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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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Erten M. MehNet: a vigesimal-based model by amino acid melting points generates unique ID numbers for protein sequences. J Biomol Struct Dyn 2024:1-7. [PMID: 38230442 DOI: 10.1080/07391102.2024.2302937] [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: 10/24/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024]
Abstract
The amino acid encoding plays a pivotal role in machine learning-based methods for predicting protein structure and function, as well as in protein mapping techniques. Additionally, the classification of protein sequences presents its own challenges. The current study aims to assign a constant value to each amino acid, thereby creating distinctions among protein sequences. The datasets used in this study were obtained from the UniProt Knowledgebase. Subsequently, these datasets underwent preprocessing steps, and identical sequences were categorized under the same headings. Each amino acid was ranked based on its respective melting point and was assigned a vigesimal digit. These generated vigesimal digits were subsequently converted to decimal values. The centerpiece of this methodology was the melting point hashing table, which was given the name 'MehNet'. Ultimately, each protein sequence was assigned a unique identification number. This approach successfully digitized protein sequences. Notably, experiments involving randomly distributed vigesimal digits for amino acids did not yield results as promising as those achieved with MehNet. The model's classification phase, which utilizes a k-nearest neighbors (kNN) classifier, demonstrates exceptional performance in miscellaneous viral sequences. It achieves high accuracy rates, with an overall accuracy of 99.75%. Notably, it achieves an outstanding accuracy of 99.92% for the Influenza C class, highlighting its ability to distinguish closely related viral sequences.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mehmet Erten
- Department of Medical Biochemistry, Fethi Sekin City Hospital, Elazığ, Turkey
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3
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Wei C, Shi M, Dong S, Li Z, Zhao B, Liu D, Li G, Cen J, Yu L, Liang X, Shi L. SIRT5-related lysine demalonylation of GSTP1 contributes to cardiomyocyte pyroptosis suppression in diabetic cardiomyopathy. Int J Biol Sci 2024; 20:585-605. [PMID: 38169591 PMCID: PMC10758093 DOI: 10.7150/ijbs.83306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024] Open
Abstract
Sirtuin 5 (SIRT5), localized in the mitochondria, has been identified as a protein desuccinylase and demalonylase in the mitochondria since the depletion of SIRT5 boosted the global succinylation and malonylation of mitochondrial proteins. We investigated the role of SIRT5 in diabetic cardiomyopathy (DCM) and identified the mechanism regarding lysine demalonylation in this process. Wild-type and SIRT5 knockout mice were induced with DCM, and primary cardiomyocytes and cardiac fibroblasts extracted from wild-type and SIRT5 knockout mice were subjected to high glucose (HG). SIRT5 deficiency exacerbated myocardial injury in DCM mice, aggravated HG-induced oxidative stress and mitochondrial dysfunction in cardiomyocytes, and intensified cardiomyocyte senescence, pyroptosis, and DNA damage. DCM-induced SIRT5 loss diminished glutathione S-transferase P (GSTP1) protein stability, represented by significantly increased lysine malonylation (Mal-Lys) modification of GSTP1. SIRT5 overexpression alleviated DCM-related myocardial injury, which was reversed by GSTP1 knockdown. Reduced SIRT5 transcription in DCM resulted from the downregulation of SPI1. SPI1 promoted the transcription of SIRT5, thereby ameliorating DCM-associated myocardial injury. However, SIRT5 deletion resulted in a significant reversal of the protective effect of SPI1. These observations suggest that SPI1 activates SIRT5 transcriptionally to mediate GSTP1 Mal-Lys modification and protein stability, thus ameliorating DCM-associated myocardial injury.
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Affiliation(s)
- Can Wei
- Department of Pathophysiology, Harbin Medical University, Harbin 150086, Heilongjiang, P.R. China
| | - Meixin Shi
- Department of Pathophysiology, Harbin Medical University, Harbin 150086, Heilongjiang, P.R. China
| | - Shiyun Dong
- Department of Pathophysiology, Harbin Medical University, Harbin 150086, Heilongjiang, P.R. China
| | - Zhitao Li
- Department of Pathophysiology, Harbin Medical University, Harbin 150086, Heilongjiang, P.R. China
| | - Bingbing Zhao
- Department of Pathophysiology, Harbin Medical University, Harbin 150086, Heilongjiang, P.R. China
| | - Dan Liu
- Department of Cadre ward, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150081, Heilongjiang, P.R. China
| | - Guopeng Li
- Department of Pathophysiology, Harbin Medical University, Harbin 150086, Heilongjiang, P.R. China
| | - Jie Cen
- Department of Pathophysiology, Harbin Medical University, Harbin 150086, Heilongjiang, P.R. China
| | - Ligen Yu
- Department of Pathophysiology, Harbin Medical University, Harbin 150086, Heilongjiang, P.R. China
| | - Xiao Liang
- Department of Cardiology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150081, Heilongjiang, P.R. China
| | - Lili Shi
- Department of Cadre Ward, The First Affiliated Hospital of Harbin Medical University, Harbin, 150081, Heilongjiang, P.R. China
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Esmaili F, Pourmirzaei M, Ramazi S, Shojaeilangari S, Yavari E. A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1266-1285. [PMID: 37863385 PMCID: PMC11082408 DOI: 10.1016/j.gpb.2023.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 01/16/2023] [Accepted: 03/23/2023] [Indexed: 10/22/2023]
Abstract
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.
