# A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases

F. Tesson, R. Planel, A. Egorov, H. Georjon, H. Vaysset, B. Brancotte, B. Néron, E. Mordret, G. Atkinson, A. Bernheim, et al.

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## To cite this version:

F. Tesson, R. Planel, A. Egorov, H. Georjon, H. Vaysset, et al., A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases. 2024. [hal-04600530](https://hal.science/hal-04600530)

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Research article

Published
2024-09-25

**Cite as**
Florian Tesson, Rémi Planel, Artyom A Egorov, Héloïse Georjon, Hugo Vaysset, Bryan Brancotte, Bertrand Néron, Ernest Mordret, Gemma Catherine Atkinson, Aude Bernheim and Jean Cury (2024)
*A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases*, Peer Community Journal, 4: e91.

**Correspondence**
florian.tesson@pasteur.fr
jean.cury@pasteur.fr

**Peer-review**
Peer reviewed and
recommended by
PCI Genomics,
https://doi.org/10.24072/pci.genomics.100373

<figure><img src="image_1.png" /></figure>

This article is licensed
under the Creative Commons
Attribution 4.0 License.

# A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases

Florian Tesson<sup>1,2</sup>, Rémi Planel<sup>3</sup>, Artyom A Egorov<sup>4</sup>, Héloïse Georjon<sup>1,1</sup>, Hugo Vaysset<sup>1,5,6</sup>, Bryan Brancotte<sup>3</sup>, Bertrand Néron<sup>3</sup>, Ernest Mordret<sup>1,1</sup>, Gemma Catherine Atkinson<sup>1,4</sup>, Aude Bernheim<sup>1,1</sup>, and Jean Cury<sup>1,1</sup>

Volume 4 (2024), article e91

https://doi.org/10.24072/pcjournal.470

## Abstract

In recent years, a vast number of novel antiphage defense mechanisms were uncovered. To facilitate the exploration of mechanistic, ecological, and evolutionary aspects related to antiphage defense systems, we released DefenseFinder in 2021 (Tesson et al., 2022). DefenseFinder is a bioinformatic program designed for the systematic identification of known antiphage defense mechanisms. The initial release of DefenseFinder v1.0.0 included 60 systems. Over the past three years, the number of antiphage systems incorporated into DefenseFinder has grown to 152. The increasing number of known systems makes it a challenge to enter the field and makes the interpretation of detections of antiphage systems difficult. Moreover, the rapid development of sequence-based predictions of structures offers novel possibilities of analysis and should be easily available. To overcome these challenges, we present a hub of resources on defense systems, including: 1) an updated version of DefenseFinder with a web-service search function, 2) a community-curated repository of knowledge on the systems, and 3) precomputed databases, which include annotations done on RefSeq genomes and structure predictions generated by AlphaFold. These pages can be freely accessed for users as a starting point on their journey to better understand a given system. We anticipate that these resources will foster the use of bioinformatics in the study of antiphage systems and will serve the community of researchers who study antiphage systems. This resource is available at: https://defensefinder.mdmlab.fr.

<footnote n="1">¹Institut Pasteur, Université Paris Cité, INSERM U1284, Molecular Diversity of Microbes lab, 75015, Paris, France,</footnote>

<footnote n="2">²Université Paris Cité, INSERM, IAME, 75018 Paris, France, ³Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France, ⁴Department of Experimental Medical Science, and Lund University</footnote>

<footnote>Virus Centre, Lund University, 221 00 Lund, Sweden, ⁵Institut Curie, Université PSL, Sorbonne Université, CNRS, Laboratoire Physico Chimie Curie, UMR 168, 75005 Paris, France, ⁶AgroParisTech, 91120, Palaiseau, France</footnote>
1 **A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice,**

2 **Wiki and Databases.**

3 Tesson F.¹,⁴, Planel R³, Egorov A², Georjon H.¹, Vaysset H.¹,⁵,⁶, Brancotte B³, Néron B³, Mordret E.¹,

4 Atkinson G², Bernheim A*¹, Cury J.¹*.

5

6 ¹Institut Pasteur, Université Paris Cité, INSERM U1284, Molecular Diversity of Microbes lab, 75015, Paris, France

7 ²Department of Experimental Medical Science, and Lund University Virus Centre, Lund University, 221 00 Lund, Sweden

8 ³Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France

10 ⁴Université Paris Cité, INSERM, IAME, 75018 Paris, France

11 ⁵Institut Curie, Université PSL, Sorbonne Université, CNRS, Laboratoire Physico Chimie Curie, UMR 168, 75005 Paris, France

13 ⁶Agroparistech, Paris, France

14

15 **Abstract**

16

17 In recent years, a vast number of novel antiphage defense mechanisms were uncovered. To
18 facilitate the exploration of mechanistic, ecological, and evolutionary aspects related to antiphage
19 defense systems, we released DefenseFinder in 2021 (Tesson et al., 2022). DefenseFinder is a
20 bioinformatic program designed for the systematic identification of all known antiphage defense
21 mechanisms. The initial release of DefenseFinder v1.0.0 included 60 systems. Over the past three
22 years, the number of antiphage systems incorporated into DefenseFinder has grown to 152. The
23 increasing number of known systems makes it a challenge to enter the field and makes the
24 interpretation of detections of antiphage systems difficult. Moreover, the rapid development of
25 sequence-based predictions of structures offers novel possibilities of analysis and should be easily
26 available. To overcome these challenges, we present a hub of resources on defense systems
27 including 1) a search tool (DefenseFinder as a web service) 2) a knowledge base, and 3)
28 precomputed databases (results of DefenseFinder ran on RefSeq database, predicted structure
29 database computed with AlphaFold). These pages can be freely accessed for users as a starting
30 point on their journey to better understand a given system. We anticipate that these resources
31 will foster the use of bioinformatics in the study of antiphage systems and will serve the community
32 of researchers who study antiphage systems. This resource is available at:
33 https://defensefinder.mdmlab.fr.
# Introduction

In the last few years, a considerable number of newly discovered antiphage defense mechanisms have come to light (Georjon & Bernheim, 2023). The most widespread mechanisms, Restriction-Modification and CRISPR-Cas systems, target foreign nucleic acids (Tock & Dryden, 2005; Nussenzweig & Marraffini, 2020). However, recent discoveries have revealed an important diversity of molecular modalities by which bacteria defend themselves against phages. This diversity of mechanisms includes nucleotide depletion (Ka et al., 2020; Hsueh et al., 2022; Tal et al., 2022; Rousset et al., 2023; Duncan-Lowey et al., 2023; Shen et al., 2023; Tang et al., 2023), membrane disruption (Durmaz & Klaenhammer, 2007; Duncan-Lowey et al., 2021; Johnson et al., 2022; Zeng et al., 2022), and production of antiviral molecules (Bernheim et al., 2021). Importantly, many defense mechanisms remain unknown.

The discovery of diverse antiphage systems not only provides new insights into bacterial immunity mechanisms but also transforms the exploration of interactions between phages and bacteria and microbial evolution. Evaluating the impact of different defense systems in naturally occurring *Vibrio* isolates has revealed that a rapid turnover of a few mobile genetic elements encoding defense systems can completely alter their susceptibility to phages (Hussain et al., 2021; Piel et al., 2022). Ongoing research is delving into the broader phylogenetic scale to explore the role of defense systems and their potential implications for phage therapy strategies (Gaborieau et al., 2023; Costa et al., 2023). Furthermore, examples indicate that bacterial defense systems can be co-regulated and operate synergistically in multi-layered defense strategies (Tesson & Bernheim, 2023). Beyond experimental approaches, there is a growing interest in identifying the antiphage systems encoded in diverse species or environments to understand the diversity of antiviral strategies employed by bacteria.

To investigate mechanistic, ecological, and evolutionary questions related to antiphage defense systems, we created in 2021 DefenseFinder (Tesson et al., 2022), a tool to detect known antiphage defense systems systematically. At the time of publication of DefenseFinder v1.0.0, the tool encompassed 60 systems. In the last 3 years, the number of antiviral systems included in DefenseFinder has grown to 152. The increasing number of systems has been accompanied by emerging challenges in comprehending DefenseFinder results biologically. For non-specialist users, the analysis can be quite intricate, demanding a high level of knowledge in this rapidly evolving field.

To bridge this gap, we decided to create a website dedicated to defense systems, encompassing an improved version of a web service to run DefenseFinder and diverse databases to illuminate and increase the understandability of bioinformatic detection of antiphage defense systems. Here, we provide the release of 92 new defense systems models and a software update as well as 3 databases. 1/ A collaborative knowledge base (wiki) summarizing information on known defense systems. 2/ A structure database, with experimentally determined and AlphaFold2 predicted structures. 3/ A precomputed database of DefenseFinder results in over 20,000 complete genomes. Besides, we designed the website to keep it as up-to-date as possible. Wiki pages are easily editable by anyone and reviewed by experts in the field before publication on the website. They can also be edited automatically to generate sections that aggregate data, such as the phylogenetic distribution of a system. All those website components are also integrated within the DefenseFinder webserver output to easily find information on a system found in a genome.

# Results

## Updates of DefenseFinder program

To improve detections performed by DefenseFinder, we updated both the program and the models. Since the release of DefenseFinder models v1.0.0, we have constantly updated the defense system
## Introduction

In the last few years, a considerable number of newly discovered antiphage defense mechanisms have come to light ¹. The most widespread mechanisms, Restriction-Modification and CRISPR-Cas systems, target foreign nucleic acids ²,³. However, recent discoveries have revealed an important diversity of molecular modalities by which bacteria defend themselves against phages among which nucleotide depletion ⁴⁻¹⁰, membrane disruption ¹¹⁻¹⁴, production of antiphage molecules ¹⁵. Importantly, many antiphage defense mechanisms remain unknown.

