ABSTRACT
BACKGROUND: N-Glycosylation is one of the most important post-translational mechanisms in eukaryotes. N-glycosylation predominantly occurs in N-X-[S/T] sequon where X is any amino acid other than proline. However, not all N-X-[S/T] sequons in proteins are glycosylated. Therefore, accurate prediction of N-glycosylation sites is essential to understand Nglycosylation mechanism. OBJECTIVE: In this article, our motivation is to develop a computational method to predict Nglycosylation sites in eukaryotic protein sequences. METHODS: In this article, we report a random forest method, Nglyc, to predict N-glycosylation site from protein sequence, using 315 sequence features. The method was trained using a dataset of 600 N-glycosylation sites and 600 non-glycosylation sites and tested on the dataset containing 295 Nglycosylation sites and 253 non-glycosylation sites. Nglyc prediction was compared with NetNGlyc, EnsembleGly and GPP methods. Further, the performance of Nglyc was evaluated using human and mouse N-glycosylation sites. RESULT: Nglyc method achieved an overall training accuracy of 0.8033 with all 315 features. Performance comparison with NetNGlyc, EnsembleGly and GPP methods shows that Nglyc performs better than the other methods with high sensitivity and specificity rate. CONCLUSION: Our method achieved an overall accuracy of 0.8248 with 0.8305 sensitivity and 0.8182 specificity. Comparison study shows that our method performs better than the other methods. Applicability and success of our method was further evaluated using human and mouse N-glycosylation sites. Nglyc method is freely available at https://github.com/bioinformaticsML/ Ngly.
Subject(s)
Computational Biology/methods , Proteins/chemistry , Sequence Analysis, Protein/methods , Animals , Databases, Protein , Glycosylation , Humans , Mice , SoftwareABSTRACT
Pheromones play a pivotal role in intra-species communication for reproduction and social behavior in a variety of mammals, such as boars. For boars, saliva is a rich source of pheromones, however, the identification of additional sources and relative abundance of pheromones in various body fluids of sows is also essential to understand the reproductive behaviors of pigs. The present study was designed to identify the source(s) of pheromones in sows. We collected urine, feces, saliva and cervical mucus/vaginal wash samples from sows at pre-estrus, estrus and post-estrus phases, and from gilts and exposed boars to each of these potential sources of pheromones. All the boars tested spent more time sniffing and hyper-salivating in response to urine from sows in estrus than that from sows not in estrus. The sniffing behavior of boars towards estrus samples differed from that towards the samples from non-estrus sows (Pâ¯<â¯0.005) and gilts (Pâ¯<â¯0.001). Further, hypersalivation behavior of boars differed between estrus samples and gilt samples (Pâ¯<â¯0.05) and estrus samples compared to pre-estrus samples (Pâ¯<â¯0.05). This is an indication that pheromones are abundant in the estrus samples. We conclude that urine of estrus sows can be a rich source of pheromones and the same can be used to identify, purify and characterize novel pheromone molecules.
Subject(s)
Estrus/physiology , Pheromones/physiology , Sexual Behavior, Animal/physiology , Sus scrofa/physiology , Animals , Female , Pheromones/urine , Swine , WeaningABSTRACT
Mitochondria are important sub-cellular organelles in eukaryotes. Defects in mitochondrial system lead to a variety of disease. Therefore, detailed knowledge of mitochondrial proteome is vital to understand mitochondrial system and their function. Sequence databases contain large number of mitochondrial proteins but they are mostly not annotated. In this study, we developed a support vector machine approach, SubmitoLoc, to predict mitochondrial sub cellular locations of proteins based on various sequence derived properties. We evaluated the predictor using 10-fold cross validation. Our method achieved 88.56 % accuracy using all features. Average sensitivity and specificity for four-subclass prediction is 85.37% and 87.25% respectively. High prediction accuracy suggests that SubmitoLoc will be useful for researchers studying mitochondrial biology and drug discovery.