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Affiliation(s)
- Farzaneh Esmaili
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Mahdi Pourmirzaei
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
| | - Shahin Ramazi
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115-111, Iran.
| | - Seyedehsamaneh Shojaeilangari
- Biomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran 33535-111, Iran
| | - Elham Yavari
- Department of Information Technology, Tarbiat Modares University, Tehran 14115-111, Iran
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Zhu L, Wang X, Li F, Song J. PreAcrs: a machine learning framework for identifying anti-CRISPR proteins. BMC Bioinformatics 2022; 23:444. [PMID: 36284264 PMCID: PMC9597991 DOI: 10.1186/s12859-022-04986-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification. RESULTS Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins. CONCLUSIONS In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git .
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Affiliation(s)
- Lin Zhu
- grid.263488.30000 0001 0472 9649Institute for Advanced Study, Shenzhen University, Shenzhen, China
| | - Xiaoyu Wang
- grid.1002.30000 0004 1936 7857Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800 Australia
| | - Fuyi Li
- grid.1008.90000 0001 2179 088XDepartment of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC Australia
| | - Jiangning Song
- grid.1002.30000 0004 1936 7857Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800 Australia ,grid.1002.30000 0004 1936 7857Monash Data Futures Institute, Monash University, Melbourne, VIC 3800 Australia
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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: 2] [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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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: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Liu Y, Liu Y, Wang GA, Cheng Y, Bi S, Zhu X. BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site for Homo sapiens. FRONTIERS IN BIOINFORMATICS 2022; 2:834153. [PMID: 36304324 PMCID: PMC9580886 DOI: 10.3389/fbinf.2022.834153] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/20/2022] [Indexed: 12/21/2022] Open
Abstract
As one of the most important posttranslational modifications (PTMs), protein lysine glycation changes the characteristics of the proteins and leads to the dysfunction of the proteins, which may cause diseases. Accurately detecting the glycation sites is of great benefit for understanding the biological function and potential mechanism of glycation in the treatment of diseases. However, experimental methods are expensive and time-consuming for lysine glycation site identification. Instead, computational methods, with their higher efficiency and lower cost, could be an important supplement to the experimental methods. In this study, we proposed a novel predictor, BERT-Kgly, for protein lysine glycation site prediction, which was developed by extracting embedding features of protein segments from pretrained Bidirectional Encoder Representations from Transformers (BERT) models. Three pretrained BERT models were explored to get the embeddings with optimal representability, and three downstream deep networks were employed to build our models. Our results showed that the model based on embeddings extracted from the BERT model pretrained on 556,603 protein sequences of UniProt outperforms other models. In addition, an independent test set was used to evaluate and compare our model with other existing methods, which indicated that our model was superior to other existing models.
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Affiliation(s)
| | | | | | | | - Shoudong Bi
- *Correspondence: Shoudong Bi, ; Xiaolei Zhu,
| | - Xiaolei Zhu
- *Correspondence: Shoudong Bi, ; Xiaolei Zhu,
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9
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Lv H, Zhang Y, Wang JS, Yuan SS, Sun ZJ, Dao FY, Guan ZX, Lin H, Deng KJ. iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice. Brief Bioinform 2021; 23:6447435. [PMID: 34864888 DOI: 10.1093/bib/bbab486] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/05/2021] [Accepted: 10/23/2021] [Indexed: 12/13/2022] Open
Abstract
Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lin-group.cn/server/iRice-MS.
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Affiliation(s)
- Hao Lv
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, China
| | - Jia-Shu Wang
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Shi-Shi Yuan
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Zi-Jie Sun
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Fu-Ying Dao
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Zheng-Xing Guan
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Hao Lin
- Center for Informational Biology at University of Electronic Science and Technology of China, China
| | - Ke-Jun Deng
- Center for Informational Biology at University of Electronic Science and Technology of China, China
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Elmassry MM, Bisht K, Colmer-Hamood JA, Wakeman CA, San Francisco MJ, Hamood AN. Malonate utilization by Pseudomonas aeruginosa affects quorum-sensing and virulence and leads to formation of mineralized biofilm-like structures. Mol Microbiol 2021; 116:516-537. [PMID: 33892520 DOI: 10.1111/mmi.14729] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/06/2021] [Accepted: 04/16/2021] [Indexed: 01/02/2023]
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen that uses malonate among its many carbon sources. We recently reported that, when grown in blood from trauma patients, P. aeruginosa expression of malonate utilization genes was upregulated. In this study, we explored the role of malonate utilization and its contribution to P. aeruginosa virulence. We grew P. aeruginosa strain PA14 in M9 minimal medium containing malonate (MM9) or glycerol (GM9) as a sole carbon source and assessed the effect of the growth on quorum sensing, virulence factors, and antibiotic resistance. Growth of PA14 in MM9, compared to GM9, reduced the production of elastases, rhamnolipids, and pyoverdine; enhanced the production of pyocyanin and catalase; and increased its sensitivity to norfloxacin. Growth in MM9 decreased extracellular levels of N-acylhomoserine lactone autoinducers, an effect likely associated with increased pH of the culture medium; but had little effect on extracellular levels of PQS. At 18 hr of growth in MM9, PA14 formed biofilm-like structures or aggregates that were associated with biomineralization, which was related to increased pH of the culture medium. These results suggest that malonate significantly impacts P. aeruginosa pathogenesis by influencing the quorum sensing systems, the production of virulence factors, biofilm formation, and antibiotic resistance.
