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# Unifying antimicrobial peptide datasets for robust deep learning-based classification

Shuang Peng, Loïc Rajjou

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## To cite this version:

Shuang Peng, Loïc Rajjou. Unifying antimicrobial peptide datasets for robust deep learning-based classification. Data in Brief, 2024, 56, pp.110822. 10.1016/j.dib.2024.110822. hal-05166317

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Data Article

# Unifying antimicrobial peptide datasets for robust deep learning-based classification

<figure><img src="image_4.png" /></figure>

Shuang Peng, Loïc Rajjou*

Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France

## ARTICLE INFO

**Article history:**

Received 1 June 2024

Accepted 5 August 2024

Available online 22 August 2024

Dataset link: amp_dataset_viri (Original data)

Dataset link: amp_dataset_faba (Original data)

Dataset link: Unifying Antimicrobial

Peptide Datasets for Robust Deep

Learning-Based Classification (Original data)

**Keywords:**

Antimicrobial Peptides (AMPs)

Plant

Sequence redundancy

Computational biology

Database analysis

## ABSTRACT

Leguminous crops are vital to sustainable agriculture due to their ability to fix atmospheric nitrogen, improving soil fertility and reducing the need for synthetic fertilizers. Additionally, they are an excellent source of protein for both human consumption and animal feed. Antimicrobial peptides (AMPs), found in various leguminous seeds, exhibit broad-spectrum antimicrobial activity through diverse mechanisms, including interaction with microbial cell membranes and interference with cellular processes, making them valuable for enhancing crop resilience and food safety. In the field of plant sciences, computational biology methods have been instrumental in the discovery and optimization of AMPs. These methods enable rapid exploration of sequence space and the prediction of AMPs using deep learning technologies. Optimizing AMP annotations through computational design offers a strategic approach to enhance efficacy and minimize potential side effects, providing a viable alternative to conventional antimicrobial agents. However, the presence of overlapping sequences across multiple databases poses a challenge for creating a reliable dataset for AMP prediction. To address this, we conducted a comprehensive analysis of sequence redundancy across various AMP databases. These databases encompass a wide range of AMPs from different sources and with specific functions, including both naturally occurring and artificially synthesized AMPs. Our analysis revealed significant overlap, underscoring the need for a non-redundant AMP sequence database. We present the development of a

## ARTICLE INFO

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: loic.rajjou@agroparistech.fr (L. Rajjou).</footnote>

<footnote>Social media: 👄 @Loic_Rajjou (L. Rajjou)</footnote>
new database that consolidates unique AMP sequences derived from leguminous seeds, aiming to create a more refined dataset for the binary classification and prediction of plant-derived AMPs. This database will support the advancement of sustainable agricultural practices by enhancing the use of plant-based AMPs in agroecology, contributing to improved crop protection and food security.

© 2024 The Author(s). Published by Elsevier Inc.

This is an open access article under the CC BY license

(http://creativecommons.org/licenses/by/4.0/)

<table><caption>Specifications Table</caption><tr><td>Subject</td><td>Biological Sciences / Bioinformatics</td></tr><tr><td>Specific subject area</td><td>Deep learning modelling of binary sequence classification to predict AntiMicrobial Peptide</td></tr><tr><td>Type of data</td><td>asta (amino acid sequence), huggingface dataset.</td></tr><tr><td>Data collection</td><td>The dataset for the study on antimicrobial peptides (AMPs) was gathered through a rigorous analysis of sequence redundancy across multiple AMP databases, encompassing both naturally occurring and artificially synthesized AMPs. The Dover analyzer (http://mobiosd-hub.com/doveranalyzer/) was used to identify and eliminate redundant AMP entries, thereby unveiling the intricate relationship among 28 distinct AMP databases. To enrich the dataset, non-AMP data was sourced from the UniProtKB database (https://www.uniprot.org/uniprotkb), with a specific focus on plant proteins meeting predetermined criteria regarding sequence length and taxonomy. The non-AMP dataset underwent hierarchical redundancy removal using CD-HIT (https://www.bioinformatics.org/cd-hit/), a widely adopted clustering tool tailored for large sequence databases. Subsequent to data collection, various preprocessing steps were implemented to ensure data integrity and compatibility for downstream analysis. Special characters were systematically removed from the dataset entries to standardize the format and facilitate uniform processing. Furthermore, to distinguish between AMPs and non-AMPs, peptides shared across both datasets were identified and subsequently excluded from the final datasets. The processed AMP and non-AMP datasets were integrated with the Hugging Face dataset module (https://huggingface.co/datasets), a versatile platform catering to the needs of machine learning and data science endeavours. Drawing upon the functionalities offered by the Hugging Face framework ensured efficient integration and facilitated accessibility for subsequent analysis and model development.</td></tr><tr><td>Data source location</td><td>The data for this study were collected from various publicly available databases, which are typically hosted online and accessible globally (see Table 1). The specific geographical coordinates of the data sources may vary, as they are often maintained by institutions or organizations worldwide. Additionally, the final datasets were integrated and stored using the resources available at our institution, namely the Recherche Data Gouv platform.</td></tr><tr><td>Data accessibility</td><td><strong>Please note:</strong> All raw data referred to in this article must be made publicly available in a data repository prior to publication. Please indicate here where your data are hosted (the URL must be working at the time of submission and editors and reviewers must have anonymous access to the repository):<br>Repository name: ...<br>Data identification number: doi:10.1093/bioinformatics/btv180.<br>Direct URL to data:<br>https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/NZOIRX&version=DRAFT<br>https://huggingface.co/datasets/ps29/amp_dataset_viri<br>https://huggingface.co/datasets/ps29/amp_dataset_faba<br>Instructions for accessing these data: ...</td></tr><tr><td>Related research article</td><td>None</td></tr></table>
# 1. Value of the Data

This section states why these data are of value to the scientific community. Please **provide between 3 and 6 bullet points** and answer at least the questions below (delete the questions afterwards). Each bullet point should be a maximum of 150 words long, and should not include conclusions or inferences:

* The paper describes the creation of a non-redundant Antimicrobial peptides (AMPs) dataset by analyzing sequence overlap across multiple databases, aiming at training robust binary AMP classifier.

* The out-performed AMP classifier accelerated the discovery and optimization of AMPs, which offer a potential solution of the escalating antibiotic resistance issue.

* The model performance is strongly related to the size of training data size. This dataset re-tries the AMPs from the databases in comprehensive way, which could be used as the knowledge base for those researchers who want to develop the specific predictors on their own.

* In addition, despite the large number of available models, the number of standardized datasets is still very limited. This dataset is expected to be one of the standardized datasets in the future.

* Finally, in the context of deep learning modeling, the more standardized Hugging Face release makes it easier to access and use the dataset.

# 2. Background

The excessive use of synthetic chemical pesticides has led to environmental and health concerns, including the development of resistance among various harmful agricultural pests and pathogens [1]. The search for sustainable and effective biotic control options is a significant focus of contemporary agricultural research [2].

Antimicrobial peptides (AMPs), especially those extracted from plant resources, offer a promising avenue for developing new, environmentally friendly pest control solutions. These peptides are an important part of the innate immune system of various organisms [3], with broad activity against microorganisms by binding, penetrating, and interfering with microbial cells [4,5].

The in-silico approach enhances the design accuracy and discovery of potential AMPs through pattern recognition, sequence alignment, and machine learning [6,7]. This method reduces time and costs by efficiently exploring a sequence space for antimicrobial activity prediction.

The impact of dataset size on predictive model performance is critical, and increasing AMP databases play a vital role as the AMP repository. However, many AMP databases have overlapping sequences for various purposes [8]. This study aims to analyze sequence redundancy in different AMP databases and create a non-redundant sequence database for AMP binary classification.

# 3. Data Description

Two AMP classification datasets (*Fabaceae* and *Viridiplantae*) were organized according to the non-amp sources, where the training set of *Fabaceae* contained 58,119 entries, the validation set contained 18,231 entries, and the test set contained 14,530 entries, and the training set of *Viridiplantae* contained 91,131 entries, the validation set contains 28,548 entries, and the test set contains 22,783 entries.

