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Adnan A, Hongya W, Ali F, Khalid M, Alghushairy O, Alsini R. A bi-layer model for identification of piwiRNA using deep neural learning. J Biomol Struct Dyn 2024; 42:5725-5733. [PMID: 37608578 DOI: 10.1080/07391102.2023.2243523] [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: 05/08/2023] [Accepted: 06/15/2023] [Indexed: 08/24/2023]
Abstract
piwiRNA is a kind of non-coding RNA (ncRNA) that cannot be translated into proteins. It helps in understanding the study of gametes generation and regulation of gene expression over both transcriptional and post-transcriptional levels. piwiRNA has the function of instructing deadenylation, animal fertility, silencing transposons, fighting viruses, and regulating endogenous genes. Due to the great significance of piwiRNA, prediction of piwiRNA is essential for crucial cellular functions. Several predictors were established for prediction of piwiRNA. However, improving the prediction of piwiRNA is highly desirable. In the current study, we developed a more promising predictor named, BLP-piwiRNA. The features are explored by reverse complement k-mer, gapped-k-mer composition, and k-mer composition. The feature set of all descriptors is fused and the best features are selected by cascade and relief feature selection strategies. The best feature sets are provided to random forest (RF), deep neural network (DNN), and support vector machine (SVM). The models validation are examined by 10-fold test. DNN with optimal features of Cascade feature selection approach secured the highest prediction results. The results illustrate that BLP-piwiRNA effectively outperforms the existing studies. The proposed approach would be beneficial for both research community and drug development industry. BLP-piwiRNA would serve as novel biomarkers and therapeutic targets for tumor diagnostics and treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Adnan Adnan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Wang Hongya
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Farman Ali
- Department of Software Engineering, Sarhad University of Science and Information Technology, Peshawar, Pakistan
| | - Majdi Khalid
- Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Omar Alghushairy
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Raed Alsini
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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2
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Jia J, Wu G, Li M. iGly-IDN: Identifying Lysine Glycation Sites in Proteins Based on Improved DenseNet. J Comput Biol 2024; 31:161-174. [PMID: 38016151 DOI: 10.1089/cmb.2023.0112] [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] [Indexed: 11/30/2023] Open
Abstract
Lysine glycation is one of the most significant protein post-translational modifications, which changes the properties of the proteins and causes them to be dysfunctional. Accurately identifying glycation sites helps to understand the biological function and potential mechanism of glycation in disease treatments. Nonetheless, the experimental methods are ordinarily inefficient and costly, so effective computational methods need to be developed. In this study, we proposed the new model called iGly-IDN based on the improved densely connected convolutional networks (DenseNet). First, one hot encoding was adopted to obtain the original feature maps. Afterward, the improved DenseNet was adopted to capture feature information with the importance degrees during the feature learning. According to the experimental results, Acc reaches 66%, and Mathews correlation coefficient reaches 0.33 on the independent testing data set, which indicates that the iGly-IDN can provide more effective glycation site identification than the current predictors.
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Affiliation(s)
- Jianhua Jia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
| | - Genqiang Wu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China
- College of Modern Economics and Management, Jiangxi University of Finance and Economics, Nanchang, China
| | - Meifang Li
- School of Computer Information Engineering, Nanchang Institute of Technology, Nanchang, China
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3
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Adejor J, Tumukunde E, Li G, Lin H, Xie R, Wang S. Impact of Lysine Succinylation on the Biology of Fungi. Curr Issues Mol Biol 2024; 46:1020-1046. [PMID: 38392183 PMCID: PMC10888112 DOI: 10.3390/cimb46020065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/24/2024] Open
Abstract
Post-translational modifications (PTMs) play a crucial role in protein functionality and the control of various cellular processes and secondary metabolites (SMs) in fungi. Lysine succinylation (Ksuc) is an emerging protein PTM characterized by the addition of a succinyl group to a lysine residue, which induces substantial alteration in the chemical and structural properties of the affected protein. This chemical alteration is reversible, dynamic in nature, and evolutionarily conserved. Recent investigations of numerous proteins that undergo significant succinylation have underscored the potential significance of Ksuc in various biological processes, encompassing normal physiological functions and the development of certain pathological processes and metabolites. This review aims to elucidate the molecular mechanisms underlying Ksuc and its diverse functions in fungi. Both conventional investigation techniques and predictive tools for identifying Ksuc sites were also considered. A more profound comprehension of Ksuc and its impact on the biology of fungi have the potential to unveil new insights into post-translational modification and may pave the way for innovative approaches that can be applied across various clinical contexts in the management of mycotoxins.
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Affiliation(s)
- John Adejor
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Elisabeth Tumukunde
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Guoqi Li
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Hong Lin
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Rui Xie
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Shihua Wang
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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4
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Ahmed SS, Rifat ZT, Rahman MS, Rahman MS. Succinylated lysine residue prediction revisited. Brief Bioinform 2023; 24:6865109. [PMID: 36460620 DOI: 10.1093/bib/bbac510] [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: 07/12/2022] [Revised: 09/30/2022] [Accepted: 10/25/2022] [Indexed: 12/04/2022] Open
Abstract
Lysine succinylation is a kind of post-translational modification (PTM) that plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy, and attention has been given in the literature to develop such models, albeit with only moderate success in the context of different evaluation metrics. One crucial aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids. In contrast, some others used them without considering the inter-dependency among the properties. The combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by combining all the properties. We propose three deep learning architectures: CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC outperforms the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we successfully achieve better sensitivity and specificity by varying the threshold value.
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Affiliation(s)
- Shehab Sarar Ahmed
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
| | - Zaara Tasnim Rifat
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
| | - M Saifur Rahman
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
| | - M Sohel Rahman
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
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Chandra A, Tünnermann L, Löfstedt T, Gratz R. Transformer-based deep learning for predicting protein properties in the life sciences. eLife 2023; 12:82819. [PMID: 36651724 PMCID: PMC9848389 DOI: 10.7554/elife.82819] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/06/2023] [Indexed: 01/19/2023] Open
Abstract
Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model-the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.
