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.

Help

Input

The input sequences must be supplied in FASTA format. Each sequence should contains only standard amino acid residues in a single letter code format i.e., A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y. An example dataset having 3 protein sequences is given below.

>Seq_1
MMINYWNPIEEIDTVRRQLDHLFEDAIDTGKSSNYPSWAPAVELWDTGDALILKAFLPGVNADSLDIQATRESISIA GERHPEDLQEGTKRLFSDINYGHFRRASKLPVAIQNTKVEASFEQGLLTLRLPKVEEEQNKVVKVNLLNKGETSQAA LEASHNS
>Seq_2 MMSIVLRDPFRSFERMYPLGWEPFQELESWRREMDRMFGRLMPISKSDGEKSLTFMPSAEMDETDQEIHLKFELPGL DAKDLDIEVTKDAVYIRGERKTEIEAESEGTVRSEFHYGKFERVIPMPSPIKTDNVQAEYNNGVLSLTLSKSEEDMK KSVKVEVS
>Seq_3 MVDSQDHYSVLQVSPQATPADIKTAFRRLVRQYHPDLNPNNPRAAEAFQKICTAYEVLSNRDRRVIYDHNDGPRSPF VEEPGSVVLQDAQQYFMQGVQKANRRNYATAIADFTQAIHLDKNYLEAYMGRCQARFALGHNREVLQDCDHILSIQP QSAQAFFFRGRSCYRLGHLDLSIESYSQAIALEKDYAQAFYHRGIAYIKVKERLAIRDLQAAASLFRQQGHIGHYQR AIATLKDLNRKPANILLNLPRNGATLATTALTTLPRLLVNPSGAALQVWQSQLPYQSIGISLICAGLAMGCVVGGAH LFAPKPWTVSDSQVMLLSLVAFSSLVLAGSLIRRMVGKGGSKGGSWSGDLLVAGSAMLPVGVWAFLSGLAMQLGQIE LIALSLFASSFTILGLYSGYTKIGQIAEPIAALAVPTIVMISYGVTALLYRALMLQLV

Output

The output (results) are displayed in a tabular format with four columns. The first column represents the serial number, second column represents the sequence identifier, third column represents the types of predicted HSP (with subtype of DnaJ if predicted as HSP40) or non-HSP and the fourth column represents the probabilities with which they are predicted in the respective classes. For example, the second sequence is predicted as HSP20 with probability 0.959, whereas the third sequence is predicted as HSP40 with probability 0.993 and again this HSP40 is predicted as Type-III DnaJ protein with probability 0.992.