Each of the two datasets corresponds to a HuggingFace Dataset, and each entry contains the five features (index, id, sequence, length and label).
# 4. Experimental Design, Materials and Methods

## 4.1. Data Source

The 28 databases used to construct the AMP dataset are described in Table 1. There were 22 AMP datasets in [1] used AMP data source, with APD database updated [3] and the other 21 unchanged. In addition, 6 new databases (BaAMPs, dbAMP, DRAMP General, DRAMP Patent, DRAMP Clinical, DRAMP Specific) have been added.

The non-AMPs were retrieved from the UniProtKB database. Catering to the research purpose of predicting AMPs from plant resources, especially from legume seeds, the protein selection was limited to the plant kingdom. Database searches were conducted according to the following criteria:

* length: from 5 to 155;

* taxonomy_id: taxonomic levels, contains *Fabaceae* [3803], *Viridiplantae* [33090];

* keyword: Gene Ontology Annotation (Molecular Function), eliminates Antimicrobial [KW-0929], Pathogenesis-related protein [KW-0568], Antiviral protein [KW-0930]

Two non-AMP data sets, *Fabaceae* and *Viridiplantae*, were generated, with 59,606 and 682,607 entries respectively.

## 4.2. Data Preprocessing

Dover analyzer was used to remove redundant AMPs. The results revealed the relationship between the 28 AMP databases. The percentage of non-redundant amp content in all databases is shown Table 2, which shows that all AMP databases contain more than 50% non-redundant AMP content. Two heatmaps show the percentage of overlap between different databases, where Table 2 represents the percentage of row databases contained in the column databases, while Table 2 represents the percentage of row databases that are completely stored in each number of databases. A total of 44,099 AMP entries were obtained after preprocessing.

The redundant non-AMPs in *Viridiplantae* dataset were hierarchically removed by CD-HIT (Table 2).

All special characters except common amino acids in AMP and non-AMP were removed through preprocessing. Finally, the peptides shared between AMP and non-AMP were deleted.

## 4.3. Dataset Preparation

The AMPs and non-AMPs (including *Fabaceae* and *Viridiplantae*) were used to build the dataset for amp prediction modeling. Where there were 8,876 amps from 11 validated databases selected for test data (Table 3) and the remaining 35,229 amps used for training data (Table 4).

The *Fabaceae* and *Viridiplantae* dataset were divided into training set and test set by equidistant sampling, containing 78,685 / 19,672 and 37,420 / 9,355 non-AMPs respectively.

The dataset was then integrated through HuggingFace dataset module.
<table><caption>**Table 1**

