![]() ![]() ![]() based ‘ProtLifePred server’ takes into account the ubiquitination (a process that involves post-translational modification of proteins) process of proteins ![]() SProtP is a web server to recognize the short-lived proteins (half-life < 30 minutes) in 293 T cells (a variant of human embryonic kidney cell line) Stability Prediction tool predicts the stability of HIV-derived peptides in cytosolic extracts from human peripheral blood mononuclear cells The estimation of half-life is done for three experimental models namely: yeast in vivo, mammalian reticulocytes (immature red blood cells) in vitro and Escherichia coli in vivo For example, ProtParam is a tool that helps to estimate half-life of a protein stored in Swiss-Prot/TrEMBL or a user entered protein. In past, computational methods for predicting half-life of proteins/peptides in blood and kidney cells/cell lines have been developed. In view of its importance, there is a need to develop an in silico method for predicting 1) half-life of peptides as well as 2) prediction of mutations required in a peptide, in order to increase its intestinal half-life. , yet these experimental procedures are costly and time consuming. Although, a number of experimental techniques like peptide modification are available in order to improve peptide stability with high accuracy A major concern associated with the use of peptide therapeutics is improving their stability by protection against degrading proteases Their high susceptibility to proteases in the gut and serum (especially for cationic peptides) and fast degradation rate due to their arginine and lysine content makes them low orally bioavailable This is because of their undesirable physicochemical properties like large molecular size, high susceptibility to enzymatic degradation (proteases), hepatic and renal clearance, etc.Īmongst the above factors, peptide stability is one of the most difficult tasks to maintain. However, designing and formulating an oral peptide has been considered as a challenging job. Additionally, orally available peptides are highly accepted by patients, which increases the therapeutic value of the drug. Among all routes of drug delivery, oral is the most preferred route because oral formulations are less expensive and less prone to infection caused by inappropriate use/reuse of needles. In addition, alternate routes such as pulmonary Presently, most of the therapeutic peptides are used in the form of injection via subcutaneous, intravenous/intramuscular route. have been developed to predict and design cell penetrating, tumor homing, anticancer, antiviral and toxic peptides, respectively. Also, a number of computation tools such as CellPPD Owing to their immense therapeutic importance, peptides have been curated from literature and stored in form of databases such as Hemolytik are used for treating various diseases like diabetes and immunoregulatory disorders The web server provides three facilities i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides.ĭue to rapid advancement in peptide and peptidomimetic techniques, pharmaceutical companies are focusing towards peptides-based therapeutics Based on above models, we have developed a web server named HLP ( Half Life Prediction), for predicting and designing peptides with desired half-life. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Secondly, models developed on HL16 dataset showed maximum R/R 2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R 2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. 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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |