ArticleFrontiers in pharmacology2018
Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features.
Article in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
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- Prediction of inhibitory peptides against E.coli with desired MIC value.Scientific reports · 2025Article
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- Antiprotozoal peptide prediction using machine learning with effective feature selection techniques.Heliyon · 2024Article
- AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria.Antibiotics (Basel, Switzerland) · 2024Article
- Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery.PloS one · 2024Article
- Unsupervised encoding selection through ensemble pruning for biomedical classification.BioData mining · 2023Article
- Antimicrobial peptides as drugs with double response againstFrontiers in microbiology · 2023Review
- Multiple machine learning methods aided virtual screening of NaJournal of cellular and molecular medicine · 2023Article
- Polypharmacological Cell-Penetrating Peptides from Venomous Marine Animals Based on Immunomodulating, Antimicrobial, and Anticancer Properties.Marine drugs · 2022Article
- Accurate Prediction of Anti-hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit.Interdisciplinary sciences, computational life sciences · 2022Article
- Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors.BMC bioinformatics · 2022Article
- CSM-peptides: A computational approach to rapid identification of therapeutic peptides.Protein science : a publication of the Protein Society · 2022Article
- Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction.Briefings in bioinformatics · 2022Article
- MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Ensemble-AHTPpred: A Robust Ensemble Machine Learning Model Integrated With a New Composite Feature for Identifying Antihypertensive Peptides.Frontiers in genetics · 2022Article
- Synthetic approaches to potent heterocyclic inhibitors of tuberculosis: A decade review.Frontiers in pharmacology · 2022Review
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3 authors.
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Abstract
Tuberculosis is one of the leading cause of death worldwide, particularly due to evolution of drug resistant strains. Antitubercular peptides may provide an alternate approach to combat antibiotic tolerance. Sequence analysis reveals that certain residues (e.g., Lysine, Arginine, Leucine, Tryptophan) are more prevalent in antitubercular peptides. This study describes the models developed for predicting antitubercular peptides by using sequence features of the peptides. We have developed support vector machine based models using different sequence features like amino acid composition, binary profile of terminus residues, dipeptide composition. Our ensemble classifiers that combines models based on amino acid composition and N5C5 binary pattern, achieves highest Acc of 73.20% with 0.80 AUROC on our main dataset. Similarly, the ensemble classifier achieved maximum Acc 75.62% with 0.83 AUROC on secondary dataset. Beside this, hybrid model achieves Acc of 75.87 and 78.54% with 0.83 and 0.86 AUROC on main and secondary dataset, respectively. In order to facilitate scientific community in designing of antitubercular peptides, we implement above models in a user friendly webserver (http://webs.iiitd.edu.in/raghava/antitbpred/).
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