The discovery of diverse antiphage systems not only provides new insights into bacterial immunity mechanisms but also transforms the exploration of interactions between phages and bacteria, as well as microbial evolution. Evaluating the impact of different defense systems in naturally occurring Vibrio isolates has revealed that a rapid turnover of a few mobile genetic elements encoding defense systems can completely alter their susceptibility to phages ¹⁶,¹⁷. Ongoing research is delving into the broader phylogenetic scale to explore the role of antiphage systems and their potential implications for phage therapy strategies ¹⁸,¹⁹. Furthermore, examples indicate that antiphage systems can be co-regulated and operate synergistically in multi-layered defense strategies²⁰. Beyond experimental approaches, there is a growing interest in identifying the antiphage systems encoded in diverse species or environments, to understand the diversity of antiviral strategies employed by bacteria.

To investigate mechanistic, ecological and evolutionary questions related to antiphage defense systems, we created in 2021 DefenseFinder²¹, a tool to systematically detect all known anti phage defense systems. At the time of publication of DefenseFinder v1.0.0, the tool encompassed 60 systems. In the last 3 years, the number of antiphage systems included in DefenseFinder has grown to reach 152. The increasing number of systems has been accompanied by emerging challenges in comprehending DefenseFinder results biologically. For non-specialist users, the analysis can be quite intricate, demanding a high level of knowledge in this rapidly evolving field.

To bridge this gap, we decided to create a website dedicated to defense systems, encompassing both an improved version of a web service to run DefenseFinder and diverse databases to illuminate and increase the understandability of bioinformatic detection of antiphage defense systems. Here, we provide the release of 92 new defense systems models and a software update as well as 3 databases. 1/ A collaborative knowledge base (wiki) summarizing information on known defense systems. 2/ A structure database, with experimentally determined as well as AlphaFold2 predicted structures. 3/ A precomputed database of DefenseFinder results over more than 20 000 complete genomes. Besides, we designed the website to keep it as up to date as possible. Wiki pages are easily editable by anyone and reviewed by experts in the field before publication on the website. They can also be edited automatically to generate sections that aggregate data such as the phylogenetic distribution of a system. The information of the web server is integrated into the rest of the website.
models included in DefenseFinder by adding a total of 92 defense systems and 132 subsystems (Figure 1A). Among those systems, 63 were discovered and described after releasing the first version of DefenseFinder models. Other systems were missed in the first version and are now added after a deeper literature review of the field. Those systems encompass the “Abi” group discovered between 1990 and 2006. DefenseFinder models v1.0.0 included 3 of these systems, while the latest version v1.2.3 detects 22 Abi systems (Chopin et al., 2005). Beyond Abi systems, we also added SanaTA (Sberro et al., 2013), MazEF (Hazan & Engelberg-Kulka, 2004), antiphage defense systems identified in mycobacterium prophages (Dedrick et al., 2017; Mageeney et al., 2020) and pAgo (Bobadilla Ugarte et al., 2023). The defense system named “Rst_DprA-PPRT” (Rousset et al., 2022) was renamed “ShosTA” as it was initially discovered in 2013 (Sberro et al., 2013) but without phage activity testing. Finally, for Lamassu-Fam (Millman et al., 2022), we separated the systems into different subsystems according to the evolving literature on the topic. Models were also adapted to the latest version of MacSyFinder (Néron et al., 2023).

To evaluate the proportion of newly detected systems, we ran DefenseFinder v1.2.3 on the RefSeq complete genome database of prokaryotes (Bacteria N = 22,422; Archaea N = 381). We found a total of 152,386 different systems, among which 33% correspond to systems present in v1.2.3 but absent from v1.0.0 (Figure 1B). Without restriction-modification and CRISPR-Cas systems, which are by far the most abundant systems, newly added systems now represent 54% of the detected systems (41-73% across phyla) (Figure 1B).

The previous version of DefenseFinder used a “profile coverage” parameter of 0.1. This value was chosen to detect defense systems when the protein was split. This low coverage threshold was balanced with a high score threshold for single-gene systems and the necessity to find the different components of the system colocalizing in the genome for multi-gene systems. We changed that threshold to 0.4 as a default value in the latest version. This threshold was set to maintain the possibility of finding incomplete genes or a gene with a domain replacement while removing very low coverage off-target hits. Users can still manually change the coverage threshold (--coverage-profile) to decide on how conservative detection should be depending on the biological problem at hand. For ease of use, we also added the possibility to directly input a nucleic acid fasta file. We use pyrodigal v3.0.1 (Larralde, 2022) to identify and translate the coding regions, which are then processed by DefenseFinder. Finally, we added non-regressive tests in a continuous integration pipeline to make the development of future features more robust against introduction of bugs.

<figure><img src="image_2.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_3.png" /></figure>

**Figure 1** - Evolution of DefenseFinder models. A. Evolution of the number of defense systems across the different versions. B. Proportion of the currently detected systems which were added in v1.0.0 (Old and RM/Cas) or in more recent versions (New) of DefenseFinder models across different phyla of prokaryotes.
79 **Results**

80

81 **Updates of DefenseFinder program**

82 To improve detections performed by DefenseFinder, we updated both the program and the
83 models.

84

85 Since the release of DefenseFinder models v1.0.0, we have constantly updated the defense
86 system models included in DefenseFinder by adding a total of 92 defense systems and 132
87 subsystems (Figure 1A). Among those systems, 63 were discovered and described after the
88 release of the first version of DefenseFinder models. Other systems were missed in the first
89 version and are now added after a deeper literature review of the field. Those systems encompass
90 the "Abi" group discovered between 1990 and 2006. DefenseFinder models v1.0.0 included 3 of
91 these systems, while the latest version v1.2.3 detects the 22 different ones 22. Beyond Abi
92 systems, we also added: SanaTA 23, MazEF 24, antiphage defense systems identified in
93 mycobacterium prophages 25,26 and pAgo 27. The defense system named "Rst_DprA-PPRT " 28
94 was renamed "ShosTA" as it was initially discovered in 2013 23 but without phage activity testing.
95 Finally, for Lamassu-Fam 29, we separated the systems into different subsystems according to
96 the evolving literature on the topic. Models were also adapted to the latest version of MacSyFinder
97 30.
98

98

99 To evaluate the proportion of newly detected systems, we ran DefenseFinder v1.2.3 on the
100 RefSeq complete genome database of prokaryotes (Bacteria N = 22,422; Archaea N = 381). We
101 found a total of 152,386 different systems, among which 33% correspond to systems present in
102 v1.2.3 but absent from v1.0.0 (Figure 1B). Without restriction-modification and CRISPR-Cas
103 systems, which are by far the most abundant systems, newly added systems now represent 54%
104 of the detected systems (41-73% across phyla) (Figure 1B).
105

105

106 The previous version of DefenseFinder used a "profile coverage" parameter of 0.1. This value
107 was chosen to detect defense systems when the protein was split. This low coverage threshold
108 was balanced with a high score threshold for single gene systems and the necessity to find the
109 different components of the system colocalizing in the genome for multi-genes systems. In the
110 latest version, we changed that threshold to 0.4 as a default value. This threshold was set to
111 maintain the possibility of finding incomplete genes or with a domain replacement while removing
112 very low coverage off target hits. Users can still manually change the coverage threshold (--
113 coverage-profile) to decide on how conservative detection should be depending on the biological
114 problem at hand. For ease of use, we also added the possibility to directly input a nucleic acid
115 fasta file. We use pyrodigal v3.0.1³¹ to identify and annotate the coding regions, which are then
116 processed by DefenseFinder. Finally, we added non-regressive tests in a continuous integration
117 pipeline to make the development of future features more robust.
118

118

119
## An improved web service

DefenseFinder is a Python program running in the command line. To enhance the software's usability, DefenseFinder has been available as a web service since its launch. Some useful features were missing, such as the possibility of accessing previous analyses. We updated the web service to add new features: a simple interface for depositing fasta nucleic acid or fasta amino acid entries on the "Home" page (Figure 2A). Results of previous analyses are now displayed in the "Analysis" page. On this page, jobs can be accessed and renamed at convenience for 1 month without any action on the web service. All the different DefenseFinder outputs are displayed in dynamic tables that can be easily downloaded. The results are displayed in a genome browser to better visualize the hits in their context (Figure 2B). All results are displayed, including orphan HMMs, which do not form a system. Those orphan HMMs should be used cautiously and analyzed using their score and profile coverage. From the result table, getting more information on a given system is now easier via a link to the collaborative knowledge base. Importantly for the community, the latest version of DefenseFinder has been packaged in Galaxy (The Galaxy Community, 2022) and can be run on any Galaxy instance.

A

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B

<figure><img src="image_5.png" /><figcaption>**Figure 2** - Architecture of DefenseFinder Webservice. A. Homepage of the DefenseFinder webservice. B. Results page of the webservice. Three different output tables and a visualization of the chromosome with the different defense system hits are available.</figcaption></figure>

## A collaborative knowledge base for Defense systems

DefenseFinder output constitutes 3 tables (`defense_findervsystems`, `defense_findergenes`, and `defense_findermhmer`) containing respectively one line per detected system, detected genes inside a system, and defensive HMM profiles hits. Given the large (and ever-growing) number of defense systems, this output can be hard to analyze. Thus, we decided to create a participative knowledge base of defense systems available at https://defensefinder.mdmlab.fr/wiki/. This website aims to provide information to understand better bioinformatic predictions provided by DefenseFinder and to allow a dynamic, up-to-date sharing of knowledge on antiphage systems. The wiki provides a few pages on general concepts of the field and a page per defense system detected by DefenseFinder. The different pages are accessible directly from the results of the DefenseFinder web service or can be explored individually and are all summarized in a table containing the main references, part of their
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 1. Evolution of DefenseFinder models.** A. Evolution of the number of defense systems across the different versions. B. Proportion of the currently detected systems which were added in v1.0.0 (Old and RM/Cas) or in more recent versions (New) of DefenseFinder models across different phyla of prokaryotes.</figcaption></figure>

**An improved web service**

DefenseFinder is a python program running in the command line. To enhance the software's usability, DefenseFinder has been available as a web service since its launch. Some useful features were however missing such as the possibility to access previous analyses. We updated the web service to add new features: a simple interface for depositing fasta nucleic acid or fasta amino acid entries on the "Home" page (Figure 2A). Results of previous analyses are now displayed in the "Analysis" page. On this page, jobs can be accessed and renamed at convenience for 1 month without any action on the web service. All the different DefenseFinder outputs are displayed in dynamic tables that can be easily downloaded. The results are displayed in a genome browser to better visualize the hits in their context (Figure 2B). From the result table, it is now easier to get more information on a given system via a link to the collaborative knowledge base.

Importantly for the community, the latest version of DefenseFinder has been packaged in Galaxy 32 and can be run on any Galaxy instance.
mechanism (sensor, activator, effector) when this information is available in the literature and the Pfam domain present in the system (Figure 3A).

Each defense system page is organized into distinct sections (Figure 3B). 1/ Description: summarizing simple information such as the system's discovery, protein names, and known domains. 2/ The molecular mechanism: summarizing known mechanisms and highlighting instances where the mechanism remains unknown. 3/ Genomic architecture: a detailed breakdown of system components and associated domains is provided, accompanied by examples of genomic loci organization. 4/ Distribution among prokaryotes: the system distribution across phylogenetic phyla using DefenseFinder results on the RefSeq complete genome database. 5/ 3D Structure of the system: showcases both experimentally validated 3D structures (if available) and predicted structures using AlphaFold2 (Jumper et al., 2021) for validated systems. Foldseek (van Kempen et al., 2023) results starting from the given predicted structure are also precomputed and available. 6/ Experimental validation: shows the tested system, the expression organism and against which phage (s) it was shown to be effective. 7/ References: relevant publications (denoted with a star) are included to facilitate a deeper understanding of each system.

Each system featured in DefenseFinder models v1.2.3 has a dedicated page on the knowledge base. These pages were populated through collaborative efforts, including a hackathon organized internally and contributions from other researchers from the community. Recognizing the rapid pace of defense system discovery and mechanism characterization, we opted for a collaborative approach by making the website open for updates. To foster a collaborative framework, we based the website on a Gitlab repository, with pages written in Markdown and designed to be easy to use by people that are not Git experts. To modify and add information to wiki pages, contributors can click on "Edit a page" (found at the bottom of every page), add some content (and add themselves as contributors) and create an automatic merge request that will be verified by at least one expert in the field before being merged. When describing a new system, the discoverers are encouraged to request the creation of a new page to describe it with all the necessary information.

## Precomputed results of DefenseFinder on 22,738 complete genomes.

Running DefenseFinder on a large database is time and resource-consuming; thus, we created a precomputed database. DefenseFinder v1.1.0 with models v1.2.3 was run on the complete genomes database from RefSeq (From July 2022, Bacteria N = 22,422, Archaea N = 381). A total of 152,386 defense systems were detected from 152 different defense systems (264 subtypes). Those precomputed results can be visualized on the "RefSeq DB" tab of the DefenseFinder website: https://defensefinder.mdmlab.fr/wiki/refseq/. Results are displayed in an interactive table, allowing research by system, accession, or taxonomy (Figure 4A). This table is linked to interactive graphs displaying the relative abundances of the system in the phylogeny and distribution of systems (Figure 4B and 4C). Results (tables and graphs) can be downloaded from the website either as a whole or only a subset using different filters. Sequences of detected proteins are accessible through a link to their NCBI protein page.

## Precomputed 3D structure predictions of defense systems

Many mechanisms of the newly described systems remain to be elucidated. Multiple studies were conducted to elucidate such mechanisms (Koga et al., 2011; Johnson et al., 2022; LeRoux et al., 2021; Hsueh et al., 2022; Tal et al., 2022; Zhang et al., 2022; Wein et al., 2023). Often, the 3D structure of the system was an important step towards understanding its mechanism (Deep et al., 2022; Duncan-Lowey et al., 2023; Shen et al., 2023; Tang et al., 2023; Antine et al., 2024; Li et al., 2024). Recent developments in structure predictions, such as the development of AlphaFold2 (Jumper et al., 2021) or ESMFold (Lin et al., 2023), allow good-quality predictions of proteins and protein complexes. We thus created a database of predicted structures for known defense systems.
A

Defense Finder

Welcome on DefenseFinder's web page. On this site, you can freely use (without any login) the DefenseFinder webservice (see below) and get help to navigate the ever expanding world of defense systems. There is a collaborative knowledge base of defense systems that provide essential information on any given defense systems. You can further explore defense systems across all fully sequenced bacteria and get insight on the structure of each protein of those systems thanks to protein structure prediction using alghaford for monomer and dimers.

We encourage you to contribute to this knowledge database, and if you see any non up-to-date information, let us know, or **contribute yourself**

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

If you are using DefenseFinder please cite:

Systematic and quantitative view of the antiviral arsenal of prokaryotes

Tesson F, Hervé A., Touchon M., d'Humieres C., Cury J., Bernheim A., Nature Communication, 2022

MacSyFinder v2: Improved modelling and search engine to identify molecular systems in genomes

Néron, B., Denilis, R., Coluzzi, C., Touchon, M., Rocha, E.P.C., Abby, S., Peer Community, Journal, 2023

A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases

Tesson, F., Planel, R., Egorov, A., Georjon, H., Vaysseet, H., Brancotte, B., Néron, B., Mordret, E., Bernheim, A., Atkinson, G., & Cury, J., Preprint, 2024

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

B

Home / Analyses / BE fasta

<figure><img src="image_10.png" /></figure>

SYSTEMS

GENES

HMMER

<figure><img src="image_11.png" /><figcaption>Figure 2. Architecture of DefenseFinder Webservice. A. Homepage of the DefenseFinder webservice. B. Results page of the Webservice. Three different output tables are available and a visualization of the chromosome with the different defense systems hits.</figcaption></figure>

146 A collaborative knowledge base for Defense systems

147 DefenseFinder output constitutes 3 tables (defense.finder.systems, defense.finder.genes and defense.finder.hmmer) containing respectively one line per detected system, detected genes
148 inside a system and defensive HMM profiles hits. This output can be hard to analyze, given the
149 large (and ever-growing) number of defense systems. Thus, we decided to create a participative
150 knowledge base of defense systems available at https://defensefinder.mdmlab.fr/wiki/. This
151 website aims to provide information to better understand bioinformatic predictions provided by
152 DefenseFinder and to allow a dynamic, up-to-date sharing of knowledge on antiphage systems.
153 The wiki provides a few pages on general concepts of the field and a page per defense system
154 detected by DefenseFinder. The different pages are accessible directly from the results of the
155 DefenseFinder web service or can be explored individually and are all summarized in a table
156 containing the main references, part of their mechanism (sensor, activator, effector) when this
157 information is available in the literature and the Pfam domain present in the system (Figure 3A).
158
159

160 Each defense system page is organized into distinct sections (Figure 3B). 1/ Description: 161 summarizing simple information such as the system's discovery, protein names and known 162 domains. 2/ The molecular mechanism: summarizing known mechanisms and highlighting 163 instances where the mechanism remains unknown. 3/ Genomic architecture: a detailed 164 breakdown of system components and associated domains is provided, accompanied by 165 examples of genomic loci organization. 4/ Distribution among prokaryotes: the system 166 distribution across phylogenetic phylums using DefenseFinder results on the RefSeq complete
A

<figure><img src="image_6.png" /></figure>

<table><thead><tr><th>System ↑</th><th>Article</th><th>Sensor</th><th>Activator</th><th>Effector</th><th>PFAM</th><th>Contributors</th></tr></thead><tbody><tr><td>Avs</td><td>Diverse e... <i>ABSTRACT</i> Gao Linyi, Altae-Tran Han, ... Science (New York, N.Y.) (...)</td><td>Sensing of phage protein</td><td>Direct binding</td><td>Diverse effectors (Nucleic acid degrading, putative Nucleotide modifying, putative Membrane disrupting)</td><td>Metallo-beta-lactamase superfamily<br>SIR2-like domain (+1 others)</td><td>Alex Linyi Gao<br>Nathalie Bechon</td></tr><tr><td>CapRel</td><td>Direct act... <i>ABSTRACT</i> Zhang Tong, Tamman Hed... Nature (2022)</td><td>Sensing of phage protein</td><td>Direct</td><td>Nucleic acid degrading (pyrophosphorylates tRNAs)</td><td>Region found in RelA / SpoT proteins</td><td>Héloïse Georjon<br>Florian Tesson</td></tr><tr><td>Paris</td><td>Phages a... <i>ABSTRACT</i> Roussel François, Depardi... Cell Host & Microbe (2022)</td><td>Sensing of phage protein</td><td>Unknown</td><td>Unknown</td><td></td><td>Lucas Paoli</td></tr><tr><td>Pif</td><td>Molecu... <i>ABSTRACT</i> Cram D, Ray A, Skurray R. Molecular & general genet...</td><td>Sensing of phage protein</td><td>Unknown</td><td>Membrane disrupting (?)</td><td>KAP family P-loop domain</td><td>Lucas Paoli</td></tr></tbody></table>

B

CapRel

Contributors: Héloïse Georjon, Florian Tesson

Description

CapRel is a fused toxin-antitoxin system that is active against diverse phages when expressed in *Escherichia coli* (Zhang et al., 2022). CapRel belongs to the family of toxSAS toxin-antitoxin systems. CapRel is an Abortive infection system that is found in Cyanobacteria, Actinobacteria, Proteobacteria, Spirochetes, Bacteroidetes, and Firmicutes, as well as in some temperate phages.

