Classification and Prediction of Antimicrobial Peptides Using N-gram Representation and Machine Learning

Othman, Manal; Ratna, Sujay; Tewari, Anant; Kang, Anthony; Du, Katherine; Vaisman, Iosif I.

doi:10.1145/3107411.3108215

Cited by 3 publications

(2 citation statements)

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“…Frequencies of the N-grams with distinct compositions can be calculated and compared with their expected frequencies based on the observed frequencies of individual amino acids. The following is the equation for the N-gram likelihood used in this study ( Othman et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

Shao,

Zhao,

Wei

et al. 2024

Front. Microbiol.

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IntroductionAntimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria.MethodsN-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness.ResultsThe models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models.DiscussionHydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.

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Section: Methodsmentioning

confidence: 99%

AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

Shao,

Zhao,

Wei

et al. 2024

Front. Microbiol.

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“…[39,52] These models show good classification performance with a maximum of 94% at predicting whether a peptide is active against gram-positive bacteria, given only the sequence of amino acids and their chemical descriptors. This is as good as more opaque and complex strategies such as multilayer artificial neural networks [61] and N-gram representation random forest modeling, [62] and better than a linear SVM, with the advantage of chemically meaningful interpretations. Additionally, these models were used to identify potentially multifunctional peptides that are both antifouling and antimicrobial.…”

Section: Con CL U S I Onsmentioning

confidence: 99%

Classifying antimicrobial and multifunctional peptides with Bayesian network models

2018

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Bayesian network models are finding success in characterizing enzyme‐catalyzed reactions, slow conformational changes, predicting enzyme inhibition, and genomics. In this work, we apply them to statistical modeling of peptides by simultaneously identifying amino acid sequence motifs and using a motif‐based model to clarify the role motifs may play in antimicrobial activity. We construct models of increasing sophistication, demonstrating how chemical knowledge of a peptide system may be embedded without requiring new derivation of model fitting equations after changing model structure. These models are used to construct classifiers with good performance (94% accuracy, Matthews correlation coefficient of 0.87) at predicting antimicrobial activity in peptides, while at the same time being built of interpretable parameters. We demonstrate use of these models to identify peptides that are potentially both antimicrobial and antifouling, and show that the background distribution of amino acids could play a greater role in activity than sequence motifs do. This provides an advancement in the type of peptide activity modeling that can be done and the ease in which models can be constructed.

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Machine Learning Prediction of Antimicrobial Peptides

Wang

Vaisman

Hoek

2022

Methods in Molecular Biology

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Classification and Prediction of Antimicrobial Peptides Using N-gram Representation and Machine Learning

Cited by 3 publications

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AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

Classifying antimicrobial and multifunctional peptides with Bayesian network models

Machine Learning Prediction of Antimicrobial Peptides

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