Comparisons of Different Feature Sets for Predicting Carbohydrate-Binding Proteins From Amino Acid Sequences Using Support Vector Machine

2012 ◽  
pp. 519-529
Author(s):  
Suchandra Payal ◽  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri
Keyword(s):  
Support Vector Machine ◽  
Amino Acid ◽  
Binding Proteins ◽  
Amino Acid Sequences ◽  
Support Vector ◽  
Carbohydrate Binding ◽  
Feature Sets ◽  
Carbohydrate Binding Proteins

scholarly journals Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Seizi Someya ◽  
Masanori Kakuta ◽  
Mizuki Morita ◽  
Kazuya Sumikoshi ◽  
Wei Cao ◽  
...  
Keyword(s):  
Amino Acid ◽  
Support Vector Machines ◽  
Binding Proteins ◽  
Prediction Method ◽  
Amino Acid Sequences ◽  
Support Vector ◽  
Carbohydrate Binding ◽  
Genome Database ◽  
Vector Machines ◽  
Carbohydrate Binding Proteins

Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs). We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC) curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

scholarly journals Carbohydrate-binding proteins in bovine kidney have consensus amino acid sequences of annexin family proteins.

1992 ◽  
Vol 267 (29) ◽  
pp. 20536-20539
Author(s):  
K Kojima ◽  
H.K. Ogawa ◽  
N Seno ◽  
K Yamamoto ◽  
T Irimura ◽  
...  
Keyword(s):  
Amino Acid ◽  
Binding Proteins ◽  
Amino Acid Sequences ◽  
Carbohydrate Binding ◽  
Bovine Kidney ◽  
Carbohydrate Binding Proteins ◽  
Consensus Amino Acid

ABC-Gly: identifying protein lysine glycation sites with artificial bee colony algorithm

2019 ◽  
Vol 17 ◽  
Author(s):  
Yanqiu Yao ◽  
Xiaosa Zhao ◽  
Qiao Ning ◽  
Junping Zhou
Keyword(s):  
Support Vector Machine ◽  
Amino Acid ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Training Dataset ◽  
Support Vector ◽  
Supplementary File ◽  
Feature Subset ◽  
Lipid Molecule ◽  
Bee Colony

Background: Glycation is a nonenzymatic post-translational modification process by attaching a sugar molecule to a protein or lipid molecule. It may impair the function and change the characteristic of the proteins which may lead to some metabolic diseases. In order to understand the underlying molecular mechanisms of glycation, computational prediction methods have been developed because of their convenience and high speed. However, a more effective computational tool is still a challenging task in computational biology. Methods: In this study, we showed an accurate identification tool named ABC-Gly for predicting lysine glycation sites. At first, we utilized three informative features, including position-specific amino acid propensity, secondary structure and the composition of k-spaced amino acid pairs to encode the peptides. Moreover, to sufficiently exploit discriminative features thus can improve the prediction and generalization ability of the model, we developed a two-step feature selection, which combined the Fisher score and an improved binary artificial bee colony algorithm based on support vector machine. Finally, based on the optimal feature subset, we constructed the effective model by using Support Vector Machine on the training dataset. Results: The performance of the proposed predictor ABC-Gly was measured with the sensitivity of 76.43%, the specificity of 91.10%, the balanced accuracy of 83.76%, the area under the receiver-operating characteristic curve (AUC) of 0.9313, a Matthew’s Correlation Coefficient (MCC) of 0.6861 by 10-fold cross-validation on training dataset, and a balanced accuracy of 59.05% on independent dataset. Compared to the state-of-the-art predictors on the training dataset, the proposed predictor achieved significant improvement in the AUC of 0.156 and MCC of 0.336. Conclusion: The detailed analysis results indicated that our predictor may serve as a powerful complementary tool to other existing methods for predicting protein lysine glycation. The source code and datasets of the ABC-Gly were provided in the Supplementary File 1.

scholarly journals Screening for carbohydrate-binding proteins in extracts of Uruguayan plants

2003 ◽  
Vol 36 (7) ◽  
pp. 851-860 ◽  
Author(s):  
A. Plá ◽  
E. Alonso ◽  
F. Batista-Viera ◽  
L. Franco Fraguas
Keyword(s):  
Binding Proteins ◽  
Carbohydrate Binding ◽  
Carbohydrate Binding Proteins
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