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Yang Q, Xu S, Jiang W, Meng F, Wang S, Sun Z, Chen N, Peng D, Liu J, Xing S. Systematic qualitative proteome-wide analysis of lysine malonylation profiling in Platycodon grandiflorus. Amino Acids 2025; 57:9. [PMID: 39812870 PMCID: PMC11735498 DOI: 10.1007/s00726-024-03432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 11/25/2024] [Indexed: 01/16/2025]
Abstract
In recent years, it was found that lysine malonylation modification can affect biological metabolism and play an important role in plant life activities. Platycodon grandiflorus, an economic crop and medicinal plant, had no reports on malonylation in the related literature. This study qualitatively introduces lysine malonylation in P. grandiflorus. A total of 888 lysine malonylation-modified proteins in P. grandiflorus were identified, with a total of 1755 modification sites. According to the functional annotation, malonylated proteins were closely related to catalysis, binding, and other reactions. Subcellular localization showed that related proteins were enriched in chloroplasts, cytoplasm, and nuclei, indicating that this modification could regulate various metabolic processes. Motif analysis showed the enrichment of Alanine (A), Cysteine (C), Glycine (G), and Valine (V) amino acids surrounding malonylated lysine residues. Metabolic pathway and protein-protein interaction network analyses suggested these modifications are mainly involved in plant photosynthesis. Moreover, malonylated proteins are also involved in stress and defense responses. This study shows that lysine malonylation can affect a variety of biological processes and metabolic pathways, and the contents are reported for the first time in P. grandiflorus, which can provide important information for further research on P. grandiflorus and lysine malonylation's role in environment stress, photosynthesis, and secondary metabolites enrichment.
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Affiliation(s)
- Qingshan Yang
- College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230012, China
| | - Shaowei Xu
- College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230012, China
| | - Weimin Jiang
- Hunan Key Laboratory for Conservation and Utilization of Biological Resources in the Nanyue Mountainous Region, College of Life Sciences and Environment, Hengyang Normal University, Hengyang, 421008, Hunan, China
| | - Fei Meng
- College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230012, China
- Institute of Traditional Chinese Medicine Resources Protection and Development, Anhui Academy of Chinese Medicine, Hefei, 230012, China
| | - Shuting Wang
- College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230012, China
| | - Zongping Sun
- Engineering Technology Research Center of Anti-Aging, Chinese Herbal Medicine, Fuyang Normal University, Fuyang, 236037, China
| | - Na Chen
- Joint Research Center for Chinese Herbal Medicine of Anhui of IHM, Hefei Comprehensive National Science Center, Bozhou, 236814, China
| | - Daiyin Peng
- College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230012, China
- Institute of Traditional Chinese Medicine Resources Protection and Development, Anhui Academy of Chinese Medicine, Hefei, 230012, China
- MOE-Anhui Joint Collaborative Innovation Center for Quality Improvement of Anhui Genuine Chinese Medicinal Materials, Hefei, 230038, China
| | - Juan Liu
- College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230012, China.
| | - Shihai Xing
- College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230012, China.
- Institute of Traditional Chinese Medicine Resources Protection and Development, Anhui Academy of Chinese Medicine, Hefei, 230012, China.
- Joint Research Center for Chinese Herbal Medicine of Anhui of IHM, Hefei Comprehensive National Science Center, Bozhou, 236814, China.
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2
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Qin Z, Ren H, Zhao P, Wang K, Liu H, Miao C, Du Y, Li J, Wu L, Chen Z. Current computational tools for protein lysine acylation site prediction. Brief Bioinform 2024; 25:bbae469. [PMID: 39316944 PMCID: PMC11421846 DOI: 10.1093/bib/bbae469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/20/2024] [Accepted: 09/07/2024] [Indexed: 09/26/2024] Open
Abstract
As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.
