DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.     

WHEN DOES A MEASUREMENT OR EVENT OCCUR?

Within quantum mechanics, a complete set of commutting observables can be found which describe the attributes of a system at a given time. However, the correct way to describe attributes of a system in time is still an open question. We discuss the difficulties in extending the standard approach of quantum mechanics to describe attributes of a system in time. We find that measuring when an event occurred and measuring that it occurred, are complimentary in Bohr's sense. To exemplify the differences between measurements at a given time and in time, we will compare Rovelli's recent proposal (quant-ph/9802020), to determine “at what time does a measurement occurred” with another model of a continuous measurement in time. Rovelli's scheme answers the question “has the measurement already occurred at a certain time?”, but does not answer to the more difficult question: “when did the measurement occur?” We also discuss the use of the probability current to measure the time at which a particle arrives to a certain location.     

Book Review: Statistical Mechanics: Fundamentals and Modem Applications

Journal of Statistical Physics -