1
|
Khandelwal M, Rout RK. PRMxAI: protein arginine methylation sites prediction based on amino acid spatial distribution using explainable artificial intelligence. BMC Bioinformatics 2023; 24:376. [PMID: 37794362 PMCID: PMC10548713 DOI: 10.1186/s12859-023-05491-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 09/21/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Protein methylation, a post-translational modification, is crucial in regulating various cellular functions. Arginine methylation is required to understand crucial biochemical activities and biological functions, like gene regulation, signal transduction, etc. However, some experimental methods, including Chip-Chip, mass spectrometry, and methylation-specific antibodies, exist for the prediction of methylated proteins. These experimental methods are expensive and tedious. As a result, computational methods based on machine learning play an efficient role in predicting arginine methylation sites. RESULTS In this research, a novel method called PRMxAI has been proposed to predict arginine methylation sites. The proposed PRMxAI extract sequence-based features, such as dipeptide composition, physicochemical properties, amino acid composition, and information theory-based features (Arimoto, Havrda-Charvat, Renyi, and Shannon entropy), to represent the protein sequences into numerical format. Various machine learning algorithms are implemented to select the better classifier, such as Decision trees, Naive Bayes, Random Forest, Support vector machines, and K-nearest neighbors. The random forest algorithm is selected as the underlying classifier for the PRMxAI model. The performance of PRMxAI is evaluated by employing 10-fold cross-validation, and it yields 87.17% and 90.40% accuracy on mono-methylarginine and di-methylarginine data sets, respectively. This research also examines the impact of various features on both data sets using explainable artificial intelligence. CONCLUSIONS The proposed PRMxAI shows the effectiveness of the features for predicting arginine methylation sites. Additionally, the SHapley Additive exPlanation method is used to interpret the predictive mechanism of the proposed model. The results indicate that the proposed PRMxAI model outperforms other state-of-the-art predictors.
Collapse
Affiliation(s)
- Monika Khandelwal
- Computer Science and Engineering Department, National Institute of Technology Srinagar, Hazratbal, Srinagar, J&K 190006 India
| | - Ranjeet Kumar Rout
- Computer Science and Engineering Department, National Institute of Technology Srinagar, Hazratbal, Srinagar, J&K 190006 India
| |
Collapse
|
2
|
Khandelwal M, Kumar Rout R, Umer S, Mallik S, Li A. Multifactorial feature extraction and site prognosis model for protein methylation data. Brief Funct Genomics 2023; 22:20-30. [PMID: 36310537 DOI: 10.1093/bfgp/elac034] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/23/2022] [Accepted: 09/28/2022] [Indexed: 01/24/2023] Open
Abstract
Integrated studies (multi-omics studies) comprising genetic, proteomic and epigenetic data analyses have become an emerging topic in biomedical research. Protein methylation is a posttranslational modification that plays an essential role in various cellular activities. The prediction of methylation sites (arginine and lysine) is vital to understand the molecular processes of protein methylation. However, current experimental techniques used for methylation site predictions are tedious and expensive. Hence, computational techniques for predicting methylation sites in proteins are necessary. For predicting methylation sites, various computational methods have been proposed in recent years. Most existing methods require structural and evolutionary information for retrieving features, acquiring this information is not always convenient. Thus, we proposed a novel method, called multi-factorial feature extraction and site prognosis model (MufeSPM), for the prediction of protein methylation sites based on information theory features (Renyi, Shannon, Havrda-Charvat and Arimoto entropy), amino acid composition and physicochemical properties acquired from protein methylation data. A random forest algorithm was used to predict methylation sites in protein sequences. This paper also studied the impact of different features and classifiers on arginine and lysine methylation data sets. For the R methylation data set, MufeSPM yielded 82.45%($\pm $ 3.47) accuracy, and for the K methylation data set, it provided an average accuracy of 71.94%($\pm $ 2.12). Additionally, the area under the receiver operating characteristic curve for different classifiers in predicting methylation site was provided. The experimental results signify that MufeSPM performs better than the state-of-the-art predictors.
Collapse
Affiliation(s)
- Monika Khandelwal
- Computer Science & Engineering, National Institute of Technology Srinagar, Hazratbal, Srinagar, 190006, Jammu and Kashmir, India
| | - Ranjeet Kumar Rout
- Computer Science & Engineering, National Institute of Technology Srinagar, Hazratbal, Srinagar, 190006, Jammu and Kashmir, India
| | - Saiyed Umer
- Computer Science & Engineering, Aliah University, Kolkata, 700016, West Bengal, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Huntington Ave, Boston, 02115, MA, USA
| | - Aimin Li
- School of Computer Science and Engineering, Xi'an University of Technology, Jinhua S Rd, 710048, Shaanxi, China
| |
Collapse
|
3
|
Ahmed S, Rahman A, Hasan MAM, Rahman J, Islam MKB, Ahmad S. predML-Site: Predicting Multiple Lysine PTM Sites With Optimal Feature Representation and Data Imbalance Minimization. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3624-3634. [PMID: 34546927 DOI: 10.1109/tcbb.2021.3114349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Identifying of post-translational modifications (PTM) is crucial in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Computational methods for predicting multiple PTM at the same lysine residues, often referred to as K-PTM, is still evolving. This paper presents a novel computational tool, abbreviated as predML-Site, for predicting KPTM, such as acetylation, crotonylation, methylation, succinylation from an uncategorized peptide sample involving single, multiple, or no modification. For informative feature representation, multiple sequence encoding schemes, such as the sequence-coupling, binary encoding, k-spaced amino acid pairs, amino acid factor have been used with ANOVA and incremental feature selection. As a core predictor, a cost-sensitive SVM classifier has been adopted which effectively mitigates the effect of class-label imbalance in the dataset. predML-Site predicts multi-label PTM sites with 84.18% accuracy using the top 91 features. It has also achieved 85.34% aiming and 86.58% coverage rate which are much better than the existing state-of-the-art predictors on the same rigorous validation test. This performance indicates that predML-Site can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, predML-Site has been deployed as a user-friendly web-server at http://103.99.176.239/predML-Site.
Collapse
|
4
|
Zhao J, Jiang H, Zou G, Lin Q, Wang Q, Liu J, Ma L. CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence. Front Genet 2022; 13:1036862. [PMID: 36324513 PMCID: PMC9618650 DOI: 10.3389/fgene.2022.1036862] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/04/2022] [Indexed: 11/30/2022] Open
Abstract
Protein arginine methylation (PRme), as one post-translational modification, plays a critical role in numerous cellular processes and regulates critical cellular functions. Though several in silico models for predicting PRme sites have been reported, new models may be required to develop due to the significant increase of identified PRme sites. In this study, we constructed multiple machine-learning and deep-learning models. The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe. CNNArginineMe performed best in AUC scoring metrics in comparisons with several reported predictors. Additionally, we employed CNNArginineMe to predict arginine methylation proteome and performed functional analysis. The arginine methylated proteome is significantly enriched in the amyotrophic lateral sclerosis (ALS) pathway. CNNArginineMe is freely available at https://github.com/guoyangzou/CNNArginineMe.
Collapse
Affiliation(s)
- Jiaojiao Zhao
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Haoqiang Jiang
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Guoyang Zou
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Qian Lin
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China
| | - Qiang Wang
- Oncology Department, Shandong Second Provincial General Hospital, Jinan, China
| | - Jia Liu
- Department of Pharmacology, School of Pharmacy, Qingdao University, Qingdao, China
| | - Leina Ma
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China
- *Correspondence: Leina Ma,
| |
Collapse
|
5
|
A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery. MEDICAL ONCOLOGY (NORTHWOOD, LONDON, ENGLAND) 2022; 39:206. [PMID: 36175717 DOI: 10.1007/s12032-022-01815-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/29/2022] [Indexed: 10/14/2022]
Abstract
Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.
Collapse
|
6
|
MethEvo: an accurate evolutionary information-based methylation site predictor. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07738-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
7
|
Ali SD, Tayara H, Chong KT. Interpretable machine learning identification of arginine methylation sites. Comput Biol Med 2022; 147:105767. [DOI: 10.1016/j.compbiomed.2022.105767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/12/2022] [Accepted: 06/18/2022] [Indexed: 11/25/2022]
|
8
|
Kakkanas A, Karamichali E, Koufogeorgou EI, Kotsakis SD, Georgopoulou U, Foka P. Targeting the YXXΦ Motifs of the SARS Coronaviruses 1 and 2 ORF3a Peptides by In Silico Analysis to Predict Novel Virus-Host Interactions. Biomolecules 2022; 12:1052. [PMID: 36008946 PMCID: PMC9405953 DOI: 10.3390/biom12081052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/21/2022] [Accepted: 07/25/2022] [Indexed: 02/08/2023] Open
Abstract
The emerging SARS-CoV and SARS-CoV-2 belong to the family of "common cold" RNA coronaviruses, and they are responsible for the 2003 epidemic and the current pandemic with over 6.3 M deaths worldwide. The ORF3a gene is conserved in both viruses and codes for the accessory protein ORF3a, with unclear functions, possibly related to viral virulence and pathogenesis. The tyrosine-based YXXΦ motif (Φ: bulky hydrophobic residue-L/I/M/V/F) was originally discovered to mediate clathrin-dependent endocytosis of membrane-spanning proteins. Many viruses employ the YXXΦ motif to achieve efficient receptor-guided internalisation in host cells, maintain the structural integrity of their capsids and enhance viral replication. Importantly, this motif has been recently identified on the ORF3a proteins of SARS-CoV and SARS-CoV-2. Given that the ORF3a aa sequence is not fully conserved between the two SARS viruses, we aimed to map in silico structural differences and putative sequence-driven alterations of regulatory elements within and adjacently to the YXXΦ motifs that could predict variations in ORF3a functions. Using robust bioinformatics tools, we investigated the presence of relevant post-translational modifications and the YXXΦ motif involvement in protein-protein interactions. Our study suggests that the predicted YXXΦ-related features may confer specific-yet to be discovered-functions to ORF3a proteins, significant to the new virus and related to enhanced propagation, host immune regulation and virulence.
Collapse
Affiliation(s)
- Athanassios Kakkanas
- Laboratory of Molecular Virology, Hellenic Pasteur Institute, 115-21 Athens, Greece; (A.K.); (E.K.); (E.I.K.); (U.G.)
| | - Eirini Karamichali
- Laboratory of Molecular Virology, Hellenic Pasteur Institute, 115-21 Athens, Greece; (A.K.); (E.K.); (E.I.K.); (U.G.)
| | - Efthymia Ioanna Koufogeorgou
- Laboratory of Molecular Virology, Hellenic Pasteur Institute, 115-21 Athens, Greece; (A.K.); (E.K.); (E.I.K.); (U.G.)
| | - Stathis D. Kotsakis
- Laboratory of Bacteriology, Hellenic Pasteur Institute, 115-21 Athens, Greece;
| | - Urania Georgopoulou
- Laboratory of Molecular Virology, Hellenic Pasteur Institute, 115-21 Athens, Greece; (A.K.); (E.K.); (E.I.K.); (U.G.)
| | - Pelagia Foka
- Laboratory of Molecular Virology, Hellenic Pasteur Institute, 115-21 Athens, Greece; (A.K.); (E.K.); (E.I.K.); (U.G.)
| |
Collapse
|
9
|
Rahman A, Ahmed S, Al Mehedi Hasan M, Ahmad S, Dehzangi I. Accurately predicting nitrosylated tyrosine sites using probabilistic sequence information. Gene 2022; 826:146445. [PMID: 35358650 DOI: 10.1016/j.gene.2022.146445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 02/16/2022] [Accepted: 03/18/2022] [Indexed: 11/04/2022]
Abstract
Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.
Collapse
Affiliation(s)
- Afrida Rahman
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Sabit Ahmed
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md Al Mehedi Hasan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.
| |
Collapse
|
10
|
Malebary SJ, Alzahrani E, Khan YD. A comprehensive tool for accurate identification of methyl-Glutamine sites. J Mol Graph Model 2021; 110:108074. [PMID: 34768228 DOI: 10.1016/j.jmgm.2021.108074] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/15/2021] [Accepted: 11/02/2021] [Indexed: 11/16/2022]
Abstract
Methylation is a biochemical process involved in nearly all of the human body functions. Glutamine is considered an indispensable amino acid that is susceptible to methylation via post-translational modification (PTM). Modern research has proved that methylation plays a momentous role in the progression of most types of cancers. Therefore, there is a need for an effective method to predict glutamine sites vulnerable to methylation accurately and inexpensively. The motive of this study is the formulation of an accurate method that could predict such sites with high accuracy. Various computationally intelligent classifiers were employed for their formulation and evaluation. Rigorous validations prove that deep learning performs best as compared to other classifiers. The accuracy (ACC) and the area under the receiver operating curve (AUC) obtained by 10-fold cross-validation was 0.962 and 0.981, while with the jackknife testing, it was 0.968 and 0.980, respectively. From these results, it is concluded that the proposed methodology works sufficiently well for the prediction of methyl-glutamine sites. The webserver's code, developed for the prediction of methyl-glutamine sites, is freely available at https://github.com/s20181080001/WebServer.git. The code can easily be set up by any intermediate-level Python user.
Collapse
Affiliation(s)
- Sharaf J Malebary
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia.
| | - Ebraheem Alzahrani
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah, 21589, Saudi Arabia.
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.
| |
Collapse
|
11
|
Basu R, Dutta S, Pal A, Sengupta M, Chattopadhyay S. Calmodulin7: recent insights into emerging roles in plant development and stress. PLANT MOLECULAR BIOLOGY 2021; 107:1-20. [PMID: 34398355 DOI: 10.1007/s11103-021-01177-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/27/2021] [Indexed: 05/25/2023]
Abstract
Analyses of the function of Arabidopsis Calmodulin7 (CAM7) in concert with multiple regulatory proteins involved in various signal transduction processes. Calmodulin (CaM) plays various regulatory roles in multiple signaling pathways in eukaryotes. Arabidopsis CALMODULIN 7 (CAM7) is a unique member of the CAM family that works as a transcription factor in light signaling pathways. CAM7 works in concert with CONSTITUTIVE PHOTOMORPHOGENIC 1 and ELONGATED HYPOCOTYL 5, and plays an important role in seedling development. Further, it is involved in the regulation of the activity of various Ca2+-gated channels such as cyclic nucleotide gated channel 6 (CNGC6), CNGC14 and auto-inhibited Ca2+ ATPase 8. Recent studies further indicate that CAM7 is also an integral part of multiple signaling pathways including hormone, immunity and stress. Here, we review the recent advances in understanding the multifaceted role of CAM7. We highlight the open-ended questions, and also discuss the diverse aspects of CAM7 characterization that need to be addressed for comprehensive understanding of its cellular functions.
Collapse
Affiliation(s)
- Riya Basu
- Department of Biotechnology, National Institute of Technology, Mahatma Gandhi Avenue, Durgapur, West Bengal, 713209, India
| | - Siddhartha Dutta
- Department of Biotechnology, National Institute of Technology, Mahatma Gandhi Avenue, Durgapur, West Bengal, 713209, India
- Department of Biotechnology, University of Engineering and Management, University Area, Plot, Street Number 03, Action Area III, B/5, Newtown, Kolkata, West Bengal, 700156, India
| | - Abhideep Pal
- Department of Biotechnology, National Institute of Technology, Mahatma Gandhi Avenue, Durgapur, West Bengal, 713209, India
| | - Mandar Sengupta
- Department of Biotechnology, National Institute of Technology, Mahatma Gandhi Avenue, Durgapur, West Bengal, 713209, India
| | - Sudip Chattopadhyay
- Department of Biotechnology, National Institute of Technology, Mahatma Gandhi Avenue, Durgapur, West Bengal, 713209, India.
| |
Collapse
|
12
|
Lumbanraja FR, Mahesworo B, Cenggoro TW, Sudigyo D, Pardamean B. SSMFN: a fused spatial and sequential deep learning model for methylation site prediction. PeerJ Comput Sci 2021; 7:e683. [PMID: 34541311 PMCID: PMC8409337 DOI: 10.7717/peerj-cs.683] [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: 04/15/2021] [Accepted: 07/30/2021] [Indexed: 05/12/2023]
Abstract
BACKGROUND Conventional in vivo methods for post-translational modification site prediction such as spectrophotometry, Western blotting, and chromatin immune precipitation can be very expensive and time-consuming. Neural networks (NN) are one of the computational approaches that can predict effectively the post-translational modification site. We developed a neural network model, namely the Sequential and Spatial Methylation Fusion Network (SSMFN), to predict possible methylation sites on protein sequences. METHOD We designed our model to be able to extract spatial and sequential information from amino acid sequences. Convolutional neural networks (CNN) is applied to harness spatial information, while long short-term memory (LSTM) is applied for sequential data. The latent representation of the CNN and LSTM branch are then fused. Afterwards, we compared the performance of our proposed model to the state-of-the-art methylation site prediction models on the balanced and imbalanced dataset. RESULTS Our model appeared to be better in almost all measurement when trained on the balanced training dataset. On the imbalanced training dataset, all of the models gave better performance since they are trained on more data. In several metrics, our model also surpasses the PRMePred model, which requires a laborious effort for feature extraction and selection. CONCLUSION Our models achieved the best performance across different environments in almost all measurements. Also, our result suggests that the NN model trained on a balanced training dataset and tested on an imbalanced dataset will offer high specificity and low sensitivity. Thus, the NN model for methylation site prediction should be trained on an imbalanced dataset. Since in the actual application, there are far more negative samples than positive samples.
Collapse
Affiliation(s)
- Favorisen Rosyking Lumbanraja
- Department of Computer Science, Faculty of Mathematics and Natural Science, University of Lampung, Bandar Lampung, Lampung, Indonesia
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, West Jakarta, Jakarta, Indonesia
- Statistics Departement, School of Computer Science, Bina Nusantara University, West Jakarta, Jakarta, Indonesia
| | - Tjeng Wawan Cenggoro
- Bioinformatics and Data Science Research Center, Bina Nusantara University, West Jakarta, Jakarta, Indonesia
- Computer Science Departement, School of Computer Science, Bina Nusantara University, West Jakarta, Jakarta, Indonesia
| | - Digdo Sudigyo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, West Jakarta, Jakarta, Indonesia
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, West Jakarta, Jakarta, Indonesia
- Computer Science Department, BINUS Graduate Program - Master of Computer Science, Bina Nusantara University, West Jakarta, Jakarta, Indonesia
| |
Collapse
|
13
|
Islam MKB, Rahman J, Hasan MAM, Ahmad S. predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance. Comput Biol Chem 2021; 94:107553. [PMID: 34384997 DOI: 10.1016/j.compbiolchem.2021.107553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 06/22/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.
Collapse
Affiliation(s)
- Md Khaled Ben Islam
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia; Department of Computer Science & Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.
| | - Julia Rahman
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia; Department of Computer Science & Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.
| | - Md Al Mehedi Hasan
- Department of Computer Science & Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Department of Computer Science & Engineering, Rajshahi University, Rajshahi, Bangladesh
| |
Collapse
|
14
|
Chaudhari M, Thapa N, Roy K, Newman RH, Saigo H, B K C D. DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins. Mol Omics 2021; 16:448-454. [PMID: 32555810 DOI: 10.1039/d0mo00025f] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Methylation, which is one of the most prominent post-translational modifications on proteins, regulates many important cellular functions. Though several model-based methylation site predictors have been reported, all existing methods employ machine learning strategies, such as support vector machines and random forest, to predict sites of methylation based on a set of "hand-selected" features. As a consequence, the subsequent models may be biased toward one set of features. Moreover, due to the large number of features, model development can often be computationally expensive. In this paper, we propose an alternative approach based on deep learning to predict arginine methylation sites. Our model, which we termed DeepRMethylSite, is computationally less expensive than traditional feature-based methods while eliminating potential biases that can arise through features selection. Based on independent testing on our dataset, DeepRMethylSite achieved efficiency scores of 68%, 82% and 0.51 with respect to sensitivity (SN), specificity (SP) and Matthew's correlation coefficient (MCC), respectively. Importantly, in side-by-side comparisons with other state-of-the-art methylation site predictors, our method performs on par or better in all scoring metrics tested.
Collapse
Affiliation(s)
- Meenal Chaudhari
- Department of Computational Science and Engineering, North Carolina Agricultural & Technical State University, Greensboro, NC 27411, USA
| | - Niraj Thapa
- Department of Computational Science and Engineering, North Carolina Agricultural & Technical State University, Greensboro, NC 27411, USA
| | - Kaushik Roy
- Department of Computer Science, North Carolina Agricultural & Technical State University, Greensboro, NC 27411, USA
| | - Robert H Newman
- Department of Biology, North Carolina Agricultural & Technical State University, Greensboro, NC 27411, USA
| | - Hiroto Saigo
- Department of Informatics, Kyushu University, Fukuoka 819-0395, Japan
| | - Dukka B K C
- Electrical Engineering and Computer Science Department, Wichita State University, Wichita, KS 67260, USA.
| |
Collapse
|
15
|
Proteome-wide Prediction of Lysine Methylation Leads to Identification of H2BK43 Methylation and Outlines the Potential Methyllysine Proteome. Cell Rep 2021; 32:107896. [PMID: 32668242 DOI: 10.1016/j.celrep.2020.107896] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 04/29/2020] [Accepted: 06/22/2020] [Indexed: 12/15/2022] Open
Abstract
Protein Lys methylation plays a critical role in numerous cellular processes, but it is challenging to identify Lys methylation in a systematic manner. Here we present an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We develop MethylSight, a program that predicts Kme events solely on the physicochemical properties of residues surrounding the putative methylation sites, which then requires validation by targeted MS. Using this approach, we identify 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts that Lys methylation is a prevalent post-translational modification in the human proteome. Our work provides a useful resource for guiding systematic exploration of the role of Lys methylation in human health and disease.
Collapse
|
16
|
Ahmed S, Rahman A, Hasan MAM, Islam MKB, Rahman J, Ahmad S. predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance. PLoS One 2021; 16:e0249396. [PMID: 33793659 PMCID: PMC8016359 DOI: 10.1371/journal.pone.0249396] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 03/18/2021] [Indexed: 12/14/2022] Open
Abstract
Post-translational modification (PTM) involves covalent modification after the biosynthesis process and plays an essential role in the study of cell biology. Lysine phosphoglycerylation, a newly discovered reversible type of PTM that affects glycolytic enzyme activities, and is responsible for a wide variety of diseases, such as heart failure, arthritis, and degeneration of the nervous system. Our goal is to computationally characterize potential phosphoglycerylation sites to understand the functionality and causality more accurately. In this study, a novel computational tool, referred to as predPhogly-Site, has been developed to predict phosphoglycerylation sites in the protein. It has effectively utilized the probabilistic sequence-coupling information among the nearby amino acid residues of phosphoglycerylation sites along with a variable cost adjustment for the skewed training dataset to enhance the prediction characteristics. It has achieved around 99% accuracy with more than 0.96 MCC and 0.97 AUC in both 10-fold cross-validation and independent test. Even, the standard deviation in 10-fold cross-validation is almost negligible. This performance indicates that predPhogly-Site remarkably outperformed the existing prediction tools and can be used as a promising predictor, preferably with its web interface at http://103.99.176.239/predPhogly-Site.
Collapse
Affiliation(s)
- Sabit Ahmed
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
- * E-mail:
| | - Afrida Rahman
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md. Al Mehedi Hasan
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md Khaled Ben Islam
- Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh
| | - Julia Rahman
- Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| |
Collapse
|
17
|
Awais M, Hussain W, Khan YD, Rasool N, Khan SA, Chou KC. iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:596-610. [PMID: 31144645 DOI: 10.1109/tcbb.2019.2919025] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association, cytoskeletal dynamics, and enzymatic inhibition/activation. Phosphohistidine (PhosH) has a key role in a number of biological processes, including central metabolism to signalling in eukaryotes and bacteria. Thus, identification of phosphohistidine sites in a protein sequence is crucial, and experimental identification can be expensive, time-taking, and laborious. To address this problem, here, we propose a novel computational model namely iPhosH-PseAAC for prediction of phosphohistidine sites in a given protein sequence using pseudo amino acid composition (PseAAC), statistical moments, and position relative features. The results of the proposed predictor are validated through self-consistency testing, 10-fold cross-validation, and jackknife testing. The self-consistency validation gave the 100 percent accuracy, whereas, for cross-validation, the accuracy achieved is 94.26 percent. Moreover, jackknife testing gave 97.07 percent accuracy for the proposed model. Thus, the proposed model iPhosH-PseAAC for prediction of iPhosH site has the great ability to predict the PhosH sites in given proteins.
Collapse
|
18
|
Comprehensive in silico mutational-sensitivity analysis of PTEN establishes signature regions implicated in pathogenesis of Autism Spectrum Disorders. Genomics 2020; 113:999-1017. [PMID: 33152507 DOI: 10.1016/j.ygeno.2020.10.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/22/2020] [Accepted: 10/30/2020] [Indexed: 01/18/2023]
Abstract
An extensively studied cancer and Autism Spectrum Disorders (ASD) gene like PTEN provided an exclusive opportunity to map its mutational-landscape, compare and establish plausible genotypic predictors of ASD-associated phenotypic outcomes. Our exhaustive in silico analysis on 4252 SNPs using >30 tools identified increased mutational-density in exon7. Phosphatase domain, although evolutionarily conserved, had the most nsSNPs localised within signature regions. The evolutionarily variable C-terminal side contained the highest truncating-SNPs outside signature regions of C2 domain and most PTMs within C-tail site which displayed maximum intolerance to polymorphisms, and permitted benign but destabilising nsSNPs that enhanced its intrinsically-disordered nature. ASD-associated SNPs localised within ATP-binding motifs and Nuclear-Localising-Sequences were the most potent triggers of ASD manifestation. These, along with variations within P, WPD and TI loops, M1 within phosphatase domain, M2 and MoRFs of C2 domain, caused severe long-range conformational fluctuations altering PTEN's dynamic stability- not observed in variations outside signature regions. 3'UTR-SNPs affected 44 strong miRNA brain-specific targets; several 5' UTR-SNPs targeted transcription-factor POLR2A and 10 pathogenic Splice-Affecting-Variants were identified.
