Prediction of lysine HMGylation sites using multiple feature extraction and fuzzy support vector machine.

Protein 3-hydroxyl-3-methylglutarylation (HMGylation) is newly discovered lysine acylation modification in mitochondrion. The accurate identification of HMGylation sites is the premise and key to further explore the molecular mechanisms of HMGylation. In this study, a novel bioinformatics tool named HMGPred is developed to predict HMGylation sites. Multiple effective features, including amino acid composition, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs, are integrated to encode HMGylation sites. And F-score feature ranking with incremental feature selection was used to eliminate redundant features. Moreover, a fuzzy support vector machine algorithm is used to effectively reduce the influence of noise problem by assigning different samples to different fuzzy membership degrees. As illustrated by 10-fold cross-validation, HMGPred achieves a satisfactory performance with an area under receiver operating characteristic curve of 0.9110. Feature analysis indicates that some k-spaced amino acid pair features, such as 'KxxxT' and 'DxxxE', play a critical role in the prediction of HMGylation sites. The results of prediction and analysis might be helpful for investigating the mechanisms of HMGylation. For the convenience of experimental researchers, HMGPred is implemented as a web server at http://123.206.31.171/HMGPred/.

Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components.

Lysine formylation is a newly discovered post-translational modification in histones, which plays a crucial role in epigenetics of chromatin function and DNA binding. In this study, a novel bioinformatics tool named CKSAAP_FormSite is proposed to predict lysine formylation sites. An effective feature extraction method, the composition of k-spaced amino acid pairs, is employed to encode formylation sites. Moreover, a biased support vector machine algorithm is proposed to solve the class imbalance problem in the prediction of formylation sites. As illustrated by 10-fold cross-validation, CKSAAP_FormSite achieves an satisfactory performance with an AUC of 0.8234. Therefore, CKSAAP_FormSite can be a useful bioinformatics tool for the prediction of formylation sites. Feature analysis shows that some amino acid pairs, such as 'KA', 'SxxxxK' and 'SxxxA' around formylation sites may play an important role in the prediction. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of formylation.

Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.

BACKGROUND: As a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet. METHODS: In this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm. RESULTS: Experimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites. CONCLUSION: A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.

Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components.

INTRODUCTION: Neddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation. OBJECTIVE: As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. METHODS: In this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction. RESULTS: Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy. CONCLUSION: Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at 123.206.31.171/NeddPred/.

Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm.

Lipoylation is a highly conserved post-translational modification which has been found to be involved in many biological processes and closely associated with various metabolic diseases. The accurate identification of lipoylation sites is necessary to elucidate the underlying molecular mechanisms of lipoylation. As the traditional experimental methods are time consuming and expensive, it is desired to develop computational methods to predict lipoylation sites. In this study, a novel predictor named LipoPred is proposed to predict lysine lipoylation sites. On the one hand, an effective feature extraction method, bi-profile bayes encoding, is employed to encode lipoylation sites. On the other hand, a fuzzy support vector machine algorithm is proposed to solve the class imbalance and noise problem in the prediction of lipoylation sites. As illustrated by 10-fold cross-validation, LipoPred achieves an excellent performance with a Matthew's correlation coefficient of 0.9930. Therefore, LipoPred can be a useful bioinformatics tool for the prediction of lipoylation sites. Feature analysis shows that some residues around lipoylation sites may play an important role in the prediction. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of lipoylation. A user-friendly web-server for LipoPred is established at 123.206.31.171/LipoPred/.

Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.

Lysine glutarylation is new type of protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of glutarylation, it is important to identify glutarylated substrates and their corresponding glutarylation sites accurately. In this study, a novel bioinformatics tool named GlutPred is developed to predict glutarylation sites by using multiple feature extraction and maximum relevance minimum redundancy feature selection. On the one hand, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs features are incorporated to encode glutarylation sites. And the maximum relevance minimum redundancy method and the incremental feature selection algorithm are adopted to remove the redundant features. On the other hand, a biased support vector machine algorithm is used to handle the imbalanced problem in glutarylation sites training dataset. As illustrated by 10-fold cross-validation, the performance of GlutPred achieves a satisfactory performance with a Sensitivity of 64.80%, a Specificity of 76.60%, an Accuracy of 74.90% and a Matthew's correlation coefficient of 0.3194. Feature analysis shows that some k-spaced amino acid pair features play the most important roles in the prediction of glutarylation sites. The conclusions derived from this study might provide some clues for understanding the molecular mechanisms of glutarylation.

Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm.