Antimicrobial peptide database as input to amp-dover, including a description of entry source, target, current availability, last update date, total number of entries and references.</caption><thead><tr><td>Databases</td><td>Type</td><td>Focused on</td><td>Available</td><td>Last update</td><td>Entries</td><td>Reference</td></tr></thead><tbody><tr><td>Peptaibol</td><td>Specific</td><td>Peptaibol family</td><td>Absent</td><td>2004</td><td>316</td><td>10.1093/nar/gkh077</td></tr><tr><td>Defensins</td><td>Specific</td><td>Defensin family of AMPs</td><td>Absent</td><td>2007</td><td>536</td><td>10.1093/nar/gk866</td></tr><tr><td>AVPdb</td><td>Specific</td><td>Antiviral peptides</td><td>Present</td><td>2013</td><td>2,059</td><td>10.1093/nar/gkt1191</td></tr><tr><td>MilkAMP</td><td>Specific</td><td>AMPs of dairy origin</td><td>Present</td><td>2013</td><td>385</td><td>10.1007/s13594-013-0153-2</td></tr><tr><td>AMSDb</td><td>Specific</td><td>Eukaryotic AMPs</td><td>Absent</td><td>01/11/2004</td><td>893</td><td>10.2174/1381612023395475</td></tr><tr><td>AMPPer</td><td>Specific</td><td>Eukaryotic AMPs</td><td>Present</td><td>01/02/2007</td><td>988</td><td>10.1093/bioinformatics/btm068</td></tr><tr><td>PenBase</td><td>Specific</td><td>Penaeidin family of AMPs</td><td>Absent</td><td>01/07/2008</td><td>28</td><td>10.1016/j.dci.2005.04.003</td></tr><tr><td>RAPD</td><td>Specific</td><td>Recombinantly produced AMPs</td><td>Absent</td><td>01/03/2010</td><td>179</td><td>10.1111/j.1574-6968.2008.01357.x</td></tr><tr><td>DAMPD</td><td>General</td><td>General AMPs</td><td>Absent</td><td>01/09/2011</td><td>1,232</td><td>10.1093/nar/gkr1063</td></tr><tr><td>PhytAMP</td><td>Specific</td><td>AMPs from plants</td><td>Present</td><td>01/01/2012</td><td>273</td><td>10.1093/nar/gkn655</td></tr><tr><td>DADP</td><td>Specific</td><td>Anuran defense peptides</td><td>Absent</td><td>01/03/2012</td><td>2,571</td><td>10.1093/bioinformatics/bts141</td></tr><tr><td>Bagel I</td><td>Specific</td><td>Bacteriocins</td><td>Absent</td><td>01/01/2013</td><td>158</td><td>10.1093/nar/gkq365</td></tr><tr><td>Bagel II</td><td>Specific</td><td>Bacteriocins</td><td>Absent</td><td>01/01/2013</td><td>228</td><td>10.1093/nar/gkq365</td></tr><tr><td>Bagel III</td><td>Specific</td><td>Bacteriocins</td><td>Absent</td><td>01/01/2013</td><td>93</td><td>10.1093/nar/gkq365</td></tr><tr><td>LAMP Experimental</td><td>General</td><td>General AMPs</td><td>Present</td><td>01/03/2013</td><td>3,191</td><td>10.1371/journal.pone.0066557</td></tr><tr><td>LAMP Patent</td><td>Specific</td><td>Patented AMPs</td><td>Present</td><td>01/03/2013</td><td>1,491</td><td>10.1371/journal.pone.0066557</td></tr><tr><td>YADAMP</td><td>General</td><td>General AMPs</td><td>Absent</td><td>01/03/2013</td><td>2,525</td><td>10.1016/j.ijantimicag.2011.12.003</td></tr><tr><td>Bactibase</td><td>Specific</td><td>Bacteriocins</td><td>Present</td><td>01/10/2014</td><td>227</td><td>10.1186/1471-2180-10-22</td></tr><tr><td>BaAMPs</td><td>Specific</td><td>Against microbial biofilms</td><td>Absent</td><td>13/09/2019</td><td>221</td><td>10.1186/1471-2180-10-22</td></tr><tr><td>dbAMP</td><td>General</td><td>General</td><td>Present</td><td>01/06/2021</td><td>18,345</td><td>10.1093/nar/gkab1080</td></tr><tr><td>APD3</td><td>General</td><td>General AMPs</td><td>Present</td><td>01/08/2021</td><td>3,273</td><td>10.1093/nar/gkv1278</td></tr><tr><td>CAMP Patent</td><td>Specific</td><td>Patented AMPs</td><td>Absent</td><td>01/11/2013</td><td>1,716</td><td>10.1093/nar/gkv1051</td></tr><tr><td>CAMP Structure</td><td>General</td><td>3D structures of AMPs</td><td>Absent</td><td>01/11/2013</td><td>682</td><td>10.1093/nar/gkv1051</td></tr><tr><td>CAMP Validated</td><td>General</td><td>General AMPs</td><td>Absent</td><td>01/11/2013</td><td>2,602</td><td>10.1093/nar/gkv1051</td></tr><tr><td>DRAMP General</td><td>General</td><td>General</td><td>Present</td><td>04/07/2023</td><td>6,034</td><td>10.1093/nar/gkab651</td></tr><tr><td>DRAMP Patent</td><td>Specific</td><td>Patented AMPs</td><td>Present</td><td>04/07/2023</td><td>16,110</td><td>10.1093/nar/gkab651</td></tr><tr><td>DRAMP Clinical</td><td>Specific</td><td>Clinical AMPs</td><td>Present</td><td>04/07/2023</td><td>40</td><td>10.1093/nar/gkab651</td></tr><tr><td>DRAMP Specific</td><td>Specific</td><td>Specific AMPs</td><td>Present</td><td>04/07/2023</td><td>6,097</td><td>10.1093/nar/gkab651</td></tr></tbody></table>
<caption>**Table 2**</caption>

The total number of sequences before/after filtration and unique sequences percentage in each database.

