DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins.
BACKGROUND
Insecticide 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.


RESULTS
Five 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.


CONCLUSIONS
This 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.
Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
Prabina Kumar
PK
Meher
Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
Tanmaya Kumar
TK
Sahu
Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.|Department of Bioinformatics, Janta Vedic College, Baraut, Baghpat, 250611, Uttar Pradesh, India.
Anjali
A
Banchariya
Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. rao.cshl.work@gmail.com.
Atmakuri Ramakrishna
AR
Rao
D000818:Animals; D003577:Cytochrome P-450 Enzyme System / Q000378:metabolism*; D006801:Humans; D007306:Insecticides / Q000032:analysis / Q000378:metabolism*; D011506:Proteins / Q000737:chemistry*; D005680:gamma-Aminobutyric Acid / Q000378:metabolism*
D007306:Insecticides; D011506:Proteins; D005680:gamma-Aminobutyric Acid; D003577:Cytochrome P-450 Enzyme System
Cytochrome P450; Di-peptide composition; GABA; Insecticide resistance; SVM