universidade Estadual Paulista, Institute of Chemistry, Department of Biochemistry and Technological Chemistry, Araraquara 14800-000, São Paulo, Brazil

b

art ic l e i nf o

a b s t r a c t

Article history: Received 26 August 2014 Accepted 12 January 2015

Purpose: Toxin–antitoxin (TA) systems are diverse and abundant genetic modules in prokaryotic cells that are typically formed by two genes encoding a stable toxin and a labile antitoxin. Because TA systems are able to repress growth or kill cells and are considered to be important actors in cell persistence (multidrug resistance without genetic change), these modules are considered potential targets for alternative drug design. In this scenario, structural information for the proteins in these systems is highly valuable. In this report, we describe the development of a web-based system, named BtoxDB, that stores all protein structural data on TA systems. Methods: The BtoxDB database was implemented as a MySQL relational database using PHP scripting language. Web interfaces were developed using HTML, CSS and JavaScript. The data were collected from the PDB, UniProt and Entrez databases. These data were appropriately filtered using specialized literature and our previous knowledge about toxin–antitoxin systems. Results: The database provides three modules (“Search”, “Browse” and “Statistics”) that enable searches, acquisition of contents and access to statistical data. Direct links to matching external databases are also available. Conclusions: The compilation of all protein structural data on TA systems in one platform is highly useful for researchers interested in this content. BtoxDB is publicly available at http://www.gurupi.uft.edu.br/ btoxdb. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Toxin–antitoxin (TA) systems Structural database Persistence Alternative antimicrobials Drug targets

1. Introduction Toxin–antitoxin (TA) systems are genetic modules found in plasmids and chromosomes of both bacteria and archaea [1]. These systems are composed of a toxin and an antitoxin that neutralizes the toxic effect of the toxin. In general, the antitoxin is less stable than the toxin and is rapidly degraded in specific conditions, leaving the toxin free to act on its cellular targets [2]. The toxin is always a protein, but the antitoxin can be an RNA or a protein [3]. TA systems are currently grouped into five types based on the mode of interaction between toxin and antitoxin and the nature of the antitoxin [3]. In type I, the antitoxin is an antisense RNA that suppresses toxic effect by binding to its mRNA [4,5]. In type II, both the toxin and antitoxin are translated to produce proteins, and the antitoxin neutralizes the toxicity of the toxin protein by binding

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þ 55 63 8497 7326; fax: þ55 63 3311 3504. E-mail address: [email protected] (L.C.B. Barbosa). 1 The authors contributed equally to this work.

http://dx.doi.org/10.1016/j.compbiomed.2015.01.010 0010-4825/& 2015 Elsevier Ltd. All rights reserved.

directly to it [2,6]. In type III, a RNA antitoxin directly neutralizes the protein toxin [7,8]. In type IV, the antitoxin is a protein that neutralizes the toxin, acting as an antagonist [9]. In type V, antitoxin directly cleaves the toxin-encoding mRNA [10]. The TA system has been related to several events in cell physiology such as plasmid maintenance [11,12], stress resistance [13], protection from bacteriophages [7], persistence [14] and regulation of biofilm formation [15]. The mode of action of TA systems has served as a model for the development of biotechnological products of interest to molecular biology [3,16]. TA-system components have been used to enhance clonal selection and protein expression in living bacterial cells [17–19]. Furthermore, because TA systems are able to repress growth or kill cells and are widely present in bacterial genomes, they are considered as potential targets for the development of new antibacterial drugs [3,20,21]. Antibiotic-resistant strains of pathogenic bacteria constitute a major health problem worldwide. Resistance mechanisms may be due to genetic changes that block antibiotic activity. There are also persisters, which are bacterial cells that are highly tolerant to

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147

Fig. 1. Data flow of the BtoxDB database.

different types of antibiotics but are genetically identical to their nontolerant kind [22]. Growth arrest is the most established hypothesis to explain the multidrug persistence displayed by certain subpopulations. In this context, TA systems are probably the major causes of the phenomenon and act as a starvation response [23]. Consequently, the interest in persister cells has increased dramatically in recent years, and research in this area could have important implications for the development of novel therapeutics to eradicate persisting subpopulations [23]. Pharmaceutical companies have dedicated major efforts to obtain new versions of old classes of antibiotics, abandoning programs to develop new types of antimicrobial drugs from unexplored targets. In consequence, there is a diminished overall capacity to generate novel antimicrobials [20,24–26]. Therefore, there is a need for innovative antimicrobials to solve this problem, and TA modules could be a way to improve antimicrobial treatments. One strategy is to obtain a putative disruptor of the TA interaction [3,16,20,21,27]. Another strategy is to design small molecules based on toxin–cellular target interaction that can poison the cell in manner similar to the natural action of toxin compounds [3,28,29]. Considering the importance of TA modules as new potential unexplored targets for the development of alternative antimicrobials and the importance of structural data for this purpose, this study presents a web-based system, named BtoxDB, that was created to maintain a resource of all available protein structural information on TA systems.

(Structured Query Language) and PHP (Hypertext Preprocessor) scripting languages. BtoxDB runs on a Linux platform with an Apache webserver. Web interfaces were developed using HTML (Hypertext Markup Language), CSS (Cascading Style Sheet) and JavaScript languages. AJAX (Asynchronous Javascript and XML) was used to create dynamic pages allowing asynchronous data queries. All charts that provide data visualization are dynamically constructed using Google Charts (https://developers.google.com/chart/). The BtoxDB database was developed to provide the following three modules: (i) a “Search” module, which performs a search from keywords; (ii) a “Browse” module, which retrieves data from predefined options; and (iii) a “Statistics” module, which returns statistical data from the database. 2.2. Data collection and curation The main data resources were the PDB (http://www.rcsb.org/pdb/ home/home.do) and UniProt (www.uniprot.org) databases. When necessary, additional information was obtained from the Entrez database (http://www.ncbi.nlm.nih.gov). All data from these databases were extracted using sequence homology searches and keyword searches for relevant toxin–antitoxin modules. These data were filtered for redundancy using specialized literature and also our previous knowledge about toxin–antitoxin systems. The data flow of the BtoxDB is briefly illustrated in Fig. 1.

3. Results and discussion 2. Materials and methods 2.1. Database construction and architecture The BtoxDB is a record-based database implemented as a MySQL relational database. It is managed and updated using the SQL

Some bioinformatics tools have been developed to facilitate research on TA systems such as RASTA-Bacteria [30], which is a tool for identifying toxin–antitoxin loci in prokaryotes. TADB database [31] is a resource for type II toxin–antitoxin loci in bacteria and archaea. In addition to genomic data, this database also provides some structural

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Fig. 2. An example of the results of a search of articles using the term “MazE”.

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Fig. 3. An example of the results for the CcdA family using the “Browse” module.

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Fig. 4. Detailed result for a specific entry.

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Table 1 Toxin structures grouped by microorganism in the BtoxDB database. Microorganism

Molecule

PDB

Chains

Experimental method

No. of molecules

Aliivibrio fischeri

CcdB CcdB CcdB CcdB CcdB MazF MazF MazF ParE Doc Doc Doc Doc Fst CcdB CcdB CcdB CcdB CcdB CcdB CcdB MazF Kid Kid YoeB YoeB YoeB HipA HipA HipA HipA HipA HipA RelE RelE RelE RelE RelE RelE RelE RelE RelE MazF HipA HipA HipA HipA HipA MqsR YafQ AbiQ RelE YoeB RelE VapC VapC ToxN ToxN ToxN RelE VapC VapC MazF MazF MazF MazF Zeta Zeta RelE CcdB

2KMT 3JSC 3JRZ 4ELY 3TCJ 1NE8 4MDX 4ME7 3KXE 3DD7 3K33 3KH2 3DD9 2KV5 3VUB 2VUB 1VUB 4VUB 1X75 3G7Z 3HPW 1UB4 1M1F 2C06 2A6Q 2A6R 2A6S 2WIU 3TPB 3TPD 3TPE 3TPT 3TPV 2KC8 2KC9 3KIS 3KIU 3KIQ 3KIX 4FXE 4FXH 4FXI 3NFC 3HZI 3DNV 3DNT 3DNU 3FBR 3HI2 1Z8M 4GLK 3BPQ 3OEI 3G5O 3DBO 3H87 2XDD 2XD0 2XDB 1WMI 3ZVK 3TND 2MF2 4MZP 4MZT 4MZM 1GVN 3Q8X 2KHE 4ELZ