<table><caption>**Table 3**</caption><thead><tr><td>Database</td><td>Total</td><td>Non-duplicate</td><td>Percent</td></tr></thead><tbody><tr><td>MilkAMP_clean</td><td>385</td><td>313</td><td>81,3</td></tr><tr><td>DRAMP_Clinical_clean</td><td>34</td><td>28</td><td>82,35</td></tr><tr><td>Bagel_I_clean</td><td>158</td><td>154</td><td>97,47</td></tr><tr><td>DRAMP_Specific_clean</td><td>6097</td><td>6044</td><td>99,13</td></tr><tr><td>YADAMP_clean</td><td>2525</td><td>2525</td><td>100</td></tr><tr><td>DRAMP_Patent_clean</td><td>16104</td><td>14212</td><td>88,25</td></tr><tr><td>PenBase_clean</td><td>28</td><td>28</td><td>100</td></tr><tr><td>RAPD_clean</td><td>179</td><td>122</td><td>68,16</td></tr><tr><td>AVPdb_clean</td><td>2059</td><td>1817</td><td>88,25</td></tr><tr><td>AMSDb_clean</td><td>893</td><td>858</td><td>96,08</td></tr><tr><td>LAMP_Patent_clean</td><td>1491</td><td>1491</td><td>100</td></tr><tr><td>DRAMP_General_clean</td><td>6027</td><td>5823</td><td>96,62</td></tr><tr><td>Bagel_III_clean</td><td>93</td><td>71</td><td>76,34</td></tr><tr><td>APD_clean</td><td>3273</td><td>3238</td><td>98,93</td></tr><tr><td>AMPPer_clean</td><td>988</td><td>948</td><td>95,95</td></tr><tr><td>Bactibase_clean</td><td>227</td><td>215</td><td>94,71</td></tr><tr><td>DAMPD_clean</td><td>1232</td><td>1199</td><td>97,32</td></tr><tr><td>CAMP_Structure_clean</td><td>929</td><td>708</td><td>76,21</td></tr><tr><td>DADP_clean</td><td>2571</td><td>2460</td><td>95,68</td></tr><tr><td>CAMP_Patent_clean</td><td>1716</td><td>1675</td><td>97,61</td></tr><tr><td>Peptaibol_clean</td><td>316</td><td>198</td><td>62,66</td></tr><tr><td>LAMP_Experimental_clean</td><td>3191</td><td>3190</td><td>99,97</td></tr><tr><td>Defensins_clean</td><td>536</td><td>507</td><td>94,59</td></tr><tr><td>dbAMP_clean</td><td>18345</td><td>18312</td><td>99,82</td></tr><tr><td>Bagel_II_clean</td><td>228</td><td>217</td><td>95,18</td></tr><tr><td>CAMP_Validated_clean</td><td>16640</td><td>15507</td><td>93,19</td></tr><tr><td>BaAMPs_clean</td><td>212</td><td>188</td><td>88,68</td></tr><tr><td>PhytAMP_clean</td><td>273</td><td>272</td><td>99,63</td></tr></tbody></table>

Results of hierarchical protein clustering on the *Viridiplantae* dataset using cd-hit, the first column shows the similarity metric for each clustering hierarchy, and the last two columns correspond to the input and output corresponding protein sequence entries.

<table><thead><tr><td>Clustering percentage</td><td>Input entries</td><td>Output entries</td></tr></thead><tbody><tr><td>80,00 %</td><td>442,923</td><td>365,347</td></tr><tr><td>70,00 %</td><td>365,347</td><td>344,001</td></tr><tr><td>60,00 %</td><td>344,001</td><td>304,472</td></tr><tr><td>50,00 %</td><td>304,472</td><td>224,918</td></tr><tr><td>40,00 %</td><td>224,918</td><td>98,859</td></tr><tr><td>30,00 %</td><td>98,859</td><td>98,389</td></tr><tr><td>20,00 %</td><td>98,389</td><td>98,357</td></tr><tr><td>10,00 %</td><td>98,357</td><td>98,357</td></tr><tr><td>5,00 %</td><td>98,357</td><td>98,357</td></tr><tr><td>1,00 %</td><td>98,357</td><td>98,357</td></tr></tbody></table>
<caption>**Table 4**</caption>

AMP database selection for the test set, containing input and output entries and the percentage of non-redundant peptides to itself.