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Affiliation(s)
- Zhaohui Qin
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Haoran Ren
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Kaiyuan Wang
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Huixia Liu
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Chunbo Miao
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Yanxiu Du
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Junzhou Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Liuji Wu
- National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
| | - Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Key Laboratory of Rice Molecular Breeding and High Efficiency Production, College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
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3
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Shrestha P, Kandel J, Tayara H, Chong KT. Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model. Nat Commun 2024; 15:6699. [PMID: 39107330 PMCID: PMC11303401 DOI: 10.1038/s41467-024-51071-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
Post-translational modifications (PTMs) are pivotal in modulating protein functions and influencing cellular processes like signaling, localization, and degradation. The complexity of these biological interactions necessitates efficient predictive methodologies. In this work, we introduce PTMGPT2, an interpretable protein language model that utilizes prompt-based fine-tuning to improve its accuracy in precisely predicting PTMs. Drawing inspiration from recent advancements in GPT-based architectures, PTMGPT2 adopts unsupervised learning to identify PTMs. It utilizes a custom prompt to guide the model through the subtle linguistic patterns encoded in amino acid sequences, generating tokens indicative of PTM sites. To provide interpretability, we visualize attention profiles from the model's final decoder layer to elucidate sequence motifs essential for molecular recognition and analyze the effects of mutations at or near PTM sites to offer deeper insights into protein functionality. Comparative assessments reveal that PTMGPT2 outperforms existing methods across 19 PTM types, underscoring its potential in identifying disease associations and drug targets.
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Affiliation(s)
- Palistha Shrestha
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea
| | - Jeevan Kandel
- Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea.
- Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea.
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4
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Ramazi S, Tabatabaei SAH, Khalili E, Nia AG, Motarjem K. Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences. Database (Oxford) 2024; 2024:baad094. [PMID: 38245002 PMCID: PMC10799748 DOI: 10.1093/database/baad094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/22/2024]
Abstract
The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety of biological functions, such as cellular regulation and pathogenesis. This modification plays a crucial role in metabolic pathways, mitochondrial functions, fatty acid oxidation and other life processes. However, accurately identifying malonylation sites is crucial to understand the molecular mechanism of malonylation, and the experimental identification can be a challenging and costly task. Recently, approaches based on machine learning (ML) have been suggested to address this issue. It has been demonstrated that these procedures improve accuracy while lowering costs and time constraints. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features and inefficient underlying classifiers. As a result, there is an urgent need for effective predictors and calculation methods. In this study, we provide a comprehensive analysis and review of existing prediction models, tools and benchmark datasets for predicting malonylation sites in protein sequences followed by a comparison study. The review consists of the specifications of benchmark datasets, explanation of features and encoding methods, descriptions of the predictions approaches and their embedding ML or deep learning models and the description and comparison of the existing tools in this domain. To evaluate and compare the prediction capability of the tools, a new bunch of data has been extracted based on the most updated database and the tools have been assessed based on the extracted data. Finally, a hybrid architecture consisting of several classifiers including classical ML models and a deep learning model has been proposed to ensemble the prediction results. This approach demonstrates the better performance in comparison with all prediction tools included in this study (the source codes of the models presented in this manuscript are available in https://github.com/Malonylation). Database URL: https://github.com/A-Golshan/Malonylation.
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Affiliation(s)
| | - Seyed Amir Hossein Tabatabaei
- Department of Computer Science, Faculty of Mathematical Sciences, University of Guilan, Namjoo St. Postal, Rasht 41938-33697, Iran
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Jalal AleAhmad, Tehran 14117-13116, Iran
| | - Elham Khalili
- Department of Plant Sciences, Faculty of Science, Tarbiat Modares University, Jalal AleAhmad, Tehran 14117-13116, Iran
| | - Amirhossein Golshan Nia
- Department of Mathematics and Computer Science, Amirkabir University of Technology, No. 350, Hafez Ave, Tehran 15916-34311, Iran
| | - Kiomars Motarjem
- Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Jalal AleAhmad, Tehran 14117-13116, Iran
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Desaire H, Go EP, Hua D. Advances, obstacles, and opportunities for machine learning in proteomics. CELL REPORTS. PHYSICAL SCIENCE 2022; 3:101069. [PMID: 36381226 PMCID: PMC9648337 DOI: 10.1016/j.xcrp.2022.101069] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.
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Affiliation(s)
- Heather Desaire
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
| | - Eden P. Go
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
| | - David Hua
- Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA
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6
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Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2499:285-322. [PMID: 35696087 DOI: 10.1007/978-1-0716-2317-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.
