تقرير
Ridge Regression Estimated Linear Probability Model Predictions of N-glycosylation in Proteins with Structural and Sequence Data
| العنوان: | Ridge Regression Estimated Linear Probability Model Predictions of N-glycosylation in Proteins with Structural and Sequence Data |
|---|---|
| المؤلفون: | Gana, Rajaram, Naha, Swagata, Mazumder, Raja, Goldman, Radoslav, Vasudevan, Sona |
| سنة النشر: | 2018 |
| المجموعة: | Quantitative Biology |
| مصطلحات موضوعية: | Quantitative Biology - Quantitative Methods, 62J05, 62J07 |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1803.06002 |
| رقم الأكسشن: | edsarx.1803.06002 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |