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A large-scale structural classification of antimicrobial peptides.

ABSTRACT: Antimicrobial peptides (AMPs) are potent drug candidates against microbial organisms such as bacteria, fungi, parasites, and viruses. AMPs have abundant sequences and structures, two fundamental resources for bioinformatics researches, but analyses on how they associate with each other are either nonexistent or limited to partial classification and data. We thus present A Database of Anti-Microbial peptides (ADAM), which contains 7,007 unique sequences and 759 structures, to systematically establish comprehensive associations between AMP sequences and structures through structural folds and to provide an easy access to view their relationships. 30 distinct AMP structural fold clusters with more than one structure are detected and about a thousand AMPs are associated with at least one structural fold cluster. According to ADAM, AMP structural folds are limited-AMPs only cover about 3% of the overall protein fold space.

SUBMITTER: Lee HT

PROVIDER: S-EPMC4426897 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A large-scale structural classification of antimicrobial peptides.

Lee Hao-Ting HT Lee Chen-Che CC Yang Je-Ruei JR Lai Jim Z C JZ Chang Kuan Y KY

BioMed research international 20150427

Antimicrobial peptides (AMPs) are potent drug candidates against microbial organisms such as bacteria, fungi, parasites, and viruses. AMPs have abundant sequences and structures, two fundamental resources for bioinformatics researches, but analyses on how they associate with each other are either nonexistent or limited to partial classification and data. We thus present A Database of Anti-Microbial peptides (ADAM), which contains 7,007 unique sequences and 759 structures, to systematically estab ...[more]

PMID: 26000295

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Project description:Antimicrobial peptides (AMPs), produced by a wide range of organisms, have attracted attention due to their potential use as novel antibiotics. The majority of these peptides are cationic and are thought to function by permeabilizing the bacterial membrane, either by making pores or by dissolving it ('carpet' model). A key hypothesis in the literature is that antimicrobial and hemolytic activity correlate with binding affinity to anionic and zwitterionic membranes, respectively. Here we test this hypothesis by using binding free energy data collected from the literature and theoretical binding energies calculated from implicit membrane models for 53 helical AMPs. We indeed find a correlation between binding energy and biological activity, depending on membrane anionic content: antibacterial activity correlates best with transfer energy to membranes with anionic lipid fraction higher than 30% and hemolytic activity correlates best with transfer energy to a 10% anionic membrane. However, the correlations are weak, with correlation coefficient up to 0.4. Weak correlations of the biological activities have also been found with other physical descriptors of the peptides, such as surface area occupation, which correlates significantly with antibacterial activity; insertion depth, which correlates significantly with hemolytic activity; and structural fluctuation, which correlates significantly with both activities. The membrane surface coverage by many peptides at the MIC is estimated to be much lower than would be required for the 'carpet' mechanism. Those peptides that are active at low surface coverage tend to be those identified in the literature as pore-forming. The transfer energy from planar membrane to cylindrical and toroidal pores was also calculated for these peptides. The transfer energy to toroidal pores is negative in almost all cases while that to cylindrical pores is more favorable in neutral than in anionic membranes. The transfer energy to pores correlates with the deviation from predictions of the 'carpet' model.

Project description:MotivationMetagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.ResultsWe propose a new rank-flexible machine learning-based compositional approach for taxonomic assignment of metagenomics reads and show that it benefits from increasing the number of fragments sampled from reference genome to tune its parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning the method involves training machine learning models on about 10(8) samples in 10(7) dimensions, which is out of reach of standard softwares but can be done efficiently with modern implementations for large-scale machine learning. The resulting method is competitive in terms of accuracy with well-established alignment and composition-based tools for problems involving a small to moderate number of candidate species and for reasonable amounts of sequencing errors. We show, however, that machine learning-based compositional approaches are still limited in their ability to deal with problems involving a greater number of species and more sensitive to sequencing errors. We finally show that the new method outperforms the state-of-the-art in its ability to classify reads from species of lineage absent from the reference database and confirm that compositional approaches achieve faster prediction times, with a gain of 2-17 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise.Availability and implementationData and codes are available at http://cbio.ensmp.fr/largescalemetagenomicsContactpierre.mahe@biomerieux.comSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

A large-scale structural classification of antimicrobial peptides.

Publications

A large-scale structural classification of antimicrobial peptides.

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