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Affiliation(s)
- Abel Chandra
- Department of Computing Science, Umeå UniversityUmeåSweden
| | - Laura Tünnermann
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural SciencesUmeåSweden
| | - Tommy Löfstedt
- Department of Computing Science, Umeå UniversityUmeåSweden
| | - Regina Gratz
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural SciencesUmeåSweden
- Department of Forest Ecology and Management, Swedish University of Agricultural SciencesUmeåSweden
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Wang H, Li H, Gao W, Xie J. PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy. Anal Biochem 2022; 658:114935. [PMID: 36206844 DOI: 10.1016/j.ab.2022.114935] [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: 07/08/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 12/30/2022]
Abstract
Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating cell function. In recent years, many models have been developed to predict ubiquitination sites in humans, mice and yeast. However, few studies have predicted ubiquitination sites in Arabidopsis thaliana. In view of this, a deep network model named PrUb-EL is proposed to predict ubiquitination sites in Arabidopsis thaliana. Firstly, six features based on the protein sequence are extracted with amino acid index database (AAindex), dipeptide deviates from the expected mean (DDE), dipeptide composition (DPC), blocks substitution matrix (BLOSUM62), enhanced amino acid composition (EAAC) and binary encoding. Secondly, the synthetic minority over-sampling technique (SMOTE) is utilized to process the imbalanced data set. Then a new classifier named DG is presented, which includes Dense block, Residual block and Gated recurrent unit (GRU) block. Finally, each of six feature extraction methods is integrated into the DG model, and the ensemble learning strategy is used to gain the final prediction result. Experimental results show that PrUb-EL has good predictive ability with the accuracy (ACC) and area under the ROC curve (auROC) values of 91.00% and 97.70% using 5-fold cross-validation, respectively. Note that the values of ACC and auROC are 88.58% and 96.09% in the independent test, respectively. Compared with previous studies, our model has significantly improved performance thus it is an excellent method for identifying ubiquitination sites in Arabidopsis thaliana. The datasets and code used for the article are available at https://github.com/Tom-Wangy/PreUb-EL.git.
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Affiliation(s)
- Houqiang Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Hong Li
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Weifeng Gao
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Jin Xie
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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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.
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8
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Jia J, Wu G, Li M, Qiu W. pSuc-EDBAM: Predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module. BMC Bioinformatics 2022; 23:450. [PMCID: PMC9620660 DOI: 10.1186/s12859-022-05001-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/25/2022] [Indexed: 11/10/2022] Open
Abstract
Background Lysine succinylation is a newly discovered protein post-translational modifications. Predicting succinylation sites helps investigate the metabolic disease treatments. However, the biological experimental approaches are costly and inefficient, it is necessary to develop efficient computational approaches. Results In this paper, we proposed a novel predictor based on ensemble dense blocks and an attention module, called as pSuc-EDBAM, which adopted one hot encoding to derive the feature maps of protein sequences, and generated the low-level feature maps through 1-D CNN. Afterward, the ensemble dense blocks were used to capture feature information at different levels in the process of feature learning. We also introduced an attention module to evaluate the importance degrees of different features. The experimental results show that Acc reaches 74.25%, and MCC reaches 0.2927 on the testing dataset, which suggest that the pSuc-EDBAM outperforms the existing predictors. Conclusions The experimental results of ten-fold cross-validation on the training dataset and independent test on the testing dataset showed that pSuc-EDBAM outperforms the existing succinylation site predictors and can predict potential succinylation sites effectively. The pSuc-EDBAM is feasible and obtains the credible predictive results, which may also provide valuable references for other related research. To make the convenience of the experimental scientists, a user-friendly web server has been established (http://bioinfo.wugenqiang.top/pSuc-EDBAM/), by which the desired results can be easily obtained.
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Affiliation(s)
- Jianhua Jia
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
| | - Genqiang Wu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
| | - Meifang Li
- grid.410729.90000 0004 1759 3199Computer Department, Nanchang Institute of Technology, Nanchang, 330044 China
| | - Wangren Qiu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
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Improving protein succinylation sites prediction using embeddings from protein language model. Sci Rep 2022; 12:16933. [PMID: 36209286 PMCID: PMC9547369 DOI: 10.1038/s41598-022-21366-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/26/2022] [Indexed: 12/29/2022] Open
Abstract
Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease.
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10
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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]
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11
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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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Zuo Y, Hong Y, Zeng X, Zhang Q, Liu X. MLysPRED: graph-based multi-view clustering and multi-dimensional normal distribution resampling techniques to predict multiple lysine sites. Brief Bioinform 2022; 23:6661182. [PMID: 35953081 DOI: 10.1093/bib/bbac277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/11/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Posttranslational modification of lysine residues, K-PTM, is one of the most popular PTMs. Some lysine residues in proteins can be continuously or cascaded covalently modified, such as acetylation, crotonylation, methylation and succinylation modification. The covalent modification of lysine residues may have some special functions in basic research and drug development. Although many computational methods have been developed to predict lysine PTMs, up to now, the K-PTM prediction methods have been modeled and learned a single class of K-PTM modification. In view of this, this study aims to fill this gap by building a multi-label computational model that can be directly used to predict multiple K-PTMs in proteins. In this study, a multi-label prediction model, MLysPRED, is proposed to identify multiple lysine sites using features generated from human protein sequences. In MLysPRED, three kinds of multi-label sequence encoding algorithms (MLDBPB, MLPSDAAP, MLPSTAAP) are proposed and combined with three encoding strategies (CHHAA, DR and Kmer) to convert preprocessed lysine sequences into effective numerical features. A multidimensional normal distribution oversampling technique and graph-based multi-view clustering under-sampling algorithm were first proposed and incorporated to reduce the proportion of the original training samples, and multi-label nearest neighbor algorithm is used for classification. It is observed that MLysPRED achieved an Aiming of 92.21%, Coverage of 94.98%, Accuracy of 89.63%, Absolute-True of 81.46% and Absolute-False of 0.0682 on the independent datasets. Additionally, comparison of results with five existing predictors also indicated that MLysPRED is very promising and encouraging to predict multiple K-PTMs in proteins. For the convenience of the experimental scientists, 'MLysPRED' has been deployed as a user-friendly web-server at http://47.100.136.41:8181.
