Computational prediction of species-specific malonylation sites via enhanced characteristic strategy

Wang, Lina; Shi, Shao-Ping; Xu, Haodong; Wen, Pingping; Qiu, Jian‐Ding

doi:10.1093/bioinformatics/btw755

Cited by 53 publications

(43 citation statements)

References 44 publications

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“…To investigate effects of the parameter N (length of amino acid residues in the upstream or the downstream of the butyrylation sites) on the predictive performances, we conducted 3-fold cross validation over the training set. Most approaches for predicting PTM sites generally set N to the interval of 10 to 15 (Hou et al, 2014;Huang et al, 2014;Xu et al, 2015a;Hasan et al, 2016;Jia et al, 2016a;Jia et al, 2016b;Xu et al, 2016;Wang et al, 2017). For example, the iSulf-Cys for predicting s-sulfenylation sites (Xu et al, 2016) adopted a window of 21 residues (i.e., N=10), while the iSuc-PseOpt (Jia et al, 2016a), a tool for predicting lysine succinylation sites, used N=15 amino acid residues of the upstream/downstream of the modified site.…”

Section: Resultsmentioning

confidence: 99%

An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation

Huang

Zheng

et al. 2020

Front. Genet.

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Butyrylation plays a crucial role in the cellular processes. Due to limit of techniques, it is a challenging task to identify histone butyrylation sites on a large scale. To fill the gap, we propose an approach based on information entropy and machine learning for computationally identifying histone butyrylation sites. The proposed method achieves 0.92 of area under the receiver operating characteristic (ROC) curve over the training set by 3-fold cross validation and 0.80 over the testing set by independent test. Feature analysis implies that amino acid residues in the down/upstream of butyrylation sites would exhibit specific sequence motif to a certain extent. Functional analysis suggests that histone butyrylation was most possibly associated with four pathways (systemic lupus erythematosus, alcoholism, viral carcinogenesis and transcriptional misregulation in cancer), was involved in binding with other molecules, processes of biosynthesis, assembly, arrangement or disassembly and was located in such complex as consists of DNA, RNA, protein, etc. The proposed method is useful to predict histone butyrylation sites. Analysis of feature and function improves understanding of histone butyrylation and increases knowledge of functions of butyrylated histones.

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Section: Resultsmentioning

confidence: 99%

An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation

Huang

Zheng

et al. 2020

Front. Genet.

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“…In recent years, SVM has also been widely used in the field of bioinformatics. In the field of proteomics research, it has been widely used to predict membrane protein types [ 65 , 66 ], G protein-coupled receptors [ 67 , 68 ], protein structure [ 69 – 73 ], protein-protein interaction [ 74 – 76 ], protein subcellular localization [ 77 – 80 ], protein post-translational modification sites [ 81 – 84 ] and other protein structure and function of the study.…”

Section: Methodsmentioning

confidence: 99%

Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction

Qiu

et al. 2018

BMC Genomics

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BackgroundApoptosis is associated with some human diseases, including cancer, autoimmune disease, neurodegenerative disease and ischemic damage, etc. Apoptosis proteins subcellular localization information is very important for understanding the mechanism of programmed cell death and the development of drugs. Therefore, the prediction of subcellular localization of apoptosis protein is still a challenging task.ResultsIn this paper, we propose a novel method for predicting apoptosis protein subcellular localization, called PsePSSM-DCCA-LFDA. Firstly, the protein sequences are extracted by combining pseudo-position specific scoring matrix (PsePSSM) and detrended cross-correlation analysis coefficient (DCCA coefficient), then the extracted feature information is reduced dimensionality by LFDA (local Fisher discriminant analysis). Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the apoptosis proteins. The overall prediction accuracy of 99.7, 99.6 and 100% are achieved respectively on the three benchmark datasets by the most rigorous jackknife test, which is better than other state-of-the-art methods.ConclusionThe experimental results indicate that our method can significantly improve the prediction accuracy of subcellular localization of apoptosis proteins, which is quite high to be able to become a promising tool for further proteomics studies. The source code and all datasets are available at https://github.com/QUST-BSBRC/PsePSSM-DCCA-LFDA/.

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“…machine learning algorithms and peptide similarity) to build their models. For example, Position-Specific lysine (K) Acetylation Predictor (PSKAcePred) [50], lysine Malonylation Predictor (MaloPred) [51], lysine (K) Acetylation predictor (KA-predictor) [52] and the k-nearest neighbor (KNN) score [51] all calculate the similarity of two peptides, which is subsequently used as input feature in machine learning algorithms. Table 1.…”

Section: State-of-the-art Computational Approaches For Lysine Ptm Prementioning

confidence: 99%

“…), (ii) the position of predicted PTM sites in the protein sequence, (iii) the peptide containing the predicted PTM site and (iv) the prediction score/confidence. Among the predictors with available web servers, EnsemblePail [115], PSKAcePred [50], NetGlycate 1.0 [20], Glycation Sites prediction by using Bi-Profile Bayes (BPB GlySite) [116], PLMLA [113], identify Succinylation sites by using Pseudo Amino Acid Composition (iSuc-PseAAC) [117], Mal Lys [118], MaloPred [51], Prediction of Methylation Sites (PMeS) [119], Methylated lysine(K) sites predictor (MethK) [120], Prediction Species-Specific Methylation sites (PSSMe) [121], RUBI [67], GPS-MSP [48], SUMOhydro [112], SUMOsp [76], Succinylation Sites predictor (SuccinSite2.0) [65], Ubiquitination Sites predictor based on Composition of K-Spaced Amino Acid Pairs (CKSAAP UbSite) [60], human Ubiquitination Sites predictor based on Composition of K-Spaced Amino Acid Pairs (hCKSAAP UbSite) [61], Ubiquitination sites Prober (UbiProber) [98] and Ubiquitination Sites prediction based on Evolutionary Screening Algorithm (ESA UbSite) [100] provide detailed output information including the PTM site and prediction score. BPB GlySite [116], GPS MSP [48], SUMOsp [76], SuccinSite2.0 [65], Joint Analyzer of Sumoylation Site and SIMs (JASSA) [122], CKSAAP UbSite [60], hCKSAAP UbSite [61] and RUBI [67] allow users to download the prediction results in 'TEXT' format for further analysis.…”

Section: Spcmentioning

confidence: 99%

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Chen

Liu

et al. 2018

Briefings in Bioinformatics

105

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Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.

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Computational prediction of species-specific malonylation sites via enhanced characteristic strategy

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 53 publications

References 44 publications

An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation

An Information Entropy-Based Approach for Computationally Identifying Histone Lysine Butyrylation

Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

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