DRAMP 2.0, an updated data repository of antimicrobial peptides

Kang, Xinyue; Dong, Fang; Shi, Cheng; Liu, Shicai; Sun, Jian; Chen, Jiaxin; Li, Haiqi; Xu, Hong‐Xi; Lao, Xingzhen; Zheng, Heng

doi:10.1038/s41597-019-0154-y

Cited by 277 publications

(189 citation statements)

References 60 publications

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“…The establishment of the database mainly uses Apache, PHP, MySQL, and the establishment method is the same as our previous research (Kang et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Applications of machine‐learning methods for the discovery of NDM‐1 inhibitors

et al. 2020

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The emergence of New Delhi metal beta‐lactamase (NDM‐1)‐producing bacteria and their worldwide spread pose great challenges for the treatment of drug‐resistant bacterial infections. These bacteria can hydrolyze most β‐lactam antibacterials. Unfortunately, there are no clinically useful NDM‐1 inhibitors. In the current work, we manually collected NDM‐1 inhibitors reported in the past decade and established the first NDM‐1 inhibitor database. Four machine‐learning models were constructed using the structural and property characteristics of the collected compounds as input training set to discover potential NDM‐1 inhibitors. In order to distinguish between high active inhibitors and putative positive drugs, a three‐classification strategy was introduced in our study. In detail, the commonly used positive and negative divisions are converted into strongly active, weakly active, and inactive. The accuracy of the best prediction model designed based on this strategy reached 90.5%, compared with 69.14% achieved by the traditional docking‐based virtual screening method. Consequently, the best model was used to virtually screen a natural product library. The safety of the selected compounds was analyzed by the ADMET prediction model based on machine learning. Seven novel NDM‐1 inhibitors were identified, which will provide valuable clues for the discovery of NDM‐1 inhibitors.

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“…The establishment of the database mainly uses Apache, PHP, MySQL, and the establishment method is the same as our previous research (Kang et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Applications of machine‐learning methods for the discovery of NDM‐1 inhibitors

et al. 2020

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“…In addition, there are many cases of peptides with promising properties currently being evaluated pre-clinically (Koo and Seo, 2019). Other pre-clinical examples are described in the review by Duncan and O'Neill (Duncan and O'Neil, 2013) and in the database DRAMP 2.0 (Kang et al, 2019).…”

Section: Potential Applicationsmentioning

confidence: 99%

Antifungal Peptides as Therapeutic Agents

Ullivarri

Arbulu

García-Gutiérrez

et al. 2020

Front. Cell. Infect. Microbiol.

174

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Fungi have been used since ancient times in food and beverage-making processes and, more recently, have been harnessed for the production of antibiotics and in processes of relevance to the bioeconomy. Moreover, they are starting to gain attention as a key component of the human microbiome. However, fungi are also responsible for human infections. The incidence of community-acquired and nosocomial fungal infections has increased considerably in recent decades. Antibiotic resistance development, the increasing number of immunodeficiency-and/or immunosuppression-related diseases and limited therapeutic options available are triggering the search for novel alternatives. These new antifungals should be less toxic for the host, with targeted or broader antimicrobial spectra (for diseases of known and unknown etiology, respectively) and modes of actions that limit the potential for the emergence of resistance among pathogenic fungi. Given these criteria, antimicrobial peptides with antifungal properties, i.e., antifungal peptides (AFPs), have emerged as powerful candidates due to their efficacy and high selectivity. In this review, we provide an overview of the bioactivity and classification of AFPs (natural and synthetic) as well as their mode of action and advantages over current antifungal drugs. Additionally, natural, heterologous and synthetic production of AFPs with a view to greater levels of exploitation is discussed. Finally, we evaluate the current and potential applications of these peptides, along with the future challenges relating to antifungal treatments.

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“…Due to their unique mechanism(s) of action and wide spectrum of biological activities, AMPs are emerging as a promising alternative to conventional anti-microbial agents for the management of antibiotic-resistant and biofilm-associated infections [24,25]. Although mainly intended for topical use due to potential toxicity and reduced stability under physiological conditions, many different peptides with anti-microbial and/or immunomodulatory properties have already been tested for efficacy in clinical studies [26,27]. In this context, previous studies of our group focused on the evaluation of the anti-microbial properties of the semi-synthetic peptide lin-SB056-1, with particular attention to the development of strategies to improve its therapeutic potential against P. aeruginosa lung infections [17][18][19].…”

Section: Discussionmentioning

confidence: 99%

The Anti-Microbial Peptide (Lin-SB056-1)2-K Reduces Pro-Inflammatory Cytokine Release through Interaction with Pseudomonas aeruginosa Lipopolysaccharide

et al. 2020

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The ability of many anti-microbial peptides (AMPs) to modulate the host immune response has highlighted their possible therapeutic use to reduce uncontrolled inflammation during chronic infections. In the present study, we examined the anti-inflammatory potential of the semi-synthetic peptide lin-SB056-1 and its dendrimeric derivative (lin-SB056-1)2-K, which were previously found to have anti-microbial activity against Pseudomonas aeruginosa in in vivo-like models mimicking the challenging environment of chronically infected lungs (i.e., artificial sputum medium and 3-D lung mucosa model). The dendrimeric derivative exerted a stronger anti-inflammatory activity than its monomeric counterpart towards lung epithelial- and macrophage-cell lines stimulated with P. aeruginosa lipopolysaccharide (LPS), based on a marked decrease (up to 80%) in the LPS-induced production of different pro-inflammatory cytokines (i.e., IL-1β, IL-6 and IL-8). Accordingly, (lin-SB056-1)2-K exhibited a stronger LPS-binding affinity than its monomeric counterpart, thereby suggesting a role of peptide/LPS neutralizing interactions in the observed anti-inflammatory effect. Along with the anti-bacterial and anti-biofilm properties, the anti-inflammatory activity of (lin-SB056-1)2-K broadens its therapeutic potential in the context of chronic (biofilm-associated) infections.

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DRAMP 2.0, an updated data repository of antimicrobial peptides

Cited by 277 publications

References 60 publications

Applications of machine‐learning methods for the discovery of NDM‐1 inhibitors

Applications of machine‐learning methods for the discovery of NDM‐1 inhibitors

Antifungal Peptides as Therapeutic Agents

The Anti-Microbial Peptide (Lin-SB056-1)2-K Reduces Pro-Inflammatory Cytokine Release through Interaction with Pseudomonas aeruginosa Lipopolysaccharide

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