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Affiliation(s)
- Yun Zuo
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Yue Hong
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Hunan University, Changsha, China
| | - Qiang Zhang
- School of Computer Science and Technology, Dalian University of Technology (DLUT), China
| | - Xiangrong Liu
- Department of Computer Science, Xiamen University, Xiamen 361005, China
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Xia Y, Jiang M, Luo Y, Feng G, Jia G, Zhang H, Wang P, Ge R. SuccSPred2.0: A Two-Step Model to Predict Succinylation Sites Based on Multifeature Fusion and Selection Algorithm. J Comput Biol 2022; 29:1085-1094. [PMID: 35714347 DOI: 10.1089/cmb.2022.0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Protein succinylation is a novel type of post-translational modification in recent decade years. It played an important role in biological structure and functions verified by experiments. However, it is time consuming and laborious for the wet experimental identification of succinylation sites. Traditional technology cannot adapt to the rapid growth of the biological sequence data sets. In this study, a new computational method named SuccSPred2.0 was proposed to identify succinylation sites in the protein sequences based on multifeature fusion and maximal information coefficient (MIC) method. SuccSPred2.0 was implemented based on a two-step strategy. At first, high-dimension features were reduced by linear discriminant analysis to prevent overfitting. Subsequently, MIC method was employed to select the important features binding classifiers to predict succinylation sites. From the compared experiments on 10-fold cross-validation and independent test data sets, SuccSPred2.0 obtained promising improvements. Comparative experiments showed that SuccSPred2.0 was superior to previous tools in identifying succinylation sites in the given proteins.
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Affiliation(s)
- Yixiao Xia
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Minchao Jiang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Yizhang Luo
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Guanwen Feng
- Xi'an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Gangyong Jia
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Hua Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Pu Wang
- Computer School, Hubei University of Arts and Science, Xiangyang, China
| | - Ruiquan Ge
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
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14
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Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2499:285-322. [PMID: 35696087 DOI: 10.1007/978-1-0716-2317-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.
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15
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Jia J, Wu G, Qiu W. pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm. Front Cell Dev Biol 2022; 10:894874. [PMID: 35686053 PMCID: PMC9170990 DOI: 10.3389/fcell.2022.894874] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Being a new type of widespread protein post-translational modifications discovered in recent years, succinylation plays a key role in protein conformational regulation and cellular function regulation. Numerous studies have shown that succinylation modifications are closely associated with the development of many diseases. In order to gain insight into the mechanism of succinylation, it is vital to identify lysine succinylation sites. However, experimental identification of succinylation sites is time-consuming and laborious, and traditional identification tools are unable to meet the rapid growth of datasets. Therefore, to solve this problem, we developed a new predictor named pSuc-FFSEA, which can predict succinylation sites in protein sequences by feature fusion and stacking ensemble algorithm. Specifically, the sequence information and physicochemical properties were first extracted using EBGW, One-Hot, continuous bag-of-words, chaos game representation, and AAF_DWT. Following that, feature selection was performed, which applied LASSO to select the optimal subset of features for the classifier, and then, stacking ensemble classifier was designed using two-layer stacking ensemble, selecting three classifiers, SVM, broad learning system and LightGBM classifier, as the base classifiers of the first layer, using logistic regression classifier as the meta classifier of the second layer. In order to further improve the model prediction accuracy and reduce the computational effort, bayesian optimization algorithm and grid search algorithm were utilized to optimize the hyperparameters of the classifier. Finally, the results of rigorous 10-fold cross-validation indicated our predictor showed excellent robustness and performed better than the previous prediction tools, which achieved an average prediction accuracy of 0.7773 ± 0.0120. Besides, for the convenience of the most experimental scientists, a user-friendly and comprehensive web-server for pSuc-FFSEA has been established at https://bio.cangmang.xyz/pSuc-FFSEA, by which one can easily obtain the expected data and results without going through the complicated mathematics.
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Affiliation(s)
- Jianhua Jia
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, China
| | - Genqiang Wu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, China
| | - Wangren Qiu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, China
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16
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Wang H, Zhao H, Zhang J, Han J, Liu Z. A parallel model of DenseCNN and ordered-neuron LSTM for generic and species-specific succinylation site prediction. Biotechnol Bioeng 2022; 119:1755-1767. [PMID: 35320585 DOI: 10.1002/bit.28091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 03/12/2022] [Accepted: 03/19/2022] [Indexed: 11/07/2022]
Abstract
Lysine succinylation (Ksucc) regulates various metabolic processes, participates in vital life processes, ans is involved in the occurrence and development of numerous diseases. Accurate recognition of succinylation sites can reveal underlying functional mechanisms and pathogenesis. However, most remain undetected. Moreover, a deep learning architecture focusing on generic and species-specific predictions is still lacking. Thus, we proposed a deep learning-based framework named Deep-Ksucc, combining a dense convolutional network (DenseCNN) and ordered-neuron long short-term memory (OnLSTM) in parallel, which took the cascading characteristics of sequence information and physicochemical properties as the input. The results of the generic and species-specific predictions indicated that Deep-Ksucc can identify sequence patterns of different organisms and recognize plenty of succinylation sites. The case study showed that Deep-Ksucc can serve as a reliable tool for biology verification and computer-aided recognition of succinylation sites. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Hong Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jing Zhang
- Engineering Training Center, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiale Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Zhihao Liu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
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17
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Zhang D, Wang S. A protein succinylation sites prediction method based on the hybrid architecture of LSTM network and CNN. J Bioinform Comput Biol 2022; 20:2250003. [PMID: 35191361 DOI: 10.1142/s0219720022500032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The succinylation modification of protein participates in the regulation of a variety of cellular processes. Identification of modified substrates with precise sites is the basis for understanding the molecular mechanism and regulation of succinylation. In this work, we picked and chose five superior feature codes: CKSAAP, ACF, BLOSUM62, AAindex, and one-hot, according to their performance in the problem of succinylation sites prediction. Then, LSTM network and CNN were used to construct four models: LSTM-CNN, CNN-LSTM, LSTM, and CNN. The five selected features were, respectively, input into each of these four models for training to compare the four models. Based on the performance of each model, the optimal model among them was chosen to construct a hybrid model DeepSucc that was composed of five sub-modules for integrating heterogeneous information. Under the 10-fold cross-validation, the hybrid model DeepSucc achieves 86.26% accuracy, 84.94% specificity, 87.57% sensitivity, 0.9406 AUC, and 0.7254 MCC. When compared with other prediction tools using an independent test set, DeepSucc outperformed them in sensitivity and MCC. The datasets and source codes can be accessed at https://github.com/1835174863zd/DeepSucc.
