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A biomimetic approach for enhancing the in vivo half-life of peptides.

ABSTRACT: The tremendous therapeutic potential of peptides has not yet been realized, mainly owing to their short in vivo half-life. Although conjugation to macromolecules has been a mainstay approach for enhancing protein half-life, the steric hindrance of macromolecules often harms the binding of peptides to target receptors, compromising the in vivo efficacy. Here we report a new strategy for enhancing the in vivo half-life of peptides without compromising their potency. Our approach involves endowing peptides with a small molecule that binds reversibly to the serum protein transthyretin. Although there are a few molecules that bind albumin reversibly, we are unaware of designed small molecules that reversibly bind other serum proteins and are used for half-life extension in vivo. We show here that our strategy was effective in enhancing the half-life of an agonist for GnRH receptor while maintaining its binding affinity, which was translated into superior in vivo efficacy.

SUBMITTER: Penchala SC

PROVIDER: S-EPMC4575266 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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A biomimetic approach for enhancing the in vivo half-life of peptides.

Penchala Sravan C SC Miller Mark R MR Pal Arindom A Dong Jin J Madadi Nikhil R NR Xie Jinghang J Joo Hyun H Tsai Jerry J Batoon Patrick P Samoshin Vyacheslav V Franz Andreas A Cox Trever T Miles Jesse J Chan William K WK Park Miki S MS Alhamadsheh Mamoun M MM

Nature chemical biology 20150907 10

The tremendous therapeutic potential of peptides has not yet been realized, mainly owing to their short in vivo half-life. Although conjugation to macromolecules has been a mainstay approach for enhancing protein half-life, the steric hindrance of macromolecules often harms the binding of peptides to target receptors, compromising the in vivo efficacy. Here we report a new strategy for enhancing the in vivo half-life of peptides without compromising their potency. Our approach involves endowing ...[more]

PMID: 26344696

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Project description:BackgroundIn 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.ResultsIn 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.ConclusionIn 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/).

Dataset Information

A biomimetic approach for enhancing the in vivo half-life of peptides.

Publications

A biomimetic approach for enhancing the in vivo half-life of peptides.

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