Prediction and Analysis of Quorum Sensing Peptides Based on Sequence Features

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).

AVP‐IC50Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)

“…These features include amino acid composition, N/C terminal residues and their physicochemical properties like hydrophobicity, secondary structure, and so forth. These have also been reported previously for prediction of other important peptides viz., ABCpred, AntiCP, QSPpred, and so forth. Evans et al have also described HIV coreceptor tropism prediction on the basis of the amino‐acid sites and physiochemical characteristics of the V3 loop sequence of the HIV envelope.…”

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides Using Informative Physicochemical Properties

“…Currently, there are only two computational methods [28,29] developed for discriminating QSPs from Non-QSPs using the information of peptide sequences as summarized in Table 1. The first QSP predictor was proposed by Akanksha et al [29] Figure 1. Structures of selected quorum sensing peptides that have been experimentally elucidated, where red and yellow colors represent alpha-helix and loop structures, respectively.…”

ge-CRISPR - An integrated pipeline for the prediction and analysis of sgRNAs genome editing efficiency for CRISPR/Cas system

“…SVM light (v6.02) (http://svmlight.joachims.org/) package was used for developing predictive models in various algorithms like QSPpred52, AVPpred etc 53…”