A systematic identification of species-specific protein succinylation sites using joint element features information

Hasan, Md. Mehedi; Khatun, Mst. Shamima; Mollah, Md. Nurul Haque; Cao, Yong; Guo, Dianjing

doi:10.2147/ijn.s140875

Cited by 53 publications

(60 citation statements)

References 34 publications

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“…Next, the performance of DeepSuccinylSite was compared with other succinylation site predictors using an independent test set as mentioned in the benchmark dataset earlier. During these analyses, some of the most widely used tools for succinylation site prediction, such as iSuc-PseAAC [8], iSuc-PseOpt [9], pSuc-Lys [10], Suc-cineSite [11], SuccineSite2.0 [12], GPSuc [13] and PSuccE [14], were considered. All these methods use the same training and independent test data sets as in Table 6.…”

Section: Independent Test Comparisons With Existing Modelsmentioning

confidence: 99%

“…In recent years, machine learning has become a costeffective method for prediction of different PTM sites. Some of the machine learning based succinylation site prediction approaches are iSuc-PseAAC [8], iSuc-PseOpt [9], pSuc-Lys [10], SuccineSite [11], SuccineSite2.0 [12], GPSuc [13] and PSuccE [14] . Although results have been promising, the potential for bias is present due to manual selection of features along with the possible absence of unknown features that contribute to succinylation.…”

Section: Introductionmentioning

confidence: 99%

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DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction

et al. 2020

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Background: Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to − 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure. Results: Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.48 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation predictors, DeepSuccinylSite represents a significant improvement in overall accuracy for prediction of succinylation sites. Conclusion: Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein succinylation.

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Section: Independent Test Comparisons With Existing Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction

et al. 2020

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“…In the prediction PTM sites, this specific problem is particularly prominent due to the sequence diversity. For instance, some sequence motifs are very weak and not available with the sequence evolutionary information [37][38][39][40][41][42]. To address this problem, we can search PSI-BLAST [32,43,44] against the NCBI NR database to generate a profile (i.e., position-specific scoring matrix [45][46][47][48][49][50].…”

Section: Feature For the Computational Prediction Of Lysine Ptm Sitesmentioning

confidence: 99%

Computational Modeling of Lysine Post-Translational Modification: An Overview

Hasan¹,

Khatun²,

Kurata³

2018

Curr Synthetic Sys Biol

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“…position-speci ic scoring matrix (PSSM)). Such sequence pro iles re lect the conservation and variation between protein sequences through the evolutionary information [39][40][41][42].…”

Section: Feature Representationmentioning

confidence: 99%

Opinion Prediction of protein Post-Translational Modification sites: An overview

Hasan¹,

Khatun²

2017

Ann Proteom Bioinform

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BackgroundPost-translational modi ication (PTM) refers to the covalent and enzymatic modi ication of proteins during or after protein biosynthesis. In the protein biosynthesis process, the ribosomal mRNA is translated into polypeptide chains, which may further undergo PTM to form the product of mature protein [1]. PTM is a common biological mechanism of both eukaryotic and prokaryotic organisms, which regulates the protein functions, the proteolytic cleavage of regulatory subunits or the degradation of entire proteins and affects all aspects of cellular life. The PTM of a protein can also determine the cell signaling state, turnover, localization, and interactions with other proteins [2]. Therefore, the analysis of proteins and their PTMs are particularly important for the study of heart disease, cancer, neurodegenerative diseases and diabetes [3,4]. Although the characterization of PTMs gets invaluable insight into the cellular functions in etiological processes, there are still challenges. Technically, the major challenges in studying PTMs are the development of speci ic detection and puri ication methods.The PTMs of proteins have been detected by a variety of experimental techniques including the mass spectrometry (MS) [5,6], liquid chromatography [7], radioactive chemical method [8], chromatin immune precipitation (ChIP) [9], western blotting [10], and eastern blotting [7]. The MS technique is one of the mainstay routes in detecting PTMs in a high-throughput manner. The new MS and capillary liquid chromatography instrumentation have made revolutionary advance in enrichment strategies in our growing knowledge of various PTMs [11]. The last decade of the actual description of many PTMs complexity has emerged through the diverse technologies and thousands of precise modi ication sites can now be identi ied with high con idence [12][13][14][15][16][17][18][19][20]. A similar strategy of fragmentation for PTM identi ication is the beam-type collisioninduced dissociation, also called higher energy collisional dissociation [21]. These types of fragmentation are characterized by the higher activation energy. Most of the fragmentation methods of precursor ions are based on the radical anions or thermal electrons [22]. These methods are advantageous over collisionally activated dissociation methods for detecting the unstable PTMs (e.g. O-GlcNAc and phosphorylation), due to the peptide support fragmentation method is effectively independent of the amino acid sequence [23][24][25]. To date, more than 350 types of PTMs have been experimentally discovered in vivo [26]. The common PTMs are phosphorylation, ubiquitination, succinylation, acetylation, pupylation, sumoylation, glycosylation, and so on. In addition, pupylation referring to the modi ication of lysine residues with a prokaryotic, ubiquitin-like protein (i.e. Pup) is another PTM in bacteria.In general, the experimental analysis of PTMs often requires labor-intensive sample preparations and hazardous or expensive chemical reagents. For instance, in the radioactive...

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A systematic identification of species-specific protein succinylation sites using joint element features information

Cited by 53 publications

References 34 publications

DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction

DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction

Computational Modeling of Lysine Post-Translational Modification: An Overview

Opinion Prediction of protein Post-Translational Modification sites: An overview

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