ArticlePloS one2015
Prediction and analysis of quorum sensing peptides based on sequence features.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- IQSPred-PLM: An Interpretable Quorum Sensing Peptides Prediction Model Based on Protein Language Model.Interdisciplinary sciences, computational life sciences · 2026Article
- TPT: a compact CNN-transformer encoder for efficient microbial small protein modeling.Frontiers in microbiology · 2026Article
- Mapping the Antibiofilm Peptide Space with Similarity Networks and Curated Negative Sets.ACS omega · 2025Article
- Tryptic oncopeptide secreted from the gut bacterium Cronobacter malonaticus PO3 promotes colorectal cancer.Scientific reports · 2025Article
- Computational approaches for identifying neuropeptides: A comprehensive review.Molecular therapy. Nucleic acids · 2025Review
- iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.Current medicinal chemistry · 2025Article
- Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.Briefings in bioinformatics · 2024Article
- AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors.Bioinformatics (Oxford, England) · 2024Article
- Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.Current medicinal chemistry · 2024Review
- BBSdb, an open resource for bacterial biofilm-associated proteins.Frontiers in cellular and infection microbiology · 2024Article
- PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework.Bioinformatics (Oxford, England) · 2023Article
- Design of novel anti-quorum sensing peptides targeting LuxO to combatIn silico pharmacology · 2023Article
- Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.Computational and structural biotechnology journal · 2023Review
- CSM-peptides: A computational approach to rapid identification of therapeutic peptides.Protein science : a publication of the Protein Society · 2022Article
- Biofilm-Molecules (Basel, Switzerland) · 2022Article
- Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning.Molecular diversity · 2022Article
- Modeling the time course of ComX: towards molecular process control for Bacillus wild-type cultivations.AMB Express · 2021Article
- CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model.BMC bioinformatics · 2021Article
- Peptipedia: a user-friendly web application and a comprehensive database for peptide research supported by Machine Learning approach.Database : the journal of biological databases and curation · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew's correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T200p+200n). Developed models performed equally well on the validation dataset (V20p+20n). The server also integrates several useful analysis tools like "QSMotifScan", "ProtFrag", "MutGen" and "PhysicoProp". Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm "QSPpred" (freely available at: http://crdd.osdd.net/servers/qsppred).
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