Enantiomers of 2-methylglutamate and 2-methylglutamine selectively impact mouse brain metabolism and behavior

Mini-review: Recent advances in post-translational modification site prediction based on deep learning

Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.

A comprehensive tool for accurate identification of methyl-Glutamine sites

Methylation is a biochemical process involved in nearly all of the human body functions. Glutamine is considered an indispensable amino acid that is susceptible to methylation via post-translational modification (PTM). Modern research has proved that methylation plays a momentous role in the progression of most types of cancers. Therefore, there is a need for an effective method to predict glutamine sites vulnerable to methylation accurately and inexpensively. The motive of this study is the formulation of an accurate method that could predict such sites with high accuracy. Various computationally intelligent classifiers were employed for their formulation and evaluation. Rigorous validations prove that deep learning performs best as compared to other classifiers. The accuracy (ACC) and the area under the receiver operating curve (AUC) obtained by 10-fold cross-validation was 0.962 and 0.981, while with the jackknife testing, it was 0.968 and 0.980, respectively. From these results, it is concluded that the proposed methodology works sufficiently well for the prediction of methyl-glutamine sites. The webserver's code, developed for the prediction of methyl-glutamine sites, is freely available at https://github.com/s20181080001/WebServer.git. The code can easily be set up by any intermediate-level Python user.

Enantiomers of 4-aminopentanoic acid act as false GABAergic neurotransmitters and impact mouse behavior

Imbalance in the metabolic pathway linking excitatory and inhibitory neurotransmission has been implicated in multiple psychiatric and neurologic disorders. Recently, we described enantiomer-specific effects of 2-methylglutamate, which is not decarboxylated to the corresponding methyl analogue of gamma-aminobutyric acid (GABA): 4-aminopentanoic acid (4APA). Here, we tested the hypothesis that 4APA also has enantiomer-specific actions in brain. Mouse cerebral synaptosome uptake (nmol/mg protein over 30 min) of (R)-4APA or (S)-4APA was time and temperature dependent; however, the R enantiomer had greater uptake, reduction of endogenous GABA concentration, and release following membrane depolarization than did the S enantiomer. (S)-4APA exhibited some weak agonist (GABA_A α4β3δ, GABA_A α5β2γ2, and GABA_B B1/B2) and antagonist (GABA_A α6β2γ2) activity while (R)-4APA showed weak agonist activity only with GABAA α5β2γ2. Both 4APA enantiomers (100 mg/kg IP) were detected in mouse brain 10 min after injection, and by 1 hr had reached concentrations that were stable over 6 hr; both enantiomers were cleared rapidly from mouse serum over 6 hr. Two-month-old mice had no mortality following 100-900 mg/kg IP of each 4APA enantiomer but did have similar dose-dependent reduction in distance moved in a novel cage. Neither enantiomer at 30 or 100 mg/kg impacted outcomes in 23 measures of well-being, activity chamber, or withdrawal from hot plate. Our results suggest that enantiomers of 4APA are active in mouse brain, and that (R)-4APA may act as a novel false neurotransmitter of GABA. Future work will focus on disease models and on possible applications as neuroimaging agents.