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Affiliation(s)
- Die Zhang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
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18
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iSuc-ChiDT: a computational method for identifying succinylation sites using statistical difference table encoding and the chi-square decision table classifier. BioData Min 2022; 15:3. [PMID: 35144656 PMCID: PMC8832670 DOI: 10.1186/s13040-022-00290-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 01/30/2022] [Indexed: 11/23/2022] Open
Abstract
Background Lysine succinylation is a type of protein post-translational modification which is widely involved in cell differentiation, cell metabolism and other important physiological activities. To study the molecular mechanism of succinylation in depth, succinylation sites need to be accurately identified, and because experimental approaches are costly and time-consuming, there is a great demand for reliable computational methods. Feature extraction is a key step in building succinylation site prediction models, and the development of effective new features improves predictive accuracy. Because the number of false succinylation sites far exceeds that of true sites, traditional classifiers perform poorly, and designing a classifier to effectively handle highly imbalanced datasets has always been a challenge. Results A new computational method, iSuc-ChiDT, is proposed to identify succinylation sites in proteins. In iSuc-ChiDT, chi-square statistical difference table encoding is developed to extract positional features, and has a higher predictive accuracy and fewer features compared to common position-based encoding schemes such as binary encoding and physicochemical property encoding. Single amino acid and undirected pair-coupled amino acid composition features are supplemented to improve the fault tolerance for residue insertions and deletions. After feature selection by Chi-MIC-share algorithm, the chi-square decision table (ChiDT) classifier is constructed for imbalanced classification. With a training set of 4748:50,551(true: false sites), ChiDT clearly outperforms traditional classifiers in predictive accuracy, and runs fast. Using an independent testing set of experimentally identified succinylation sites, iSuc-ChiDT achieves a sensitivity of 70.47%, a specificity of 66.27%, a Matthews correlation coefficient of 0.205, and a global accuracy index Q9 of 0.683, showing a significant improvement in sensitivity and overall accuracy compared to PSuccE, Success, SuccinSite, and other existing succinylation site predictors. Conclusions iSuc-ChiDT shows great promise in predicting succinylation sites and is expected to facilitate further experimental investigation of protein succinylation. Supplementary Information The online version contains supplementary material available at 10.1186/s13040-022-00290-1.
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Khan S, Khan M, Iqbal N, Amiruddin Abd Rahman M, Khalis Abdul Karim M. Deep-piRNA: Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features. COMPUTERS, MATERIALS & CONTINUA 2022; 72:2243-2258. [DOI: 10.32604/cmc.2022.022901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 11/11/2021] [Indexed: 09/02/2023]
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20
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Zhu Y, Yin S, Zheng J, Shi Y, Jia C. O-glycosylation site prediction for Homo sapiens by combining properties and sequence features with support vector machine. J Bioinform Comput Biol 2021; 20:2150029. [PMID: 34806952 DOI: 10.1142/s0219720021500293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
O-glycosylation is a protein posttranslational modification important in regulating almost all cells. It is related to a large number of physiological and pathological phenomena. Recognizing O-glycosylation sites is the key to further investigating the molecular mechanism of protein posttranslational modification. This study aimed to collect a reliable dataset on Homo sapiens and develop an O-glycosylation predictor for Homo sapiens, named Captor, through multiple features. A random undersampling method and a synthetic minority oversampling technique were employed to deal with imbalanced data. In addition, the Kruskal-Wallis (K-W) test was adopted to optimize feature vectors and improve the performance of the model. A support vector machine, due to its optimal performance, was used to train and optimize the final prediction model after a comprehensive comparison of various classifiers in traditional machine learning methods and deep learning. On the independent test set, Captor outperformed the existing O-glycosylation tool, suggesting that Captor could provide more instructive guidance for further experimental research on O-glycosylation. The source code and datasets are available at https://github.com/YanZhu06/Captor/.
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Affiliation(s)
- Yan Zhu
- School of Science, Dalian Maritime University, Dalian 116026, P. R. China
| | - Shuwan Yin
- School of Science, Dalian Maritime University, Dalian 116026, P. R. China
| | - Jia Zheng
- School of Science, Dalian Maritime University, Dalian 116026, P. R. China
| | - Yixia Shi
- School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524048, P. R. China
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian 116026, P. R. China
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21
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Ahmed S, Rahman A, Hasan MAM, Ahmad S, Shovan SM. Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance. Sci Rep 2021; 11:18882. [PMID: 34556767 PMCID: PMC8460736 DOI: 10.1038/s41598-021-98458-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/08/2021] [Indexed: 02/08/2023] Open
Abstract
Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named 'iMul-kSite' for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that 'iMul-kSite' can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, 'iMul-kSite' has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite .
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Affiliation(s)
- Sabit Ahmed
- grid.443086.d0000 0004 1755 355XComputer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204 Bangladesh
| | - Afrida Rahman
- grid.443086.d0000 0004 1755 355XComputer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204 Bangladesh
| | - Md. Al Mehedi Hasan
- grid.443086.d0000 0004 1755 355XComputer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204 Bangladesh
| | - Shamim Ahmad
- grid.412656.20000 0004 0451 7306Computer Science and Engineering, University of Rajshahi, Rajshahi, 6205 Bangladesh
| | - S. M. Shovan
- grid.443086.d0000 0004 1755 355XComputer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204 Bangladesh
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Akmal MA, Hussain W, Rasool N, Khan YD, Khan SA, Chou KC. Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2045-2056. [PMID: 31985438 DOI: 10.1109/tcbb.2020.2968441] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.
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Wang H, Zhao H, Yan Z, Zhao J, Han J. MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network. Biomolecules 2021; 11:biom11060872. [PMID: 34208298 PMCID: PMC8231176 DOI: 10.3390/biom11060872] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 12/26/2022] Open
Abstract
Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for disease treatment and research of related drugs. However, most existing computational methods for the prediction of succinylation sites are still based on machine learning. With the increasing volume of data and complexity of feature representations, it is necessary to explore effective deep learning methods to recognize succinylation sites. In this paper, we propose a multilane dense convolutional attention network, MDCAN-Lys. MDCAN-Lys extracts sequence information, physicochemical properties of amino acids, and structural properties of proteins using a three-way network, and it constructs feature space. For each sub-network, MDCAN-Lys uses the cascading model of dense convolutional block and convolutional block attention module to capture feature information at different levels and improve the abstraction ability of the network. The experimental results of 10-fold cross-validation and independent testing show that MDCAN-Lys can recognize more succinylation sites, which is consistent with the conclusion of the case study. Thus, it is worthwhile to explore deep learning-based methods for the recognition of succinylation sites.
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Structure, Biosynthesis, and Biological Activity of Succinylated Forms of Bacteriocin BacSp222. Int J Mol Sci 2021; 22:ijms22126256. [PMID: 34200765 PMCID: PMC8230399 DOI: 10.3390/ijms22126256] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 01/21/2023] Open
Abstract
BacSp222 is a multifunctional peptide produced by Staphylococcus pseudintermedius 222. This 50-amino acid long peptide belongs to subclass IId of bacteriocins and forms a four-helix bundle molecule. In addition to bactericidal functions, BacSp222 possesses also features of a virulence factor, manifested in immunomodulatory and cytotoxic activities toward eukaryotic cells. In the present study, we demonstrate that BacSp222 is produced in several post-translationally modified forms, succinylated at the ε-amino group of lysine residues. Such modifications have not been previously described for any bacteriocins. NMR and circular dichroism spectroscopy studies have shown that the modifications do not alter the spatial structure of the peptide. At the same time, succinylation significantly diminishes its bactericidal and cytotoxic potential. We demonstrate that the modification of the bacteriocin is an effect of non-enzymatic reaction with a highly reactive intracellular metabolite, i.e., succinyl-coenzyme A. The production of succinylated forms of the bacteriocin depends on environmental factors and on the access of bacteria to nutrients. Our study indicates that the production of succinylated forms of bacteriocin occurs in response to the changing environment, protects producer cells against the autotoxicity of the excreted peptide, and limits the pathogenicity of the strain.
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25
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LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9923112. [PMID: 34159204 PMCID: PMC8188601 DOI: 10.1155/2021/9923112] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/25/2021] [Accepted: 05/03/2021] [Indexed: 11/17/2022]
Abstract
Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods were developed to deal with this challenge, few considered semantic relationship between residues. We combined long short-term memory (LSTM) and convolutional neural network (CNN) into a deep learning method for predicting succinylation site. The proposed method obtained a Matthews correlation coefficient of 0.2508 on the independent test, outperforming state of the art methods. We also performed the enrichment analysis of succinylation proteins. The results showed that functions of succinylation were conserved across species but differed to a certain extent with species. On basis of the proposed method, we developed a user-friendly web server for predicting succinylation sites.
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26
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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.
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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
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Yang Y, Wang H, Li W, Wang X, Wei S, Liu Y, Xu Y. Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks. BMC Bioinformatics 2021; 22:171. [PMID: 33789579 PMCID: PMC8010967 DOI: 10.1186/s12859-021-04101-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/23/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. METHOD We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. RESULTS In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN . CONCLUSIONS The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
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Affiliation(s)
- Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Wen Li
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaobo Wang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Shizhao Wei
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yulong Liu
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.
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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.
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29
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Khan YD, Alzahrani E, Alghamdi W, Ullah MZ. Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200424085947] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Background:
Allergens are antigens that can stimulate an atopic type I human
hypersensitivity reaction by an immunoglobulin E (IgE) reaction. Some proteins are naturally
allergenic than others. The challenge for toxicologists is to identify properties that allow proteins
to cause allergic sensitization and allergic diseases. The identification of allergen proteins is a very
critical and pivotal task. The experimental identification of protein functions is a hectic, laborious
and costly task; therefore, computer scientists have proposed various methods in the field of
computational biology and bioinformatics using various data science approaches. Objectives:
Herein, we report a novel predictor for the identification of allergen proteins.
Methods:
For feature extraction, statistical moments and various position-based features have been
incorporated into Chou’s pseudo amino acid composition (PseAAC), and are used for training of a
neural network.
Results:
The predictor is validated through 10-fold cross-validation and Jackknife testing, which
gave 99.43% and 99.87% accurate results.
Conclusions:
Thus, the proposed predictor can help in predicting the Allergen proteins in an
efficient and accurate way and can provide baseline data for the discovery of new drugs and
biomarkers.