Collapse
|
19
|
Zhang X, Chen L. Prediction of membrane protein types by fusing protein-protein interaction and protein sequence information. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2020; 1868:140524. [PMID: 32858174 DOI: 10.1016/j.bbapap.2020.140524] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/17/2020] [Accepted: 07/30/2020] [Indexed: 11/30/2022]
Abstract
Membrane proteins are gatekeepers to the cell and essential for determination of the function of cells. Identification of the types of membrane proteins is an essential problem in cell biology. It is time-consuming and expensive to identify the type of membrane proteins with traditional experimental methods. The alternative way is to design effective computational methods, which can provide quick and reliable predictions. To date, several computational methods have been proposed in this regard. Several of them used the features extracted from the sequence information of individual proteins. Recently, networks are more and more popular to tackle different protein-related problems, which can organize proteins in a system level and give an overview of all proteins. However, such form weakens the essential properties of proteins, such as their sequence information. In this study, a novel feature fusion scheme was proposed, which integrated the information of protein sequences and protein-protein interaction network. The fused features of a protein were defined as the linear combination of sequence features of all proteins in the network, where the combination coefficients were the probabilities yielded by the random walk with restart algorithm with the protein as the seed node. Several models with such fused features and different classification algorithms were built and evaluated. Their performance for predicting the type of membrane proteins was improved compared with the models only with the sequence features or network information.
Collapse
Affiliation(s)
- Xiaolin Zhang
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China.
| |
Collapse
|
20
|
Abstract
During the last three decades or so, many efforts have been made to study the protein cleavage
sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease
and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly
clear <i>via</i> this mini-review that the motivation driving the aforementioned studies is quite wise,
and that the results acquired through these studies are very rewarding, particularly for developing peptide
drugs.
Collapse
Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
| |
Collapse
|
21
|
Chou KC. An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule. Curr Pharm Des 2020; 25:4223-4234. [PMID: 31782354 DOI: 10.2174/1381612825666191129164042] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/25/2019] [Indexed: 11/22/2022]
Abstract
OBJECTIVE One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information. METHODS To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed. RESULTS AND CONCLUSION It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.
Collapse
Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, Massachusetts 02478, United States.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
22
|
Zheng L, Huang S, Mu N, Zhang H, Zhang J, Chang Y, Yang L, Zuo Y. RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2019:5650975. [PMID: 31802128 PMCID: PMC6893003 DOI: 10.1093/database/baz131] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 10/16/2019] [Accepted: 10/17/2019] [Indexed: 12/12/2022]
Abstract
By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook. Database URL: http://bioinfor.imu.edu.cn/raacbook
Collapse
Affiliation(s)
- Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Nengjiang Mu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Haoyue Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Jiayu Zhang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Yu Chang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Baojian Road No.157, Harbin 150081, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Zhaojun Road No.24, Hohhot, 010070, China
| |
Collapse
|
23
|
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/).
Collapse
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
| |
Collapse
|
24
|
Zheng W, Wuyun Q, Cheng M, Hu G, Zhang Y. Two-Level Protein Methylation Prediction using structure model-based features. Sci Rep 2020; 10:6008. [PMID: 32265459 PMCID: PMC7138832 DOI: 10.1038/s41598-020-62883-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 03/16/2020] [Indexed: 01/26/2023] Open
Abstract
Protein methylation plays a vital role in cell processing. Many novel methods try to predict methylation sites from protein sequence by sequence information or predicted structural information, but none of them use protein tertiary structure information in prediction. In particular, most of them do not build models for predicting methylation types (mono-, di-, tri-methylation). To address these problems, we propose a novel method, Met-predictor, to predict methylation sites and methylation types using a support vector machine-based network. Met-predictor combines a variety of sequence-based features that are derived from protein sequences with structure model-based features, which are geometric information extracted from predicted protein tertiary structure models, and are firstly used in methylation prediction. Met-predictor was tested on two independent test sets, where the addition of structure model-based features improved AUC from 0.611 and 0.520 to 0.655 and 0.566 for lysine and from 0.723 and 0.640 to 0.734 and 0.643 for arginine. When compared with other state-of-the-art methods, Met-predictor had 13.1% (3.9%) and 8.5% (16.4%) higher accuracy than the best of other methods for methyllysine and methylarginine prediction on the independent test set I (II). Furthermore, Met-predictor also attains excellent performance for predicting methylation types.
Collapse
Affiliation(s)
- Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, PR China
| | - Qiqige Wuyun
- Computer Science and Engineering Department, Michigan State University, East Lansing, MI, 48823, USA.,School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, PR China
| | - Micah Cheng
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, PR China.
| | - Yanping Zhang
- Department of Mathematics, School of Mathematics and Physics, Hebei University of Engineering, Handan, 056038, PR China.
| |
Collapse
|
25
|
Huang G, Zheng Y, Wu YQ, Han GS, Yu ZG. An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation. Front Genet 2020; 10:1325. [PMID: 32117407 PMCID: PMC7033570 DOI: 10.3389/fgene.2019.01325] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 12/05/2019] [Indexed: 12/14/2022] Open
Abstract
Butyrylation plays a crucial role in the cellular processes. Due to limit of techniques, it is a challenging task to identify histone butyrylation sites on a large scale. To fill the gap, we propose an approach based on information entropy and machine learning for computationally identifying histone butyrylation sites. The proposed method achieves 0.92 of area under the receiver operating characteristic (ROC) curve over the training set by 3-fold cross validation and 0.80 over the testing set by independent test. Feature analysis implies that amino acid residues in the down/upstream of butyrylation sites would exhibit specific sequence motif to a certain extent. Functional analysis suggests that histone butyrylation was most possibly associated with four pathways (systemic lupus erythematosus, alcoholism, viral carcinogenesis and transcriptional misregulation in cancer), was involved in binding with other molecules, processes of biosynthesis, assembly, arrangement or disassembly and was located in such complex as consists of DNA, RNA, protein, etc. The proposed method is useful to predict histone butyrylation sites. Analysis of feature and function improves understanding of histone butyrylation and increases knowledge of functions of butyrylated histones.
Collapse
Affiliation(s)
- Guohua Huang
- Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, China
| | - Yang Zheng
- Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, China
| | - Yao-Qun Wu
- Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China
| | - Guo-Sheng Han
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China.,School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
| |
Collapse
|
26
|
Chou KC. Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis. Curr Top Med Chem 2019; 19:2283-2300. [DOI: 10.2174/1568026619666191018100141] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 08/18/2019] [Accepted: 08/26/2019] [Indexed: 01/27/2023]
Abstract
Stimulated by the 5-steps rule during the last decade or so, computational proteomics has achieved remarkable progresses in the following three areas: (1) protein structural class prediction; (2) protein subcellular location prediction; (3) post-translational modification (PTM) site prediction. The results obtained by these predictions are very useful not only for an in-depth study of the functions of proteins and their biological processes in a cell, but also for developing novel drugs against major diseases such as cancers, Alzheimer’s, and Parkinson’s. Moreover, since the targets to be predicted may have the multi-label feature, two sets of metrics are introduced: one is for inspecting the global prediction quality, while the other for the local prediction quality. All the predictors covered in this review have a userfriendly web-server, through which the majority of experimental scientists can easily obtain their desired data without the need to go through the complicated mathematics.
Collapse
Affiliation(s)
- Kuo-Chen Chou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| |
Collapse
|
27
|
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.
Collapse
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
| |
Collapse
|
28
|
Malebary SJ, Rehman MSU, Khan YD. iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule. PLoS One 2019; 14:e0223993. [PMID: 31751380 PMCID: PMC6874067 DOI: 10.1371/journal.pone.0223993] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/02/2019] [Indexed: 01/22/2023] Open
Abstract
Among different post-translational modifications (PTMs), one of the most important one is the lysine crotonylation in proteins. Its importance cannot be undermined related to different diseases and essential biological practice. The key step for finding the hidden mechanisms of crotonylation along with their occurrence sites is to completely apprehend the mechanism behind this biological process. In previously reported studies, researchers have used different techniques, like position weighted matrix (PWM), support vector machine (SVM), k nearest neighbors (KNN), and many others. However, the maximum prediction accuracy achieved was not such high. To address this, herein, we propose an improved predictor for lysine crotonylation sites named iCrotoK-PseAAC, in which we have incorporated various position and composition relative features along with statistical moments into PseAAC. The results of self-consistency testing were 100% accurate, while the 10-fold cross validation gave 99.0% accuracy. Based on the validation and comparison of model, it is concluded that the iCrotoK-PseAAC is more accurate than the previously proposed models.
Collapse
Affiliation(s)
- Sharaf Jameel Malebary
- Department of Information Technology, King Abdul Aziz University, Rabigh, Kingdom of Saudi Arabia
| | - Muhammad Safi ur Rehman
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| |
Collapse
|
29
|
Chou KC. Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs. Curr Med Chem 2019; 26:4918-4943. [PMID: 31060481 DOI: 10.2174/0929867326666190507082559] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 01/29/2019] [Accepted: 01/31/2019] [Indexed: 12/16/2022]
Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
Collapse
Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
| |
Collapse
|
30
|
Abstract
The smallest unit of life is a cell, which contains numerous protein molecules. Most
of the functions critical to the cell’s survival are performed by these proteins located in its different
organelles, usually called ‘‘subcellular locations”. Information of subcellular localization
for a protein can provide useful clues about its function. To reveal the intricate pathways at the
cellular level, knowledge of the subcellular localization of proteins in a cell is prerequisite.
Therefore, one of the fundamental goals in molecular cell biology and proteomics is to determine
the subcellular locations of proteins in an entire cell. It is also indispensable for prioritizing
and selecting the right targets for drug development. Unfortunately, it is both timeconsuming
and costly to determine the subcellular locations of proteins purely based on experiments.
With the avalanche of protein sequences generated in the post-genomic age, it is highly
desired to develop computational methods for rapidly and effectively identifying the subcellular
locations of uncharacterized proteins based on their sequences information alone. Actually,
considerable progresses have been achieved in this regard. This review is focused on those
methods, which have the capacity to deal with multi-label proteins that may simultaneously
exist in two or more subcellular location sites. Protein molecules with this kind of characteristic
are vitally important for finding multi-target drugs, a current hot trend in drug development.
Focused in this review are also those methods that have use-friendly web-servers established so
that the majority of experimental scientists can use them to get the desired results without the
need to go through the detailed mathematics involved.
Collapse
Affiliation(s)
- Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, United States
| |
Collapse
|
31
|
Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via CHOU’S 5-Step Rule. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09931-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
32
|
Kumari B, Kumar R, Kumar M. Identifying residues that determine palmitoylation using association rule mining. Bioinformatics 2019; 35:2887-2890. [PMID: 30649192 DOI: 10.1093/bioinformatics/btz003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 09/15/2018] [Accepted: 01/07/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION In eukaryotes, palmitoylation drives several essential cellular mechanisms like protein sorting, protein stability and protein-protein interaction. Several amino acids namely Cys, Gly, Ser, Thr and Lys undergo palmitoylation. But very little is known about the amino acid patterns that promote palmitoylation. RESULTS We deduced presence of statistically significant amino acids around palmitoylation sites and their association with different palmitoylated residues i.e. Cys, Gly and Ser. The results suggest that palmitoylation, irrespective of its target residue, generally occurs at sites where Cys, Leu, Lys, Arg, Ser and Met are abundant. Furthermore, functional properties of the three types of palmitoylated proteins were compared. We observed similar functional behavior of Cys and Gly palmitoylated proteins but proteins with Ser palmitoylation showed distinctiveness from remaining two. Motif-wise functional conservation was also observed in Cys palmitoylated proteins. We also did functional annotation of predicted human palmitoylome. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Bandana Kumari
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
| | - Ravindra Kumar
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
| | - Manish Kumar
- Department of Biophysics, University of Delhi South Campus, New Delhi, India
| |
Collapse
|
33
|
Wei L, Xing P, Shi G, Ji Z, Zou Q. Fast Prediction of Protein Methylation Sites Using a Sequence-Based Feature Selection Technique. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1264-1273. [PMID: 28222000 DOI: 10.1109/tcbb.2017.2670558] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Protein methylation, an important post-translational modification, plays crucial roles in many cellular processes. The accurate prediction of protein methylation sites is fundamentally important for revealing the molecular mechanisms undergoing methylation. In recent years, computational prediction based on machine learning algorithms has emerged as a powerful and robust approach for identifying methylation sites, and much progress has been made in predictive performance improvement. However, the predictive performance of existing methods is not satisfactory in terms of overall accuracy. Motivated by this, we propose a novel random-forest-based predictor called MePred-RF, integrating several discriminative sequence-based feature descriptors and improving feature representation capability using a powerful feature selection technique. Importantly, unlike other methods based on multiple, complex information inputs, our proposed MePred-RF is based on sequence information alone. Comparative studies on benchmark datasets via vigorous jackknife tests indicate that our proposed MePred-RF method remarkably outperforms other state-of-the-art predictors, leading by a 4.5 percent average in terms of overall accuracy. A user-friendly webserver that implements the proposed method has been established for researchers' convenience, and is now freely available for public use through http://server.malab.cn/MePred-RF. We anticipate our research tool to be useful for the large-scale prediction and analysis of protein methylation sites.
Collapse
|
34
|
Zhao W, Li GP, Wang J, Zhou YK, Gao Y, Du PF. Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions. J Theor Biol 2019; 473:38-43. [DOI: 10.1016/j.jtbi.2019.04.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/22/2019] [Accepted: 04/29/2019] [Indexed: 12/11/2022]
|
35
|
Ning Q, Ma Z, Zhao X. dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components. J Theor Biol 2019; 470:43-49. [DOI: 10.1016/j.jtbi.2019.03.011] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 03/09/2019] [Accepted: 03/13/2019] [Indexed: 10/27/2022]
|
36
|
Barukab O, Khan YD, Khan SA, Chou KC. iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components. Curr Genomics 2019; 20:306-320. [PMID: 32030089 PMCID: PMC6983959 DOI: 10.2174/1389202920666190819091609] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 08/04/2019] [Accepted: 08/06/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological pro-cesses. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites. METHODOLOGY In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are in-corporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and in-dependent testing. RESULTS Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing. CONCLUSION The proposed model has better performance as compared to the existing predictors, how-ever, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.
Collapse
Affiliation(s)
| | | | - Sher Afzal Khan
- Address correspondence to this author at the Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia; and Department of Computer Sciences, Abdul Wali Khan University, Mardan, Pakistan; E-mail:
| | | |
Collapse
|
37
|
Ilyas S, Hussain W, Ashraf A, Khan YD, Khan SA, Chou KC. iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule. Curr Genomics 2019; 20:275-292. [PMID: 32030087 PMCID: PMC6983956 DOI: 10.2174/1389202920666190809095206] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/02/2019] [Accepted: 07/26/2019] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming. OBJECTIVE Herein, we propose a computational predictor named iMethylK_pseAAC to identify lysine methylation sites. METHODS Firstly, we constructed feature vectors based on PseAAC using position and composition rel-ative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing. RESULTS The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing. CONCLUSION It is concluded that iMethylK_pseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl_pseACC, BPB_pPMS and PMeS.
Collapse
Affiliation(s)
| | | | | | - Yaser Daanial Khan
- Address correspondence to this author at the Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore, Pakistan; Tel: +923054440271; E-mail:
| | | | | |
Collapse
|
38
|
Pan Q, Guo Y, Guo L, Liao S, Zhao C, Wang S, Liu HF. Mechanistic Insights of Chemicals and Drugs as Risk Factors for Systemic Lupus Erythematosus. Curr Med Chem 2019; 27:5175-5188. [PMID: 30947650 DOI: 10.2174/0929867326666190404140658] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 03/25/2019] [Accepted: 03/27/2019] [Indexed: 12/21/2022]
Abstract
Systemic Lupus Erythematosus (SLE) is a chronic and relapsing heterogenous autoimmune disease that primarily affects women of reproductive age. Genetic and environmental risk factors are involved in the pathogenesis of SLE, and susceptibility genes have recently been identified. However, as gene therapy is far from clinical application, further investigation of environmental risk factors could reveal important therapeutic approaches. We systematically explored two groups of environmental risk factors: chemicals (including silica, solvents, pesticides, hydrocarbons, heavy metals, and particulate matter) and drugs (including procainamide, hydralazine, quinidine, Dpenicillamine, isoniazid, and methyldopa). Furthermore, the mechanisms underlying risk factors, such as genetic factors, epigenetic change, and disrupted immune tolerance, were explored. This review identifies novel risk factors and their underlying mechanisms. Practicable measures for the management of these risk factors will benefit SLE patients and provide potential therapeutic strategies.
Collapse
Affiliation(s)
- Qingjun Pan
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Yun Guo
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Linjie Guo
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Shuzhen Liao
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Chunfei Zhao
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Sijie Wang
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| | - Hua-Feng Liu
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Affiliated Hospital of Guangdong Medical University, 57th South Renmin Road, Zhanjiang 524001, Guangdong, China
| |
Collapse
|
39
|
Han Q, Yang C, Lu J, Zhang Y, Li J. Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Beta-lyase Plays in Hyperoxaluria. Curr Med Chem 2019; 26:4944-4963. [PMID: 30907303 DOI: 10.2174/0929867326666190325095223] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 02/17/2019] [Accepted: 02/22/2019] [Indexed: 11/22/2022]
Abstract
Hyperoxaluria, excessive urinary oxalate excretion, is a significant health problem worldwide. Disrupted oxalate metabolism has been implicated in hyperoxaluria and accordingly, an enzymatic disturbance in oxalate biosynthesis can result in the primary hyperoxaluria. Alanine glyoxylate aminotransferase-1 and glyoxylate reductase, the enzymes involving glyoxylate (precursor for oxalate) metabolism, have been related to primary hyperoxalurias. Some studies suggest that other enzymes such as glycolate oxidase and alanine glyoxylate aminotransferase-2 might be associated with primary hyperoxaluria as well, but evidence of a definitive link is not strong between the clinical cases and gene mutations. There are still some idiopathic hyperoxalurias, which require a further study for the etiologies. Some aminotransferases, particularly kynurenine aminotransferases, can convert glyoxylate to glycine. Based on biochemical and structural characteristics, expression level, subcellular localization of some aminotransferases, a number of them appear able to catalyze the transamination of glyoxylate to glycine more efficiently than alanine glyoxylate aminotransferase-1. The aim of this minireview is to explore other undermining causes of primary hyperoxaluria and stimulate research toward achieving a comprehensive understanding of underlying mechanisms leading to the disease. Herein, we reviewed all aminotransferases in the liver for their functions in glyoxylate metabolism. Particularly, kynurenine aminotransferase-I and III were carefully discussed regarding their biochemical and structural characteristics, cellular localization, and enzyme inhibition. Kynurenine aminotransferase-III is, so far, the most efficient putative mitochondrial enzyme to transaminate glyoxylate to glycine in mammalian livers, might be an interesting enzyme to look over in hyperoxaluria etiology of primary hyperoxaluria and should be carefully investigated for its involvement in oxalate metabolism.
