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Mini-review: Recent advances in post-translational modification site prediction based on deep learning. Comput Struct Biotechnol J 2022; 20:3522-3532. [PMID: 35860402 PMCID: PMC9284371 DOI: 10.1016/j.csbj.2022.06.045] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022] Open
Abstract
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.
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Key Words
- AAindex, Amino acid index
- ATP, Adenosine triphosphate
- AUC, Area under curve
- Ac, Acetylation
- BE, Binary encoding
- BLOSUM, Blocks substitution matrix
- Bi-LSTM, Bidirectional LSTM
- CKSAAP, Composition of k-spaced amino acid Pairs
- CNN, Convolutional neural network
- CNNOH, CNN with the one-hot encoding
- CNNWE, CNN with the word-embedding encoding
- CNNrgb, CNN red green blue
- CV, Cross-validation
- DC-CNN, Densely connected convolutional neural network
- DL, Deep learning
- DNNs, Deep neural networks
- Deep learning
- E. coli, Escherichia coli
- EBGW, Encoding based on grouped weight
- EGAAC, Enhanced grouped amino acids content
- IG, Information gain
- K, Lysine
- KNN, k nearest neighbor
- LASSO, Least absolute shrinkage and selection operator
- LSTM, Long short-term memory
- LSTMWE, LSTM with the word-embedding encoding
- M.musculus, Mus musculus
- MDC, Modular densely connected convolutional networks
- MDCAN, Multilane dense convolutional attention network
- ML, Machine learning
- MLP, Multilayer perceptron
- MMI, Multivariate mutual information
- Machine learning
- Mass spectrometry
- NMBroto, Normalized Moreau-Broto autocorrelation
- P, Proline
- PSP, PhosphoSitePlus
- PSSM, Position-specific scoring matrix
- PTM, Post-translational modifications
- Ph, Phosphorylation
- Post-translational modification
- Prediction
- PseAAC, Pseudo-amino acid composition
- R, Arginine
- RF, Random forest
- RNN, Recurrent neural network
- ROC, Receiver operating characteristic
- S, Serine
- S. typhimurium, Salmonella typhimurium
- S.cerevisiae, Saccharomyces cerevisiae
- SE, Squeeze and excitation
- SEV, Split to Equal Validation
- ST, Source and target
- SUMO, Small ubiquitin-like modifier
- SVM, Support vector machines
- T, Threonine
- Ub, Ubiquitination
- Y, Tyrosine
- ZSL, Zero-shot learning
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Malebary SJ, Alzahrani E, Khan YD. A comprehensive tool for accurate identification of methyl-Glutamine sites. J Mol Graph Model 2021; 110:108074. [PMID: 34768228 DOI: 10.1016/j.jmgm.2021.108074] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/15/2021] [Accepted: 11/02/2021] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Sharaf J Malebary
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia.
| | - Ebraheem Alzahrani
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah, 21589, Saudi Arabia.
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.
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Wawro AM, Gajera CR, Baker SA, Leśniak RK, Montine KS, Fischer CR, Saw NL, Shamloo M, Montine TJ. Enantiomers of 4-aminopentanoic acid act as false GABAergic neurotransmitters and impact mouse behavior. J Neurochem 2021; 158:1074-1082. [PMID: 34273193 DOI: 10.1111/jnc.15474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/07/2021] [Accepted: 07/11/2021] [Indexed: 11/27/2022]
Abstract
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 (GABAA α4β3δ, GABAA α5β2γ2, and GABAB B1/B2) and antagonist (GABAA 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.
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Affiliation(s)
- Adam M Wawro
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Steven A Baker
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | | | | | - Nay L Saw
- Behavioral and Functional Neuroscience Laboratory, Stanford University, Stanford, CA, USA
| | - Mehrdad Shamloo
- Behavioral and Functional Neuroscience Laboratory, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Stanford University, Stanford, CA, USA
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