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Affiliation(s)
- Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, C II Johar Town, Lahore 54770, Pakistan
| | - Ebraheem Alzahrani
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
| | - Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 80221, Jeddah, Saudi Arabia
| | - Malik Zaka Ullah
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
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Shujaat M, Wahab A, Tayara H, Chong KT. pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters. Genes (Basel) 2020; 11:genes11121529. [PMID: 33371507 PMCID: PMC7767505 DOI: 10.3390/genes11121529] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/11/2020] [Accepted: 12/18/2020] [Indexed: 01/13/2023] Open
Abstract
A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools.
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Affiliation(s)
- Muhammad Shujaat
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea or (M.S.); (A.W.)
- Department of Computer Sciences, Bahria University, Lahore 54000, Pakistan
| | - Abdul Wahab
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea or (M.S.); (A.W.)
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea
- Correspondence: (H.T.); (K.T.C.)
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea or (M.S.); (A.W.)
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
- Correspondence: (H.T.); (K.T.C.)
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RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix. Genes (Basel) 2020; 11:genes11121524. [PMID: 33419274 PMCID: PMC7766696 DOI: 10.3390/genes11121524] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 11/29/2022] Open
Abstract
Background: Post-translational modification (PTM) is a biological process that is associated with the modification of proteome, which results in the alteration of normal cell biology and pathogenesis. There have been numerous PTM reports in recent years, out of which, lysine phosphoglycerylation has emerged as one of the recent developments. The traditional methods of identifying phosphoglycerylated residues, which are experimental procedures such as mass spectrometry, have shown to be time-consuming and cost-inefficient, despite the abundance of proteins being sequenced in this post-genomic era. Due to these drawbacks, computational techniques are being sought to establish an effective identification system of phosphoglycerylated lysine residues. The development of a predictor for phosphoglycerylation prediction is not a first, but it is necessary as the latest predictor falls short in adequately detecting phosphoglycerylated and non-phosphoglycerylated lysine residues. Results: In this work, we introduce a new predictor named RAM-PGK, which uses sequence-based information relating to amino acid residues to predict phosphoglycerylated and non-phosphoglycerylated sites. A benchmark dataset was employed for this purpose, which contained experimentally identified phosphoglycerylated and non-phosphoglycerylated lysine residues. From the dataset, we extracted the residue adjacency matrix pertaining to each lysine residue in the protein sequences and converted them into feature vectors, which is used to build the phosphoglycerylation predictor. Conclusion: RAM-PGK, which is based on sequential features and support vector machine classifiers, has shown a noteworthy improvement in terms of performance in comparison to some of the recent prediction methods. The performance metrics of the RAM-PGK predictor are: 0.5741 sensitivity, 0.6436 specificity, 0.0531 precision, 0.6414 accuracy, and 0.0824 Mathews correlation coefficient.
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Naseer S, Hussain W, Khan YD, Rasool N. Optimization of serine phosphorylation prediction in proteins by comparing human engineered features and deep representations. Anal Biochem 2020; 615:114069. [PMID: 33340540 DOI: 10.1016/j.ab.2020.114069] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 11/15/2020] [Accepted: 12/14/2020] [Indexed: 02/01/2023]
Abstract
Deep representations can be used to replace human-engineered representations, as such features are constrained by certain limitations. For the prediction of protein post-translation modifications (PTMs) sites, research community uses different feature extraction techniques applied on Pseudo amino acid compositions (PseAAC). Serine phosphorylation is one of the most important PTM as it is the most occurring, and is important for various biological functions. Creating efficient representations from large protein sequences, to predict PTM sites, is a time and resource intensive task. In this study we propose, implement and evaluate use of Deep learning to learn effective protein data representations from PseAAC to develop data driven PTM detection systems and compare the same with two human representations.. The comparisons are performed by training an xgboost based classifier using each representation. The best scores were achieved by RNN-LSTM based deep representation and CNN based representation with an accuracy score of 81.1% and 78.3% respectively. Human engineered representations scored 77.3% and 74.9% respectively. Based on these results, it is concluded that the deep features are promising feature engineering replacement to identify PhosS sites in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.
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Affiliation(s)
- Sheraz Naseer
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan.
| | - Waqar Hussain
- National Center of Artificial Intelligence, Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan; Center for Professional & Applied Studies, Lahore, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan
| | - Nouman Rasool
- Center for Professional & Applied Studies, Lahore, Pakistan
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33
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Ao C, Yu L, Zou Q. Prediction of bio-sequence modifications and the associations with diseases. Brief Funct Genomics 2020; 20:1-18. [PMID: 33313647 DOI: 10.1093/bfgp/elaa023] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 12/22/2022] Open
Abstract
Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.
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PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids. Genes (Basel) 2020; 11:genes11121431. [PMID: 33260770 PMCID: PMC7761138 DOI: 10.3390/genes11121431] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/23/2020] [Accepted: 11/23/2020] [Indexed: 12/23/2022] Open
Abstract
Post-translational modification (PTM) is a critical biological reaction which adds to the diversification of the proteome. With numerous known modifications being studied, pupylation has gained focus in the scientific community due to its significant role in regulating biological processes. The traditional experimental practice to detect pupylation sites proved to be expensive and requires a lot of time and resources. Thus, there have been many computational predictors developed to challenge this issue. However, performance is still limited. In this study, we propose another computational method, named PupStruct, which uses the structural information of amino acids with a radial basis kernel function Support Vector Machine (SVM) to predict pupylated lysine residues. We compared PupStruct with three state-of-the-art predictors from the literature where PupStruct has validated a significant improvement in performance over them with statistical metrics such as sensitivity (0.9234), specificity (0.9359), accuracy (0.9296), precision (0.9349), and Mathew’s correlation coefficient (0.8616) on a benchmark dataset.
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Yang XF, Zhou YK, Zhang L, Gao Y, Du PF. Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions. Curr Bioinform 2020. [DOI: 10.2174/1574893614666190902151038] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background:
Long non-coding RNAs (lncRNAs) are transcripts with a length more
than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown
that the biological functions of lncRNAs are intimately related to their subcellular localizations.
