title: Designing of peptides with desired half-life in intestine-like environment.
abstract: BACKGROUND
  In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life.
  
  
  RESULTS
  In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides.
  
  
  CONCLUSIONS
  In summary, this study describes a web server 'HLP' that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).
authors.forename: Arun
authors.initials: A
authors.lastname: Sharma
authors.forename: Deepak
authors.initials: D
authors.lastname: Singla
authors.forename: Mamoon
authors.initials: M
authors.lastname: Rashid
authors.affiliation: Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.
authors.forename: Gajendra Pal Singh
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D055598:Chemical Phenomena; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D015195:Drug Design; D006207:Half-Life; D020407:Internet; D007413:Intestinal Mucosa / Q000378:metabolism*; D009154:Mutation; D010455:Peptides / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D012984:Software
chemical_list: D010455:Peptides