Prediction of Neddylation Sites Using the Composition of k-spaced Amino Acid Pairs and Fuzzy SVM

2020 ◽  
Vol 15 (7) ◽  
pp. 725-731
Author(s):  
Zhe Ju ◽  
Shi-Yun Wang
Keyword(s):  
Amino Acid ◽  
Molecular Mechanisms ◽  
Feature Selection Method ◽  
Class Imbalance ◽  
Support Vector ◽  
Post Translational Modification ◽  
Fuzzy Support Vector Machine ◽  
Accurate Identification ◽  
Isopeptide Bonds ◽  
User Friendly

Introduction: Neddylation is the process of ubiquitin-like protein NEDD8 attaching substrate lysine via isopeptide bonds. As a highly dynamic and reversible post-translational modification, lysine neddylation has been found to be involved in various biological processes and closely associated with many diseases. Objective: The accurate identification of neddylation sites is necessary to elucidate the underlying molecular mechanisms of neddylation. As traditional experimental methods are often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. Methods: In this study, a novel predictor named CKSAAP_NeddSite is developed to detect neddylation sites. An effective feature encoding technology, the composition of k-spaced amino acid pairs, is used to encode neddylation sites. And the F-score feature selection method is adopted to remove the redundant features. Moreover, a fuzzy support vector machine algorithm is employed to overcome the class imbalance and noise problem. Results: As illustrated by 10-fold cross-validation, CKSAAP_NeddSite achieves an AUC of 0.9848. Independent tests also show that CKSAAP_NeddSite significantly outperforms existing neddylation sites predictor. Therefore, CKSAAP_NeddSite can be a useful bioinformatics tool for the prediction of neddylation sites. Feature analysis shows that some residues around neddylation sites may play an important role in the prediction. Conclusion: The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of neddylation. A user-friendly web-server for CKSAAP_NeddSite is established at 123.206.31.171/CKSAAP_NeddSite.

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

2020 ◽  
Vol 20 (8) ◽  
pp. 592-601
Author(s):  
Zhe Ju ◽  
Shi-Yun Wang
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Class Imbalance ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Operating Characteristic Curve ◽  
Matthew’S Correlation Coefficient ◽  
User Friendly

Introduction: Neddylation is a highly dynamic and reversible post-translatiNeddylation 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.onal modification which has been found to be involved in various biological processes and closely associated with many diseases. The accurate identification of neddylation sites is necessary to elucidate the underlying molecular mechanisms of neddylation. As the traditional experimental methods are time consuming and expensive, it is desired to develop computational methods to predict neddylation sites. In this study, a novel predictor named NeddPred is proposed to predict lysine neddylation sites. An effective feature extraction method, bi-profile bayes encoding, is employed to encode neddylation sites. Moreover, a fuzzy support vector machine algorithm is proposed to solve the class imbalance and noise problem in the prediction of neddylation sites. As illustrated by 10-fold cross-validation, NeddPred achieves an excellent performance with a Matthew's correlation coefficient of 0.7082 and an area under receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing neddylation sites predictor NeddyPreddy. Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly web-server for NeddPred is established at 123.206.31.171/NeddPred/. 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/.

Discriminative Feature Selection Based on Imbalance SVDD for Fault Detection of Semiconductor Manufacturing Processes

2016 ◽  
Vol 25 (11) ◽  
pp. 1650143 ◽  
Author(s):  
Jian Wang ◽  
Jian Feng ◽  
Zhiyan Han
Keyword(s):  
Feature Selection ◽  
Fault Detection ◽  
Semiconductor Industry ◽  
Feature Selection Method ◽  
Class Imbalance ◽  
Support Vector ◽  
Support Vector Data Description ◽  
The Real ◽  
Discriminative Feature ◽  
New Feature

Feature selection has become a key step of fault detection. Unfortunately, the class imbalance in the modern semiconductor industry makes feature selection quite challenging. This paper analyzes the challenges and indicates the limitations of the traditional supervised and unsupervised feature selection methods. To cope with the limitations, a new feature selection method named imbalanced support vector data description-radius-recursive feature selection (ISVDD-radius-RFE) is proposed. When selecting features, the ISVDD-radius-RFE has three advantages: (1) ISVDD-radius-RFE is designed to find the most representative feature by finding the real shape of normal samples. (2) ISVDD-radius-RFE can represent the real shape of normal samples more correctly by introducing the discriminant information from fault samples. (3) ISVDD-radius-RFE is optimized for fault detection where the imbalance data is common. The kernel ISVDD-radius-RFE is also described in this paper. The proposed method is demonstrated through its application in the banana set and SECOM dataset. The experimental results confirm ISVDD-radius-RFE and kernel ISVDD-radius-RFE improve the performance of fault detection.