Therefore, it is very important to confirm the lncRNA subcellular localization.
Methods:
In this paper, we proposed a novel method to predict the subcellular localization of
lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer
nucleotide composition and sequence order correlated factors of lncRNA to formulate
lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of
Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support
vector machine (SVM) to perform the prediction.
Results:
The AUC value of the proposed method can reach 0.9695, which indicated the proposed
predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore,
the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross
validation, which clearly outperforms the existing state-of- the-art method.
Conclusion:
It is demonstrated that the proposed predictor is feasible and powerful for the prediction
of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the
source code at https://github.com/NicoleYXF/lncRNA.
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Affiliation(s)
- Xiao-Fei Yang
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Yuan-Ke Zhou
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Lin Zhang
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Yang Gao
- School of Medicine, Nankai University, Tianjin 300071, China
| | - Pu-Feng Du
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
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36
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Liu Y, Yu Z, Chen C, Han Y, Yu B. Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net. Anal Biochem 2020; 609:113903. [DOI: 10.1016/j.ab.2020.113903] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 07/27/2020] [Accepted: 08/05/2020] [Indexed: 12/18/2022]
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37
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Liu X, Liu Z, Mao X, Li Q. m7GPredictor: An improved machine learning-based model for predicting internal m7G modifications using sequence properties. Anal Biochem 2020; 609:113905. [DOI: 10.1016/j.ab.2020.113905] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 07/24/2020] [Accepted: 08/05/2020] [Indexed: 12/21/2022]
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38
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Zhu YH, Hu J, Qi Y, Song XN, Yu DJ. Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites. Comb Chem High Throughput Screen 2020; 22:455-469. [PMID: 31553288 DOI: 10.2174/1386207322666190925125524] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 06/21/2019] [Accepted: 08/23/2019] [Indexed: 11/22/2022]
Abstract
AIM AND OBJECTIVE The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding sites. Nevertheless, the severe class imbalance phenomenon, where the number of nonbinding (majority) residues is far greater than that of binding (minority) residues, has a negative impact on the performance of such machine-learning-based predictors. MATERIALS AND METHODS In this study, we aim to relieve the negative impact of class imbalance by Boosting Multiple Granular Support Vector Machines (BGSVM). In BGSVM, each base SVM is trained on a granular training subset consisting of all minority samples and some reasonably selected majority samples. The efficacy of BGSVM for dealing with class imbalance was validated by benchmarking it with several typical imbalance learning algorithms. We further implemented a protein-nucleotide binding site predictor, called BGSVM-NUC, with the BGSVM algorithm. RESULTS Rigorous cross-validation and independent validation tests for five types of proteinnucleotide interactions demonstrated that the proposed BGSVM-NUC achieves promising prediction performance and outperforms several popular sequence-based protein-nucleotide binding site predictors. The BGSVM-NUC web server is freely available at http://csbio.njust.edu.cn/bioinf/BGSVM-NUC/ for academic use.
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Affiliation(s)
- Yi-Heng Zhu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Jun Hu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yong Qi
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Xiao-Ning Song
- School of Internet of Things, Jiangnan University, Wuxi 214122, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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Arafat ME, Ahmad MW, Shovan S, Dehzangi A, Dipta SR, Hasan MAM, Taherzadeh G, Shatabda S, Sharma A. Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features. Genes (Basel) 2020; 11:E1023. [PMID: 32878321 PMCID: PMC7565944 DOI: 10.3390/genes11091023] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/19/2020] [Accepted: 08/27/2020] [Indexed: 02/07/2023] Open
Abstract
Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.
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Affiliation(s)
- Md. Easin Arafat
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Md. Wakil Ahmad
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - S.M. Shovan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh; (S.M.S.); (M.A.M.H.)
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ 08102, USA;
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Shubhashis Roy Dipta
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Md. Al Mehedi Hasan
- Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh; (S.M.S.); (M.A.M.H.)
| | - Ghazaleh Taherzadeh
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD 20742, USA
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh; (M.E.A.); (M.W.A.); (S.R.D.)
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD 4111, Australia
- Department of Medical Science Mathematics, Tokyo Medical and Dental University (TMDU), Tokyo 113-8510, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
- School of Engineering and Physics, Faculty of Science Technology and Environment, University of the South Pacific, Suva, Fiji
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40
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HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:194-207. [PMID: 32861878 PMCID: PMC7647696 DOI: 10.1016/j.gpb.2019.11.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 09/17/2019] [Accepted: 11/13/2019] [Indexed: 11/21/2022]
Abstract
As an important protein acylation modification, lysine succinylation (Ksucc) is involved in diverse biological processes, and participates in human tumorigenesis. Here, we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set, combined 10 types of informative features, and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework. We constructed a new tool named HybridSucc, which achieved area under curve (AUC) values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites, respectively. In comparison, the accuracy of HybridSucc was 17.84%–50.62% better than that of other existing tools. Using HybridSucc, we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins, including PKM2, SHMT2, and IDH2. We not only developed a high-profile tool for predicting Ksucc sites, but also generated useful candidates for further experimental consideration. The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.
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41
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Gachpazan M, Kashani H, Khazaei M, Hassanian SM, Rezayi M, Asgharzadeh F, Ghayour-Mobarhan M, Ferns GA, Avan A. The Impact of Statin Therapy on the Survival of Patients with Gastrointestinal Cancer. Curr Drug Targets 2020; 20:738-747. [PMID: 30539694 DOI: 10.2174/1389450120666181211165449] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 10/25/2018] [Accepted: 12/05/2018] [Indexed: 12/13/2022]
Abstract
Statins are 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors that may play an important role in the evolution of cancers, due to their effects on cancer cell metabolism. Statins affect several potential pathways, including cell proliferation, angiogenesis, apoptosis and metastasis. The number of trials assessing the putative clinical benefits of statins in cancer is increasing. Currently, there are several trials listed on the global trial identifier website clinicaltrials.gov. Given the compelling evidence from these trials in a variety of clinical settings, there have been calls for a clinical trial of statins in the adjuvant gastrointestinal cancer setting. However, randomized controlled trials on specific cancer types in relation to statin use, as well as studies on populations without a clinical indication for using statins, have elucidated some potential underlying biological mechanisms, and the investigation of different statins is probably warranted. It would be useful for these trials to incorporate the assessment of tumour biomarkers predictive of statin response in their design. This review summarizes the recent preclinical and clinical studies that assess the application of statins in the treatment of gastrointestinal cancers with particular emphasize on their association with cancer risk.
