Rational design of hyper-glycosylated human luteinizing hormone analogs (a bioinformatics approach). (April 2019)

• Record Type: Journal Article
• Authors: Shafaghi, Mona; Shabani, Ali Akbar; Minuchehr, Zarrin
• Is Part Of: Computational biology and chemistry. Volume 79(2019)
• Page Start: 16
• Page End: 23
• Publication Date: 2019-04
• Subjects: In silico design -- Human luteinizing hormone -- Hyper-glycosylation -- Glycoengineering; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2019.01.002
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: LH was considered for identification of the suitable positions for the addition of new N -glycan sites. Two qualified analogs with one additional glycosylation site were identified using computational strategies. The strategies can reduce laborious and time-consuming experimental analyses of the hyper-glycosylated analogs. Abstract: Glycoengineering is a recently used approach to extend serum half-life of valuable protein therapeutics. One aspect of glycoengineering is to introduce new N -glycosylation site (Asn-X-Thr/Ser, where X ≠ Pro) into desirable positions in the peptide backbone, resulting in the generation of hyper-glycosylated protein. In this study, human luteinizing hormone (LH) was considered for identification of the suitable positions for the addition of new N-linked glycosylation sites. A rational in silico approach was applied for prediction of structural and functional alterations caused by changes in amino acid sequence. As the first step, we explored the amino acid sequence of LH to find out desirable positions for introducing Asn or/and Thr to create new N -glycosylation sites. This exploration led to the identification of 38 potential N -glycan sites, and then the four acceptable ones were selected for further analysis. Three-dimensional (3D) structures of the selected analogs were generated and examined by the model evaluation methods. Finally, two analogs with one additional glycosylation site were suggested as the qualified analogs for hyper-glycosylation of the LH, which can be considered for further experimental investigations. Our computational strategy can reduce laborious and time-consuming experimental analyses of the analogs.