Collapse
Affiliation(s)
- Qian Han
- Key Laboratory of Tropical Biological Resources of Ministry of Education, Hainan University, Haikou, Hainan 570228. China
| | - Cihan Yang
- Key Laboratory of Tropical Biological Resources of Ministry of Education, Hainan University, Haikou, Hainan 570228. China
| | - Jun Lu
- Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208. China
| | - Yinai Zhang
- Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208. China
| | - Jianyong Li
- Department of Biochemistry, Virginia Tech, Blacksburg, VA 24061. United States
| |
Collapse
|
40
|
SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal Biochem 2019; 568:14-23. [DOI: 10.1016/j.ab.2018.12.019] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 12/19/2018] [Accepted: 12/22/2018] [Indexed: 02/06/2023]
|
41
|
Khan YD, Jamil M, Hussain W, Rasool N, Khan SA, Chou KC. pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J Theor Biol 2019; 463:47-55. [DOI: 10.1016/j.jtbi.2018.12.015] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 12/05/2018] [Accepted: 12/11/2018] [Indexed: 02/08/2023]
|
42
|
Chen G, Cao M, Yu J, Guo X, Shi S. Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC. J Theor Biol 2019; 461:92-101. [DOI: 10.1016/j.jtbi.2018.10.047] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 10/09/2018] [Accepted: 10/22/2018] [Indexed: 12/12/2022]
|
43
|
Jia J, Li X, Qiu W, Xiao X, Chou KC. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. J Theor Biol 2019; 460:195-203. [DOI: 10.1016/j.jtbi.2018.10.021] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 09/16/2018] [Accepted: 10/08/2018] [Indexed: 01/11/2023]
|
44
|
Bao W, Yang B, Li Z, Zhou Y. LAIPT: Lysine Acetylation Site Identification with Polynomial Tree. Int J Mol Sci 2018; 20:ijms20010113. [PMID: 30597947 PMCID: PMC6337602 DOI: 10.3390/ijms20010113] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 11/30/2018] [Accepted: 12/05/2018] [Indexed: 11/16/2022] Open
Abstract
Post-translational modification plays a key role in the field of biology. Experimental identification methods are time-consuming and expensive. Therefore, computational methods to deal with such issues overcome these shortcomings and limitations. In this article, we propose a lysine acetylation site identification with polynomial tree method (LAIPT), making use of the polynomial style to demonstrate amino-acid residue relationships in peptide segments. This polynomial style was enriched by the physical and chemical properties of amino-acid residues. Then, these reconstructed features were input into the employed classification model, named the flexible neural tree. Finally, some effect evaluation measurements were employed to test the model’s performance.
Collapse
Affiliation(s)
- Wenzheng Bao
- School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China.
| | - Bin Yang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
| | - Zhengwei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
| | - Yong Zhou
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
| |
Collapse
|
45
|
iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components. J Theor Biol 2018; 465:1-6. [PMID: 30590059 DOI: 10.1016/j.jtbi.2018.12.034] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 12/15/2018] [Accepted: 12/23/2018] [Indexed: 11/21/2022]
Abstract
The 2'-O-methylation transferase is involved in the process of 2'-O-methylation. In catalytic processes, the 2-hydroxy group of the ribose moiety of a nucleotide accept a methyl group. This methylation process is a post-transcriptional modification, which occurs in various cellular RNAs and plays a vital role in regulation of gene expressions at the post-transcriptional level. Through biochemical experiments 2'-O-methylation sites produce good results but these biochemical process and exploratory techniques are very expensive. Thus, it is required to develop a computational method to identify 2'-O-methylation sites. In this work, we proposed a simple and precise convolution neural network method namely: iRNA-PseKNC(2methyl) to identify 2'-O-methylation sites. The existing techniques use handcrafted features, while the proposed method automatically extracts the features of 2'-O-methylation using the proposed convolution neural network model. The proposed prediction iRNA-PseKNC(2methyl) method obtained 98.27% of accuracy, 96.29% of sensitivity, 100% of specificity, and 0.965 of MCC on Home sapiens dataset. The reported outcomes present that our proposed method obtained better outcomes than existing method in terms of all evaluation parameters. These outcomes show that iRNA-PseKNC(2methyl) method might be beneficial for the academic research and drug design.
Collapse
|
46
|
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.
Collapse
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
| |
Collapse
|
47
|
Ju Z, Wang SY. Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm. Anal Biochem 2018; 561-562:11-17. [DOI: 10.1016/j.ab.2018.09.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Revised: 09/09/2018] [Accepted: 09/09/2018] [Indexed: 12/14/2022]
|
48
|
Ju Z, Wang SY. Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm. J Theor Biol 2018; 457:6-13. [DOI: 10.1016/j.jtbi.2018.08.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/07/2018] [Accepted: 08/15/2018] [Indexed: 11/29/2022]
|
49
|
Mei J, Fu Y, Zhao J. Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition. J Theor Biol 2018; 456:41-48. [DOI: 10.1016/j.jtbi.2018.07.040] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Revised: 07/20/2018] [Accepted: 07/29/2018] [Indexed: 12/23/2022]
|
50
|
Jin W, Li QZ, Zuo YC, Cao YN, Zhang LQ, Hou R, Su WX. Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue. DNA Cell Biol 2018; 38:49-62. [PMID: 30346835 DOI: 10.1089/dna.2018.4276] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Breast cancer has a high mortality rate for females. Aberrant DNA methylation plays a crucial role in the occurrence and progression of breast carcinoma. By comparing DNA methylation differences between tumor breast tissue and normal breast tissue, we calculate and analyze the distributions of the hyper- and hypomethylation sites in different function regions. Results indicate that enhancer regions are often hypomethylated in breast cancer. CpG islands (CGIs) are mainly hypermethylated, while the flanking CGI (shores and shelves) is more easily hypomethylated. The hypomethylation in gene body region is related to the upregulation of gene expression, and the hypomethylation of enhancer regions is closely associated with gene expression upregulation in breast cancer. Some key hypomethylation sites in enhancer regions and key hypermethylation sites in CGIs for regulating key genes are, respectively, found, such as oncogenes ESR1 and ERBB2 and tumor suppressor genes FBLN2, CEBPA, and FAT4. This suggests that the recognizing methylation status of these genes will be useful for the diagnosis of breast cancer.
Collapse
Affiliation(s)
- Wen Jin
- 1 Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University , Hohhot, China
| | - Qian-Zhong Li
- 1 Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University , Hohhot, China .,2 The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University , Hohhot, China
| | - Yong-Chun Zuo
- 2 The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Inner Mongolia University , Hohhot, China
| | - Yan-Ni Cao
- 1 Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University , Hohhot, China
| | - Lu-Qiang Zhang
- 1 Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University , Hohhot, China
| | - Rui Hou
- 1 Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University , Hohhot, China
| | - Wen-Xia Su
- 3 College of Science, Inner Mongolia Agricultural University , Hohhot, China
| |
Collapse
|