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Affiliation(s)
- Meysam Gachpazan
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Modern Sciences and Technologies; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Kashani
- Department of Modern Sciences and Technologies; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Medical Biochemistry; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Rezayi
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Modern Sciences and Technologies; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fereshteh Asgharzadeh
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, United Kingdom
| | - Amir Avan
- Metabolic syndrome Research center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Modern Sciences and Technologies; Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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42
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Use Chou’s 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting. Mol Genet Genomics 2020; 295:1431-1442. [DOI: 10.1007/s00438-020-01711-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/11/2020] [Indexed: 01/08/2023]
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43
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Amin R, Rahman CR, Ahmed S, Sifat MHR, Liton MNK, Rahman MM, Khan MZH, Shatabda S. iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters. Bioinformatics 2020; 36:4869-4875. [DOI: 10.1093/bioinformatics/btaa609] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 05/19/2020] [Accepted: 06/24/2020] [Indexed: 11/14/2022] Open
Abstract
Abstract
Motivation
Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra- and interclass variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge.
Results
We present iPromoter-BnCNN for identification and accurate classification of six types of promoters—σ24,σ28,σ32,σ38,σ54,σ70. It is a CNN-based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with six state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset.
Availability and implementation
Our proposed tool iPromoter-BnCNN web server is freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source code can be found https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ruhul Amin
- Department of Computer Science and Engineering, United International University, Dhaka 1207, Bangladesh
| | - Chowdhury Rafeed Rahman
- Department of Computer Science and Engineering, United International University, Dhaka 1207, Bangladesh
| | - Sajid Ahmed
- Department of Computer Science and Engineering, United International University, Dhaka 1207, Bangladesh
| | - Md Habibur Rahman Sifat
- Department of Computer Science and Engineering, United International University, Dhaka 1207, Bangladesh
| | - Md Nazmul Khan Liton
- Department of Computer Science and Engineering, United International University, Dhaka 1207, Bangladesh
| | - Md Moshiur Rahman
- Department of Computer Science and Engineering, United International University, Dhaka 1207, Bangladesh
| | - Md Zahid Hossain Khan
- Department of Computer Science and Engineering, United International University, Dhaka 1207, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka 1207, Bangladesh
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44
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Kao HJ, Nguyen VN, Huang KY, Chang WC, Lee TY. SuccSite: Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:208-219. [PMID: 32592791 PMCID: PMC7647693 DOI: 10.1016/j.gpb.2018.10.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 10/01/2018] [Accepted: 10/11/2018] [Indexed: 12/14/2022]
Abstract
Protein succinylation is a biochemical reaction in which a succinyl group (-CO-CH2-CH2-CO-) is attached to the lysine residue of a protein molecule. Lysine succinylation plays important regulatory roles in living cells. However, studies in this field are limited by the difficulty in experimentally identifying the substrate site specificity of lysine succinylation. To facilitate this process, several tools have been proposed for the computational identification of succinylated lysine sites. In this study, we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid composition. Using experimentally verified lysine succinylated sites collected from public resources, the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo program. These findings enabled the adoption of an effective machine learning method, support vector machine, to train a predictive model with not only the amino acid composition, but also the composition of k-spaced amino acid pairs. After the selection of the best model using a ten-fold cross-validation approach, the selected model significantly outperformed existing tools based on an independent dataset manually extracted from published research articles. Finally, the selected model was used to develop a web-based tool, SuccSite, to aid the study of protein succinylation. Two proteins were used as case studies on the website to demonstrate the effective prediction of succinylation sites. We will regularly update SuccSite by integrating more experimental datasets. SuccSite is freely accessible at http://csb.cse.yzu.edu.tw/SuccSite/.
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Affiliation(s)
- Hui-Ju Kao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan, China
| | - Van-Nui Nguyen
- Department of Information Technology, University of Information and Communication Technology, Thai Nguyen 1000, Vietnam
| | - Kai-Yao Huang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Wen-Chi Chang
- Institute of Tropical Plant Sciences, Cheng Kung University, Tainan 701, Taiwan, China
| | - Tzong-Yi Lee
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.
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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.
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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
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Saikia S, Bordoloi M, Sarmah R. Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection. Curr Drug Targets 2020; 20:522-539. [PMID: 30394207 DOI: 10.2174/1389450120666181105152439] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 08/28/2018] [Accepted: 10/22/2018] [Indexed: 12/14/2022]
Abstract
The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including "off-target" effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.
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Affiliation(s)
- Surovi Saikia
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Manobjyoti Bordoloi
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Rajeev Sarmah
- Allied Health Sciences, Assam Down Town University, Panikhaiti, Guwahati 781026, Assam, India
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Hu Y, Lu Y, Wang S, Zhang M, Qu X, Niu B. Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs. Curr Drug Targets 2020; 20:488-500. [PMID: 30091413 DOI: 10.2174/1389450119666180809122244] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 06/19/2018] [Accepted: 06/25/2018] [Indexed: 12/14/2022]
Abstract
BACKGROUND Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. OBJECTIVE In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. RESULTS Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. CONCLUSION This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.
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Affiliation(s)
- Yan Hu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Yi Lu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Shuo Wang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Mengying Zhang
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Xiaosheng Qu
- National Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, 530023,Nanning, China
| | - Bing Niu
- School of Life Sciences, Shanghai University, Shanghai 200444, China
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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
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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
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