A Wide Antimicrobial Peptides Search Method Using Fuzzy Modeling

Fernandes, Fabiano C.; Porto, William F.; Franco, Octávio L.

doi:10.1007/978-3-642-03223-3_14

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Similarly, Fernandes et al (2009) developed a classification method based on fuzzy modelling, also focusing on data set mining. This approach is based on the linguistic model developed by Loose et al (2006) and also on the peptide's amphipathicity.…”

Section: Empirical Methods Of Amp Predictionmentioning

confidence: 99%

Prediction and Rational Design of Antimicrobial Peptides

Porto¹,

Franco²

2012

Protein Structure

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Section: Empirical Methods Of Amp Predictionmentioning

confidence: 99%

Prediction and Rational Design of Antimicrobial Peptides

Porto¹,

Franco²

2012

Protein Structure

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“…The AMPA web application (http://tcoffee.crg.cat/apps/ampa) (Torrent et al 2011) was constructed for assessing the antimicrobial domains of proteins, based on an antimicrobial propensity scale for each amino acid (related to the IC 50 values for all amino acid replacements in the AMP bactenecin 2A) and identifying the regions (412 amino acids in length) located below the threshold, which are considered putative antimicrobial domains. There is also a search method (Fernandes et al 2009) for sequence similarity and physico-chemical properties followed by a fuzzy inference system in order to find AMPs that are more appropriate for certain target domains. Finally, the Antibp server (http://www.…”

Section: Discovery and Classification Of Ampsmentioning

confidence: 99%

New trends in peptide-based anti-biofilm strategies: a review of recent achievements and bioinformatic approaches

2012

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Antimicrobial peptides (AMPs) have a broad spectrum of activity and unspecific mechanisms of action. Therefore, they are seen as valid alternatives to overcome clinically relevant biofilms and reduce the chance of acquired resistance. This paper reviews AMPs and anti-biofilm AMP-based strategies and discusses ongoing and future work. Recent studies report successful AMP-based prophylactic and therapeutic strategies, several databases catalogue AMP information and analysis tools, and novel bioinformatics tools are supporting AMP discovery and design. However, most AMP studies are performed with planktonic cultures, and most studies on sessile cells test AMPs on growing rather than mature biofilms. Promising preliminary synergistic studies have to be consubstantiated and the study of functionalized coatings with AMPs must be further explored. Standardized operating protocols, to enforce the repeatability and reproducibility of AMP anti-biofilm tests, and automated means of screening and processing the ever-expanding literature are still missing.

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“…Mining information from databases has been recognized by many researchers as a hot spot in different areas [43]. Data mining processes have been carried out to discover novel biological data, such as putative ancestral genes of circular proteins in plants [44], antimicrobial peptides [45,46] or candidates for novel translatable sequences [4]. This work describes a novel data mining approach for selecting hypothetical proteins for functional prediction.…”

Section: Data Miningmentioning

confidence: 99%

Screening and Functional Prediction of Conserved Hypothetical Proteins from Escherichia coli

MariaNeto¹

2014

J Proteomics Bioinform

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Protein structures can provide some functional evidences. Therefore structural genomics efforts to identify the functions of hypothetical proteins have brought advances in our understanding of biological systems. To this end, a new strategy to mine protein databases in the search for candidates for function prediction was here described. The strategy was applied to Escherichia coli proteins deposited in the NCBI's non-redundant database. Briefly, data mining selects small conserved hypothetical proteins without significant templates on Protein Data Bank, without transmembrane regions and with similarity to Eukaryote proteins. Through this strategy, 12 protein sequences were selected for molecular modelling, from a total of 13,306 E. coli's conserved hypothetical sequences. From these, only three sequences could be modelled. GI 488361128 model was similar to cupredoxins, GI 281178323 model was similar to β-barrel proteins and GI 227886634 model showed structural similarities to lipid binding proteins. However, only the GI 227886634 seems to have a function related to the similar structures, since it was the unique structure that kept the fold during the molecular dynamics simulation. The method here described can be relevant to select hypothetical sequences that can be targets for in vitro and/or in vivo functional characterization.

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A Wide Antimicrobial Peptides Search Method Using Fuzzy Modeling

Cited by 5 publications

References 4 publications

Prediction and Rational Design of Antimicrobial Peptides

Prediction and Rational Design of Antimicrobial Peptides

New trends in peptide-based anti-biofilm strategies: a review of recent achievements and bioinformatic approaches

Screening and Functional Prediction of Conserved Hypothetical Proteins from Escherichia coli

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