Designing of peptides with desired half-life in intestine-like environment

Abstract

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/).Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-282) contains supplementary material, which is available to authorized users.

Antiviral peptides as promising therapeutic drugs

“…They can derive from bait studies where an artificial peptide is tested for interaction against a specific target, such as a surface glycoprotein or an important viral enzyme [37]; or obtained in silico using specific software designed for the prediction of peptides [38,39]. In both cases, many settings are taken into consideration, such as the topology, amino acid composition, charge, and many other chemical and structural characteristics that may influence the antiviral activity of a peptide [40,41].…”

Insilico Alpha-Helical Structural Recognition of Temporin Antimicrobial Peptides and Its Interactions with Middle East Respiratory Syndrome-Coronavirus

“…Further, the biofilm active temporin peptides were also predicted using the dPABBs module (Sharma et al 2016). The half-life of the peptides was identified using the HLP, a server for predicting half-life of peptides (Sharma et al 2014).…”

New peptides with immunomodulatory activity in macrophages and antibacterial activity against multiresistant Staphylococcus aureus

“…Based on the N-terminal amino acid of each peptide, the half-life In vitro and In vivo was predicted along with the instability and aliphatic index and finally classification of the stability of the peptide. Likewise, the "HLP: Web server for predicting half-life of peptides in intestine-like environment" (Sharma et al, 2014) permitted the prediction of the half-life and stability of the new peptides in an intestine-like, proteolytic, environment.…”

“…2. Institute of Microbial Technology, HLP: Web server for predicting half-life of the peptide in intestine-like environment (Sharma et al, 2014). 3.…”