Our other prediction servers

• dSSPred
• MaLDoSS
• PreDOSS
• HSplice
• SPIDBAR
• DCDNC
• iAMPpred
• DIRprot

Useful Links

• NCBI
• HSPIR
• iHSP-PseRAAAC
• JPred

Our Recent Publications

Meher PK , Sahu TK , Banchariya A, Rao AR (2017). DIRProt: A computational approach for discriminating insecticide resistant proteins from non-resistant proteins. BMC Bioinformatics, 18: 190.

Meher PK , Sahu TK , Saini V, and Rao AR (2017). Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Scientific Reports, 7: 42362.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2016). Discriminating coding from non-coding regions based on codon structure and methylation-mediated substitution: An application in rice and cattle. Computers and Electronics in Agriculture, 129: 66–73.

Meher PK , Sahu TK , Rao AR(2016). Prediction of donor splice sites using random forest with a new sequence encoding approach. BioData mining, 9:4.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2016). Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features. Algorithms for Molecular Biology, 11:16.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2016). A computational approach for prediction of donor splice sites with improved accuracy. Journal of Theoretical Biology, 404: 285–294.

Meher PK , Sahu TK , Rao AR(2016). Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier. Gene, 592: 316–324.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2015). Determination of window size and identification of suitable method for prediction of donor splice sites in rice (Oryza sativa) genome. Journal of Plant Biochemistry and Biotechnology, 24(4): 385-392.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2014). A statistical approach for 5’ splice site prediction using short sequence motifs and without encoding sequence data. BMC Bioinformatics, 15: 362.

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Heat shock proteins (HSPs) are one of the largest groups of molecular chaperones that assist in correct folding of partially folded or denatured proteins, establishment of proper protein conformation and prevention of unalterable aggregation of damaged proteins. Besides chaperonine activities, HSPs are also involved in other functions like modulation of their synthesis, participation in signal transduction pathways, RNA processing etc. HSPs also play vital role in maintaining the overall cellular protein homeostasis. Due to broad range of activities, they have received a considerable attention of the researchers. Keeping in view the wide range of functions of HSPs, we developed this server for prediction of HSPs, their families (HSP20, HSP40, HSP60, HSP70, HSP90 and HSP100) and sub-typpes of DnaJ proteins(Type I, Type II, Type III, Type IV). The ir-HSP achieved higher accuracy as compared to the existing approaches, and thus believed to supplement the existing efforts for annotation of protein sequences.

          Figure Source: HSPIR database

Please Cite:
Meher, P. K., Sahu, T. K., Gahoi, S. and Rao, A. R. (2018). ir-HSP: improved recognition of heat shock proteins, their families and sub-types based on g-spaced di-peptide features and support vector machine. Frontiers in Genetics, 8, 235.