title: In silico approaches for designing highly effective cell penetrating peptides.
abstract: BACKGROUND
  Cell penetrating peptides have gained much recognition as a versatile transport vehicle for the intracellular delivery of wide range of cargoes (i.e. oligonucelotides, small molecules, proteins, etc.), that otherwise lack bioavailability, thus offering great potential as future therapeutics. Keeping in mind the therapeutic importance of these peptides, we have developed in silico methods for the prediction of cell penetrating peptides, which can be used for rapid screening of such peptides prior to their synthesis.
  
  
  METHODS
  In the present study, support vector machine (SVM)-based models have been developed for predicting and designing highly effective cell penetrating peptides. Various features like amino acid composition, dipeptide composition, binary profile of patterns, and physicochemical properties have been used as input features. The main dataset used in this study consists of 708 peptides. In addition, we have identified various motifs in cell penetrating peptides, and used these motifs for developing a hybrid prediction model. Performance of our method was evaluated on an independent dataset and also compared with that of the existing methods.
  
  
  RESULTS
  In cell penetrating peptides, certain residues (e.g. Arg, Lys, Pro, Trp, Leu, and Ala) are preferred at specific locations. Thus, it was possible to discriminate cell-penetrating peptides from non-cell penetrating peptides based on amino acid composition. All models were evaluated using five-fold cross-validation technique. We have achieved a maximum accuracy of 97.40% using the hybrid model that combines motif information and binary profile of the peptides. On independent dataset, we achieved maximum accuracy of 81.31% with MCC of 0.63.
  
  
  CONCLUSIONS
  The present study demonstrates that features like amino acid composition, binary profile of patterns and motifs, can be used to train an SVM classifier that can predict cell penetrating peptides with higher accuracy. The hybrid model described in this study achieved more accuracy than the previous methods and thus may complement the existing methods. Based on the above study, a user-friendly web server CellPPD has been developed to help the biologists, where a user can predict and design CPPs with much ease. CellPPD web server is freely accessible at http://crdd.osdd.net/raghava/cellppd/.
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh 160036, India.
authors.forename: Ankur
authors.initials: A
authors.lastname: Gautam
authors.forename: Kumardeep
authors.initials: K
authors.lastname: Chaudhary
authors.forename: Rahul
authors.initials: R
authors.lastname: Kumar
authors.forename: Arun
authors.initials: A
authors.lastname: Sharma
authors.forename: Pallavi
authors.initials: P
authors.lastname: Kapoor
authors.forename: Atul
authors.initials: A
authors.lastname: Tyagi
authors.forename: Gajendra P S
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D020816:Amino Acid Motifs; D057846:Cell-Penetrating Peptides / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology*; D003198:Computer Simulation; D030562:Databases, Protein; D016503:Drug Delivery Systems; D009841:Oligonucleotides / Q000235:genetics; D015202:Protein Engineering / Q000379:methods*; D017434:Protein Structure, Tertiary; D012372:ROC Curve; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D057846:Cell-Penetrating Peptides; D009841:Oligonucleotides