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Ahmed F, Sharma A, Shatabda S, Dehzangi I. DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation. Proteins 2024. [PMID: 39239684 DOI: 10.1002/prot.26734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 06/27/2024] [Accepted: 07/15/2024] [Indexed: 09/07/2024]
Abstract
Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host-pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low-cost and high-speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho-serine (pS), phospho-threonine (pT), and phospho-tyrosine (pY) sites. DeepPhoPred incorporates a two-headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep-learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available at https://github.com/faisalahm3d/DeepPhoPred.
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Affiliation(s)
- Faisal Ahmed
- Digital Health Unit, NVISION Systems and Technologies SL, Barcelona, Spain
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain
| | - Alok Sharma
- Laboratory of Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Queensland, Australia
- College of Informatics, Korea University, Seoul, South Korea
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Japan
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, New Jersey, USA
- Center for Computational and Integrative Biology (CCIB), Rutgers University, Camden, New Jersey, USA
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Bischoff E, Lang L, Zimmermann J, Luczak M, Kiefer AM, Niedner-Schatteburg G, Manolikakes G, Morgan B, Deponte M. Glutathione kinetically outcompetes reactions between dimedone and a cyclic sulfenamide or physiological sulfenic acids. Free Radic Biol Med 2023; 208:165-177. [PMID: 37541455 DOI: 10.1016/j.freeradbiomed.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/06/2023]
Abstract
Dimedone and its derivates are used as selective probes for the nucleophilic detection of sulfenic acids in biological samples. Qualitative analyses suggested that dimedone also reacts with cyclic sulfenamides. Furthermore, under physiological conditions, dimedone must compete with the highly concentrated nucleophile glutathione. We therefore quantified the reaction kinetics for a cyclic sulfenamide model peptide and the sulfenic acids of glutathione and a model peroxiredoxin in the presence or absence of dimedone and glutathione. We show that the cyclic sulfenamide is stabilized at lower pH and that it reacts with dimedone. While reactions between dimedone and sulfenic acids or the cyclic sulfenamide have similar rate constants, glutathione kinetically outcompetes dimedone as a nucleophile by several orders of magnitude. Our comparative in vitro and intracellular analyses challenge the selectivity of dimedone. Consequently, the dimedone labeling of cysteinyl residues inside living cells points towards unidentified reaction pathways or unknown, kinetically competitive redox species.
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Affiliation(s)
- Eileen Bischoff
- Fachbereich Chemie & Landesforschungszentrum OPTIMAS, RPTU Kaiserslautern, Erwin-Schrödinger Straße 54, D-67663, Kaiserslautern, Germany
| | - Lukas Lang
- Fachbereich Chemie & Landesforschungszentrum OPTIMAS, RPTU Kaiserslautern, Erwin-Schrödinger Straße 54, D-67663, Kaiserslautern, Germany
| | - Jannik Zimmermann
- Zentrum für Human- und Molekularbiologie (ZHMB), Universität des Saarlandes, Biochemie Campus, Geb. B2.2, D-66123, Saarbrücken, Germany
| | - Maximilian Luczak
- Fachbereich Chemie & Landesforschungszentrum OPTIMAS, RPTU Kaiserslautern, Erwin-Schrödinger Straße 54, D-67663, Kaiserslautern, Germany
| | - Anna Maria Kiefer
- Fachbereich Biologie, RPTU Kaiserslautern, Paul-Ehrlich Straße 23, D-67663, Kaiserslautern, Germany
| | - Gereon Niedner-Schatteburg
- Fachbereich Chemie & Landesforschungszentrum OPTIMAS, RPTU Kaiserslautern, Erwin-Schrödinger Straße 54, D-67663, Kaiserslautern, Germany
| | - Georg Manolikakes
- Fachbereich Chemie & Landesforschungszentrum OPTIMAS, RPTU Kaiserslautern, Erwin-Schrödinger Straße 54, D-67663, Kaiserslautern, Germany
| | - Bruce Morgan
- Zentrum für Human- und Molekularbiologie (ZHMB), Universität des Saarlandes, Biochemie Campus, Geb. B2.2, D-66123, Saarbrücken, Germany
| | - Marcel Deponte
- Fachbereich Chemie & Landesforschungszentrum OPTIMAS, RPTU Kaiserslautern, Erwin-Schrödinger Straße 54, D-67663, Kaiserslautern, Germany.
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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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Sohrawordi M, Hossain MA. Prediction of lysine formylation sites using support vector machine based on the sample selection from majority classes and synthetic minority over-sampling techniques. Biochimie 2021; 192:125-135. [PMID: 34627982 DOI: 10.1016/j.biochi.2021.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 10/03/2021] [Accepted: 10/05/2021] [Indexed: 12/22/2022]
Abstract
Lysine formylation is a newly discovered and mostly interested type of post-translational modification (PTM) that is generally found on core and linker histone proteins of prokaryote and eukaryote and plays various important roles on the regulation of various cellular mechanisms. Hence, it is very urgent to properly identify formylation site in protein for understanding the molecular mechanism of formylation deeply and defining drug for relevant diseases. As experimentally identification of formylation site using traditional processes are expensive and time consuming, a simple and high speedy mathematical model for predicting accurately lysine formylation sites is highly desired. A useful computational model named PLF_SVM is deigned and proposed in this study by using binary encoding (BE), amino acid composition (AAC), reverse position relative incidence matrix (RPRIM), position relative incidence matrix (PRIM), and position specific amino acid propensity (PSAAP) feature generation methods for predicting formylated and non-formylated lysine sites. Besides, the Synthetic Minority Oversampling Technique (SMOTE) and a proposed sample selection strategy named EnSVM are applied to handle the imbalance training dataset problem. Thereafter, the optimal number of features are selected by F-score method to train the model. Finally, it has been seen that PLF_SVM outperforms the state-of-the-art approaches in validation and independent test with an accuracy of 98.61% and 98.77% respectively. At https://plf-svm.herokuapp.com/, a user-friendly web tool is also created for identifying formylation sites. Therefore, the proposed method may be helpful guideline for the analysis and prediction of formylated lysine and knowing the process of cellular regulation.
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Affiliation(s)
- Md Sohrawordi
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh; Dept. of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
| | - Md Ali Hossain
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
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