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Palacios A, Acharya P, Peidl A, Beck M, Blanco E, Mishra A, Bawa-Khalfe T, Pakhrin S. SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model. NAR Genom Bioinform 2024; 6:lqae011. [PMID: 38327870 PMCID: PMC10849187 DOI: 10.1093/nargab/lqae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/17/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024] Open
Abstract
SUMOylation is an essential post-translational modification system with the ability to regulate nearly all aspects of cellular physiology. Three major paralogues SUMO1, SUMO2 and SUMO3 form a covalent bond between the small ubiquitin-like modifier with lysine residues at consensus sites in protein substrates. Biochemical studies continue to identify unique biological functions for protein targets conjugated to SUMO1 versus the highly homologous SUMO2 and SUMO3 paralogues. Yet, the field has failed to harness contemporary AI approaches including pre-trained protein language models to fully expand and/or recognize the SUMOylated proteome. Herein, we present a novel, deep learning-based approach called SumoPred-PLM for human SUMOylation prediction with sensitivity, specificity, Matthew's correlation coefficient, and accuracy of 74.64%, 73.36%, 0.48% and 74.00%, respectively, on the CPLM 4.0 independent test dataset. In addition, this novel platform uses contextualized embeddings obtained from a pre-trained protein language model, ProtT5-XL-UniRef50 to identify SUMO2/3-specific conjugation sites. The results demonstrate that SumoPred-PLM is a powerful and unique computational tool to predict SUMOylation sites in proteins and accelerate discovery.
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Affiliation(s)
- Andrew Vargas Palacios
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
| | - Pujan Acharya
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
| | - Anthony Stephen Peidl
- Department of Biology and Biochemistry, Center for Nuclear Receptors & Cell Signaling, University of Houston, Houston, TX 77204, USA
| | - Moriah Rene Beck
- Department of Chemistry and Biochemistry, Wichita State University, 1845 Fairmount St., Wichita, KS 67260, USA
| | - Eduardo Blanco
- Department of Computer Science, University of Arizona, 1040 4th St., Tucson, AZ 85721, USA
| | - Avdesh Mishra
- Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
| | - Tasneem Bawa-Khalfe
- Department of Biology and Biochemistry, Center for Nuclear Receptors & Cell Signaling, University of Houston, Houston, TX 77204, USA
| | - Subash Chandra Pakhrin
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
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Kumari S, Gupta R, Ambasta RK, Kumar P. Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme. Biochim Biophys Acta Rev Cancer 2023; 1878:188999. [PMID: 37858622 DOI: 10.1016/j.bbcan.2023.188999] [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: 06/30/2023] [Revised: 10/06/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023]
Abstract
Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PTMs alter the oncogenic signaling events and offer a novel avenue in GBM therapeutics research through PTM enzymes as potential biomarkers for drug targeting. In addition, PTMs are critical regulators of chromatin architecture, gene expression, and tumor microenvironment (TME), that play a crucial function in tumorigenesis. Moreover, the implementation of artificial intelligence and machine learning algorithms enhances GBM therapeutics research through the identification of novel PTM enzymes and residues. Herein, we briefly explain the mechanism of protein modifications in GBM etiology, and in altering the biologics of GBM cells through chromatin remodeling, modulation of the TME, and signaling pathways. In addition, we highlighted the importance of PTM enzymes as therapeutic biomarkers and the role of artificial intelligence and machine learning in protein PTM prediction.
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Affiliation(s)
- Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India; School of Medicine, University of South Carolina, Columbia, SC, United States of America
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India; Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India.
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India.
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Lin X, Gao Y, Lei F. An application of topological data analysis in predicting sumoylation sites. PeerJ 2023; 11:e16204. [PMID: 37846308 PMCID: PMC10576966 DOI: 10.7717/peerj.16204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 09/08/2023] [Indexed: 10/18/2023] Open
Abstract
Sumoylation is a reversible post-translational modification that regulates certain significant biochemical functions in proteins. The protein alterations caused by sumoylation are associated with the incidence of some human diseases. Therefore, identifying the sites of sumoylation in proteins may provide a direction for mechanistic research and drug development. Here, we propose a new computational approach for identifying sumoylation sites using an encoding method based on topological data analysis. The features of our model captured the key physical and biological properties of proteins at multiple scales. In a 10-fold cross validation, the outcomes of our model showed 96.45% of sensitivity (Sn), 94.65% of accuracy (Acc), 0.8946 of Matthew's correlation coefficient (MCC), and 0.99 of area under curve (AUC). The proposed predictor with only topological features achieves the best MCC and AUC in comparison to the other released methods. Our results suggest that topological information is an additional parameter that can assist in the prediction of sumoylation sites and provide a novel perspective for further research in protein sumoylation.