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Affiliation(s)
- Moamen M Elmassry
- Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA
| | - Karishma Bisht
- Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA
| | - Jane A Colmer-Hamood
- Department of Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA.,Department of Medical Education, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | | | - Michael J San Francisco
- Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA.,Honors College, Texas Tech University, Lubbock, TX, USA
| | - Abdul N Hamood
- Department of Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center, Lubbock, TX, USA.,Department of Surgery, Texas Tech University Health Sciences Center, Lubbock, TX, USA
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Yang Y, Wang H, Li W, Wang X, Wei S, Liu Y, Xu Y. Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks. BMC Bioinformatics 2021; 22:171. [PMID: 33789579 PMCID: PMC8010967 DOI: 10.1186/s12859-021-04101-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/23/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. METHOD We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. RESULTS In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN . CONCLUSIONS The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
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Affiliation(s)
- Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Wen Li
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaobo Wang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Shizhao Wei
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yulong Liu
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.
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12
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Jing XY, Li FM. Predicting Cell Wall Lytic Enzymes Using Combined Features. Front Bioeng Biotechnol 2021; 8:627335. [PMID: 33585423 PMCID: PMC7874139 DOI: 10.3389/fbioe.2020.627335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/04/2020] [Indexed: 11/13/2022] Open
Abstract
Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.
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Affiliation(s)
- Xiao-Yang Jing
- College of Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Feng-Min Li
- College of Science, Inner Mongolia Agricultural University, Hohhot, China
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13
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RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix. Genes (Basel) 2020; 11:genes11121524. [PMID: 33419274 PMCID: PMC7766696 DOI: 10.3390/genes11121524] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 11/29/2022] Open
Abstract
Background: Post-translational modification (PTM) is a biological process that is associated with the modification of proteome, which results in the alteration of normal cell biology and pathogenesis. There have been numerous PTM reports in recent years, out of which, lysine phosphoglycerylation has emerged as one of the recent developments. The traditional methods of identifying phosphoglycerylated residues, which are experimental procedures such as mass spectrometry, have shown to be time-consuming and cost-inefficient, despite the abundance of proteins being sequenced in this post-genomic era. Due to these drawbacks, computational techniques are being sought to establish an effective identification system of phosphoglycerylated lysine residues. The development of a predictor for phosphoglycerylation prediction is not a first, but it is necessary as the latest predictor falls short in adequately detecting phosphoglycerylated and non-phosphoglycerylated lysine residues. Results: In this work, we introduce a new predictor named RAM-PGK, which uses sequence-based information relating to amino acid residues to predict phosphoglycerylated and non-phosphoglycerylated sites. A benchmark dataset was employed for this purpose, which contained experimentally identified phosphoglycerylated and non-phosphoglycerylated lysine residues. From the dataset, we extracted the residue adjacency matrix pertaining to each lysine residue in the protein sequences and converted them into feature vectors, which is used to build the phosphoglycerylation predictor. Conclusion: RAM-PGK, which is based on sequential features and support vector machine classifiers, has shown a noteworthy improvement in terms of performance in comparison to some of the recent prediction methods. The performance metrics of the RAM-PGK predictor are: 0.5741 sensitivity, 0.6436 specificity, 0.0531 precision, 0.6414 accuracy, and 0.0824 Mathews correlation coefficient.
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14
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Liu X, Wang L, Li J, Hu J, Zhang X. Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration : Malonylation site prediction. BMC Genomics 2020; 21:812. [PMID: 33225896 PMCID: PMC7682087 DOI: 10.1186/s12864-020-07166-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 10/20/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Malonylation is a recently discovered post-translational modification that is associated with a variety of diseases such as Type 2 Diabetes Mellitus and different types of cancers. Compared with experimental identification of malonylation sites, computational method is a time-effective process with comparatively low costs. RESULTS In this study, we proposed a novel computational model called Mal-Prec (Malonylation Prediction) for malonylation site prediction through the combination of Principal Component Analysis and Support Vector Machine. One-hot encoding, physio-chemical properties, and composition of k-spaced acid pairs were initially performed to extract sequence features. PCA was then applied to select optimal feature subsets while SVM was adopted to predict malonylation sites. Five-fold cross-validation results showed that Mal-Prec can achieve better prediction performance compared with other approaches. AUC (area under the receiver operating characteristic curves) analysis achieved 96.47 and 90.72% on 5-fold cross-validation of independent data sets, respectively. CONCLUSION Mal-Prec is a computationally reliable method for identifying malonylation sites in protein sequences. It outperforms existing prediction tools and can serve as a useful tool for identifying and discovering novel malonylation sites in human proteins. Mal-Prec is coded in MATLAB and is publicly available at https://github.com/flyinsky6/Mal-Prec , together with the data sets used in this study.
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Affiliation(s)
- Xin Liu
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, 221004 Jiangsu China
| | - Liang Wang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, 221004 Jiangsu China
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, 221000 Jiangsu China
| | - Jian Li
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70118 USA
| | - Junfeng Hu
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, 221004 Jiangsu China
| | - Xiao Zhang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, 221004 Jiangsu China
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15
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Chung CR, Chang YP, Hsu YL, Chen S, Wu LC, Horng JT, Lee TY. Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins. Sci Rep 2020; 10:10541. [PMID: 32601280 PMCID: PMC7324624 DOI: 10.1038/s41598-020-67384-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: 01/06/2020] [Accepted: 06/03/2020] [Indexed: 12/22/2022] Open
Abstract
Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level,
the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information,
and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo (https://fdblab.csie.ncu.edu.tw/kmalo/home.html), which can help facilitate the functional investigation of protein malonylation on mammals and plants.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Ya-Ping Chang
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Yu-Lin Hsu
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Siyu Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, 32001, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan. .,Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41359, Taiwan.
