Ridge Regression Estimated Linear Probability Model Predictions of N-glycosylation in Proteins with Structural and Sequence Data
Quantitative Methods
2018-03-26 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1803.06002,
title = {Ridge Regression Estimated Linear Probability Model Predictions of N-glycosylation in Proteins with Structural and Sequence Data},
author = {Rajaram Gana and Swagata Naha and Raja Mazumder and Radoslav Goldman and Sona Vasudevan},
journal= {arXiv preprint arXiv:1803.06002},
year = {2018}
}
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20 pages