Molecular mechanism

The CapRel system of Salmonella temperate phage SJ46 is normally found in a closed conformation, which is thought to maintain CapRel in an auto-inhibited state. However, during phage SECPh27 infection, binding of the major phage capsid protein (p057) to CapRel releases it from its inhibited state, allowing pyrophosphorylation of tRNAs by the toxin domain and resulting in translation inhibition (Zhang et al., 2022). Other phage capsid proteins can be recognized by CapRel, as observed during infection by phage Bas8.

Different CapRel homologs confer defense against different phages, suggesting variable phage specificity of CapRel system which seems to be mediated by the C-terminal region of CapRel.

Example of genomic structure

The CapRel is composed of 1 protein: CapRel.
Here is an example found in the RefSeq database:

<figure><img src="image_7.png" /></figure>

The CapRel system in *Campylobacter* sp. RM12175 (GCF_002139875.1, NZ_C0P018792) is composed of 1 protein: CapRel (WP_086315994.1)

Distribution of the system among prokaryotes

Among the 22,818 complete genomes of RefSeq, the CapRel is detected in 398 genomes (1.74 %). The system was detected in 217 different species.

<figure><img src="image_8.png" /></figure>

Structure

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

Experimental validation

<figure><img src="image_11.png" /></figure>

References

* Direct activation of a bacterial innate immune system by a viral capsid protein
  Zhang Tong, Tamman Hedvig, Coppieters T Wallant Kyo, Kurata Tatsuki, Lefloux Micheie, Srikant Siriram, Brodiazhen...
  Nature (2022)

<caption>**Figure 3** - Architecture of the DefenseFinderWiki collaborative knowledge base. A. Table presenting all the different defense systems. The table can be filtered by system, type of sensor/activator or effector and Pfam inside the system. B. An example of one page of the knowledge base covering the CapRel defense system.</caption>
167 genome database. 5/ **3D Structure of the system**: showcases both experimentally validated 3D

168 structures (if available) and predicted structures using AlphaFold2 ³³ for validated systems.

169 Foldseek ³⁴ results starting from the given predicted structure are also precomputed and available.

170 6/ **Experimental validation**: shows the tested system, the expression organism and against

171 which phage(s) it was shown to be effective. 7/ **References**: relevant publications (denoted with

172 a star) are included to facilitate a deeper understanding of each system.
A

<figure><img src="image_12.png" /></figure>

B

<figure><img src="image_13.png" /></figure>

C

<figure><img src="image_14.png" /></figure>

**Figure 4** - DefenseFinder results database. A. Main table used to search through the DefenseFinder results. Searching through this table can be done using only keywords or by using a combination of conditions on the system, the phylogeny, or the name of the replicon. B. Interactive bar charts of the count of each system on the left and the count of each taxonomic order reactive to the filter in the main table. C. Interactive heatmap displaying the count of each system in each taxonomic order reactive to the main table. The taxonomic level can be modified from superkingdom to the species level for both the barchart and heatmap.

Experimentally validated proteins were retrieved for each system, and the 3D predicted structure was computed using AlphaFold2. For some systems, we could not find the original protein sequence or accession of the experimentally validated system. Some subsystems (CBASS (Millman, 2020a), Retron (Mestre et al., 2020; Millman, 2020b), Lamassu (Millman et al., 2022), etc.) were not experimentally validated and are included in DefenseFinder. In such cases, we selected a different representative from DefenseFinder for structural prediction (See Methods). Recent studies demonstrated that many systems function as complexes (Deep et al., 2022; Duncan-Lowey et al., 2023; Shen et al., 2023; Tang et al., 2023; Antine et al., 2024). To provide insight on the possible complexes, all possible dimers (homo and heterodimers) were computed for each system. For each predicted complex, pDockQ scores (Bryant et al., 2022) were computed to assess the probability of the protein-protein interaction. For several systems with three or more proteins, complexes with up to four proteins were also computed in 1:1 stoichiometry. To discover similar folds in structural databases, we ran FoldSeek (van Kempen et al., 2023) using the predicted 3D structure of the
A

≡ Knowledge database of all known defense systems

WEBSERVICE

WIKI

REFSEQ DB

STRUCTURES DB

HELP

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

B

CapRel

Contributors: Hélène Georjon, Florian Tesson

Description

CapRel is a fused toxin-antitoxin system that is active against diverse phages when expressed in *Escherichia coli* (Zhang et al., 2022). CapRel belongs to the family of toxinSAS toxin-antitoxin systems. CapRel is an Abortive infection system which is found in Cyanobacteria, Actinobacteria, and Proteobacteria. Spirochetes, Bacteroidetes, and Firmicutes, as well as in some temperate phages.

Molecular mechanism

The CapRel system of Salmonella temperate phage SJ46 is normally found in a closed conformation, which is thought to maintain CapRel in an auto-inhibited state. However during phage SECpN27 infection, binding of the major phage capsid protein (Gp57) to CapRel releases it from its inhibited state, allowing pyrophosphorylation of tRNAs by the toxin domain and resulting in translation inhibition (Zhang et al., 2022). Other phage capsid proteins can be recognized by CapRel, as observed during infection by phage Ba50.

Different CapRel homologues confer defense against different phages, suggesting variable phage specificity of CapRel system which seems to be mediated by the C-terminal region of CapRel.

Example of genomic structure

The CapRel is composed of 1 protein: CapRel.
Here is an example found in the RefSeq database:

<figure><img src="image_14.png" /></figure>

The CapRel system in Campylobacter sp. RM12175 (GCF_002139075.1, NZ_0P016793) is composed of 1 protein: CapRel (WP_006315994.1)

Distribution of the system among prokaryotes

Among the 22,003 complete genomes of RefSeq, the CapRel is detected in 398 genomes (1.75%).
The system was detected in 217 different species.

<figure><img src="image_15.png" /></figure>

Proportion of genome encoding the CapRel system for the 14 phyla with more than 50 genomes in the RefSeq database

Structure

CapRel

Example 1

Select PDB

Experimental validation

<figure><img src="image_16.png" /></figure>

References

<figure><img src="image_17.png" /></figure>

Direct activation of a bacterial innate immune system by a viral capsid protein

ABSTRACT

Zhang Tong, Tainianman Hedvig, Cappelers 1, Watant Ryo, Karatata Tatsuke, LiRouan Michele, Srikrant Sriram, Brodiachenko Tetlana, Ce Nature (2022)

<figure><img src="image_18.png" /></figure>

EDIT ON GITLAB

<figure><img src="image_19.png" /></figure>

BUTTERS_GP57R

CARD_NLR

<caption>Figure 3. Architecture of the DefenseFinderWiki collaborative knowledge base. A. Table presenting all the different defense systems. The table can be filtered by system, type of sensor/activator or effector and Pfam inside the system. B. An example of one page of the knowledge base covering the CapRel defense system.</caption>

173 174 175 176 177 178
monomers against the PDB (Burley et al., 2023) and AlphaFold Uniprot (UniProt Consortium, 2023; Varadi et al., 2022) databases, and provided the precomputed results in the structure table.

In total, we provide more than 1,500 predictions of monomers, homodimers, or heterodimers. Results are available on the DefenseFinder structure database at: https://defensefinder.mdmlab.fr/wiki/structure/ as a table with all predicted structures present in the database (Figure 5A). Users can search for specific proteins or systems as well as filter all the structure by monomers, dimers, or quality statistics (pLDDT, iptm+ptm or pDockQ for multimers). Predicted structures can be visualized on the website using Mol* (Sehnal et al., 2021) with the predicted structure's confidence score (pLDDT) (Figure 5B). Results can also be downloaded individually or in bulk directly on the website.

A

<figure><img src="image_15.png" /></figure>

<table><thead><tr><th>Structure</th><th>Foldseek</th><th>System</th><th>Sub-system</th><th>Proteins in structure</th><th>System genes</th><th>Sys id</th><th>Completed</th><th>Prediction type</th><th>N genes in sys</th><th>pLDDT</th><th>iptm+ptm</th><th>pDockQ</th></tr></thead><tbody><tr><td>⊕ ±</td><td></td><td>Paris</td><td>PARIS_J</td><td>AAA_15</td><td></td><td>0</td><td>✓</td><td>monomer</td><td>2</td><td>83.09139</td><td></td><td></td></tr><tr><td>⊕ ±</td><td></td><td>Paris</td><td>PARIS_J</td><td>DUF4435</td><td></td><td>0</td><td>✓</td><td>monomer</td><td>2</td><td>90.4483</td><td></td><td></td></tr><tr><td>⊕ ±</td><td></td><td>Paris</td><td>PARIS_J</td><td>AAA_15 DUF4435</td><td>AAA_15 DUF4435</td><td>0</td><td>✓</td><td>multimer(dimer)</td><td>2</td><td>0.70877</td><td>0.1787</td><td></td></tr><tr><td>⊕ ±</td><td></td><td>Paris</td><td>PARIS_J</td><td>AAA_15 AAA_15</td><td>AAA_15 DUF4435</td><td>0</td><td>✓</td><td>multimer(dimer)</td><td>2</td><td>0.74363</td><td>0.50861</td><td></td></tr><tr><td>⊕ ±</td><td></td><td>Paris</td><td>PARIS_J</td><td>DUF4435 DUF4435</td><td>AAA_15 DUF4435</td><td>0</td><td>✓</td><td>multimer(dimer)</td><td>2</td><td>0.88372</td><td>0.59781</td><td></td></tr></tbody></table>

B

<figure><img src="image_16.png" /><figcaption>**Figure 5** - DefenseFinder predicted structures' database: Organization of the structure database. **A.** The dynamic table to search across the 1,543 predicted structures. **B.** Example of protein structure visualization with a dimer of AriA_AAA15 from the Paris defense system.</figcaption></figure>
Each system featured in DefenseFinder models v1.2.3 has a dedicated page on the knowledge base. These pages were populated through collaborative efforts, including a hackathon organized internally and contributions from other researchers from the community. Recognizing the rapid pace of defense system discovery and mechanism characterization, we opted for a collaborative approach by making the website open for updates. To foster a collaborative framework, we based the website on a Gitlab repository, with pages written in Markdown and designed to be easy to use by people that are not Git experts. To modify and add information to wiki pages, contributors can click on "Edit a page" (found at the bottom of every page), add some content (and add themselves as contributors) and create an automatic merge request that will be verified by at least one expert in the field before being merged. When describing a new system, the discoverers are encouraged to request the creation of a new page to describe it with all the necessary information.

## Precomputed results of DefenseFinder on 22,738 complete genomes.

Running DefenseFinder on a large database is time and resource consuming, thus we created a precomputed database. DefenseFinder v1.1.0 with models v1.2.3 was run on the complete genomes database from RefSeq (From July 2022, Bacteria N = 22,422, Archaea N = 381). A total of 152,386 defense systems were detected from 152 different defense systems (264 subtypes). Those precomputed results can be visualized on the "RefSeq DB" tab of the DefenseFinder website: https://defensefinder.mdmlab.fr/wiki/refseq/. Results are displayed in an interactive table allowing research by system, accession, or taxonomy (Figure 4A). This table is linked to interactive graphs displaying the relative abundances of the system in the phylogeny and distribution of systems (Figure 4B and 4C). Results (tables and graphs) can be downloaded from the website either as a whole or only a subset using different filters. Sequences of detected proteins are accessible through a link to their NCBI protein page.
## Conclusion and perspectives

The field of antiphage systems is growing rapidly, which can be difficult to follow for many researchers and students whose primary focus is not antiphage systems. Typically, literature reviews can become quickly outdated. At the same time, bioinformatics predictions guide many studies on antiphage systems. We offer a comprehensive resource for studying antiphage systems to lower the barrier of bioinformatics predictions and to have flexible and fast-evolving access to knowledge on antiphage systems.

The integration of DefenseFinderWiki enables the outputs produced by DefenseFinder to be linked to wiki-style pages, consolidating information on various defense systems thereby improving the understanding of the defense arsenal. Identifying defense systems can pose computational challenges, particularly when applied to large datasets. Here, we ran DefenseFinder on the RefSeq database. The resulting data is showcased through a dynamic table, complemented by visualizations depicting their distribution across the prokaryotic taxonomy. The final section of the website is devoted to protein structures, showcasing visual representations of predicted structures for all identified defense systems.

The ongoing commitment to regular updates of DefenseFinder ensures the incorporation of newly discovered defense systems, thereby enhancing the platform's utility and adaptability within the scientific community. To match the speed of discovery, we built the platform as a collaborative database. This will allow researchers from the community to add information about previously described systems and to create new pages for undescribed defense systems. This database is easy to modify via Gitlab, even for non-Git users, while taking advantage of a version-controlled system.

We will continue to develop the community aspect of the knowledge base by providing tutorials and organizing workshops to encourage people to contribute to the project. New updates will be made to increase the information on the website (new predicted structure, alphafoldDB (Varadi et al., 2022), increase in the number of genomes, sequence availability). We plan to add in a future release, a new section where users can test whether a system is related to a known one or not. If the system is new, we will provide a form to add the new system both for DefenseFinder and the website.

Overall, we are providing a new hub, gathering many resources that we hope will be useful for those exploring antiphage defense systems. This website aims to support newcomers in the field, including students, seasoned researchers, and enthusiasts of defense systems, by providing a platform to initiate work or deepen their understanding and knowledge in this domain.

## Materials and Methods

### Website development

This website comprises two distinct components: the webservice and the knowledge base (wiki and databases) each functioning separately. On the frontend, both utilize Vue.js and its ecosystem, particularly Nuxt.

**The Webservice:** The backend is built on top of Django and Django Ninja, designed for user-friendly and intuitive API development. It establishes communication with the Pasteur Galaxy (Mareuil et al., 2017) instance to execute the DefenseFinder tool and retrieve results. Subsequently, outcomes are stored in a PostgreSQL database accessible through anonymous sessions.

**The knowledge base:** Devoid of a backend, this is a fully static website implemented using Nuxt and Nuxt Content. Nuxt Content, a Git-based Headless CMS, facilitates the creation and management of static pages via Markdown or JSON files stored in a GitLab repository. This architectural approach alleviates the need for database and backend maintenance, delegating these responsibilities to GitLab, which seamlessly handles authentication, permissions, modification history, and a rich web editor. Therefore, the content is easily editable and accessible. The wiki also provides an interface for
<figure><img src="image_20.png" /><figcaption>**Figure 4. DefenseFinder results database A.** Main table used to search through the DefenseFinder results. Searching through this table can be done using only keywords or by using a combination of conditions on the system, the phylogeny, or the name of the replicon. **B.** Interactive bar charts of the count of each system on the left and the count of each taxonomic order reactive to the filter in the main table. **C.** Interactive heatmap displaying the count of each system in each taxonomic order reactive to the main table. The taxonomic level can be modified from superkingdom to the species level for both the barchart and heatmap.</figcaption></figure>

## Precomputed 3D structures predictions of defense systems

Many mechanisms of the newly described systems remain to be elucidated. Multiple studies were conducted to elucidate such mechanisms<sup>5,9,13,35-38</sup>. Often, the 3D structure of the system was an important step towards the understanding of its mechanism<sup>4,8,10,39-41</sup>. Recent developments in structure predictions, such as the development of AlphaFold2 or ESMFold<sup>33,42</sup> allow good quality predictions of proteins and protein complexes. We thus created a database of predicted structure for known defense systems.

For each system, experimentally validated proteins were retrieved, and the 3D predicted structure was computed using AlphaFold2. For systems and subsystems where protein accessions were impossible to retrieve, we selected another representative. Recent studies demonstrated that many systems function as complexes<sup>4,8,10,39,40</sup>. To provide insight on the possible complexes, all possible dimers (homo and heterodimers) were computed for each system. For each predicted complex, pDockQ scores<sup>43</sup> were computed to assess the probability of the protein-protein
searching through large datasets (RefSeq and structure predictions) using Meilisearch (self-hosted) with filters and complex queries.

The plots are generated using Observable Plot and D3.

Both websites undergo automated deployment to a Kubernetes cluster through a GitLab CI workflow. Additionally, a custom linter can be executed against the markdown content via the GitLab web interface.

## Protein selection for structure prediction

Protein accessions of experimentally validated systems were retrieved and used for structure prediction for each system and subsystem. For several subsystems, it was not possible to retrieve experimentally validated sequences for two reasons: no protein sequences or accessions in the original paper or, it's a subsystem with no experimental validation. For those systems, one of the best system hits from DefenseFinder was randomly selected and used for the protein structure prediction. Best hits were selected based on their hit scores and profile coverage (fourth quantile of hit score for each gene of the system and more than 75% of profile coverage).

AlphaFold2 (v2.3.1) (Jumper et al., 2021) was used to predict protein structures, using the AlphaFold-Multimer (Evans et al., 2022) protocol for complexes. Monomer and complex models were sorted based on pLDDT (mean of predicted Local Distance Difference Test) and iptm+ptm (weighted sum of interface and all residues template-modeling scores), respectively. The models with the highest scores were taken for subsequent analysis. Additionally, for dimer structures, pDockQ scores (predicted DockQ) (Bryant et al., 2022) were calculated to assess interface quality; (models with acceptable quality are considered if they have DockQ ≥ 0.23). For all monomeric structures we used FoldSeek (v5) (to perform structure similarity searches against the PDB (Burley et al., 2023) and AlphaFold Uniprot (UniProt Consortium, 2023; Varadi et al., 2022) databases.

## Precomputed DefenseFinder results

For the precomputed database, we used all the RefSeq complete genomes (O'Leary et al., 2016) of both Bacteria (N = 22,422) and Archaea (N = 381) from July 2022. DefenseFinder v1.2.0 (Tesson et al., 2022) using models v1.2.3 was run on all genomes separately using standard settings (coverage 0.4).

## Pfam annotation

We ran hmmsearch HMMER 3.3.2 (Eddy, 2011) on proteins that were detected by DefenseFinder with a coverage above 75%, to make sure they are complete, against Pfam-A database v33.1 (Mistry et al., 2021). For each protein family of a given defense system, PFAM that hits at least 50% of the members of the family are assigned to the protein family and therefore to the system.