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, 518172, China. .,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 518172, China.
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16
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AHMAD WAKIL, ARAFAT EASIN, TAHERZADEH GHAZALEH, SHARMA ALOK, DIPTA SHUBHASHISROY, DEHZANGI ABDOLLAH, SHATABDA SWAKKHAR. Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:77888-77902. [PMID: 33354488 PMCID: PMC7751949 DOI: 10.1109/access.2020.2989713] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).
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Affiliation(s)
- WAKIL AHMAD
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - EASIN ARAFAT
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - GHAZALEH TAHERZADEH
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, 20742, USA
| | - ALOK SHARMA
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
- CREST, JST, Tokyo, 102-8666, Japan
| | - SHUBHASHIS ROY DIPTA
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
| | - ABDOLLAH DEHZANGI
- Department of Computer Science, Morgan State University, Baltimore, MD, 21251, USA
| | - SWAKKHAR SHATABDA
- Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh
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Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma. Nat Commun 2020; 11:1778. [PMID: 32286325 PMCID: PMC7156652 DOI: 10.1038/s41467-020-15671-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 03/23/2020] [Indexed: 12/17/2022] Open
Abstract
TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin- stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis. Translocation renal cell carcinoma is an aggressive form of renal cancer that is often misdiagnosed to other subtypes. Here the authors demonstrated that by using machine learning and H&E stained whole-slide images, an accurate diagnose of this particular type of renal cancer can be achieved.
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RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites. Comput Struct Biotechnol J 2020; 18:852-860. [PMID: 32322367 PMCID: PMC7160427 DOI: 10.1016/j.csbj.2020.02.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 01/27/2020] [Accepted: 02/19/2020] [Indexed: 12/19/2022] Open
Abstract
Malonylation, which has recently emerged as an important lysine modification, regulates diverse biological activities and has been implicated in several pervasive disorders, including cardiovascular disease and cancer. However, conventional global proteomics analysis using tandem mass spectrometry can be time-consuming, expensive and technically challenging. Therefore, to complement and extend existing experimental methods for malonylation site identification, we developed two novel computational methods for malonylation site prediction based on random forest and deep learning machine learning algorithms, RF-MaloSite and DL-MaloSite, respectively. DL-MaloSite requires the primary amino acid sequence as an input and RF-MaloSite utilizes a diverse set of biochemical, physiochemical and sequence-based features. While systematic assessment of performance metrics suggests that both ‘RF-MaloSite’ and ‘DL-MaloSite’ perform well in all metrics tested, our methods perform particularly well in the areas of accuracy, sensitivity and overall method performance (assessed by the Matthew’s Correlation Coefficient). For instance, RF-MaloSite exhibited MCC scores of 0.42 and 0.40 using 10-fold cross-validation and an independent test set, respectively. Meanwhile, DL-MaloSite was characterized by MCC scores of 0.51 and 0.49 based on 10-fold cross-validation and an independent set, respectively. Importantly, both methods exhibited efficiency scores that were on par or better than those achieved by existing malonylation site prediction methods. The identification of these sites may also provide important insights into the mechanisms of crosstalk between malonylation and other lysine modifications, such as acetylation, glutarylation and succinylation. To facilitate their use, both methods have been made freely available to the research community at https://github.com/dukkakc/DL-MaloSite-and-RF-MaloSite.
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19
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Abstract
Aims:Post-Translational Modifications (PTMs), which include more than 450 types, can be regarded as the fundamental cellular regulation.Background:Recently, experiments demonstrated that the lysine malonylation modification is a significant process in several organisms and cells. Meanwhile, malonylation plays an important role in the regulation of protein subcellular localization, stability, translocation to lipid rafts and many other protein functions.Objective:Identification of malonylation will contribute to understanding the molecular mechanism in the field of biology. Nevertheless, several existing experimental approaches, which can hardly meet the need of the high speed data generation, are expensive and time-consuming. Moreover, some machine learning methods can hardly meet the high-accuracy need in this issue.Methods:In this study, we proposed a method, named MSIT that means malonylation sites identification tree, utilized the amino acid residues and profile information to identify the lysine malonylation sites with the tree structural neural network in the peptides sequence level.Methods:The proposed algorithm can get 0.8699 of F1 score and 89.34% in true positive ratio in E. coli. MSIT outperformed existing malonylation site identification methods and features on different species datasets.Conclusion:Based on these measures, it can be demonstrated that MSIT will be helpful in identifying candidate malonylation sites.
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Affiliation(s)
- Wenzheng Bao
- School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China
| | - De-Shuang Huang
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
| | - Yue-Hui Chen
- School of Information, University of Jinan, Jinan 250022, China
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20
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Chandra A, Sharma A, Dehzangi A, Shigemizu D, Tsunoda T. Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix. BMC Mol Cell Biol 2019; 20:57. [PMID: 31856704 PMCID: PMC6923822 DOI: 10.1186/s12860-019-0240-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 11/20/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However, the computational techniques proposed so far hold limitations to correctly predict this covalent modification. RESULTS We propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of amino acids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices of amino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision, accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253, 0.9193, 0.8330, 0.9306, respectively. CONCLUSIONS The proposed predictor, based on the feature of evolutionary information and support vector machine classifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine residues when compared against the existing predictors. The data and software of this work can be acquired from https://github.com/abelavit/Bigram-PGK.
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Affiliation(s)
- Abel Chandra
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji.
| | - Alok Sharma
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji. .,Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, 4111, Australia. .,Department of Medical Science Mathematics, Medical Research Institute, 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. .,CREST, JST, Tokyo, 102-8666, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Daichi Shigemizu
- Department of Medical Science Mathematics, Medical Research Institute, 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.,CREST, JST, Tokyo, 102-8666, Japan.,Medical Genome Center, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan
| | - Tatsuhiko Tsunoda
- Department of Medical Science Mathematics, Medical Research Institute, 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.,CREST, JST, Tokyo, 102-8666, Japan.,Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, 108-8639, Japan
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21
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Basith S, Manavalan B, Shin TH, Lee G. SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:131-141. [PMID: 31542696 PMCID: PMC6796762 DOI: 10.1016/j.omtn.2019.08.011] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 07/30/2019] [Accepted: 08/08/2019] [Indexed: 12/19/2022]
Abstract
DNA N6-adenine methylation (6mA) is an epigenetic modification in prokaryotes and eukaryotes. Identifying 6mA sites in rice genome is important in rice epigenetics and breeding, but non-random distribution and biological functions of these sites remain unclear. Several machine-learning tools can identify 6mA sites but show limited prediction accuracy, which limits their usability in epigenetic research. Here, we developed a novel computational predictor, called the Sequence-based DNA N6-methyladenine predictor (SDM6A), which is a two-layer ensemble approach for identifying 6mA sites in the rice genome. Unlike existing methods, which are based on single models with basic features, SDM6A explores various features, and five encoding methods were identified as appropriate for this problem. Subsequently, an optimal feature set was identified from encodings, and corresponding models were developed individually using support vector machine and extremely randomized tree. First, all five single models were integrated via ensemble approach to define the class for each classifier. Second, two classifiers were integrated to generate a final prediction. SDM6A achieved robust performance on cross-validation and independent evaluation, with average accuracy and Matthews correlation coefficient (MCC) of 88.2% and 0.764, respectively. Corresponding metrics were 4.7%-11.0% and 2.3%-5.5% higher than those of existing methods, respectively. A user-friendly, publicly accessible web server (http://thegleelab.org/SDM6A) was implemented to predict novel putative 6mA sites in rice genome.
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Affiliation(s)
- Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | | | - Tae Hwan Shin
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.
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22
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Zhang Y, Xie R, Wang J, Leier A, Marquez-Lago TT, Akutsu T, Webb GI, Chou KC, Song J. Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Brief Bioinform 2019; 20:2185-2199. [PMID: 30351377 PMCID: PMC6954445 DOI: 10.1093/bib/bby079] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/28/2018] [Accepted: 08/01/2018] [Indexed: 11/15/2022] Open
Abstract
As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.
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Affiliation(s)
- Yanju Zhang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Ruopeng Xie
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Jiawei Wang
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
| | - André Leier
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, VIC 3800, Australia
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, USA
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jiangning Song
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, VIC 3800, Australia
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, VIC 3800, Australia
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23
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Chen Z, Liu X, Li F, Li C, Marquez-Lago T, Leier A, Akutsu T, Webb GI, Xu D, Smith AI, Li L, Chou KC, Song J. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Brief Bioinform 2019; 20:2267-2290. [PMID: 30285084 PMCID: PMC6954452 DOI: 10.1093/bib/bby089] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 08/17/2018] [Accepted: 08/18/2018] [Indexed: 12/22/2022] Open
Abstract
Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.
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Affiliation(s)
- Zhen Chen
- School of Basic Medical Science, Qingdao University, Dengzhou Road, Qingdao, Shandong, China
| | - Xuhan Liu
- Medicinal Chemistry, Leiden Academic Centre for Drug Research,Einsteinweg, Leiden, The Netherlands
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Chen Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Institute of Molecular Systems Biology, ETH Zürich,Auguste-Piccard-Hof, Zürich, Switzerland
| | - Tatiana Marquez-Lago
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - André Leier
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research,Kyoto University, Uji, Kyoto, Japan
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - Dakang Xu
- Faculty of Medical Laboratory Science, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Molecular and Translational Science, Faculty of Medicine, Hudson Institute of Medical Research, Monash University, Melbourne, VIC, Australia
| | - Alexander Ian Smith
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Lei Li
- School of Basic Medical Science, Qingdao University, Dengzhou Road, Qingdao, Shandong, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
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24
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Feature subset selection combining maximal information entropy and maximal information coefficient. APPL INTELL 2019. [DOI: 10.1007/s10489-019-01537-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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25
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Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging. Neuroradiology 2019; 61:1229-1237. [DOI: 10.1007/s00234-019-02244-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 06/05/2019] [Indexed: 02/07/2023]
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26
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Chandra AA, Sharma A, Dehzangi A, Tsunoda T. EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction. BMC Genomics 2019; 19:984. [PMID: 30999859 PMCID: PMC7402405 DOI: 10.1186/s12864-018-5383-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 12/17/2018] [Indexed: 01/21/2023] Open
Abstract