scholarly journals ANPrAod: Identify Antioxidant Proteins by Fusing Amino Acid Clustering Strategy and N -Peptide Combination

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qilemuge Xi ◽  
Hao Wang ◽  
Liuxi Yi ◽  
Jian Zhou ◽  
Yuchao Liang ◽  
...  
Keyword(s):  
Amino Acid ◽  
Protein Identification ◽  
Critical Role ◽  
Feature Representation ◽  
New Drugs ◽  
Support Vector ◽  
Accurate Identification ◽  
Promising Tool ◽  
Antioxidant Proteins ◽  
Representation Scheme

Antioxidant proteins perform significant functions in disease control and delaying aging which can prevent free radicals from damaging organisms. Accurate identification of antioxidant proteins has important implications for the development of new drugs and the treatment of related diseases, as they play a critical role in the control or prevention of cancer and aging-related conditions. Since experimental identification techniques are time-consuming and expensive, many computational methods have been proposed to identify antioxidant proteins. Although the accuracy of these methods is acceptable, there are still some challenges. In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.

scholarly journals Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

2021 ◽  
Vol 9 ◽  
Author(s):  
Jian Zhou ◽  
Suling Bo ◽  
Hao Wang ◽  
Lei Zheng ◽  
Pengfei Liang ◽  
...  
Keyword(s):  
Amino Acid ◽  
Protein Modification ◽  
Catalytic Mechanism ◽  
Feature Representation ◽  
Accurate Identification ◽  
Cluster Strategy ◽  
Representation Scheme ◽  
User Friendly ◽  
Optimal Feature

The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.

Prediction of Protein Ubiquitination Sites in Arabidopsis thaliana

2019 ◽  
Vol 14 (7) ◽  
pp. 614-620 ◽  
Author(s):  
Jiajing Chen ◽  
Jianan Zhao ◽  
Shiping Yang ◽  
Zhen Chen ◽  
Ziding Zhang
Keyword(s):  
Arabidopsis Thaliana ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Feature Selection Method ◽  
Prediction Method ◽  
Support Vector ◽  
Biological Processes ◽  
Protein Ubiquitination ◽  
Ubiquitination Site ◽  
Site Identification

Background: As one of the most important reversible protein post-translation modification types, ubiquitination plays a significant role in the regulation of many biological processes, such as cell division, signal transduction, apoptosis and immune response. Protein ubiquitination usually occurs when ubiquitin molecule is attached to a lysine on a target protein, which is also known as “lysine ubiquitination”. Objective: In order to investigate the molecular mechanisms of ubiquitination-related biological processes, the crucial first step is the identification of ubiquitination sites. However, conventional experimental methods in detecting ubiquitination sites are often time-consuming and a large number of ubiquitination sites remain unidentified. In this study, a ubiquitination site prediction method for Arabidopsis thaliana was developed using a Support Vector Machine (SVM). Methods: We collected 3009 experimentally validated ubiquitination sites on 1607 proteins in A. thaliana to construct the training set. Three feature encoding schemes were used to characterize the sequence patterns around ubiquitination sites, including AAC, Binary and CKSAAP. The maximum Relevance and Minimum Redundancy (mRMR) feature selection method was employed to reduce the dimensionality of input features. Five-fold cross-validation and independent tests were used to evaluate the performance of the established models. Results: As a result, the combination of AAC and CKSAAP encoding schemes yielded the best performance with the accuracy and AUC of 81.35% and 0.868 in the independent test. We also generated an online predictor termed as AraUbiSite, which is freely accessible at: http://systbio.cau.edu.cn/araubisite. Conclusion: We developed a well-performed prediction tool for large-scale ubiquitination site identification in A. thaliana. It is hoped that the current work will speed up the process of identification of ubiquitination sites in A. thaliana and help to further elucidate the molecular mechanisms of ubiquitination in plants.

