Ridge Regression Estimated Linear Probability Model Predictions of N-glycosylation in Proteins with Structural and Sequence Data

Autor:	Gana, Rajaram, Naha, Swagata, Mazumder, Raja, Goldman, Radoslav, Vasudevan, Sona
Rok vydání:	2018
Předmět:	Quantitative Biology - Quantitative Methods 62J05 62J07
Druh dokumentu:	Working Paper
Popis:	Absent experimental evidence, a robust methodology to predict the likelihood of N-glycosylation in human proteins is essential for guiding experimental work. Based on the distribution of amino acids in the neighborhood of the NxS/T sequon (N-site); the structural attributes of the N-site that include Accessible Surface Area, secondary structural elements, main-chain phi-psi, turn types; the relative location of the N-site in the primary sequence; and the nature of the glycan bound, the ridge regression estimated linear probability model is used to predict this likelihood. This model yields a Kolmogorov-Smirnov (Gini coefficient) statistic value of about 74% (89%), which is reasonable. Comment: 20 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1803.06002 Zobrazit plný text záznamu View this record from Arxiv