Background Post-translational modification (PTM), which is a biological process, tends to modify proteome that leads to changes in normal cell biology and pathogenesis. In the recent times, there has been many reported PTMs. Out of the many modifications, phosphoglycerylation has become particularly the subject of interest. The experimental procedure for identification of phosphoglycerylated residues continues to be an expensive, inefficient and time-consuming effort, even with a large number of proteins that are sequenced in the post-genomic period. Computational methods are therefore being anticipated in order to effectively predict phosphoglycerylated lysines. Even though there are predictors available, the ability to detect phosphoglycerylated lysine residues still remains inadequate. Results We have introduced a new predictor in this paper named EvolStruct-Phogly that uses structural and evolutionary information relating to amino acids to predict phosphoglycerylated lysine residues. Benchmarked data is employed containing experimentally identified phosphoglycerylated and non-phosphoglycerylated lysines. We have then extracted the three structural information which are accessible surface area of amino acids, backbone torsion angles, amino acid’s local structure conformations and profile bigrams of position-specific scoring matrices. Conclusion EvolStruct-Phogly showed a noteworthy improvement in regards to the performance when compared with the previous predictors. The performance metrics obtained are as follows: sensitivity 0.7744, specificity 0.8533, precision 0.7368, accuracy 0.8275, and Mathews correlation coefficient of 0.6242. The software package and data of this work can be obtained from https://github.com/abelavit/EvolStruct-Phogly or www.alok-ai-lab.com
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Affiliation(s)
| | - Alok Sharma
- School of Engineering & Physics, University of the South Pacific, Suva, Fiji. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia. .,CREST, JST, Tokyo, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, MD, USA
| | - Tatushiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,CREST, JST, Tokyo, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
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27
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Xu Y, Yang Y, Wang Z, Shao Y. Prediction of Acetylation and Succinylation in Proteins Based on Multilabel Learning RankSVM. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666180830101540] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
In vivo, one of the most efficient biological mechanisms for expanding the genetic code and regulating cellular physiology is protein post-translational modification (PTM). Because PTM can provide very useful information for both basic research and drug development, identification of PTM sites in proteins has become a very important topic in bioinformatics. Lysine residue in protein can be subjected to many types of PTMs, such as acetylation, succinylation, methylation and propionylation and so on. In order to deal with the huge protein sequences, the present study is devoted to developing computational techniques that can be used to predict the multiple K-type modifications of any uncharacterized protein timely and effectively. In this work, we proposed a method which could deal with the acetylation and succinylation prediction in a multilabel learning. Three feature constructions including sequences and physicochemical properties have been applied. The multilabel learning algorithm RankSVM has been first used in PTMs. In 10-fold cross-validation the predictor with physicochemical properties encoding got accuracy 73.86%, abslute-true 64.70%, respectively. They were better than the other feature constructions. We compared with other multilabel algorithms and the existing predictor iPTM-Lys. The results of our predictor were better than other methods. Meanwhile we also analyzed the acetylation and succinylation peptides which could illustrate the results.
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Affiliation(s)
- Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Zu Wang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Yuanhai Shao
- School of Economics and Management, Hainan University, Haikou 570228, China
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28
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Xu Y, Yang Y, Wang H, Shao Y. Lysine Malonylation Identification in E. coli with Multiple Features. CURR PROTEOMICS 2019. [DOI: 10.2174/1570164615666181005104614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Motivation: Lysine malonylation in eukaryote proteins had been found in 2011 through
high-throughput proteomic analysis. However, it was poorly understood in prokaryotes. Recent researches
have shown that maonylation in E. coli was significantly enriched in protein translation, energy
metabolism pathways and fatty acid biosynthesis.
Results:
In this work we proposed a predictor to identify the lysine malonylation sites in E. coli
through physicochemical properties, binary code and sequence frequency by support vector machine
algorithm. The experimentally determined lysine malonylation sites were retrieved from the first and
largest malonylome dataset in prokaryotes up to date. The physicochemical properties plus position
specific amino acid sequence propensity features got the best results with AUC (the area under the
Receive Operating Character curve) 0.7994, MCC (Mathew correlation coefficient) 0.4335 in 10-fold
cross-validation. Meanwhile the AUC values were 0.7800, 0.7851 and 0.8050 in 6-fold, 8-fold and
LOO (leave-one-out) cross-validation, respectively. All the ROC curves were close to each other
which illustrated the robustness and performance of the proposed predictor. We also analyzed the sequence
propensities through TwoSampleLogo and found some peptides differences with t-test p<0.01.
The predictor had shown better results than those of other methods K-Nearest Neighbors, C4.5
decision tree, Naïve Bayes and Random Forest. Functional analysis showed that malonylated proteins
were involved in many transcription activities and diverse biological processes. Meanwhile we also
developed an online package which could be freely downloaded https://github.com/Sunmile/
Malonylation E.coli.
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Affiliation(s)
- Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
| | - Yuanhai Shao
- School of Economics and Management, Hainan University, Haikou 570228, China
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29
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Chen Z, He N, Huang Y, Qin WT, Liu X, Li L. Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites. GENOMICS PROTEOMICS & BIOINFORMATICS 2019; 16:451-459. [PMID: 30639696 PMCID: PMC6411950 DOI: 10.1016/j.gpb.2018.08.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 06/20/2018] [Accepted: 08/08/2018] [Indexed: 12/27/2022]
Abstract
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTMWE) for the prediction of mammalian malonylation sites. LSTMWE performs better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTMWE is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTMWE and the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence. LEMP is available at http://www.bioinfogo.org/lemp.
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Affiliation(s)
- Zhen Chen
- School of Basic Medicine, Qingdao University, Qingdao 266021, China
| | - Ningning He
- School of Basic Medicine, Qingdao University, Qingdao 266021, China
| | - Yu Huang
- School of Data Science and Software Engineering, Qingdao University, Qingdao 266021, China
| | - Wen Tao Qin
- Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario N6A 5C1, Canada
| | - Xuhan Liu
- Department of Information Technology, Beijing Oriental Yamei Gene Technology Institute Co. Ltd., Beijing 100078, China.
| | - Lei Li
- School of Basic Medicine, Qingdao University, Qingdao 266021, China; School of Data Science and Software Engineering, Qingdao University, Qingdao 266021, China; Qingdao Cancer Institute, Qingdao University, Qingdao 266021, China.