Pfam annotation of the example of genomic locus was done on each protein of the system using hmmsearch "with—ga_cut" argument. If two PFAM hits were overlapping in a single protein sequence, only the best hit (hit_score) was kept.

## New profile modeling

New HMM profiles were built using the same method as in the first version of DefenseFinder (see methods in (Tesson et al., 2022). For protein with a single representative available, the first profile was made either by blasting one homolog (if only one is available) on BLAST RefSeq (nr) (Camacho et al., 2009) database (filter with 30% identity and 70% coverage). If a first detection is available in supplementary material, the first profile is done using those proteins.
227 interaction. For several systems with more than 2 proteins, complexes with up to 4 proteins were
228 also computed in 1:1 stoichiometry.
229 To discover similar folds in structural databases, we ran FoldSeek 34 using the predicted 3D
230 structure of the monomers against the PDB 44 and AlphaFold Uniprot 45,46 databases, and provided
231 the precomputed results in the structure table.

232
233 In total, we provide more than 1,500 predictions of homodimers or dimers. Results are available
234 on the DefenseFinder structure database at: https://defensefinder.mdmlab.fr/wiki/structure/ as a
235 table with all predicted structures present in the database (Figure 5A). Users can search for
236 specific proteins or systems as well as filter all the structure by monomers, dimers, or quality
237 statistics (pLDDT, iptm+ptm or pDockQ for multimers). Predicted structures can be visualized on
238 the website using Mol* 47 with the confidence score (pLDDT) of the predicted structure (Figure
239 5B). Results can also be downloaded one by one or in bulk directly on the website.

240
241
242
243
### Addition of new models

Using the architecture of MacSyFinder v2 (Néron et al., 2023), we added new definitions and profiles inside the previous MacSyFinder models. Inconsistencies between models were then checked and reduced: overlapping systems and systems blocking the detection of the other.

### Funding

F. T., H. G., H. V., E. M., A. B. and J. C. are supported by the CRI Research Fellowship to A.B. from the Bettencourt Schueller Foundation, the ATIP-Avenir program from INSERM (R21042KS/RSE22002KSA), the Emergence program from the University of Paris-Cité (RSFVJ21IDXB6_DANA) ERC Starting Grant (PECAN 101040529) and the core funding of Institut Pasteur. H.G. PhD is funded by Generare Bioscience. G. C. A. and A. E. acknowledge support from the Knut and Alice Wallenberg Foundation (project grant 2020-0037) and the Swedish Research Council (Vetenskapsrådet) (grants 2019-01085, 2022-01603 and 2023-02353). The AlphaFold2 computations were enabled by the supercomputing resources Berzelius provided by the National Supercomputer Centre (NSC) at Linköping University and the Knut and Alice Wallenberg foundation. Additional computational resources for structural modeling were provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing at NSC, Chalmers University Centre for Computational Science and Engineering (C3SE), and PDC Centre for High Performance Computing, KTH Royal Institute of Technology, partially funded by the Swedish Research Council through grants 2018-05973 and 2022-06725.

### Data availability

DefenseFinder website is available at: https://defensefinder.mdmlab.fr/. This website is free and open to all users and there is no login requirement. The source code of the wiki pages at the time of submission is available on Zenodo (https://doi.org/10.5281/zenodo.13762622; Tesson et al. 2024) and the active repository is hosted on the GitLab instance of the Pasteur Institute (https://gitlab.pasteur.fr/mdm-lab/wiki) and is under GPLv3 license.

### Acknowledgement

We are grateful to all the members of MDMLab for their help in filling most of the knowledge base pages with relevant information. We thank Nathalie Béchon, Alex L. Gao, Adi Millman, François Rousset, Avigail Stokar-Avihail and Daan Swarts who accepted to write pages on their favorite subjects. We thank the IT Department of Institut Pasteur, including Thomas Menard, in particular, for providing access to the Kubernetes cluster and initial training. Preprint version 4 of this article has been peer-reviewed and recommended by Peer Community In Genomics (https://doi.org/10.24072/pci.genomics.100373; Wang, 2024)

### Conflict of interest disclosure

The authors declare that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.
<figure><img src="image_21.png" /><figcaption>**Figure 5. DefenseFinder predicted structures' database:** Organization of the structure database. **A.** The dynamic table to search across the 1,543 predicted structures. **B.** Example of protein structure visualization with a dimer of AriA_AAA15 from the Paris defense system.</figcaption></figure>

## Conclusion and perspectives

The field of antiphage systems is growing at a rapid pace which can be difficult to follow for many researchers and students whose primary focus of studies is not antiphage systems. Typically, reviews of literature can become quickly outdated. At the same time, bioinformatics predictions allow guiding many studies on antiphage systems. We offer here a comprehensive resource for the study of antiphage systems with the aim of lowering the barrier of bioinformatics predictions and to have flexible and fast evolving access to knowledge on antiphage systems.

The integration of DefenseFinderWiki enables the outputs produced by DefenseFinder to be linked to wiki-style pages consolidating information on various defense systems, thereby

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Bryant, P., Pozzati, G., and Elofsson, A. (2022). Improved prediction of protein-protein interactions using AlphaFold2. *Nat Commun* 13, 1265. https://doi.org/10.1038/s41467-022-28865-w

Burley, S.K., Bhikadiya, C., Bi, C., Bittrich, S., Chao, H., Chen, L., Craig, P.A., Crichlow, G.V., Dalenberg, K., Duarte, J.M., et al. (2023). RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. *Nucleic Acids Res* 51, D488–D508. https://doi.org/10.1093/nar/gkac1077

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Dedrick, R.M., Jacobs-Sera, D., Bustamante, C.A.G., Garlena, R.A., Mavrich, T.N., Pope, W.H., Reyes, J.C.C., Russell, D.A., Adair, T., Alvey, R., et al. (2017). Prophage-mediated defence against viral attack and viral counter-defence. *Nat Microbiol* 2, 1–13. https://doi.org/10.1038/nmicrobiol.2016.251

Deep, A., Gu, Y., Gao, Y.-Q., Ego, K.M., Herzik, M.A., Zhou, H., and Corbett, K.D. (2022). The SMC-family Wadjet complex protects bacteria from plasmid transformation by recognition and cleavage of closed-circular DNA. *Molecular Cell* 82, 4145-4159.e7. https://doi.org/10.1016/j.molcel.2022.09.008

Duncan-Lowey, B., McNamara-Bordewick, N.K., Tal, N., Sorek, R., and Kranzusch, P.J. (2021). Effector-mediated membrane disruption controls cell death in CBASS antiphage defense. *Molecular Cell* 81, 5039-5051.e5. https://doi.org/10.1016/j.molcel.2021.10.020

Duncan-Lowey, B., Tal, N., Johnson, A.G., Rawson, S., Mayer, M.L., Doron, S., Millman, A., Melamed, S., Fedorenko, T., Kacen, A., et al. (2023). Cryo-EM structure of the RADAR supramolecular anti-phage defense complex. *Cell* 186, 987-998.e15. https://doi.org/10.1016/j.cell.2023.01.012

Durmaz, E., and Klaenhammer, T.R. (2007). Abortive Phage Resistance Mechanism AbiZ Speeds the Lysis Clock To Cause Premature Lysis of Phage-Infected Lactococcus lactis. *J Bacteriol* 189, 1417–1425. https://doi.org/10.1128/JB.00904-06

Eddy, S.R. (2011). Accelerated Profile HMM Searches. *PLOS Computational Biology* 7, e1002195. https://doi.org/10.1371/journal.pcbi.1002195

Evans, R., O'Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2022). Protein complex prediction with AlphaFold-Multimer (Bioinformatics) https://doi.org/10.1101/2021.10.04.463034
260 improving the understanding of the defense arsenal. Identifying defense systems can pose
261 computational challenges, particularly when applied to large datasets. Here, we ran
262 DefenseFinder on the RefSeq database. The resulting data is showcased through a dynamic
263 table, complemented by visualizations depicting their distribution across the prokaryotic
264 taxonomy. The final section of the website is devoted to protein structures, showcasing visual
265 representations of predicted structures for all identified defense systems.

266 The ongoing commitment to regular updates of DefenseFinder ensures the incorporation of newly
267 discovered defense systems, thereby enhancing the platform's utility and adaptability within the
268 scientific community. To match the speed of the discovery, we built the platform as a collaborative
269 database. This will allow researchers from the community to add information about previously
270 described systems and to create new pages for undescribed defense systems. This database is
271 easy to modify via Gitlab even for non-Gitlab users while taking advantage of a version-controlled
272 system.

273 Overall, we are providing a new hub, gathering many resources that we hope will be useful for
274 those embarking on the exploration of antiphage defense systems. This website aims to support
275 newcomers in the field, including students, seasoned researchers, and enthusiasts of defense
276 systems, by providing a platform to initiate work or deepen their understanding and knowledge in
277 this domain.
278
Gaborieau, B., Vaysset, H., Tesson, F., Charachon, I., Dib, N., Bernier, J., Dequidt, T., Georjon, H., Clermont, O., Hersen, P., et al. (2023). Predicting phage-bacteria interactions at the strain level from genomes. Preprint at bioRxiv, https://doi.org/10.1101/2023.11.22.567924 https://doi.org/10.1101/2023.11.22.567924.

Georjon, H., and Bernheim, A. (2023). The highly diverse antiphage defence systems of bacteria. Nat Rev Microbiol 21, 686-700. https://doi.org/10.1038/s41579-023-00934-x.