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7
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Sorkhi AG, Pirgazi J, Ghasemi V. A hybrid feature extraction scheme for efficient malonylation site prediction. Sci Rep 2022; 12:5756. [PMID: 35388017 PMCID: PMC8987080 DOI: 10.1038/s41598-022-08555-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/07/2022] [Indexed: 11/09/2022] Open
Abstract
Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities. Experimental methods are one of the due prediction approaches. But they are typically costly and time-consuming to implement. Recently, methods based on machine-learning solutions have been proposed to tackle this problem. Such practices have been shown to reduce costs and time complexities and increase accuracy. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features, and inefficient underlying classifiers. A machine learning-based method is proposed in this paper to cope with these problems. In the proposed approach, seven different features are extracted. Then, the extracted features are combined, ranked based on the Fisher's score (F-score), and the most efficient ones are selected. Afterward, malonylation sites are predicted using various classifiers. Simulation results show that the proposed method has acceptable performance compared with some state-of-the-art approaches. In addition, the XGBOOST classifier, founded on extracted features such as TFCRF, has a higher prediction rate than the other methods. The codes are publicly available at: https://github.com/jimy2020/Malonylation-site-prediction.
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Affiliation(s)
- Ali Ghanbari Sorkhi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
| | - Jamshid Pirgazi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran.
| | - Vahid Ghasemi
- Department of Computer Engineering, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
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8
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Khalili E, Ramazi S, Ghanati F, Kouchaki S. Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network. Brief Bioinform 2022; 23:bbac015. [PMID: 35152280 DOI: 10.1093/bib/bbac015] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/17/2021] [Accepted: 01/12/2022] [Indexed: 12/17/2023] Open
Abstract
Phosphorylation of proteins is one of the most significant post-translational modifications (PTMs) and plays a crucial role in plant functionality due to its impact on signaling, gene expression, enzyme kinetics, protein stability and interactions. Accurate prediction of plant phosphorylation sites (p-sites) is vital as abnormal regulation of phosphorylation usually leads to plant diseases. However, current experimental methods for PTM prediction suffers from high-computational cost and are error-prone. The present study develops machine learning-based prediction techniques, including a high-performance interpretable deep tabular learning network (TabNet) to improve the prediction of protein p-sites in soybean. Moreover, we use a hybrid feature set of sequential-based features, physicochemical properties and position-specific scoring matrices to predict serine (Ser/S), threonine (Thr/T) and tyrosine (Tyr/Y) p-sites in soybean for the first time. The experimentally verified p-sites data of soybean proteins are collected from the eukaryotic phosphorylation sites database and database post-translational modification. We then remove the redundant set of positive and negative samples by dropping protein sequences with >40% similarity. It is found that the developed techniques perform >70% in terms of accuracy. The results demonstrate that the TabNet model is the best performing classifier using hybrid features and with window size of 13, resulted in 78.96 and 77.24% sensitivity and specificity, respectively. The results indicate that the TabNet method has advantages in terms of high-performance and interpretability. The proposed technique can automatically analyze the data without any measurement errors and any human intervention. Furthermore, it can be used to predict putative protein p-sites in plants effectively. The collected dataset and source code are publicly deposited at https://github.com/Elham-khalili/Soybean-P-sites-Prediction.
