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Datta S, Nabeel Asim M, Dengel A, Ahmed S. NTpred: a robust and precise machine learning framework for in silico identification of Tyrosine nitration sites in protein sequences. Brief Funct Genomics 2024; 23:163-179. [PMID: 37248673 DOI: 10.1093/bfgp/elad018] [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: 02/14/2023] [Revised: 04/12/2023] [Accepted: 05/02/2023] [Indexed: 05/31/2023] Open
Abstract
Post-translational modifications (PTMs) either enhance a protein's activity in various sub-cellular processes, or degrade their activity which leads toward failure of intracellular processes. Tyrosine nitration (NT) modification degrades protein's activity that initiates and propagates various diseases including neurodegenerative, cardiovascular, autoimmune diseases and carcinogenesis. Identification of NT modification supports development of novel therapies and drug discoveries for associated diseases. Identification of NT modification in biochemical labs is expensive, time consuming and error-prone. To supplement this process, several computational approaches have been proposed. However these approaches fail to precisely identify NT modification, due to the extraction of irrelevant, redundant and less discriminative features from protein sequences. This paper presents the NTpred framework that is competent in extracting comprehensive features from raw protein sequences using four different sequence encoders. To reap the benefits of different encoders, it generates four additional feature spaces by fusing different combinations of individual encodings. Furthermore, it eradicates irrelevant and redundant features from eight different feature spaces through a Recursive Feature Elimination process. Selected features of four individual encodings and four feature fusion vectors are used to train eight different Gradient Boosted Tree classifiers. The probability scores from the trained classifiers are utilized to generate a new probabilistic feature space, which is used to train a Logistic Regression classifier. On the BD1 benchmark dataset, the proposed framework outperforms the existing best-performing predictor in 5-fold cross validation and independent test evaluation with combined improvement of 13.7% in MCC and 20.1% in AUC. Similarly, on the BD2 benchmark dataset, the proposed framework outperforms the existing best-performing predictor with combined improvement of 5.3% in MCC and 1.0% in AUC. NTpred is publicly available for further experimentation and predictive use at: https://sds_genetic_analysis.opendfki.de/PredNTS/.
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Affiliation(s)
- Sourajyoti Datta
- Department of Computer Science, Rheinland Pfälzische Technische Universität, Kaiserslautern, 67663, Germany
| | - Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
| | - Andreas Dengel
- Department of Computer Science, Rheinland Pfälzische Technische Universität, Kaiserslautern, 67663, Germany
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
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2
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Jimenez J, Dubey P, Carter B, Koomen JM, Markowitz J. A metabolic perspective on nitric oxide function in melanoma. Biochim Biophys Acta Rev Cancer 2024; 1879:189038. [PMID: 38061664 DOI: 10.1016/j.bbcan.2023.189038] [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: 07/19/2023] [Revised: 10/17/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023]
Abstract
Nitric oxide (NO) generated from nitric oxide synthase (NOS) exerts a dichotomous effect in melanoma, suppressing or promoting tumor progression. This dichotomy is thought to depend on the intracellular NO concentration and the cell type in which it is generated. Due to its central role in the metabolism of multiple critical constituents involved in signaling and stress, it is crucial to explore NO's contribution to the metabolic dysfunction of melanoma. This review will discuss many known metabolites linked to NO production in melanoma. We discuss the synthesis of these metabolites, their role in biochemical pathways, and how they alter the biological processes observed in the melanoma tumor microenvironment. The metabolic pathways altered by NO and the corresponding metabolites reinforce its dual role in melanoma and support investigating this effect for potential avenues of therapeutic intervention.
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Affiliation(s)
- John Jimenez
- Department of Cutaneous Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; Department of Oncologic Sciences, University of South Florida Morsani School of Medicine, Tampa, FL 33612, USA
| | - Parul Dubey
- Department of Cutaneous Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Bethany Carter
- Department of Cutaneous Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; Flow Cytometry Core Facility, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - John M Koomen
- Molecular Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA
| | - Joseph Markowitz
- Department of Cutaneous Oncology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; Department of Oncologic Sciences, University of South Florida Morsani School of Medicine, Tampa, FL 33612, USA.
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3
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Griswold-Prenner I, Kashyap AK, Mazhar S, Hall ZW, Fazelinia H, Ischiropoulos H. Unveiling the human nitroproteome: Protein tyrosine nitration in cell signaling and cancer. J Biol Chem 2023; 299:105038. [PMID: 37442231 PMCID: PMC10413360 DOI: 10.1016/j.jbc.2023.105038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/28/2023] [Accepted: 07/06/2023] [Indexed: 07/15/2023] Open
Abstract
Covalent amino acid modification significantly expands protein functional capability in regulating biological processes. Tyrosine residues can undergo phosphorylation, sulfation, adenylation, halogenation, and nitration. These posttranslational modifications (PTMs) result from the actions of specific enzymes: tyrosine kinases, tyrosyl-protein sulfotransferase(s), adenylate transferase(s), oxidoreductases, peroxidases, and metal-heme containing proteins. Whereas phosphorylation, sulfation, and adenylation modify the hydroxyl group of tyrosine, tyrosine halogenation and nitration target the adjacent carbon residues. Because aberrant tyrosine nitration has been associated with human disorders and with animal models of disease, we have created an updated and curated database of 908 human nitrated proteins. We have also analyzed this new resource to provide insight into the role of tyrosine nitration in cancer biology, an area that has not previously been considered in detail. Unexpectedly, we have found that 879 of the 1971 known sites of tyrosine nitration are also sites of phosphorylation suggesting an extensive role for nitration in cell signaling. Overall, the review offers several forward-looking opportunities for future research and new perspectives for understanding the role of tyrosine nitration in cancer biology.