Hazan, R., and Engelberg-Kulka, H. (2004). Escherichia coli mazEF-mediated cell death as a defense mechanism that inhibits the spread of phage P1. Mol Genet Genomics 272, 227-234. https://doi.org/10.1007/s00438-004-1048-y.

Hsueh, B.Y., Severin, G.B., Elg, C.A., Waldron, E.J., Kant, A., Wessel, A.J., Dover, J.A., Rhoades, C.R., Ridenhour, B.J., Parent, K.N., et al. (2022). Phage defence by deaminase-mediated depletion of deoxynucleotides in bacteria. Nat Microbiol 7, 1210-1220. https://doi.org/10.1038/s41564-022-01162-4.

Hussain, F.A., Dubert, J., Elsherbini, J., Murphy, M., VanInsberghe, D., Arevalo, P., Kauffman, K., Rodino-Janeiro, B.K., Gavin, H., Gomez, A., et al. (2021). Rapid evolutionary turnover of mobile genetic elements drives bacterial resistance to phages. Science 374, 488-492. https://doi.org/10.1126/science.abb1083.

Johnson, A.G., Wein, T., Mayer, M.L., Duncan-Lowey, B., Yirmiya, E., Oppenheimer-Shaanan, Y., Amitai, G., Sorek, R., and Kranzusch, P.J. (2022). Bacterial gasdermins reveal an ancient mechanism of cell death. Science 375, 221-225. https://doi.org/10.1126/science.abj8432.

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589. https://doi.org/10.1038/s41586-021-03819-2.

Ka, D., Oh, H., Park, E., Kim, J.-H., and Bae, E. (2020). Structural and functional evidence of bacterial antiphage protection by Thoeris defense system via NAD+ degradation. Nat Commun 11, 2816. https://doi.org/10.1038/s41467-020-16703-w.

Koga, M., Otsuka, Y., Lemire, S., and Yonesaki, T. (2011). Escherichia coli rnLA and rnB Compose a Novel Toxin-Antitoxin System. Genetics 187, 123-130. https://doi.org/10.1534/genetics.110.121798.

Larralde, M. (2022). Pyrodigal: Python bindings and interface to Prodigal, an efficient method for gene prediction in prokaryotes. Journal of Open Source Software 7, 4296. https://doi.org/10.21105/joss.04296.

LeRoux, M., Srikant, S., Littlehale, M.H., Teodoro, G., Doron, S., Badiee, M., Leung, A.K.L., Sorek, R., and Laub, M.T. (2021). The DarTG toxin-antitoxin system provides phage defense by ADP-ribosylating viral DNA. bioRxiv, 2021.09.27.462013. https://doi.org/10.1101/2021.09.27.462013.

Li, Y., Shen, Z., Zhang, M., Yang, X.-Y., Cleary, S.P., Xie, J., Marathe, I.A., Kostelic, M., Greenwald, J., Rish, A.D., et al. (2024). PtuA and PtuB assemble into an inflammasome-like oligomer for anti-phage defense. Nat Struct Mol Biol, 1-11. https://doi.org/10.1038/s41594-023-01172-8.

Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123-1130. https://doi.org/10.1126/science.ade2574.

Mageeney, C.M., Mohammed, H.T., Dies, M., Anbari, S., Cudkevich, N., Chen, Y., Buceta, J., and Ware, V.C. (2020). Mycobacterium Phage Butters-Encoded Proteins Contribute to Host Defense against Viral Attack. mSystems 5, e00534-20. https://doi.org/10.1128/mSystems.00534-20.

Mareuil, F., Doppelt-Azeroual, O., and Ménager, H. (2017). <p>A public Galaxy platform at Pasteur used as an execution engine for web services</p>. F1000Research 6. https://doi.org/10.7490/f1000research.1114334.1.

Mestre, M.R., González-Delgado, A., Gutiérrez-Rus, L.I., Martínez-Abarca, F., and Toro, N. (2020). Systematic prediction of genes functionally associated with bacterial retrons and classification of
279 **Materials and Methods**

280 *Website development*

281
282 This website comprises two distinct components: the webservice and the knowledge base (wiki and databases) each functioning separately. On the frontend, both utilize Vue.js and its ecosystem, particularly Nuxt.

285
286 The Webservice: The backend is built on top of Django and Django Ninja, designed for user-friendly and intuitive API development. It establishes communication with the Pasteur Galaxy 48
287
288 instance to execute the DefenseFinder tool and retrieve results. Subsequently, outcomes are
289 stored in a PostgreSQL database accessible through anonymous sessions.

290
291 The knowledge base: Devoid of a backend, this is a fully static website implemented using Nuxt and Nuxt Content. Nuxt Content, a Git-based Headless CMS, facilitates the creation and management of static pages via Markdown or JSON files stored in a GitLab repository. This architectural approach alleviates the need for database and backend maintenance, delegating these responsibilities to GitLab, which seamlessly handles authentication, permissions, modification history, and a rich web editor. Therefore, the content is easily editable and accessible. The wiki also provides an interface for searching through large datasets (Refseq and structure predictions) using Meilisearch (self-hosted) with filters and complex queries.

299 The plots are generated using Observable Plot and D3.

300 Both websites undergo automated deployment to a Kubernetes cluster through a GitLab CI workflow. Additionally, a custom linter can be executed against the markdown content via the GitLab web interface.

303
304 *Protein selection for structure prediction*
305 For each system and subsystem, protein accessions of experimentally validated systems were retrieved and used for structure prediction. For systems with no accession available, one of the best systems hits (hit_score and coverage) was randomly selected and used for the protein structure prediction.

309
310 *Structure prediction*
311 AlphaFold2 (v2.3.1) 33 was used to predict protein structures, using the AlphaFold-Multimer 49
312 protocol for complexes. Monomer and complex models were sorted based on pLDDT (mean of
313 predicted Local Distance Difference Test) and iptm+ptm (weighted sum of interface and all
314 residues template-modeling scores), respectively. The models with the highest scores were
taken for subsequent analysis. Additionally, for dimer structures, pDockQ scores (predicted
315 DockQ) 43 were calculated to assess interface quality; (models with acceptable quality are
316 considered if they have DockQ ≥ 0.23). For all monomeric structures we used FoldSeek (v5) 34
317 to perform structure similarity searches against the PDB 44 and AlphaFold Uniprot 45,46
318
319 databases.

320
321 *Precomputed DefenseFinder results*
the encoded tripartite systems. Nucleic Acids Research 48, 12632-12647.
https://doi.org/10.1093/nar/gkaa1149.

Millman, A., Melamed, S., Amitai, G., and Sorek, R. (2020a). Diversity and classification of cyclic-oligonucleotide-based anti-phage signalling systems. Nat Microbiol 5, 1608-1615.
https://doi.org/10.1038/s41564-020-0777-y.

Millman, A., Bernheim, A., Stokar-Avihail, A., Fedorenko, T., Voichek, M., Leavitt, A., Oppenheimer-Shaanan, Y., and Sorek, R. (2020b). Bacterial Retrons Function In Anti-Phage Defense. Cell 183, 1551-1561.e12.
https://doi.org/10.1016/j.cell.2020.09.065.

Millman, A., Melamed, S., Leavitt, A., Doron, S., Bernheim, A., Hör, J., Garb, J., Bechon, N., Brandis, A., Lopatina, A., et al. (2022). An expanded arsenal of immune systems that protect bacteria from phages. Cell Host Microbe 30, 1556-1569.e5.
https://doi.org/10.1016/j.chom.2022.09.017.

Mistry, J., Chuguransky, S., Williams, L., Qureshi, M., Salazar, G.A., Sonnhammer, E.L.L., Tosatto, S.C.E., Paladin, L., Raj, S., Richardson, L.J., et al. (2021). Pfam: The protein families database in 2021. Nucleic Acids Research 49, D412-D419.
https://doi.org/10.1093/nar/gkaa913.

Néron, B., Denise, R., Coluzzi, C., Touchon, M., Rocha, E.P.C., and Abby, S.S. (2023). MacSyFinder v2: Improved modelling and search engine to identify molecular systems in genomes. Peer Community Journal 3.
https://doi.org/10.24072/pcjournal.250.

Nussenzweig, P.M., and Marraffini, L.A. (2020). Molecular Mechanisms of CRISPR-Cas Immunity in Bacteria. Annu. Rev. Genet. 54, 93-120.
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O'Leary, N.A., Wright, M.W., Brister, J.R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016). Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44, D733-D745.
https://doi.org/10.1093/nar/gkv1189.

Piel, D., Bruto, M., Labreuche, Y., Blanquart, F., Goudenège, D., Barcia-Cruz, R., Chenivesse, S., Le Panse, S., James, A., Dubert, J., et al. (2022). Phage-host coevolution in natural populations. Nat Microbiol 7, 1075-1086.
https://doi.org/10.1038/s41564-022-01157-1.

Rousset, F., Depardieu, F., Miele, S., Dowding, J., Laval, A.-L., Lieberman, E., Garry, D., Rocha, E.P.C., Bernheim, A., and Bikard, D. (2022). Phages and their satellites encode hotspots of antiviral systems. Cell Host & Microbe 30, 740-753.e5.
https://doi.org/10.1016/j.chom.2022.02.018.