Cysteine S-sulfenylation is an important protein post-translational modification, which plays a crucial role in transcriptional regulation, cell signaling, and protein functions. To better elucidate the molecular mechanism of S-sulfenylation, it is important to identify S-sulfenylated substrates and their corresponding S-sulfenylation sites accurately. In this study, a novel bioinformatics tool named Sulf_FSVM is proposed to predict S-sulfenylation sites by using multiple feature extraction and fuzzy support vector machine algorithm. On the one hand, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs features are incorporated to encode S-sulfenylation sites. And the maximum relevance minimum redundancy method are adopted to remove the redundant features. On the other hand, a fuzzy support vector machine algorithm is used to handle the class imbalance and noise problem in S-sulfenylation sites training dataset. As illustrated by 10-fold cross-validation, the performance of Sulf_FSVM achieves a satisfactory performance with a Sensitivity of 73.26%, a Specificity of 70.78%, an Accuracy of 71.07% and a Matthew's correlation coefficient of 0.2971. Independent tests also show that Sulf_FSVM significantly outperforms existing S-sulfenylation sites predictors. Therefore, Sulf_FSVM can be a useful tool for accurate prediction of protein S-sulfenylation sites.

Prediction of protein N-formylation using the composition of k-spaced amino acid pairs.

As one of important protein post-translational modifications, N-formylation has been reported to be involved in various biological processes. The accurate identification of N-formylation sites is crucial for understanding the underlying mechanisms of N-formylation. Since the traditional experimental methods are generally labor-intensive and expensive, it is important to develop computational methods to predict N-formylation sites. In this paper, a predictor named NformPred is proposed to improve the prediction of N-formylation sites by using composition of k-spaced amino acid pairs encoding scheme and support vector machine algorithm. As illustrated by 10-fold cross-validation, NformPred achieves a promising performance with a Sensitivity of 86.00%, a Specificity of 96.25%, an Accuracy of 94.48% and a Matthew's correlation coefficient of 0.8099, which are much better than those of current computational method. Feature analysis shows that some k-spaced amino acid pairs such as 'IxxL', 'LV' and 'IxxxI' play the most important roles in the prediction of N-formylation sites. These predictive and analytical results suggest that NformPred might facilitate the identification of protein N-formylation. A free online service for NformPred is accessible at http://123.206.31.171/NformPred/.

Predicting pupylation sites in prokaryotic proteins using semi-supervised self-training support vector machine algorithm.

As one important post-translational modification of prokaryotic proteins, pupylation plays a key role in regulating various biological processes. The accurate identification of pupylation sites is crucial for understanding the underlying mechanisms of pupylation. Although several computational methods have been developed for the identification of pupylation sites, the prediction accuracy of them is still unsatisfactory. Here, a novel bioinformatics tool named IMP-PUP is proposed to improve the prediction of pupylation sites. IMP-PUP is constructed on the composition of k-spaced amino acid pairs and trained with a modified semi-supervised self-training support vector machine (SVM) algorithm. The proposed algorithm iteratively trains a series of support vector machine classifiers on both annotated and non-annotated pupylated proteins. Computational results show that IMP-PUP achieves the area under receiver operating characteristic curves of 0.91, 0.73, and 0.75 on our training set, Tung's testing set, and our testing set, respectively, which are better than those of the different error costs SVM algorithm and the original self-training SVM algorithm. Independent tests also show that IMP-PUP significantly outperforms three other existing pupylation site predictors: GPS-PUP, iPUP, and pbPUP. Therefore, IMP-PUP can be a useful tool for accurate prediction of pupylation sites. A MATLAB software package for IMP-PUP is available at https://juzhe1120.github.io/.

Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC.

As a new type of post-translational modification, lysine phosphoglycerylation plays a key role in regulating glycolytic process and metabolism in cells. Due to the traditional experimental methods are time-consuming and labor-intensive, it is important to develop computational methods to identify the potential phosphoglycerylation sites. However, the prediction performance of the existing phosphoglycerylation site predictor is not satisfactory. In this study, a novel predictor named CKSAAP_PhoglySite is developed to predict phosphoglycerylation sites by using composition of k-spaced amino acid pairs and fuzzy support vector machine. On the one hand, after many aspects of assessments, we find the composition of k-spaced amino acid pairs is more suitable for representing the protein sequence around the phosphoglycerylation sites than other encoding schemes. On the other hand, the proposed fuzzy support vector machine algorithm can effectively handle the imbalanced and noisy problem in phosphoglycerylation sites training dataset. Experimental results indicate that CKSAAP_PhoglySite outperforms the existing phosphoglycerylation site predictor Phogly-PseAAC significantly. A matlab software package for CKSAAP_PhoglySite can be freely downloaded from https://github.com/juzhe1120/Matlab_Software/blob/master/CKSAAP_PhoglySite_Matlab_Software.zip.

iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC.

