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

2008 ◽  
Vol 37 (suppl_1) ◽  
pp. D933-D937 ◽  
Author(s):  
Guangshun Wang ◽  
Xia Li ◽  
Zhe Wang
Keyword(s):  
Antimicrobial Peptide ◽  
Peptide Design ◽  
Peptide Database

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

2021 ◽  
Author(s):  
Guangshun Wang ◽  
C. Michael Zietz ◽  
Ashok Madgapalli ◽  
Shuona Wang ◽  
Zhe Wang
Keyword(s):  
Antimicrobial Peptide ◽  
Peptide Database

scholarly journals Identification of Novel Human Immunodeficiency Virus Type 1-Inhibitory Peptides Based on the Antimicrobial Peptide Database

2010 ◽  
Vol 54 (3) ◽  
pp. 1343-1346 ◽  
Author(s):  
Guangshun Wang ◽  
Karen M. Watson ◽  
Alan Peterkofsky ◽  
Robert W. Buckheit
Keyword(s):  
Antimicrobial Peptide ◽  
Original Sequence ◽  
Inhibitory Peptides ◽  
Peptide Database ◽  
Immunodeficiency Virus ◽  
Antiviral Peptides ◽  
Anti Hiv ◽  
Hiv 1 ◽  
Aurein 1.2

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.

scholarly journals A Uniform In Vitro Efficacy Dataset to Guide Antimicrobial Peptide Design

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 27 ◽  
Author(s):  
Deepesh Nagarajan ◽  
Tushar Nagarajan ◽  
Neha Nanajkar ◽  
Nagasuma Chandra
Keyword(s):  
Antimicrobial Peptides ◽  
Antimicrobial Peptide ◽  
Antimicrobial Agents ◽  
Multi Drug Resistance ◽  
Peptide Design ◽  
Drug Candidates ◽  
The Face ◽  
Conventional Antibiotics ◽  
Different Sources

Antimicrobial peptides are ubiquitous molecules that form the innate immune system of organisms across all kingdoms of life. Despite their prevalence and early origins, they continue to remain potent natural antimicrobial agents. Antimicrobial peptides are therefore promising drug candidates in the face of overwhelming multi-drug resistance to conventional antibiotics. Over the past few decades, thousands of antimicrobial peptides have been characterized in vitro, and their efficacy data are now available in a multitude of public databases. Computational antimicrobial peptide design attempts typically use such data. However, utilizing heterogenous data aggregated from different sources presents significant drawbacks. In this report, we present a uniform dataset containing 20 antimicrobial peptides assayed against 30 organisms of Gram-negative, Gram-positive, mycobacterial, and fungal origin. We also present circular dichroism spectra for all antimicrobial peptides. We draw simple inferences from this data, and we discuss what characteristics are essential for antimicrobial peptide efficacy. We expect our uniform dataset to be useful for future projects involving computational antimicrobial peptide design.

scholarly journals APD: the Antimicrobial Peptide Database

2004 ◽  
Vol 32 (90001) ◽  
pp. 590D-592 ◽  
Author(s):  
Z. Wang
Keyword(s):  
Antimicrobial Peptide ◽  
Peptide Database

scholarly journals A uniform in vitro efficacy dataset to guide antimicrobial peptide design

2018 ◽  
Author(s):  
Deepesh Nagarajan ◽  
Tushar Nagarajan ◽  
Neha Nanajkar ◽  
Nagasuma Chandra
Keyword(s):  
Antimicrobial Peptides ◽  
Antimicrobial Peptide ◽  
Antimicrobial Agents ◽  
Multi Drug Resistance ◽  
Peptide Design ◽  
Drug Candidates ◽  
The Face ◽  
Conventional Antibiotics ◽  
Different Sources

ABSTRACTAntimicrobial peptides are ubiquitous molecules that form the innate immune system of organisms across all kingdoms of life. Despite their prevalence and early origins, they continue to remain potent natural antimicrobial agents. Antimicrobial peptides are therefore promising drug candidates in the face of overwhelming multi-drug resistance to conventional antibiotics. Over the past few decades, thousands of antimicrobial peptides have been characterized in vitro, and their efficacy data is now available in a multitude of public databases. Computational antimicrobial peptide design attempts typically use such data. However, utilizing heterogenous data aggregated from different sources presents significant drawbacks. In this report, we present a uniform dataset containing 20 antimicrobial peptides assayed against 30 organisms spanning gram positive, gram negative, fungal, and mycobacterial origin. We draw inferences from the results of 600 individual MIC assays, and discuss what characteristics are essential for antimicrobial peptide efficacy. We expect our uniform dataset to be useful for future projects involving computational antimicrobial peptide design.

Optimization of antimicrobial peptide design: insights from action mechanisms

2015 ◽  
Vol 3 (6) ◽  
Author(s):  
Caleb M. Agbale ◽  
Osmel Fleitas ◽  
Isaac K. Galyuon ◽  
Octavio L. Franco
Keyword(s):  
Antimicrobial Peptide ◽  
Peptide Design ◽  
Action Mechanisms

scholarly journals Antimicrobial peptide identification using multi-scale convolutional network

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.

scholarly journals Antifungal Activity of ToAP2D Peptide Against Sporothrix globosa

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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