Prediction and analysis of quorum sensing peptides based on sequence features

PloS One
Akanksha RajputManoj Kumar

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 v...Continue Reading

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Citations

Oct 23, 2015·Nucleic Acids Research·Akanksha RajputManoj Kumar
Feb 26, 2016·Scientific Reports·Arun SharmaAnshu Bhardwaj
Mar 11, 2018·Journal of Cheminformatics·Abid QureshiManoj Kumar
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Feb 28, 2019·Frontiers in Pharmacology·Akanksha RajputManoj Kumar
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Jan 23, 2021·Briefings in Bioinformatics·Akanksha RajputManoj Kumar
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Aug 31, 2021·Briefings in Bioinformatics·Yichen GuoBin Liu
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