Rousset, F., Yirmiya, E., Nesher, S., Brandis, A., Mehlman, T., Itkin, M., Malitsky, S., Millman, A., Melamed, S., and Sorek, R. (2023). A conserved family of immune effectors cleaves cellular ATP upon viral infection. Cell 186, 3619-3631.e13.
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Sberro, H., Leavitt, A., Kiro, R., Koh, E., Peleg, Y., Qimron, U., and Sorek, R. (2013). Discovery of functional toxin/antitoxin systems in bacteria by shotgun cloning. Mol Cell 50, 136-148.
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Sehnal, D., Bittrich, S., Deshpande, M., Svobodová, R., Berka, K., Bazgier, V., Velankar, S., Burley, S.K., Koča, J., and Rose, A.S. (2021). Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures. Nucleic Acids Research 49, W431-W437.
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Shen, Z., Lin, Q., Yang, X.-Y., Fosuah, E., and Fu, T.-M. (2023). Assembly-mediated activation of the SIR2-HerA supramolecular complex for anti-phage defense. Molecular Cell 83, 4586-4599.e5.
https://doi.org/10.1016/j.molcel.2023.11.007.

Tal, N., Millman, A., Stokar-Avihail, A., Fedorenko, T., Leavitt, A., Melamed, S., Yirmiya, E., Avraham, C., Brandis, A., Mehlman, T., et al. (2022). Bacteria deplete deoxynucleotides to defend against bacteriophage infection. Nat Microbiol 7, 1200-1209.
https://doi.org/10.1038/s41564-022-01158-0.

Tang, D., Chen, Y., Chen, H., Jia, T., Chen, Q., and Yu, Y. (2023). Multiple enzymatic activities of a Sir2-HerA system cooperate for anti-phage defense. Molecular Cell 83, 4600-4613.e6.
https://doi.org/10.1016/j.molcel.2023.11.010.
For the precomputed database, we used all the RefSeq complete genomes⁵⁰ of both Archaea (N = 22,422) and Bacteria (N = 381) from July 2022. DefenseFinder v1.2.0²¹ using models v1.2.3 was run on all genomes separately using standard settings (coverage 0.4).

### Pfam annotation
We ran hmmsearch HMMER 3.3.2⁵¹ on proteins that were detected by DefenseFinder with a coverage above 75%, to make sure they are complete, against Pfam-A database v33.1⁵². For each protein family of a given defense system, PFAM that hits at least 50% of the members of the family are assigned to the protein family and therefore to the system.

Pfam annotation of the example of genomic locus was done on each protein of the system using hmmsearch with --ga_cut argument. If two PFAM hits were superposed only the best hit (hit_score) was kept.

### New profile modeling
New HMM profiles were built using the same method as in the first version of DefenseFinder (see methods in²¹. For protein with a single representative available, the first profile was made either by blasting one homolog (if only one is available) on BLAST RefSeq (nr)⁵³ database (filter with 30% identity and 70% coverage). If a first detection is available in supplementary material, the first profile is done using those proteins.

### Addition of new models
Using the architecture of MacSyFinder v2⁵⁴, we added new definitions and profiles inside the previous MacSyFinder models. Inconsistencies between models were then checked and reduced: overlapping systems and systems blocking the detection of the other.

### Funding
F. T., H. G., H. V., E. M., A. B. and J. C. are supported by the CRI Research Fellowship to A.B. from the Bettencourt Schueller Foundation, the ATIP-Avenir program from INSERM (R21042KS/RSE22002KSA), the Emergence program from the University of Paris-Cité (RSFVJ21IDXB6_DANA) ERC Starting Grant (PECAN 101040529) and the core funding of Institut Pasteur. H.G. PhD is funded by Generare Bioscience. G. C. A. and A. E. acknowledge support from the Knut and Alice Wallenberg Foundation (project grant 2020-0037) and the Swedish Research Council (Vetenskapsrådet) (grants 2019-01085, 2022-01603 and 2023-02353). The AlphaFold2 computations were enabled by the supercomputing resources Berzelius provided by the National Supercomputer Centre (NSC) at Linköping University and the Knut and Alice Wallenberg foundation. Additional computational resources for structural modeling were provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing at NSC, Chalmers University Centre for Computational Science and Engineering (C3SE), and PDC Centre for High Performance Computing, KTH Royal Institute of Technology, partially funded by the Swedish Research Council through grants 2018-05973 and 2022-06725.

### Data availability
Tesson, F., and Bernheim, A. (2023). Synergy and regulation of antiphage systems: toward the existence of a bacterial immune system? *Current Opinion in Microbiology* 71, 102238. https://doi.org/10.1016/j.mib.2022.102238.

Tesson, F., Cury, J., & Planel, R. (2024). *Code for the DefenseFinder Wiki*. Zenodo. https://doi.org/10.5281/zenodo.13762622.

Tesson, F., Hervé, A., Mordret, E., Touchon, M., d'Humières, C., Cury, J., and Bernheim, A. (2022). Systematic and quantitative view of the antiviral arsenal of prokaryotes. *Nat Commun* 13, 2561. https://doi.org/10.1038/s41467-022-30269-9.

The Galaxy Community (2022). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. *Nucleic Acids Research* 50, W345–W351. https://doi.org/10.1093/nar/gkac247.

Tock, M.R., and Dryden, D.T. (2005). The biology of restriction and anti-restriction. *Current Opinion in Microbiology* 8, 466–472. https://doi.org/10.1016/j.mib.2005.06.003.

UniProt Consortium (2023). UniProt: the Universal Protein Knowledgebase in 2023. *Nucleic Acids Res* 51, D523–D531. https://doi.org/10.1093/nar/gkac1052.

van Kempen, M., Kim, S.S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C.L.M., Söding, J., and Steinegger, M. (2023). Fast and accurate protein structure search with Foldseek. *Nat Biotechnol*, 1–4. https://doi.org/10.1038/s41587-023-01773-0.

Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. *Nucleic Acids Res* 50, D439–D444. https://doi.org/10.1093/nar/gkab1061.

Wang, S. (2024) DefenseFinder update advances prokaryotic antiviral system research. *Peer Community in Genomics*, 100373. https://doi.org/10.24072/pci.genomics.100373.

Wein, T., Johnson, A.G., Millman, A., Lange, K., Yirmiya, E., Hadary, R., Garb, J., Steinruecke, F., Hill, A.B., Kranzusch, P.J., et al. (2023). CARD-like domains mediate anti-phage defense in bacterial gasdermin systems. *bioRxiv*, 2023.05.28.542683. https://doi.org/10.1101/2023.05.28.542683.

Zeng, Z., Chen, Y., Pinilla-Redondo, R., Shah, S.A., Zhao, F., Wang, C., Hu, Z., Wu, C., Zhang, C., Whitaker, R.J., et al. (2022). A short prokaryotic Argonaute activates membrane effector to confer antiviral defense. *Cell Host & Microbe* 30, 930-943.e6. https://doi.org/10.1016/j.chom.2022.04.015.

Zhang, T., Tamman, H., Coppieters 't Wallant, K., Kurata, T., LeRoux, M., Srikant, S., Brodiazhenko, T., Cepauskas, A., Talavera, A., Martens, C., et al. (2022). Direct activation of a bacterial innate immune system by a viral capsid protein. *Nature* 612, 132–140. https://doi.org/10.1038/s41586-022-05444-z.
365 DefenseFinder website is available at: https://defensefinder.mdmlab.fr/. This website is free and 366 open to all users and there is no login requirement. The source code of the wiki pages is hosted 367 on the GitLab instance of the Pasteur Institute (https://gitlab.pasteur.fr/mdm-lab/wiki) and is under 368 GPLv3 license.

369
370 Acknowledgement
371 We are grateful to all the members of MDMLab for their help in filling most of the knowledge base
372 pages with relevant information. We thank Nathalie Béchon, Alex L. Gao, Adi Millman, François
373 Rousset, Avigail Stokar-Avihail and Daan Swarts who accepted to write pages on their favorite
374 subjects. We thank the IT Department of Institut Pasteur, including Thomas Menard, in particular,
375 for providing access to the Kubernetes cluster and initial training.

376
377
378
379 References

380

381 1. Georjon, H., and Bernheim, A. (2023). The highly diverse antiphage defence systems of bacteria. Nat Rev Microbiol 21, 686-700. 10.1038/s41579-023-00934-x.

383 2. Nussenzweig, P.M., and Marraffini, L.A. (2020). Molecular Mechanisms of CRISPR-Cas Immunity in Bacteria. Annu. Rev. Genet. 54, 93-120. 10.1146/annurev-genet-022120-112523.

386 3. Tock, M.R., and Dryden, D.T. (2005). The biology of restriction and anti-restriction. Current Opinion in Microbiology 8, 466-472. 10.1016/j.mib.2005.06.003.

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392 5. Hsueh, B.Y., Severin, G.B., Elg, C.A., Waldron, E.J., Kant, A., Wessel, A.J., Dover, J.A., Rhoades, C.R., Ridenhour, B.J., Parent, K.N., et al. (2022). Phage defence by deaminase-mediated depletion of deoxynucleotides in bacteria. Nat Microbiol 7, 1210-1220. 10.1038/s41564-022-01162-4.

396 6. Ka, D., Oh, H., Park, E., Kim, J.-H., and Bae, E. (2020). Structural and functional evidence of bacterial antiphage protection by Thoeris defense system via NAD+ degradation. Nat Commun 11, 2816. 10.1038/s41467-020-16703-w.

399 7. Rousset, F., Yirmiya, E., Nesher, S., Brandis, A., Mehlman, T., Itkin, M., Malitsky, S., Millman, A., Melamed, S., and Sorek, R. (2023). A conserved family of immune effectors cleaves cellular ATP upon viral infection. Cell 186, 3619-3631.e13. 10.1016/j.cell.2023.07.020.

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