Analysis and prediction of antibacterial peptides

By Sneh Lata, B. Sharma, Gajendra P. Raghava. BMC Bioinformatics, 2007

515 citations recorded by the source corpus.

BackgroundAntibacterial peptides are important components of the innate immune system, used by the host to protect itself from different types of pathogenic bacteria. Over the last few decades, the search for new drugs and drug targets has prompted an interest in these antibacterial peptides. We analyzed 486 antibacterial peptides, obtained from antimicrobial peptide database APD, in order to understand the preference of amino acid residues at specific positions in these peptides.ResultsIt was observed that certain types of residues are preferred over others in antibacterial peptides, particularly at the N and C terminus. These observations encouraged us to develop a method for predicting antibacterial peptides in proteins from their amino acid sequence. First, the N-terminal residues were used for predicting antibacterial peptides using Artificial Neural Network (ANN), Quantitative Matrices (QM) and Support Vector Machine (SVM), which resulted in an accuracy of 83.63%, 84.78% and 87.85%, respectively. Then, the C-terminal residues were used for developing prediction methods, which resulted in an accuracy of 77.34%, 82.03% and 85.16% using ANN, QM and SVM, respectively. Finally, ANN, QM and SVM models were developed using N and C terminal residues, which achieved an accuracy of 88.17%, 90.37% and 92.11%, respectively. All the models developed in this study were evaluated using five-fold cross validation technique. These models were also tested on an independent or blind dataset.ConclusionAmong antibacterial peptides, there is preference for certain residues at N and C termini, which helps to demarcate them from non-antibacterial peptides. Both the termini play a crucial role in imparting the antibacterial property to these peptides. Among the methods developed, SVM shows the best performance in predicting antibacterial peptides followed by QM and ANN, in that order. AntiBP (Antibacterial peptides) will help in discovering efficacious antibacterial peptides, which we hope will prove to be a boon to combat the dreadful antibiotic resistant bacteria. A user friendly web server has also been developed to help the biological community, which is accessible at http://www.imtech.res.in/raghava/antibp/.

Subfields: Microbiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/be89eb944012ae02a15777fb8453d60c37898724

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Citations in the corpus

QUBIC: a qualitative biclustering algorithm for analyses of gene expression data (2009, background): “Cheng and Church (8) proposed a quantitative measure, ‘mean squared residue’, essentially a variability measure, as a guide to search for biclusters in a gene expression data set, which has been adopted by numerous biclustering algorithms (9–11).”
CAMP: a useful resource for research on antimicrobial peptides (2009, background): “Some of the existing prediction servers for AMPs are APD (14), BACTIBASE (17), PhytAMP (16) and AntiBP (49).”
Eigengene networks for studying the relationships between co-expression modules (2007, background): “Many possible approaches for defining network modules have been proposed in the literature [25-29].”
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Wisdom of crowds for robust gene network inference (2012, methodology): “(5) Network predictions were assessed using experimentally supported interactions from E.”
Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC (2017, unknown): “The formulae given in equation (1) has made the meaning of Sn, Sp, Ac, and MCC much more intuitive and easier-to-understand, particularly for the meaning of MCC, as concurred by a series of studies published very recently19–20,37–41.”
FABIA: factor analysis for bicluster acquisition (2010, background): “Received on December 23, 2009; revised on March 26, 2010; accepted on April 20, 2010”
iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. (2013, methodology): “Also, the methods proposed in [12] and [13] did not provide any web-server, while the method in [9,10] were limited for antibacterial peptides only.”
Integrative approaches for finding modular structure in biological networks (2013, methodology): “In a biclustering method, cMonkey 69 , P values of correlated expression, sequence similarity and network topology were measured and an aggregate P value was defined as the joint membership probability.”
APD3: the antimicrobial peptide database as a tool for research and education (2015, methodology): “(44) first used the APD sequences as a positive training set to program machine-learning algorithms for AMP prediction.”
ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions (2016, methodology): “In this way, ADAGE is distinct from clustering-or biclustering-based techniques, such as cMonkey (56), which identify subsets of genes coregulated in subsets of experiments.”