RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix

Lysine Phoshoglycerylation Is Widespread in Bacteria and Overlaps with Acylation

Phosphoglycerylation is a non-enzymatic protein modification in which a phosphoglyceryl moiety is covalently bound to the ε-amino group of lysine. It is enriched in glycolytic enzymes from humans and mice and is thought to provide a feedback mechanism for regulating glycolytic flux. We report the first proteomic analysis of this post-translational modification in bacteria by profiling phosphoglyceryl-lysine during the growth of Streptococcus pyogenes in different culture media. The identity of phosphoglyceryl-lysine was confirmed by a previously unknown diagnostic cyclic immonium ion generated during MS/MS. We identified 370 lysine phosphoglycerylation sites in 123 proteins of S. pyogenes. Growth in a defined medium on 1% fructose caused a significant accumulation of phosphoglycerylation compared to growth in a rich medium containing 0.2% glucose. Re-analysis of phosphoproteomes from 14 bacterial species revealed that phosphoglycerylation is generally widespread in bacteria. Many phosphoglycerylation sites were conserved in several bacteria, including S. pyogenes. There was considerable overlap between phosphoglycerylation, acetylation, succinylation, and other acylations on the same lysine residues. Despite some exceptions, most lysine phosphoglycerylations in S. pyogenes occurred with low stoichiometry. Such modifications may be meaningless, but it is also conceivable that phosphoglycerylation, acetylation, and other acylations jointly contribute to the overall regulation of metabolism.

Transformer-based deep learning for predicting protein properties in the life sciences

Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model-the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.

PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection

A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular mechanism behind the phosphoglycerylation process to develop the drugs for related diseases. But accurately identifying of phosphoglycerylation site from a protein sequence in a laboratory is a very difficult and challenging task. Hence, the construction of an efficient computation model is greatly sought for this purpose. A little number of computational models are currently available for identifying the phosphoglycerylation sites, which are not able to reach their prediction capability at a satisfactory level. Therefore, an effective predictor named PLP_FS has been designed and constructed to identify phosphoglycerylation sites in this study. For the training purpose, an optimal number of feature sets was obtained by fusion of multiple F_Score feature selection techniques from the features generated by three types of sequence-based feature extraction methods and fitted with the support vector machine classification technique to the prediction model. On the other hand, the k-neighbor near cleaning and SMOTE methods were also implemented to balance the benchmark dataset. The suggested model in 10-fold cross-validation obtained an accuracy of 99.22%, a sensitivity of 98.17% and a specificity of 99.75% according to the experimental findings, which are better than other currently available predictors for accurately identifying the phosphoglycerylation sites.

Accurately predicting nitrosylated tyrosine sites using probabilistic sequence information

Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.