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Affiliation(s)
- Elham Khalili
- Department of Plant Science, Faculty of Science, Tarbiat Modarres University, Tehran, Iran
| | - Shahin Ramazi
- Department of Biophysics, Faculty of Biological Science, Tarbiat Modares University, Tehran, Iran
| | - Faezeh Ghanati
- Department of Plant Science, Faculty of Science, Tarbiat Modarres University, Tehran, Iran
| | - Samaneh Kouchaki
- Department of Electrical and Electronic Engineering, .Faculty of Engineering and Physical Sciences, Centre for Vision, Speech, and Signal Processing, University of Surrey, Guildford, UK
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Wang M, Song L, Zhang Y, Gao H, Yan L, Yu B. Malsite-Deep: Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108191] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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10
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Arif M, Ahmed S, Ge F, Kabir M, Khan YD, Yu DJ, Thafar M. StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2022; 220:104458. [DOI: 10.1016/j.chemolab.2021.104458] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2024]
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11
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Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs. Methods Mol Biol 2022; 2499:155-176. [PMID: 35696080 PMCID: PMC9844236 DOI: 10.1007/978-1-0716-2317-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Peroxiredoxins (Prxs) are a protein superfamily, present in all organisms, that play a critical role in protecting cellular macromolecules from oxidative damage but also regulate intracellular and intercellular signaling processes involving redox-regulated proteins and pathways. Bioinformatic approaches using computational tools that focus on active site-proximal sequence fragments (known as active site signatures) and iterative clustering and searching methods (referred to as TuLIP and MISST) have recently enabled the recognition of over 38,000 peroxiredoxins, as well as their classification into six functionally relevant groups. With these data providing so many examples of Prxs in each class, machine learning approaches offer an opportunity to extract additional information about features characteristic of these protein groups.In this study, we developed a novel computational method named "RF-Prx" based on a random forest (RF) approach integrated with K-space amino acid pairs (KSAAP) to identify peroxiredoxins and classify them into one of six subgroups. Our process performed in a superior manner compared to other machine learning classifiers. Thus the RF approach integrated with K-space amino acid pairs enabled the detection of class-specific conserved sequences outside the known functional centers and with potential importance. For example, drugs designed to target Prx proteins would likely suffer from cross-reactivity among distinct Prxs if targeted to conserved active sites, but this may be avoidable if remote, class-specific regions could be targeted instead.
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Chaudhari M, Thapa N, Ismail H, Chopade S, Caragea D, Köhn M, Newman RH, Kc DB. DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites. Front Cell Dev Biol 2021; 9:662983. [PMID: 34249915 PMCID: PMC8264445 DOI: 10.3389/fcell.2021.662983] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/20/2021] [Indexed: 11/17/2022] Open
Abstract
Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein’s primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew’s correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC.
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Affiliation(s)
- Meenal Chaudhari
- Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, United States
| | - Niraj Thapa
- Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, United States
| | - Hamid Ismail
- Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, United States
| | - Sandhya Chopade
- Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, United States
| | - Doina Caragea
- Department of Computer Science, Kansas State University, Manhattan, KS, United States
| | - Maja Köhn
- Faculty of Biology, Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Robert H Newman
- Department of Biology, North Carolina A&T State University, Greensboro, NC, United States
| | - Dukka B Kc
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS, United States
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13
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Thapa N, Chaudhari M, Iannetta AA, White C, Roy K, Newman RH, Hicks LM, Kc DB. A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites. Sci Rep 2021; 11:12550. [PMID: 34131195 PMCID: PMC8206365 DOI: 10.1038/s41598-021-91840-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/28/2021] [Indexed: 11/23/2022] Open
Abstract
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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Affiliation(s)
- Niraj Thapa
- Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Meenal Chaudhari
- Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Anthony A Iannetta
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Clarence White
- Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Kaushik Roy
- Department of Computer Science, North Carolina A&T State University, Greensboro, NC, USA
| | - Robert H Newman
- Department of Biology, North Carolina A&T State University, Greensboro, NC, USA
| | - Leslie M Hicks
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dukka B Kc
- Electrical Engineering and Computer Science Department, Wichita State University, Wichita, KS, USA.
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Arafat ME, Ahmad MW, Shovan S, Dehzangi A, Dipta SR, Hasan MAM, Taherzadeh G, Shatabda S, Sharma A. Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features. Genes (Basel) 2020; 11:E1023. [PMID: 32878321 PMCID: PMC7565944 DOI: 10.3390/genes11091023] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/19/2020] [Accepted: 08/27/2020] [Indexed: 02/07/2023] Open
Abstract
Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.
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Affiliation(s)
- Md. Easin Arafat
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Md. Wakil Ahmad
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - S.M. Shovan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh; (S.M.S.); (M.A.M.H.)
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA;
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Shubhashis Roy Dipta
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Md. Al Mehedi Hasan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh; (S.M.S.); (M.A.M.H.)
| | - Ghazaleh Taherzadeh
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD 20742, USA
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD 4111, Australia
- Department of Medical Science Mathematics, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
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