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Affiliation(s)
- Xiaoxi Lin
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
| | - Yaru Gao
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
| | - Fengchun Lei
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
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ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites. Cells 2022; 11:cells11172646. [PMID: 36078053 PMCID: PMC9454673 DOI: 10.3390/cells11172646] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 12/26/2022] Open
Abstract
Lysine SUMOylation plays an essential role in various biological functions. Several approaches integrating various algorithms have been developed for predicting SUMOylation sites based on a limited dataset. Recently, the number of identified SUMOylation sites has significantly increased due to investigation at the proteomics scale. We collected modification data and found the reported approaches had poor performance using our collected data. Therefore, it is essential to explore the characteristics of this modification and construct prediction models with improved performance based on an enlarged dataset. In this study, we constructed and compared 16 classifiers by integrating four different algorithms and four encoding features selected from 11 sequence-based or physicochemical features. We found that the convolution neural network (CNN) model integrated with residue structure, dubbed ResSUMO, performed favorably when compared with the traditional machine learning and CNN models in both cross-validation and independent tests. The area under the receiver operating characteristic (ROC) curve for ResSUMO was around 0.80, superior to that of the reported predictors. We also found that increasing the depth of neural networks in the CNN models did not improve prediction performance due to the degradation problem, but the residual structure could be included to optimize the neural networks and improve performance. This indicates that residual neural networks have the potential to be broadly applied in the prediction of other types of modification sites with great effectiveness and robustness. Furthermore, the online ResSUMO service is freely accessible.
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You X, Hu X, Feng Z, Wang Z, Hao S, Yang C. Recognizing Protein-metal Ion Ligands Binding Residues by Random Forest Algorithm with Adding Orthogonal Properties. Comput Biol Chem 2022; 98:107693. [DOI: 10.1016/j.compbiolchem.2022.107693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 11/16/2022]
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Dehzangi I, Sharma A, Shatabda S. iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features. Methods Mol Biol 2022; 2499:125-134. [PMID: 35696077 DOI: 10.1007/978-1-0716-2317-6_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Posttranslational modification (PTM) is an important biological mechanism to promote functional diversity among the proteins. So far, a wide range of PTMs has been identified. Among them, glycation is considered as one of the most important PTMs. Glycation is associated with different neurological disorders including Parkinson and Alzheimer. It is also shown to be responsible for different diseases, including vascular complications of diabetes mellitus. Despite all the efforts have been made so far, the prediction performance of glycation sites using computational methods remains limited. Here we present a newly developed machine learning tool called iProtGly-SS that utilizes sequential and structural information as well as Support Vector Machine (SVM) classifier to enhance lysine glycation site prediction accuracy. The performance of iProtGly-SS was investigated using the three most popular benchmarks used for this task. Our results demonstrate that iProtGly-SS is able to achieve 81.61%, 93.62%, and 92.95% prediction accuracies on these benchmarks, which are significantly better than those results reported in the previous studies. iProtGly-SS is implemented as a web-based tool which is publicly available at http://brl.uiu.ac.bd/iprotgly-ss/ .
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Affiliation(s)
- Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, USA.
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia.
- Department of Medical Science Mathematics, Tokyo Medical and Dental University (TMDU), Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
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Khan YD, Khan NS, Naseer S, Butt AH. iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC. PeerJ 2021; 9:e11581. [PMID: 34430072 PMCID: PMC8349168 DOI: 10.7717/peerj.11581] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 05/19/2021] [Indexed: 01/25/2023] Open
Abstract
Sumoylation is the post-translational modification that is involved in the adaption of the cells and the functional properties of a large number of proteins. Sumoylation has key importance in subcellular concentration, transcriptional synchronization, chromatin remodeling, response to stress, and regulation of mitosis. Sumoylation is associated with developmental defects in many human diseases such as cancer, Huntington's, Alzheimer's, Parkinson's, Spin cerebellar ataxia 1, and amyotrophic lateral sclerosis. The covalent bonding of Sumoylation is essential to inheriting part of the operative characteristics of some other proteins. For that reason, the prediction of the Sumoylation site has significance in the scientific community. A novel and efficient technique is proposed to predict the Sumoylation sites in proteins by incorporating Chou's Pseudo Amino Acid Composition (PseAAC) with statistical moments-based features. The outcomes from the proposed system using 10 fold cross-validation testing are 94.51%, 94.24%, 94.79% and 0.8903% accuracy, sensitivity, specificity and MCC, respectively. The performance of the proposed system is so far the best in comparison to the other state-of-the-art methods. The codes for the current study are available on the GitHub repository using the link: https://github.com/csbioinfopk/iSumoK-PseAAC.
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Affiliation(s)
- Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Nabeel Sabir Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Sheraz Naseer
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Ahmad Hassan Butt
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Punjab, Pakistan
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Aggarwal S, Tolani P, Gupta S, Yadav AK. Posttranslational modifications in systems biology. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:93-126. [PMID: 34340775 DOI: 10.1016/bs.apcsb.2021.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.
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Affiliation(s)
- Suruchi Aggarwal
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India; Department of Molecular Biology and Biotechnology, Cotton University, Guwahati, Assam, India
| | - Priya Tolani
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India
| | - Srishti Gupta
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India; School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Amit Kumar Yadav
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India.
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Dou L, Yang F, Xu L, Zou Q. A comprehensive review of the imbalance classification of protein post-translational modifications. Brief Bioinform 2021; 22:6217722. [PMID: 33834199 DOI: 10.1093/bib/bbab089] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/17/2021] [Accepted: 02/24/2021] [Indexed: 12/13/2022] Open
Abstract
Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.
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Affiliation(s)
- Lijun Dou
- University of Electronic Science and Technology of China and the Shenzhen Polytechnic, China
| | - Fenglong Yang
- University of Electronic Science and Technology of China and the Shenzhen Polytechnic, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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