The evolution of the antimicrobial peptide database ( APD ) over 18 years: Milestones and new features

ABSTRACT To identify novel anti-HIV-1 peptides based on the antimicrobial peptide database (APD; http://aps.unmc.edu/AP/main.php ), we have screened 30 candidates and found 11 peptides with 50% effective concentrations (EC50) of <10 μM and therapeutic indices (TI) of up to 17. Furthermore, among the eight peptides (with identical amino acid compositions but different sequences) generated by shuffling the sequence of an aurein 1.2 analog, two had a TI twice that of the original sequence. Because antiviral peptides in the database have an arginine/lysine (R/K) ratio of >1, increases in the Arg contents of amphibian maximin H5 and dermaseptin S9 peptides and the database-derived GLK-19 peptide improved the TIs. These examples demonstrate that the APD is a rich resource and a useful tool for developing novel HIV-1-inhibitory peptides.

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APD: the Antimicrobial Peptide Database

Nucleic Acids Research ◽

10.1093/nar/gkh025 ◽

2004 ◽

Vol 32 (90001) ◽

pp. 590D-592 ◽

Cited By ~ 442

Author(s):

Z. Wang

Keyword(s):

Antimicrobial Peptide ◽

Peptide Database

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Antimicrobial peptide identification using multi-scale convolutional network

BMC Bioinformatics ◽

10.1186/s12859-019-3327-y ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Xin Su ◽

Jing Xu ◽

Yanbin Yin ◽

Xiongwen Quan ◽

Han Zhang

Keyword(s):

Deep Learning ◽

Antimicrobial Peptide ◽

State Of The Art ◽

Peptide Identification ◽

The State ◽

Fusion Model ◽

Learning Methods ◽

Convolutional Network ◽

Multi Scale ◽

Peptide Database

Abstract Background Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

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The antimicrobial peptide database provides a platform for decoding the design principles of naturally occurring antimicrobial peptides

Protein Science ◽

10.1002/pro.3702 ◽

2019 ◽

Vol 29 (1) ◽

pp. 8-18 ◽

Cited By ~ 11

Author(s):

Guangshun Wang

Keyword(s):

Antimicrobial Peptides ◽

Antimicrobial Peptide ◽

Design Principles ◽

Naturally Occurring ◽

Peptide Database

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APD2: the updated antimicrobial peptide database and its application in peptide design

Nucleic Acids Research ◽

10.1093/nar/gkn823 ◽

2008 ◽

Vol 37 (suppl_1) ◽

pp. D933-D937 ◽

Cited By ~ 584

Author(s):

Guangshun Wang ◽

Xia Li ◽

Zhe Wang

Keyword(s):

Antimicrobial Peptide ◽

Peptide Design ◽

Peptide Database

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Graphical User Interface for Antimicrobial Peptide Database

10.23860/thesis-garg-tripti-2016 ◽

2016 ◽

Author(s):

◽

Tripti Garg

Keyword(s):

User Interface ◽

Antimicrobial Peptide ◽

Graphical User Interface ◽

Peptide Database

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Antifungal Activity of ToAP2D Peptide Against Sporothrix globosa

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.761518 ◽

2021 ◽

Vol 9 ◽

Author(s):

Tianyi Yan ◽

Fuqiu Li ◽

Jinran Li ◽

Feng Chen

Keyword(s):

Antimicrobial Peptide ◽

Treatment Time ◽

Physicochemical Characteristics ◽

Sporothrix Globosa ◽

Peptide Database ◽

Electron Microscopy Analysis ◽

Modified Peptides ◽

Aliphatic Index ◽

Apoptosis Related Protein

Improving clinical efficacy and reducing treatment time have been the focus of sporotrichosis therapy. Antimicrobial peptides ToAP2A, ToAP2C, and ToAP2D were synthesized on the basis of ToAP2 (AP02759), a peptide derived from the antimicrobial peptide database by the database filtering technology, and their physicochemical characteristics were analyzed. Compared with template peptide ToAP2, the modified peptides had much shorter length, lower molecular weight but significantly greater stability, which in return resulted in increases in the aliphatic index, hydrophilicity, and protein binding ability. Here, we show that the three derived peptides inhibit the growth of Sporothrix globosa, among which ToAP2D had the strongest anti-fungal activity. ToAP2D showed good serum stability without acute toxicity. The ToAP2D treatment inhibited the growth of S. globosa and enhanced apoptosis, which was evidenced by the upregulation of apoptosis-related protein caspase-3. The scanning electron microscopy analysis revealed deformation and rupture of S. globosa. The levels of mitochondrial membrane potential were decreased and that of the reactive oxygen species (ROS) were increased in S. globosa upon ToAP2D treatment. Moreover, ToAP2D activated metacaspase. In the in vivo study, we further demonstrated that ToAP2D inhibited the S. globosa infection of mice footpads, and its efficiency was nearly comparable to itraconazole. In summary, our results suggest that antimicrobial peptide ToAP2D has the potential for sporotrichosis therapy.

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Towards Identify Selective Antibacterial Peptides Based on Abstracts Meaning

Computational and Mathematical Methods in Medicine ◽

10.1155/2016/1505261 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11

Author(s):

Liliana I. Barbosa-Santillán ◽

Juan J. Sánchez-Escobar ◽

M. Angeles Calixto-Romo ◽

Luis F. Barbosa-Santillán

Keyword(s):

Natural Language ◽

Antimicrobial Peptide ◽

Antibacterial Peptides ◽

Peptide Database ◽

Search Tool ◽

Two Stages

We present an Identify Selective Antibacterial Peptides (ISAP) approach based on abstracts meaning. Laboratories and researchers have significantly increased the report of their discoveries related to antibacterial peptides in primary publications. It is important to find antibacterial peptides that have been reported in primary publications because they can produce antibiotics of different generations that attack and destroy the bacteria. Unfortunately, researchers used heterogeneous forms of natural language to describe their discoveries (sometimes without the sequence of the peptides). Thus, we propose that learning the words meaning instead of the antibacterial peptides sequence is possible to identify and predict antibacterial peptides reported in the PubMed engine. The ISAP approach consists of two stages: training and discovering. ISAP founds that the 35% of the abstracts sample had antibacterial peptides and we tested in the updated Antimicrobial Peptide Database 2 (APD2). ISAP predicted that 45% of the abstracts had antibacterial peptides. That is, ISAP found that 810 antibacterial peptides were not classified like that, so they are not reported in APD2. As a result, this new search tool would complement the APD2 with a set of peptides that are candidates to be antibacterial. Finally, 20% of the abstracts were not semantic related to APD2.

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