<table><thead><tr><th>Output</th><th>Input</th><th>Percentage of non-redundant peptides</th></tr></thead><tbody><tr><td>From 40 Exported</td><td>34</td><td>protein sequences to DRAMP_Clinical_clean(85%)</td></tr><tr><td>From 273 Exported</td><td>273</td><td>protein sequences to PhytAMP_clean(100%)</td></tr><tr><td>From 536 Exported</td><td>536</td><td>protein sequences to Defensins_clean(100%)</td></tr><tr><td>From 929 Exported</td><td>929</td><td>protein sequences to CAMP Structures_clean(100%)</td></tr><tr><td>From 1232 Exported</td><td>1232</td><td>protein sequences to DAMPD_clean(100%)</td></tr><tr><td>From 1491 Exported</td><td>1491</td><td>protein sequences to LAMP_Patent_clean(100%)</td></tr><tr><td>From 1716 Exported</td><td>1716</td><td>protein sequences to CAMP_Patent_clean(100%)</td></tr><tr><td>From 2525 Exported</td><td>2525</td><td>protein sequences to YADAMP_clean(100%)</td></tr><tr><td>From 2571 Exported</td><td>2571</td><td>protein sequences to DADP_clean(100%)</td></tr><tr><td>From 3191 Exported</td><td>3191</td><td>protein sequences to LAMP_Experimental_clean(100%)</td></tr><tr><td>From 3273 Exported</td><td>3273</td><td>protein sequences to APD_clean(100%)</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Heatmap of sequence overlap between each two AMP databases. The overlaps were represented by the percentage of overlap sequence in database of row.</figcaption></figure>

## Limitations

While the antimicrobial peptide dataset presented in this research offers a quantitative edge, it lacks a comprehensive depiction of the data label, including details like origin, specific antimicrobial targets, immune test parameters, and whether it is synthetically produced or predicted. It is recommended that future users of the dataset supplement it with more elaborate descriptions to enhance the efficacy of predictive model (Fig. 1 and Table 5).
<caption>**Table 5**</caption>

AMP database selection for the train/validation set, containing input and output entries and the percentage of non-redundant peptides to itself.

<table><thead><tr><td>Output</td><td>Input</td><td>Percentage of non-redundant peptides</td></tr></thead><tbody><tr><td>From 28 Exported</td><td>28</td><td>protein sequences to PenBase_clean(100%)</td></tr><tr><td>From 93 Exported</td><td>93</td><td>protein sequences to Bagel_III_clean(100%)</td></tr><tr><td>From 158 Exported</td><td>158</td><td>protein sequences to Bagel_I_clean(100%)</td></tr><tr><td>From 179 Exported</td><td>179</td><td>protein sequences to RAPD_clean(100%)</td></tr><tr><td>From 221 Exported</td><td>212</td><td>protein sequences to BaAMPs_clean(95%)</td></tr><tr><td>From 227 Exported</td><td>227</td><td>protein sequences to Bactibase_clean(100%)</td></tr><tr><td>From 228 Exported</td><td>228</td><td>protein sequences to Bagel_II_clean(100%)</td></tr><tr><td>From 316 Exported</td><td>316</td><td>protein sequences to Peptaibol_clean(100%)</td></tr><tr><td>From 385 Exported</td><td>385</td><td>protein sequences to MilkAMP_clean(100%)</td></tr><tr><td>From 893 Exported</td><td>893</td><td>protein sequences to AMSDb_clean(100%)</td></tr><tr><td>From 988 Exported</td><td>988</td><td>protein sequences to AMPer_clean(100%)</td></tr><tr><td>From 2059 Exported</td><td>2059</td><td>protein sequences to AVPdb_clean(100%)</td></tr><tr><td>From 6034 Exported</td><td>6029</td><td>protein sequences to DRAMP_General_clean(99%)</td></tr><tr><td>From 6097 Exported</td><td>6097</td><td>protein sequences to DRAMP_Specific_clean(100%)</td></tr><tr><td>From 16110 Exported</td><td>16110</td><td>protein sequences to DRAMP_Patent_clean(100%)</td></tr><tr><td>From 16640 Exported</td><td>16640</td><td>protein sequences to CAMP_Validated_clean(100%)</td></tr><tr><td>From 18345 Exported</td><td>18345</td><td>protein sequences to dbAMP_clean(100%)</td></tr></tbody></table>