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30
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AL-barakati HJ, Saigo H, Newman RH, KC DB. RF-GlutarySite: a random forest based predictor for glutarylation sites. Mol Omics 2019; 15:189-204. [DOI: 10.1039/c9mo00028c] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Glutarylation, which is a newly identified posttranslational modification that occurs on lysine residues, has recently emerged as an important regulator of several metabolic and mitochondrial processes. Here, we describe the development of RF-GlutarySite, a random forest-based predictor designed to predict glutarylation sites based on protein primary amino acid sequence.
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Affiliation(s)
- Hussam J. AL-barakati
- Department of Computational Science and Engineering
- North Carolina Agricultural & Technical State University
- Greensboro
- USA
| | - Hiroto Saigo
- Department of Informatics
- Kyushu University
- Fukuoka 819-0395
- Japan
| | - Robert H. Newman
- Department of Biology
- North Carolina Agricultural & Technical State University
- Greensboro
- USA
| | - Dukka B. KC
- Department of Computational Science and Engineering
- North Carolina Agricultural & Technical State University
- Greensboro
- USA
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31
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Wang S, Li J, Sun X, Zhang YH, Huang T, Cai Y. Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm. Comb Chem High Throughput Screen 2018; 23:304-312. [PMID: 30588879 DOI: 10.2174/1386207322666181227144318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Revised: 09/03/2018] [Accepted: 12/04/2018] [Indexed: 12/12/2022]
Abstract
BACKGROUND As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. OBJECTIVE In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. METHOD Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. RESULTS An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. CONCLUSION Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.
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Affiliation(s)
- ShaoPeng Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - JiaRui Li
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Xijun Sun
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Yu-Hang Zhang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yudong Cai
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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32
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He W, Wei L, Zou Q. Research progress in protein posttranslational modification site prediction. Brief Funct Genomics 2018; 18:220-229. [DOI: 10.1093/bfgp/ely039] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 11/15/2018] [Accepted: 11/22/2018] [Indexed: 01/24/2023] Open
Abstract
AbstractPosttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.
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Affiliation(s)
- Wenying He
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Leyi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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33
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Chandra A, Sharma A, Dehzangi A, Ranganathan S, Jokhan A, Chou KC, Tsunoda T. PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Sci Rep 2018; 8:17923. [PMID: 30560923 PMCID: PMC6299098 DOI: 10.1038/s41598-018-36203-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 11/16/2018] [Indexed: 12/22/2022] Open
Abstract
The biological process known as post-translational modification (PTM) contributes to diversifying the proteome hence affecting many aspects of normal cell biology and pathogenesis. There have been many recently reported PTMs, but lysine phosphoglycerylation has emerged as the most recent subject of interest. Despite a large number of proteins being sequenced, the experimental method for detection of phosphoglycerylated residues remains an expensive, time-consuming and inefficient endeavor in the post-genomic era. Instead, the computational methods are being proposed for accurately predicting phosphoglycerylated lysines. Though a number of predictors are available, performance in detecting phosphoglycerylated lysine residues is still limited. In this paper, we propose a new predictor called PhoglyStruct that utilizes structural information of amino acids alongside a multilayer perceptron classifier for predicting phosphoglycerylated and non-phosphoglycerylated lysine residues. For the experiment, we located phosphoglycerylated and non-phosphoglycerylated lysines in our employed benchmark. We then derived and integrated properties such as accessible surface area, backbone torsion angles, and local structure conformations. PhoglyStruct showed significant improvement in the ability to detect phosphoglycerylated residues from non-phosphoglycerylated ones when compared to previous predictors. The sensitivity, specificity, accuracy, Mathews correlation coefficient and AUC were 0.8542, 0.7597, 0.7834, 0.5468 and 0.8077, respectively. The data and Matlab/Octave software packages are available at https://github.com/abelavit/PhoglyStruct .
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Affiliation(s)
- Abel Chandra
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia.
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan.
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji.
- CREST, JST, Tokyo, 113-8510, Japan.
| | - Abdollah Dehzangi
- Department of Computer Science, Morgan State University, Baltimore, Maryland, USA
| | - Shoba Ranganathan
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Anjeela Jokhan
- Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
| | - Kuo-Chen Chou
- The Gordon Life Science Institute, Boston, MA, 02478, USA
| | - Tatsuhiko Tsunoda
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Kanagawa, Japan
- CREST, JST, Tokyo, 113-8510, Japan
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34
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Xu Y, Yang Y, Ding J, Li C. iGlu-Lys: A Predictor for Lysine Glutarylation Through Amino Acid Pair Order Features. IEEE Trans Nanobioscience 2018; 17:394-401. [DOI: 10.1109/tnb.2018.2848673] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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35
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Moehring F, Waas M, Keppel TR, Rathore D, Cowie AM, Stucky CL, Gundry RL. Quantitative Top-Down Mass Spectrometry Identifies Proteoforms Differentially Released during Mechanical Stimulation of Mouse Skin. J Proteome Res 2018; 17:2635-2648. [PMID: 29925238 PMCID: PMC6195672 DOI: 10.1021/acs.jproteome.8b00109] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Mechanotransduction refers to the processes whereby mechanical stimuli are converted into electrochemical signals that allow for the sensation of our surrounding environment through touch. Despite its fundamental role in our daily lives, the molecular and cellular mechanisms of mechanotransduction are not yet well-defined. Previous data suggest that keratinocytes may release factors that activate or modulate cutaneous sensory neuron terminals, including small molecules, lipids, peptides, proteins, and oligosaccharides. This study presents a first step toward identifying soluble mediators of keratinocyte-sensory neuron communication by evaluating the potential for top-down mass spectrometry to identify proteoforms released during 1 min of mechanical stimulation of mouse skin from naı̈ve animals. Overall, this study identified 47 proteoforms in the secretome of mouse hind paw skin, of which 14 were differentially released during mechanical stimulation, and includes proteins with known and previously unknown relevance to mechanotransduction. Finally, this study outlines a bioinformatic workflow that merges output from two complementary analysis platforms for top-down data and demonstrates the utility of this workflow for integrating quantitative and qualitative data.
