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

Sharma A, Singla D, Rashid M, Raghava GP - BMC Bioinformatics (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.

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.

Conclusion: 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/).

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Physicochemical properties and amino acid composition of top 20 peptides having longest half-life (stable peptides) and top 20 peptides having shortest half-life (unstable peptides). (A) physicochemical properties of 16mer peptides having short and long half-life; (B) physicochemical properties of 10mer peptides having short and long half-life; (C) average amino acid composition 16mer peptides having short and long half-life; (D) shows average amino acid composition of 10mer peptides having short and long half-life.
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Fig1: Physicochemical properties and amino acid composition of top 20 peptides having longest half-life (stable peptides) and top 20 peptides having shortest half-life (unstable peptides). (A) physicochemical properties of 16mer peptides having short and long half-life; (B) physicochemical properties of 10mer peptides having short and long half-life; (C) average amino acid composition 16mer peptides having short and long half-life; (D) shows average amino acid composition of 10mer peptides having short and long half-life.

Mentions: We computed and analyzed physicochemical properties of peptides in both HL10 and HL16 datasets to understand relation between property of amino acids and their half-life. Based on the half-life value, peptides were classified into two categories peptides having long half-life (highly stable) and peptides having short half-life (poorly stable or unstable). Each category have top 20 peptides, it means 20 peptides having longest half-life were classified as stable and 20 peptides having shortest half-life were classified as unstable. We observed that negatively charged, neutral and tiny types of residues are more prominent in highly stable peptides (Figure 1A and B). We have also computed amino acid composition of peptides in highly and lowest/poorly stable peptide datasets. As shown in Figure 1 (C and D), residues Ala, Asp, Glu, Gly, Gln, Ser and Thr are abundant in peptide dataset with longer half-life. As evident from Additional file1: Table S3, residues D (Asp), F (Phe), G (Gly), L (Leu), Q (Gln), R (Arg) and Y (Tyr) show significant differences (p < 0.05) in amino acid composition in 10mer dataset. Similarly, residues D (Asp), F (Phe), G (Gly), K (Lys), M (Met), N (Asn), R (Arg) and Y (Tyr) shows statistically significant differences (p < 0.05) in amino acid composition for 16mer dataset (Additional file1: Table S4).Figure 1


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

Sharma A, Singla D, Rashid M, Raghava GP - BMC Bioinformatics (2014)

Physicochemical properties and amino acid composition of top 20 peptides having longest half-life (stable peptides) and top 20 peptides having shortest half-life (unstable peptides). (A) physicochemical properties of 16mer peptides having short and long half-life; (B) physicochemical properties of 10mer peptides having short and long half-life; (C) average amino acid composition 16mer peptides having short and long half-life; (D) shows average amino acid composition of 10mer peptides having short and long half-life.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4150950&req=5

Fig1: Physicochemical properties and amino acid composition of top 20 peptides having longest half-life (stable peptides) and top 20 peptides having shortest half-life (unstable peptides). (A) physicochemical properties of 16mer peptides having short and long half-life; (B) physicochemical properties of 10mer peptides having short and long half-life; (C) average amino acid composition 16mer peptides having short and long half-life; (D) shows average amino acid composition of 10mer peptides having short and long half-life.
Mentions: We computed and analyzed physicochemical properties of peptides in both HL10 and HL16 datasets to understand relation between property of amino acids and their half-life. Based on the half-life value, peptides were classified into two categories peptides having long half-life (highly stable) and peptides having short half-life (poorly stable or unstable). Each category have top 20 peptides, it means 20 peptides having longest half-life were classified as stable and 20 peptides having shortest half-life were classified as unstable. We observed that negatively charged, neutral and tiny types of residues are more prominent in highly stable peptides (Figure 1A and B). We have also computed amino acid composition of peptides in highly and lowest/poorly stable peptide datasets. As shown in Figure 1 (C and D), residues Ala, Asp, Glu, Gly, Gln, Ser and Thr are abundant in peptide dataset with longer half-life. As evident from Additional file1: Table S3, residues D (Asp), F (Phe), G (Gly), L (Leu), Q (Gln), R (Arg) and Y (Tyr) show significant differences (p < 0.05) in amino acid composition in 10mer dataset. Similarly, residues D (Asp), F (Phe), G (Gly), K (Lys), M (Met), N (Asn), R (Arg) and Y (Tyr) shows statistically significant differences (p < 0.05) in amino acid composition for 16mer dataset (Additional file1: Table S4).Figure 1

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@imtech.res.in.

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.

Conclusion: 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/).

Show MeSH
Related in: MedlinePlus