CellPPD: Designing of Cell Penetrating Peptides

This is one of the major features of CellPPD by which users can design efficient CPPs either single or multiple peptides at a time. Users have two options: (i) user can opt only binary profile based SVM approach, and (ii) hybrid (binary profile based SVM + motif information) approach. As this sever allows users to select threshold, we suggest the users to select higher value if they are interested in high specificity (high confidence). In case the user is more interested to cover most of the CPPs (high sensitivity) then they should select lower threshold. For using hybrid model user has to select E-value (default value is 10), which is required for the MAST programme. Both approaches will generate all the possible mutants of a given peptide and predicts their status i.e., CPP or non-CPP. By this tool, users can easily identify, which amino acid(s) is important for cell penetration. Along with this, it calculates important physico-chemical properties of the peptides like hydrophilicity, hydrophobicity, charge, pI etc. Users can choose peptides according to desired physico-chemical properties.

Here, user can scan a protein sequence to identify novel putative CPPs present in that sequence. It will generate the fragments of length selected by the users, and predict their cell penetration potential along with all the important physico-chemical properties like hydrophobicity, charge, pI etc. selected by the users in the display option. So, it will assist researchers who are looking for novel CPPs prior to their synthesis.

We identified more than hundred CPP motifs by MEME software. By using this tool, user can map these motifs on their protein sequences to locate putative region of CPP. It also assists researchers to identify novel CPP sequences.

We also provide list of all the identified CPP motifs in this webservers. Users can use them to design new CPP sequences or they can use them as such.

In this server, we used motif information for the prediction of CPPs. We developed hybrid model by combining motif information and SVM, which increases the accuracy and reliability of this prediction server.