2016
DOI: 10.1007/s00203-016-1293-6
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Abstract: Staphylococcus aureus and methicillin-resistant S. aureus are major pathogens. The antimicrobial peptides and essential oils (EOs) display narrow- or broad-spectrum activity against bacteria including these strains. A centralized resource, such as a database, designed specifically for anti-S. aureus/anti-methicillin-resistant S. aureus antimicrobial peptides and EOs is therefore needed to facilitate the comprehensive investigation of their structure/activity associations and combinations. The database ANTISTAP… Show more

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Cited by 13 publications
(7 citation statements)
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“…It is well-known that an important assumption of any data-driven modeling study is related to the correctness of the input data sets, , i.e ., data-set size, curation, well-defined endpoint, and diversity. Regarding data-set size, prior AF-QSAMs to recognize AMPs used only a subset of the universe of extant AMPs (between 54 and 3500 experimentally validated AMPs), although several freely available AMP-related databases jointly present a much larger number of nonredundant AMPs. Indeed, Aguilera-Mendoza et al , compiled 22 642 nonredundant AMPs from 40 different databases. However, the models built by Xiao et al (iAMP-2L server), Bhadra et al (AmPEP method), and Chung et al (AMPfun server) for the general AMP classification used 878, 3268, and 1686 experimentally validated peptide sequences, respectively, representing only 0.039, 0.144, and 0.074% of the universe of AMPs detailed by Aguilera-Mendoza et al This low representativeness of training AMPs likely to affect adversely the generalization ability of the current AF-QSAMs.…”
Section: Introductionmentioning
confidence: 99%
“…It is well-known that an important assumption of any data-driven modeling study is related to the correctness of the input data sets, , i.e ., data-set size, curation, well-defined endpoint, and diversity. Regarding data-set size, prior AF-QSAMs to recognize AMPs used only a subset of the universe of extant AMPs (between 54 and 3500 experimentally validated AMPs), although several freely available AMP-related databases jointly present a much larger number of nonredundant AMPs. Indeed, Aguilera-Mendoza et al , compiled 22 642 nonredundant AMPs from 40 different databases. However, the models built by Xiao et al (iAMP-2L server), Bhadra et al (AmPEP method), and Chung et al (AMPfun server) for the general AMP classification used 878, 3268, and 1686 experimentally validated peptide sequences, respectively, representing only 0.039, 0.144, and 0.074% of the universe of AMPs detailed by Aguilera-Mendoza et al This low representativeness of training AMPs likely to affect adversely the generalization ability of the current AF-QSAMs.…”
Section: Introductionmentioning
confidence: 99%
“…As a part of microflora of skin and mucous membranes of healthy individuals, Staphylococcus aureus is also an opportunistic pathogen and associated with hospital acquired infections such as septicemia, pneumonia, septic arthritis, osteomyelitis, toxic shock syndrome after surgery, folliculitis, endocarditis, and urinary tract infections (UTIs) [1, 2]. Antibiotic resistance by affecting more than two million people annually is one of the biggest global challenges.…”
Section: Introductionmentioning
confidence: 99%
“…ADAPTABLE incorporates automated tools to periodically download, process, and merge data to keep synched with data sources: ADAM (Lee et al, 2015), ANTISTAPHYBASE (Zouhir et al, 2017), APD (Wang & Wang, 2004; Wang et al, 2009; Wang et al, 2016), AVPdb (Qureshi et al, 2014), BaAMPs (Di Luca et al, 2015), BACTIBASE (Hammami et al, 2007, 2010), CAMPR3 (Waghu et al, 2016), CancerPPD (Tyagi et al, 2015), ConoServer (Kaas et al, 2008, 2012), CPPsite (Gautam et al, 2012; Agrawal et al, 2016), DADP (Novković et al, 2012), DBAASP (Gogoladze et al, 2014; Pirtskhalava et al, 2016), Defensins (Seebah et al, 2007), DRAMP (Fan et al, 2016; Kang et al, 2019), Hemolytik (Gautam et al, 2014), HIPdb (Qureshi et al, 2013), InverPep (Gómez et al, 2017), LAMP (Zhao et al, 2013), MilkAMP (Théolier et al, 2014), ParaPep (Mehta et al, 2014), Peptaibol (Whitmore & Wallace, 2004), PhytAMP (Hammami et al, 2009), SATPdb (Singh et al, 2016), UniProt (The UniProt Consortium, 2018), YADAMP (Piotto et al, 2012), PubChem (Kim et al, 2019), The Microbe Directory (Shaaban et al, 2018), and the Protein Data Bank (Berman et al, 2000).…”
Section: Methodsmentioning
confidence: 99%