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1. A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites

   Thapa N, Chaudari M ( June 2021 , Scientific report)

   null (Ed.)

   Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga. 

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2. Extensive regulation of enzyme activity by phosphorylation in Escherichia coli

   https://doi.org/10.1038/s41467-021-25988-4  

   Schastnaya, Evgeniya ; Raguz Nakic, Zrinka ; Gruber, Christoph H. ; Doubleday, Peter Francis ; Krishnan, Aarti ; Johns, Nathan I. ; Park, Jimin ; Wang, Harris H. ; Sauer, Uwe ( December 2021 , Nature Communications)

   Abstract Protein serine/threonine/tyrosine (S/T/Y) phosphorylation is an essential and frequent post-translational modification in eukaryotes, but historically has been considered less prevalent in bacteria because fewer proteins were found to be phosphorylated and most proteins were modified to a lower degree. Recent proteomics studies greatly expanded the phosphoproteome of Escherichia coli to more than 2000 phosphorylation sites (phosphosites), yet mechanisms of action were proposed for only six phosphosites and fitness effects were described for 38 phosphosites upon perturbation. By systematically characterizing functional relevance of S/T/Y phosphorylation in E. coli metabolism, we found 44 of the 52 mutated phosphosites to be functional based on growth phenotypes and intracellular metabolome profiles. By effectively doubling the number of known functional phosphosites, we provide evidence that protein phosphorylation is a major regulation process in bacterial metabolism. Combining in vitro and in vivo experiments, we demonstrate how single phosphosites modulate enzymatic activity and regulate metabolic fluxes in glycolysis, methylglyoxal bypass, acetate metabolism and the split between pentose phosphate and Entner-Doudoroff pathways through mechanisms that include shielding the substrate binding site, limiting structural dynamics, and disrupting interactions relevant for activity in vivo. 

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3. Heterotrimeric G‐Protein‐Dependent Proteome and Phosphoproteome in Unstimulated Arabidopsis Roots

   https://doi.org/10.1002/pmic.201800323  

   Song, Gaoyuan ; Brachova, Libuse ; Nikolau, Basil J. ; Jones, Alan M. ; Walley, Justin W. ( December 2018 , PROTEOMICS)

   The G‐protein complex is a cytoplasmic on–off molecular switch that is set by plasma membrane receptors that activate upon binding of its cognate extracellular agonist. In animals, the default setting is the “off” resting state, while in plants, the default state is constitutively “on” but repressed by a plasma membrane receptor‐like protein. De‐repression appears to involve specific phosphorylation of key elements of the G‐protein complex and possibly target proteins that are positioned downstream of this complex. To address this possibility, protein abundance and phosphorylation state are quantified in wild type and G‐protein deficient Arabidopsis roots in the unstimulated resting state. A total of 3246 phosphorylated and 8141 non‐modified protein groups are identified. It has been found that 428 phosphorylation sites decrease and 509 sites increase in abundance in the G‐protein quadrupole mutant lacking an operable G‐protein‐complex. Kinases with known roles in G‐protein signaling including MAP KINASE 6 and FERONIA are differentially phosphorylated along with many other proteins now implicated in the control of G‐protein signaling. Taken together, these datasets will enable the discovery of novel proteins and biological processes dependent on G‐protein signaling.

    

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4. DeepNGlyPred: A Deep neural network-based approach for human N-linked glycosylation site prediction

   Pakhrin, Subash ; Aoki-Kinoshita, Kiyoko ; Caragea, Doina ; Dukka, KC ( December 2021 , Molecules)

   Abstract 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. 

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

   https://doi.org/10.3390/molecules26237314  

   Pakhrin, Subash C. ; Aoki-Kinoshita, Kiyoko F. ; Caragea, Doina ; KC, Dukka B. ( December 2021 , Molecules)

   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. 

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