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Affiliation(s)
| | | | | | - Zach W Hall
- Nitrase Therapeutics, Brisbane, California, USA
| | - Hossein Fazelinia
- Children's Hospital of Philadelphia Research Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Harry Ischiropoulos
- Children's Hospital of Philadelphia Research Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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4
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Srivathsa AV, Sadashivappa NM, Hegde AK, Radha S, Mahesh AR, Ammunje DN, Sen D, Theivendren P, Govindaraj S, Kunjiappan S, Pavadai P. A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery. Curr Pharm Des 2023; 29:1180-1192. [PMID: 37132148 DOI: 10.2174/1381612829666230428110542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/06/2023] [Accepted: 02/27/2023] [Indexed: 05/04/2023]
Abstract
Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.
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Affiliation(s)
- Anjana Vidya Srivathsa
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Nandini Markuli Sadashivappa
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Apeksha Krishnamurthy Hegde
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Srimathi Radha
- Department of Pharmaceutical Chemistry, SRM College of Pharmacy, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Chengalpattu District, Kattankulathur, Tamil Nadu, 603203, India
| | - Agasa Ramu Mahesh
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Damodar Nayak Ammunje
- Department of Pharmacology, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
| | - Debanjan Sen
- Department of Pharmaceutical Chemistry, BCDA College of Pharmacy & Technology, Hridaypur, Kolkata, 700127, West Bengal, India
| | - Panneerselvam Theivendren
- Department of Pharmaceutical Chemistry, Swamy Vivekanandha College of Pharmacy, Elayampalayam, Tiruchengode, 637205, India
| | - Saravanan Govindaraj
- Department of Pharmaceutical Chemistry, MNR College of Pharmacy, Fasalwadi, Sangareddy, 502 001, India
| | - Selvaraj Kunjiappan
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India
| | - Parasuraman Pavadai
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M.S.R. Nagar, Bengaluru, 560054, India
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Rescifina A. Progress of the "Molecular Informatics" Section in 2022. Int J Mol Sci 2023; 24:ijms24119442. [PMID: 37298393 DOI: 10.3390/ijms24119442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
This is the first Editorial of the "Molecular Informatics" Section (MIS) of the International Journal of Molecular Sciences (IJMS), which was created towards the end of 2018 (the first article was submitted on 27 September 2018) and has experienced significant growth from 2018 to now [...].
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Affiliation(s)
- Antonio Rescifina
- Department of Drug and Health Sciences, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
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Kang M, Oh JH. Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics". Int J Mol Sci 2022; 23:ijms23126610. [PMID: 35743052 PMCID: PMC9224509 DOI: 10.3390/ijms23126610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/10/2022] [Indexed: 02/04/2023] Open
Abstract
In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing, speech recognition, and natural language processing, and now rapidly becoming a dominant tool in biomedicine [...].
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Affiliation(s)
- Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV 89154, USA;
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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Rahman A, Ahmed S, Al Mehedi Hasan M, Ahmad S, Dehzangi I. Accurately predicting nitrosylated tyrosine sites using probabilistic sequence information. Gene 2022; 826:146445. [PMID: 35358650 DOI: 10.1016/j.gene.2022.146445] [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: 09/08/2021] [Revised: 02/16/2022] [Accepted: 03/18/2022] [Indexed: 11/04/2022]
Abstract
Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.
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Affiliation(s)
- Afrida Rahman
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Sabit Ahmed
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md Al Mehedi Hasan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.
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Characterization of a novel affinity binding ligand for tyrosine nitrated peptides from a phage-displayed peptide library. Talanta 2022; 241:123225. [DOI: 10.1016/j.talanta.2022.123225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/29/2021] [Accepted: 01/09/2022] [Indexed: 01/10/2023]
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9
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Amerifar S, Norouzi M, Ghandi M. A tool for feature extraction from biological sequences. Brief Bioinform 2022; 23:6563937. [PMID: 35383372 DOI: 10.1093/bib/bbac108] [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: 11/19/2021] [Revised: 03/01/2022] [Accepted: 03/03/2022] [Indexed: 11/12/2022] Open
Abstract
With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the ability of human beings, creating a splendid opportunity for machine learning methods to grow. The methods, however, are practical only when the sequences are converted into feature vectors. Many tools target this task including iLearnPlus, a Python-based tool which supports a rich set of features. In this paper, we propose a holistic tool that extracts features from biological sequences (i.e. DNA, RNA and Protein). These features are the inputs to machine learning models that predict properties, structures or functions of the input sequences. Our tool not only supports all features in iLearnPlus but also 30 additional features which exist in the literature. Moreover, our tool is based on R language which makes an alternative for bioinformaticians to transform sequences into feature vectors. We have compared the conversion time of our tool with that of iLearnPlus: we transform the sequences much faster. We convert small nucleotides by a median of 2.8X faster, while we outperform iLearnPlus by a median of 6.3X for large sequences. Finally, in amino acids, our tool achieves a median speedup of 23.9X.