A, B A A C, D A, B A A, B A, B, A, B A, C A A, B, A, B, A A A, B, A, B, A C, D A, B A, B A, B A, B A, B E, F A, B, A, B, A, C A A A A, B B A A Y Y Y Y D, E, A, B A, B, A, B, A A A, B A A B, D A A B, D C, D, B, C B A, B A, B, A, B, A A, C A, B, A, C, A, B A, B, A, B A, B, B, D B, D A C, D

NMR X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY NMR X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY NMR X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY NMR NMR X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY NMR X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY NMR X-RAY X-RAY X-RAY X-RAY X-RAY NMR X-RAY

5

Bacillus subtilis Bacillus subtilis subsp. subtilis str. 168 Caulobacter crescentus NA1000 Enterobacteria phage P1

Enterococcus faecalis Escherichia coli

Escherichia coli K-12

Helicobacter pylori 26695 Lactococcus lactis Methanocaldococcus jannaschii DSM 2661 Mycobacterium tuberculosis Mycobacterium tuberculosis H37Rv

Pectobacterium atrosepticum

Pyrococcus horikoshii OT3 Rickettsia felis Shigella flexneri Staphylococcus aureus Staphylococcus aureus subsp. aureus N315

Streptococcus pyogenes Thermus thermophilus HB8 Vibrio fischeri MJ11

C, D

C, D C, D, E, F, G, H

C, D, E, F, G, H C, D

C, D, E, F C, D

F C C, D, E, F

G, H, K, L, O, P

E E, X, Y, Z

C, D E, G C, D, E, F, G, H C, D

1 2 1 4

1 19

16

1 1 1 1 3

3

1 1 1 1 3

2 1 1 Total :70

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Table 2 Antitoxin structures grouped by microorganism in the BtoxDB database. Microorganism

Molecule

PDB

Chains

Experimental method

No. of molecules

Bacillus subtilis subsp. subtilis str. 168 Caulobacter crescentus NA1000 Enterobacteria phage P1

MazE ParD Phd Phd Phd Phd RelB CcdA CcdA CcdA CcdA MazE MazE ParD YefM HipB Phd YeeU Higa Higa RelB RelB CcdA CcdA HipB HipB GhoS MqsA MqsA MqsA MqsA MqsA MqsA CcdA RelB YefM YefM YefM RelB VapB VapB ToxI ToxI ToxI RelB VapB VapB YeeU Epsilon Epsilon

4ME7 3KXE 3DD7 3HS2 3K33 3KH2 2K29 2ADL 2ADN 2H3A 2H3C 1UB4 1MVF 2AN7 2A6Q 2WIU 3HRY 2H28 2ICT 2ICP 2KC8 4FXE 3G7Z 3HPW 3HZI 3DNV 2LLZ 3HI2 3GA8 3GN5 3FMY 3O9X 2KZ8 3TCJ 3BPQ 3OEI 3CTO 3D55 3G5O 3DBO 3H87 2XDD 2XD0 2XDB 1WMI 3ZVK 3TND 2INW 1GVN 3Q8X

E, F C, D B, D A, B, C, D, E, F, G, H B, C, D E, F, G, H A, B A, B A, B A, B A, B C D, E A, B A, B, C, D B, D A, B, C A, B A A B A, B, C C, D C B B A A, C A A, B A A, B A T A, C A, B, I, J, M, N, E, F A, B, C, D, E A, B, C, D A, D A C, D F, G, H G, H, I, U, V, W G B, D E, F, G, H B, D, F, H A, B A, C A, C

X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY NMR NMR NMR NMR NMR X-RAY X-RAY NMR X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY NMR X-RAY X-RAY X-RAY X-RAY X-RAY NMR X-RAY X-RAY X-RAY X-RAY X-RAY NMR X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY X-RAY