Prediction of Citrullination Sites on the Basis of mRMR Method and SNN

2020 ◽  
Vol 22 (10) ◽  
pp. 705-715 ◽  
Author(s):  
Min Liu ◽  
Guangzhong Liu
Keyword(s):  
Prediction Model ◽  
Evaluation Method ◽  
Nearest Neighbor ◽  
Protein Sequences ◽  
Support Vector ◽  
Post Translational Modification ◽  
K Nearest Neighbor ◽  
Accurate Identification ◽  
Citrullinated Proteins ◽  
K Nearest Neighbor Algorithm

Background: Citrullination, an important post-translational modification of proteins, alters the molecular weight and electrostatic charge of the protein side chains. Citrulline, in protein sequences, is catalyzed by a class of Peptidyl Arginine Deiminases (PADs). Dependent on Ca2+, PADs include five isozymes: PAD 1, 2, 3, 4/5, and 6. Citrullinated proteins have been identified in many biological and pathological processes. Among them, abnormal protein citrullination modification can lead to serious human diseases, including multiple sclerosis and rheumatoid arthritis. Objective: It is important to identify the citrullination sites in protein sequences. The accurate identification of citrullination sites may contribute to the studies on the molecular functions and pathological mechanisms of related diseases. Methods and Results: In this study, after an encoded training set (containing 116 positive and 348 negative samples) into the feature matrix, the mRMR method was used to analyze the 941- dimensional features which were sorted on the basis of their importance. Then, a predictive model based on a self-normalizing neural network (SNN) was proposed to predict the citrullination sites in protein sequences. Incremental Feature Selection (IFS) and 10-fold cross-validation were used as the model evaluation method. Three classical machine learning models, namely random forest, support vector machine, and k-nearest neighbor algorithm, were selected and compared with the SNN prediction model using the same evaluation methods. SNN may be the best tool for citrullination site prediction. The maximum value of the Matthews Correlation Coefficient (MCC) reached 0.672404 on the basis of the optimal classifier of SNN. Conclusion: The results showed that the SNN-based prediction methods performed better when evaluated by some common metrics, such as MCC, accuracy, and F1-Measure. SNN prediction model also achieved a better balance in the classification and recognition of positive and negative samples from datasets compared with the other three models.

scholarly journals The Prediction of Calpain Cleavage Sites with the mRMR and IFS Approaches

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Wenyi Zhang ◽  
Xin Xu ◽  
Longjia Jia ◽  
Zhiqiang Ma ◽  
Na Luo ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Solvent Accessibility ◽  
Limited Proteolysis ◽  
Cysteine Proteases ◽  
Biochemical Properties ◽  
Support Vector ◽  
Cleavage Sites ◽  
Future Direction ◽  
User Friendly ◽  
Better Than

Calpains are an important family of the Ca2+-dependent cysteine proteases which catalyze the limited proteolysis of many specific substrates. Calpains play crucial roles in basic physiological and pathological processes, and identification of the calpain cleavage sites may facilitate the understanding of the molecular mechanisms and biological function. But traditional experiment approaches to predict the sites are accurate, and are always labor-intensive and time-consuming. Thus, it is common to see that computational methods receive increasing attention due to their convenience and fast speed in recent years. In this study, we develop a new predictor based on the support vector machine (SVM) with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). And we concern the feature of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility to represent the calpain cleavage sites. Experimental results show that the performance of our predictor is better than several other state-of- the-art predictors, whose average prediction accuracy is 79.49%, sensitivity is 62.31%, and specificity is 88.12%. Since user-friendly and publicly accessible web servers represent the future direction for developing practically more useful predictors, here we have provided a web-server for the method presented in this paper.

scholarly journals PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 353 ◽  
Author(s):  
Phasit Charoenkwan ◽  
Sakawrat Kanthawong ◽  
Nalini Schaduangrat ◽  
Janchai Yana ◽  
Watshara Shoombuatong
Keyword(s):  
Molecular Mechanisms ◽  
Propensity Scores ◽  
State Of The Art ◽  
Support Vector ◽  
Dipeptide Composition ◽  
Biophysical Properties ◽  
Validation Test ◽  
Novel Method ◽  
User Friendly ◽  
Scoring Card Method

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.

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