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Affiliation(s)
- Sare Amerifar
- Bioinformatics, Tatbiat Modares University, Jalal Al Ahmad, 14115-111, Tehran, Iran
| | - Mahammad Norouzi
- Computer Science, Technical University of Darmstadt, Hochschulstr. 1, 64293, Hesse, Germany
| | - Mahmoud Ghandi
- Bioinformatics, Monte Rosa Therapeutics, Summer Street, 02210, Boston, United States
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10
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León J. Protein Tyrosine Nitration in Plant Nitric Oxide Signaling. FRONTIERS IN PLANT SCIENCE 2022; 13:859374. [PMID: 35360296 PMCID: PMC8963475 DOI: 10.3389/fpls.2022.859374] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 02/21/2022] [Indexed: 05/09/2023]
Abstract
Nitric oxide (NO), which is ubiquitously present in living organisms, regulates many developmental and stress-activated processes in plants. Regulatory effects exerted by NO lies mostly in its chemical reactivity as a free radical. Proteins are main targets of NO action as several amino acids can undergo NO-related post-translational modifications (PTMs) that include mainly S-nitrosylation of cysteine, and nitration of tyrosine and tryptophan. This review is focused on the role of protein tyrosine nitration on NO signaling, making emphasis on the production of NO and peroxynitrite, which is the main physiological nitrating agent; the main metabolic and signaling pathways targeted by protein nitration; and the past, present, and future of methodological and strategic approaches to study this PTM. Available information on identification of nitrated plant proteins, the corresponding nitration sites, and the functional effects on the modified proteins will be summarized. However, due to the low proportion of in vivo nitrated peptides and their inherent instability, the identification of nitration sites by proteomic analyses is a difficult task. Artificial nitration procedures are likely not the best strategy for nitration site identification due to the lack of specificity. An alternative to get artificial site-specific nitration comes from the application of genetic code expansion technologies based on the use of orthogonal aminoacyl-tRNA synthetase/tRNA pairs engineered for specific noncanonical amino acids. This strategy permits the programmable site-specific installation of genetically encoded 3-nitrotyrosine sites in proteins expressed in Escherichia coli, thus allowing the study of the effects of specific site nitration on protein structure and function.
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Kolbert Z, Lindermayr C. Computational prediction of NO-dependent posttranslational modifications in plants: Current status and perspectives. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2021; 167:851-861. [PMID: 34536898 DOI: 10.1016/j.plaphy.2021.09.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/04/2021] [Accepted: 09/08/2021] [Indexed: 05/11/2023]
Abstract
The perception and transduction of nitric oxide (NO) signal is achieved by NO-dependent posttranslational modifications (PTMs) among which S-nitrosation and tyrosine nitration has biological significance. In plants, 100-1000 S-nitrosated and tyrosine nitrated proteins have been identified so far by mass spectrometry. The determination of NO-modified protein targets/amino acid residues is often methodologically challenging. In the past decade, the growing demand for the knowledge of S-nitrosated or tyrosine nitrated sites has motivated the introduction of bioinformatics tools. For predicting S-nitrosation seven computational tools have been developed (GPS-SNO, SNOSite, iSNO-PseACC, iSNO-AAPAir, PSNO, PreSNO, RecSNO). Four predictors have been developed for indicating tyrosine nitration sites (GPS-YNO2, iNitro-Tyr, PredNTS, iNitroY-Deep), and one tool (DeepNitro) predicts both NO-dependent PTMs. The advantage of these computational tools is the fast provision of large amount of information. In this review, the available software tools have been tested on plant proteins in which S-nitrosated or tyrosine nitrated sites have been experimentally identified. The predictors showed distinct performance and there were differences from the experimental results partly due to the fact that the three-dimensional protein structure is not taken into account by the computational tools. Nevertheless, the predictors excellently establish experiments, and it is suggested to apply all available tools on target proteins and compare their results. In the future, computational prediction must be developed further to improve the precision with which S-nitrosation/tyrosine nitration-sites are identified.
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Affiliation(s)
- Zsuzsanna Kolbert
- Department of Plant Biology, University of Szeged, Közép fasor 52, 6726, Szeged, Hungary.
| | - Christian Lindermayr
- Institute of Biochemical Plant Pathology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstaedter Landstr. 1, D-85764, Oberschleißheim, München, Germany.
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12
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Basith S, Lee G, Manavalan B. STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction. Brief Bioinform 2021; 23:6370848. [PMID: 34532736 PMCID: PMC8769686 DOI: 10.1093/bib/bbab376] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/22/2021] [Accepted: 08/24/2021] [Indexed: 12/13/2022] Open
Abstract
Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.
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Affiliation(s)
- Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Gwang Lee
- Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea
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