1 1 4

Escherichia coli

Escherichia coli CFT073 Escherichia coli K-12

Escherichia coli O157:H7 Methanocaldococcus jannaschii DSM 2661 Mycobacterium tuberculosis

Mycobacterium tuberculosis H37Rv

Pectobacterium atrosepticum

Pyrococcus horikoshii OT3 Rickettsia felis Shigella flexneri Streptococcus pyogenes

12

2 13

1 1 3

3

3

1 1 2 2 Total: 50

data but is limited to displaying a figure of the 3D structure and a link to PDB database. In this work, the BtoxDB database was developed to create a resource of all available structural information on toxin– antitoxin systems in prokaryotes, not limited to the type II group. Furthermore, detailed data are provided through three user-friendly modules. The “Search” and “Browse” modules were developed to allow queries on entries deposited in the BtoxDB database. In the access screen for both tools, is possible to select only literature search or only structural data from crystallography or NMR experiments. The “Search“ module should be used when the user is interested in a data search based on keywords without filtering by families, superfamilies or specific species. The “Browse“ module should be used when the user is interested in browsing the complete listing of BtoxDB database entries or browsing all data from one or more toxin/antitoxin family, toxin/antitoxin superfamily or one or more species.

In both modules, the literature search initially returns entries in a table (Fig. 2). Each result, containing information about article title, authors and journal, is displayed in one row of the table. The complete reference of selected entries can be downloaded using the “Get references“ button on the bottom of the display page. For each entry, a “more” option on the right of the display page shows detailed content for each reference in a new screen. Apart from the title, authors and journal, the new screen displays the PMID and DOI numbers, both linking to the respective external databases, and the abstract of the reference. When the user searches for structural data, in both modules, a screen is shown with the data in a table containing information about the experimental method, PDB ID, molecule name and organism (Fig. 3). The PDB files or primary sequences of selected entries can be downloaded using the “Get PDBs“ and “Get sequences“ buttons, respectively, located on the bottom of the display page. For each entry, on the right of the display page,

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a “more” option shows detailed information on each structure, in a new screen, including experimental data, molecule names, 3D structure, secondary structure, primary sequences and related articles. An example of the detailed data available on a solved structure is shown in Fig. 4. The “Statistics” module provides an easy way to obtain statistical data on solved structures of TA systems. In this module, it is possible to obtain statistical data grouped by toxin/antitoxin families, toxin/antitoxin superfamilies or by microorganisms. Table 1 and Table 2 show current structures deposited in the BtoxDB database, grouped by microorganism, for toxins and antitoxins respectively. At present, the BtoxDB contains records of over 120 molecules, including TA complexes and free toxins/ antitoxins.

[6] [7]

[8]

[9]

[10]

[11]

4. Conclusions One way to combat the emergence of bacterial multidrug resistance is to increase research focused on the development of new drugs against these resistant pathogens. TA modules are potential targets for new ligands that may contribute to the elimination of persister cells that remain even after treatment with antibiotics. In this regard, structural information of TA systems is fundamental and has major academic and industrial value [32]. Considering this information, the BtoxDB database was developed to provide a user-friendly web-based resource for all available protein structural information on toxin– antitoxin systems. The database provides three modules that allow users to search, acquire contents and access statistical data and also provides direct links to external databases. The platform developed here is very useful for researchers interested in TA systems and can be freely accessed at http://www.gurupi.uft.edu.br/btoxdb/. The BtoxDB database will be updated monthly as new structural data for TA modules are published. New tools will also be incorporated into BtoxDB to allow additional data analysis.

[12]

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[14]

[15]

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[17]

[18] [19] [20] [21]

Conflict of interest statement None declared.

[22] [23]

[24]

Acknowledgments We gratefully acknowledge FAPESP, CNPq and CAPES for financial support.

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[27]

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BtoxDB: a comprehensive database of protein structural data on toxin-antitoxin systems.

Toxin-antitoxin (TA) systems are diverse and abundant genetic modules in prokaryotic cells that are typically formed by two genes encoding a stable to...
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