Prediction of lysine crotonylation sites by incorporating the composition of k -spaced amino acid pairs into Chou’s general PseAAC

Ju, Zhe; He, Jianjun

doi:10.1016/j.jmgm.2017.08.020

Cited by 83 publications

(23 citation statements)

References 44 publications

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“…As the nonhistone Kcr sites have recently been discovered, we examined their performance on the prediction of non-histone Kcr sites. Currently, two classifiers iKcr-PseEns [12] and CKSAAP_CrotSite [13] are accessible. We investigated them using our independent test dataset where the number of negatives is around ten times larger than that of positives.…”

Section: Reusults and Discusstions A The Avalilabe Classifiers mentioning

confidence: 99%

“…So far, a few computational approaches were developed based on the known Kcr sites on histone proteins, e.g. CrotPred [10], iPTM-mlys [11], iKcr-PseEns [12] and CKSAAP_CrotSite [13]. Although these algorithms have made great contributions to the Kcr prediction, their training dataset are derived from histone proteins only and the number of the Kcr sites is limited (≤169).…”

Section: Introductionmentioning

confidence: 99%

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Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks

Zhao

Chen

et al. 2020

IEEE Access

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Protein lysine crotonylation (Kcr) is an important type of post-translational modification that regulates various activities. The experimental approaches to identify the Kcr sites are time-consuming and it is necessary to develop computational prediction approaches. Previously, a few classifiers were based on over 100 Kcr sites from histone proteins. Recently, thousands of Kcr sites have been experimentally verified on non-histone proteins from the plant species Papaya. We found that the previous classifiers fail to identify non-histone Kcr sites. Therefore, it is necessary to develop classifiers for non-histone proteins. Accordingly, we constructed 11 different classifiers to recognize non-histone Kcr sites by combining different features and algorithms (such as random forest and convolutional neural network (CNN)). They were compared using both tenfold cross validation and independent test dataset. The classifier based on CNN and the word embedding approach, dubbed as pKcr, performed better than other classifiers. pKcr obtained AUC value of 0.855 and 0.853 for tenfold cross-validation and independent data test, respectively. No statistical difference of its performances on these two tests indicates that pKcr does not overfit. In the pKcr framework, a peptide is cleaved into biological characters followed by transformation into digital vectors. These vectors are input into the CNN with participation of multiple convolution kernels to automatically extract various features and pooling layers to perform feature selection. The superior performance of pKcr suggests that this algorithm is well suited for the Kcr prediction and may be applied broadly to predicting other types of PTM sites. pKcr can be available at http://www.bioinfogo.org/pkcr. INDEX TERMS Feature extraction, lysine crotonylation, convolutional neural network, word embedding.

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Section: Reusults and Discusstions A The Avalilabe Classifiers mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks

Zhao

Chen

et al. 2020

IEEE Access

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“…Crotonylation is the addition of crotonyl group to Lys side chains, regulating gene expression by modifying histones in promoters or enhancer nucleosomes (Tan et al 2011; Ju and He 2017; Wei et al 2017). Although not exclusive to the C-terminus, KxxK, and PxK minimotifs have a higher propensity for crotonylation (Wei et al 2017).…”

Section: C-termini Backgroundmentioning

confidence: 99%

The carboxy-terminus, a key regulator of protein function

Sharma

Schiller

2019

Critical Reviews in Biochemistry and Molecular Biology

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All proteins end with a carboxyl terminus that has unique biophysical properties and is often disordered. Although there are examples of important C-termini functions, a more global role for the C-terminus is not yet established. In this review, we summarize research on C-termini, a unique region in proteins that cells exploit. Alternative splicing and proteolysis increase the diversity of proteins and peptides in cells with unique C-termini. The C-termini of proteins contain minimotifs, short peptides with an encoded function generally characterized as binding, posttranslational modifications, and trafficking. Many of these activities are specific to minimotifs on the C-terminus. Approximately 13% of C-termini in the human proteome have a known minimotif, and the majority, if not all of the remaining termini have conserved motifs inferring a function that remains to be discovered. C-termini, their predictions, and their functions are collated in the C-terminome, Proteus, and Terminus Oriented Protein Function INferred Database (TopFIND) database/web systems. Many C-termini are well conserved, and some have a known role in health and disease. We envision that this summary of C-termini will guide future investigation of their biochemical and physiological significance.

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“…The composition of amino acid pairs (AAPC) [20], transforms a sequence fragment into a 441-dimensional vector, which includes 441 elements specifying the numbers of occurrences of 441 amino acid pairs divided by the total number of amino acid pairs in a fragmented sequence [21]. CKSAAP [22] is a widely used sequence encoding method that has been applied with great success to many PTM prediction problems, such as O-glycosylation [23], palmitoylation [24], ubiqutination [8], phosphorylation [25], pupylation [26], methylation [27], N-formylation [28] and crotonylation [29]. In this study, we also employed CKSAAP to classify lysine residues into glutarylation and non-glutarylation sites.…”

Section: Investigation and Encoding Of Sequence-based Featuresmentioning

confidence: 99%

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

et al. 2019

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Background: Glutarylation, the addition of a glutaryl group (five carbons) to a lysine residue of a protein molecule, is an important post-translational modification and plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified glutarylated peptides increases, it becomes imperative to investigate substrate motifs to enhance the study of protein glutarylation. We carried out a bioinformatics investigation of glutarylation sites based on amino acid composition using a public database containing information on 430 non-homologous glutarylation sites. Results: The TwoSampleLogo analysis indicates that positively charged and polar amino acids surrounding glutarylated sites may be associated with the specificity in substrate site of protein glutarylation. Additionally, the chi-squared test was utilized to explore the intrinsic interdependence between two positions around glutarylation sites. Further, maximal dependence decomposition (MDD), which consists of partitioning a large-scale dataset into subgroups with statistically significant amino acid conservation, was used to capture motif signatures of glutarylation sites. We considered single features, such as amino acid composition (AAC), amino acid pair composition (AAPC), and composition of k-spaced amino acid pairs (CKSAAP), as well as the effectiveness of incorporating MDD-identified substrate motifs into an integrated prediction model. Evaluation by five-fold cross-validation showed that AAC was most effective in discriminating between glutarylation and non-glutarylation sites, according to support vector machine (SVM). Conclusions: The SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.677, a specificity of 0.619, an accuracy of 0.638, and a Matthews Correlation Coefficient (MCC) value of 0.28. Using an independent testing dataset (46 glutarylated and 92 non-glutarylated sites) obtained from the literature, we demonstrated that the integrated SVM model could improve the predictive performance effectively, yielding a balanced sensitivity and specificity of 0.652 and 0.739, respectively. This integrated SVM model has been implemented as a web-based system (MDDGlutar), which is now freely available at http://csb.cse.yzu. edu.tw/MDDGlutar/.

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Prediction of lysine crotonylation sites by incorporating the composition of k -spaced amino acid pairs into Chou’s general PseAAC

Cited by 83 publications

References 44 publications

Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks

Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks

The carboxy-terminus, a key regulator of protein function

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

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