## Ethics Statement

The current work does not involve human subjects, animal experiments, or any data collected from social media platforms. Therefore, we confirm that our research strictly adheres to the guidelines for authors provided by Data in Brief in terms of ethical considerations.

## Data Availability

amp_dataset_viri (Original data) (Hugging Face).

amp_dataset_faba (Original data) (Hugging Face).

Unifying Antimicrobial Peptide Datasets for Robust Deep Learning-Based Classification (Original data) (Recherche Data Gouv).

## CRediT Author Statement

**Shuang Peng:** Conceptualization, Investigation, Methodology, Visualization, Data curation, Writing – original draft; **Loïc Rajjou:** Conceptualization, Investigation, Supervision, Validation, Writing – review & editing.

## Acknowledgements

This research was supported by several funding agencies. S.P. received a PhD scholarship from the Chinese Scholarship Council (CSC). The Institut Jean-Pierre Bourgin (IJPB) benefits from the support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007). This work was also part of the 3rd Programme for Future Investments (France2030) and operated by the SUCSEED project (ANR- 20-PCPA-0009) funded by the 'Growing and Protecting crops Differently' French Priority Research Program (PPR-CPA), part of the national investment plan operated by the French National Research Agency (ANR) We extend our sincere gratitude to these organizations for their financial support.
Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal rela-
tionships that could have appeared to influence the work reported in this paper.

References

[1] E. Agathokleous, C. Sonne, G. Benelli, E.J. Calabrese, R.N.C. Guedes, Low-dose chemical stimulation and pest resistance threaten global crop production, Sci. Total Environ. 878 (2023) 162989, doi:10.1016/j.scitotenv.2023.162989

[2] A. Gokul, J. Mabaso, N. Henema, L. Otomo, O.O. Bakare, A. Klein, A.I. Daniel, A. Omolola, L.-A. Niekerk, M. Nkomo, M. Keyster, Sustainable agriculture through the enhancement of microbial biocontrol agents: current challenges and new perspectives, Appl. Sci. 13 (2023) 6507, doi:10.3390/app13116507

[3] G. Wang, X. Li, Z. Wang, APD3: the antimicrobial peptide database as a tool for research and education, Nucleic Acids. Res. 44 (2016) D1087–D1093, doi:10.1093/nar/gkv1278.

[4] M. Zasloff, Antimicrobial peptides of multicellular organisms, Nature 415 (2002) 389–395, doi:10.1038/415389a.

[5] K.A. Brogden, Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria? Nat. Rev. Microbiol. 3 (2005) 238–250, doi:10.1038/nrmicro1098.

[6] G. Wang, I.I. Vaisman, M.L. van Hoek, Machine learning prediction of antimicrobial peptides, in: T. Simonson (Ed.), Computational Peptide Science, Springer US, New York, NY, 2022, pp. 1–37, doi:10.1007/978-1-0716-1855-4_1.

[7] O. Bärcenas, C. Pintado-Grima, K. Sidorczuk, F. Teufel, H. Nielsen, S. Ventura, M. Burdukiewicz, The dynamic landscape of peptide activity prediction, Comput. Struct. Biotechnol. J. 20 (2022) 6526–6533, doi:10.1016/j.csbj.2022.11.043.

[8] L. Aguilera-Mendoza, Y. Marrero-Ponce, R. Tellez-Ibarra, M.T. Llorente-Quesada, J. Salgado, S.J. Barigye, J. Liu, Overlap and diversity in antimicrobial peptide databases: compiling a non-redundant set of sequences, Bioinformatics. 31 (2015) 2553–2559, doi:10.1093/bioinformatics/btv180.