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Affiliation(s)
- Francie Moehring
- Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Matthew Waas
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Theodore R. Keppel
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Center for Biomedical Mass Spectrometry Research, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Deepali Rathore
- Center for Biomedical Mass Spectrometry Research, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Ashley M. Cowie
- Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Cheryl L. Stucky
- Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Rebekah L. Gundry
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Center for Biomedical Mass Spectrometry Research, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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36
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SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites. Sci Rep 2018; 8:11288. [PMID: 30050050 PMCID: PMC6062547 DOI: 10.1038/s41598-018-29126-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 07/02/2018] [Indexed: 12/15/2022] Open
Abstract
Protein S-sulfenylation, which results from oxidation of free thiols on cysteine residues, has recently emerged as an important post-translational modification that regulates the structure and function of proteins involved in a variety of physiological and pathological processes. By altering the size and physiochemical properties of modified cysteine residues, sulfenylation can impact the cellular function of proteins in several different ways. Thus, the ability to rapidly and accurately identify putative sulfenylation sites in proteins will provide important insights into redox-dependent regulation of protein function in a variety of cellular contexts. Though bottom-up proteomic approaches, such as tandem mass spectrometry (MS/MS), provide a wealth of information about global changes in the sulfenylation state of proteins, MS/MS-based experiments are often labor-intensive, costly and technically challenging. Therefore, to complement existing proteomic approaches, researchers have developed a series of computational tools to identify putative sulfenylation sites on proteins. However, existing methods often suffer from low accuracy, specificity, and/or sensitivity. In this study, we developed SVM-SulfoSite, a novel sulfenylation prediction tool that uses support vector machines (SVM) to identify key determinants of sulfenylation among five feature classes: binary code, physiochemical properties, k-space amino acid pairs, amino acid composition and high-quality physiochemical indices. Using 10-fold cross-validation, SVM-SulfoSite achieved 95% sensitivity and 83% specificity, with an overall accuracy of 89% and Matthew’s correlation coefficient (MCC) of 0.79. Likewise, using an independent test set of experimentally identified sulfenylation sites, our method achieved scores of 74%, 62%, 80% and 0.42 for accuracy, sensitivity, specificity and MCC, with an area under the receiver operator characteristic (ROC) curve of 0.81. Moreover, in side-by-side comparisons, SVM-SulfoSite performed as well as or better than existing sulfenylation prediction tools. Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein S-sulfenylation.
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37
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Ju Z, Wang SY. Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition. Gene 2018; 664:78-83. [DOI: 10.1016/j.gene.2018.04.055] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 03/23/2018] [Accepted: 04/18/2018] [Indexed: 01/09/2023]
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38
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Taherzadeh G, Yang Y, Xu H, Xue Y, Liew AWC, Zhou Y. Predicting lysine-malonylation sites of proteins using sequence and predicted structural features. J Comput Chem 2018; 39:1757-1763. [DOI: 10.1002/jcc.25353] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/30/2018] [Accepted: 04/08/2018] [Indexed: 12/21/2022]
Affiliation(s)
- Ghazaleh Taherzadeh
- School of Information and Communication Technology; Griffith University, Parklands Drive; Southport Queensland 4222 Australia
| | - Yuedong Yang
- School of Data and Computer Science; Sun Yat-sen University; Guangzhou 510275 China
| | - Haodong Xu
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering; Huazhong University of Science and Technology; Wuhan Hubei 430074 China
| | - Yu Xue
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering; Huazhong University of Science and Technology; Wuhan Hubei 430074 China
| | - Alan Wee-Chung Liew
- School of Information and Communication Technology; Griffith University, Parklands Drive; Southport Queensland 4222 Australia
| | - Yaoqi Zhou
- School of Information and Communication Technology; Griffith University, Parklands Drive; Southport Queensland 4222 Australia
- Institute for Glycomics, Griffith University, Parklands Dr; Southport Queensland 4222 Australia
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39
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Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance. ENERGIES 2017. [DOI: 10.3390/en10101516] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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40
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Using SF6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect. ENERGIES 2017. [DOI: 10.3390/en10081119] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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41
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Ma Y, Yang M, Lin X, Liu X, Huang H, Ge F. Malonylome Analysis Reveals the Involvement of Lysine Malonylation in Metabolism and Photosynthesis in Cyanobacteria. J Proteome Res 2017; 16:2030-2043. [DOI: 10.1021/acs.jproteome.7b00017] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Yanyan Ma
- Key
Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingkun Yang
- Key
Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Xiaohuang Lin
- Key
Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Liu
- Key
Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Huang
- Key
Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Feng Ge
- Key
Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| |
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