Abstract
BackgroundInsecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc. Resistance to different insecticides is conferred by the proteins encoded from certain class of genes of the insects. To distinguish the insecticide resistant proteins from non-resistant proteins, no computational tool is available till date. Thus, development of such a computational tool will be helpful in predicting the insecticide resistant proteins, which can be targeted for developing appropriate insecticides.ResultsFive different sets of feature viz., amino acid composition (AAC), di-peptide composition (DPC), pseudo amino acid composition (PAAC), composition-transition-distribution (CTD) and auto-correlation function (ACF) were used to map the protein sequences into numeric feature vectors. The encoded numeric vectors were then used as input in support vector machine (SVM) for classification of insecticide resistant and non-resistant proteins. Higher accuracies were obtained under RBF kernel than that of other kernels. Further, accuracies were observed to be higher for DPC feature set as compared to others. The proposed approach achieved an overall accuracy of >90% in discriminating resistant from non-resistant proteins. Further, the two classes of resistant proteins i.e., detoxification-based and target-based were discriminated from non-resistant proteins with >95% accuracy. Besides, >95% accuracy was also observed for discrimination of proteins involved in detoxification- and target-based resistance mechanisms. The proposed approach not only outperformed Blastp, PSI-Blast and Delta-Blast algorithms, but also achieved >92% accuracy while assessed using an independent dataset of 75 insecticide resistant proteins.ConclusionsThis paper presents the first computational approach for discriminating the insecticide resistant proteins from non-resistant proteins. Based on the proposed approach, an online prediction server DIRProt has also been developed for computational prediction of insecticide resistant proteins, which is accessible at http://cabgrid.res.in:8080/dirprot/. The proposed approach is believed to supplement the efforts needed to develop dynamic insecticides in wet-lab by targeting the insecticide resistant proteins.
Highlights
Insecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc
The γ-amino butyric acid (GABA) receptor is the site of target for cyclodiene insecticides [20], where the resistance to dieldrin (Rdl) is conferred by the change of a single amino acid in GABA-gated chloride ion channel encoded by Rdl gene [21]
Collection and processing of data In this study, protein sequences corresponding to four important groups of insecticide resistant genes viz., cytochrome P450, knockdown resistance (Kdr), Rdl and AChE were collected from insecticide resistance gene database
Summary
Insecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc. Though there is evidence of alteration in cuticular penetration, most of the studies have focused and evaluated the target site insensitivity and detoxification of insecticides (metabolic resistance) mechanisms. These two mechanisms have been reported to cover a wide range of resistance levels to almost all available insecticides [9]. The GABA receptor is the site of target for cyclodiene (dieldrin) insecticides [20], where the resistance to dieldrin (Rdl) is conferred by the change of a single amino acid in GABA-gated chloride ion channel encoded by Rdl gene [21]. The point mutation in the insecticide-binding site of AChE has been identified as the cause of insensitivity to these insecticides [26]
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