As one of the most critical post-translational modifications, lysine methylation plays a key role in regulating various protein functions. In order to understand the molecular mechanism of lysine methylation, it is important to identify lysine methylation sites and their methylation degrees accurately. As the traditional experimental methods are time-consuming and labor-intensive, several computational methods have been developed for the identification of methylation sites. However, the prediction accuracy of existing computational methods is still unsatisfactory. Moreover, they are only focused on predicting whether a query lysine residue is a methylation site, without considering its methylation degrees. In this paper, a novel two-level predictor named iLM-2L is proposed to predict lysine methylation sites and their methylation degrees using composition of k-spaced amino acid pairs feature coding scheme and support vector machine algorithm. The 1st level is to identify whether a query lysine residue is a methylation site, and the 2nd level is to identify which methylation degree(s) the query lysine residue belongs to if it has been predicted as a methyllysine site in the 1st level identification. The iLM-2L achieves a promising performance with a Sensitivity of 76.46%, a Specificity of 91.90%, an Accuracy of 85.31% and a Matthew's correlation coefficient of 69.94% for the 1st level as well as a Precision of 84.81%, an accuracy of 79.35%, a recall of 80.83%, an Absolute_Ture of 73.89% and a Hamming_loss of 15.63% for the 2nd level in jackknife test. As illustrated by independent test, the performance of iLM-2L outperforms other existing lysine methylation site predictors significantly. A matlab software package for iLM-2L can be freely downloaded from https://github.com/juzhe1120/Matlab_Software/blob/master/iLM-2L_Matlab_Software.rar.

Do Grittier People Have Greater Subjective Well-Being? A Meta-Analysis.

The present study conducted a meta-analysis to examine the relation between grit and subjective well-being (SWB). The association between grit (i.e., overall grit, perseverance of effort, and consistency of interest) and SWB (i.e., positive affect, negative affect, happiness, depression, life satisfaction, job satisfaction, and school satisfaction) were synthesized across 83 studies and 66,518 participants. The results based on a random-effects model showed a substantial correlation between overall grit and SWB (ρ = .46, 95% confidence interval [CI] = [.43, .48]), followed by perseverance of effort (ρ = .38, 95% CI = [.33, .43]) and consistency of interest (ρ = .23, 95% CI = [.17, .28]). The moderator analysis indicated that the correlations between overall grit/consistency of effort and SWB become weaker as age increased, and these links were stronger in affective well-being than in cognitive well-being. Moreover, grit explained unique variance in SWB even after controlling for conscientiousness. Implications and directions for further research are discussed.

Childhood maltreatment on young adult depression: A moderated mediation model of negative automatic thoughts and self-compassion.

This study examines the mediating role of negative automatic thoughts on the link between childhood maltreatment and young adult depression, and the moderating role of self-compassion in this indirect link. College students (N = 578) completed self-report questionnaires assessing the mentioned study variables. The results showed that childhood maltreatment was positively associated with young adult depression via negative automatic thoughts. Moreover, self-compassion moderated this indirect link such that participants with low self-compassion demonstrated a stronger indirect link than those with high self-compassion. These findings highlight the important role of self-compassion in countering the adverse outcomes of childhood maltreatment.

Prediction of 2-hydroxyisobutyrylation sites by integrating multiple sequence features with ensemble support vector machine.

Lysine 2-hydroxyisobutyrylation (Khib) is a new type of histone mark, which has been found to affect the association between histone and DNA. To better understand the molecular mechanism of Khib, it is important to identify 2-hydroxyisobutyrylated substrates and their corresponding Khib sites accurately. In this study, a novel bioinformatics tool named KhibPred is proposed to predict Khib sites in human HeLa cells. Three kinds of effective features, the composition of k-spaced amino acid pairs, binary encoding and amino acid factors, are incorporated to encode Khib sites. Moreover, an ensemble support vector machine is employed to overcome the imbalanced problem in the prediction. As illustrated by 10-fold cross-validation, the performance of KhibPred achieves a satisfactory performance with an area under receiver operating characteristic curve of 0.7937. Therefore, KhibPred can be a useful tool for predicting protein Khib sites. Feature analysis shows that the polarity factor features play significant roles in the prediction of Khib sites. The conclusions derived from this study might provide useful insights for in-depth investigation into the molecular mechanisms of Khib.

Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition.

As one of the most important and common protein post-translational modifications, citrullination plays a key role in regulating various biological processes and is associated with several human diseases. The accurate identification of citrullination sites is crucial for elucidating the underlying molecular mechanisms of citrullination and designing drugs for related human diseases. In this study, a novel bioinformatics tool named CKSAAP_CitrSite is developed for the prediction of citrullination sites. With the assistance of support vector machine algorithm, the highlight of CKSAAP_CitrSite is to adopt the composition of k-spaced amino acid pairs surrounding a query site as input. As illustrated by 10-fold cross-validation, CKSAAP_CitrSite achieves a satisfactory performance with a Sensitivity of 77.59%, a Specificity of 95.26%, an Accuracy of 89.37% and a Matthew's correlation coefficient of 0.7566, which is much better than those of the existing prediction method. Feature analysis shows that the N-terminal space containing pairs may play an important role in the prediction of citrullination sites, and the arginines close to N-terminus tend to be citrullinated. The conclusions derived from this study could offer useful information for elucidating the molecular mechanisms of citrullination and related experimental validations. A user-friendly web-server for CKSAAP_CitrSite is available at 123.206.31.171/CKSAAP_CitrSite/.

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

As one of the most important and common histones post-translational modifications, crotonylation plays a key role in regulating various biological processes. The accurate identification of crotonylation sites is crucial to elucidate the underlying molecular mechanisms of crotonylation. In this study, a novel bioinformatics tool named CKSAAP_CrotSite is developed to predict crotonylation sites. The highlight of CKSAAP_CrotSite is to adopt the composition of k-spaced amino acid pairs as input encoding, and the support vector machine is employed as the classifier. As illustrated by jackknife test, CKSAAP_CrotSite achieves a promising performance with a Sensitivity of 92.45%, a Specificity of 99.17%, an Accuracy of 98.11% and a Matthew's correlation coefficient of 0.9283, which is much better than those of the existing prediction methods. Feature analysis shows that some amino acid pairs such as 'KxG', 'KG' and 'PxP' may play an important role in the prediction of crotonylation sites. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of crotonylation and related experimental validations. A user-friendly web-server for CKSAAP_CrotSite is available at 123.206.31.171/CKSAAP_CrotSite/.

Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou's PseAAC.

Lysine propionylation is an important and common protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of propionylation, it is important to identify propionylated substrates and their corresponding propionylation sites accurately. In this study, a novel bioinformatics tool named PropPred is developed to predict propionylation sites by using multiple feature extraction and biased support vector machine. On the one hand, various features are incorporated, including amino acid composition, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs. And the F-score feature method and the incremental feature selection algorithm are adopted to remove the redundant features. On the other hand, the biased support vector machine algorithm is used to handle the imbalanced problem in propionylation sites training dataset. As illustrated by 10-fold cross-validation, the performance of PropPred achieves a satisfactory performance with a Sensitivity of 70.03%, a Specificity of 75.61%, an accuracy of 75.02% and a Matthew's correlation coefficient of 0.3085. Feature analysis shows that some amino acid factors play the most important roles in the prediction of propionylation sites. These analysis and prediction results might provide some clues for understanding the molecular mechanisms of propionylation. A user-friendly web-server for PropPred is established at 123.206.31.171/PropPred/.

Predicting lysine glycation sites using bi-profile bayes feature extraction.

Glycation is a nonenzymatic post-translational modification which has been found to be involved in various biological processes and closely associated with many metabolic diseases. The accurate identification of glycation sites is important to understand the underlying molecular mechanisms of glycation. As the traditional experimental methods are often labor-intensive and time-consuming, it is desired to develop computational methods to predict glycation sites. In this study, a novel predictor named BPB_GlySite is proposed to predict lysine glycation sites by using bi-profile bayes feature extraction and support vector machine algorithm. As illustrated by 10-fold cross-validation, BPB_GlySite achieves a satisfactory performance with a Sensitivity of 63.68%, a Specificity of 72.60%, an Accuracy of 69.63% and a Matthew's correlation coefficient of 0.3499. Experimental results also indicate that BPB_GlySite significantly outperforms three existing glycation sites predictors: NetGlycate, PreGly and Gly-PseAAC. Therefore, BPB_GlySite can be a useful bioinformatics tool for the prediction of glycation sites. A user-friendly web-server for BPB_GlySite is established at 123.206.31.171/BPB_GlySite/.