scholarly journals Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lifu Zhang ◽  
Benzhi Dong ◽  
Zhixia Teng ◽  
Ying Zhang ◽  
Liran Juan
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Extraction Methods ◽  
New Drugs ◽  
Training Dataset ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Vast Number ◽  
Human Proteins

Enzymes are proteins that can efficiently catalyze specific biochemical reactions, and they are widely present in the human body. Developing an efficient method to identify human enzymes is vital to select enzymes from the vast number of human proteins and to investigate their functions. Nevertheless, only a limited amount of research has been conducted on the classification of human enzymes and nonenzymes. In this work, we developed a support vector machine- (SVM-) based predictor to classify human enzymes using the amino acid composition (AAC), the composition of k-spaced amino acid pairs (CKSAAP), and selected informative amino acid pairs through the use of a feature selection technique. A training dataset including 1117 human enzymes and 2099 nonenzymes and a test dataset including 684 human enzymes and 1270 nonenzymes were constructed to train and test the proposed model. The results of jackknife cross-validation showed that the overall accuracy was 76.46% for the training set and 76.21% for the test set, which are higher than the 72.6% achieved in previous research. Furthermore, various feature extraction methods and mainstream classifiers were compared in this task, and informative feature parameters of k-spaced amino acid pairs were selected and compared. The results suggest that our classifier can be used in human enzyme identification effectively and efficiently and can help to understand their functions and develop new drugs.

scholarly journals Identifying the Types of Ion Channel-Targeted Conotoxins by Incorporating New Properties of Residues into Pseudo Amino Acid Composition

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Yun Wu ◽  
Yufei Zheng ◽  
Hua Tang
Keyword(s):  
Amino Acid ◽  
Ion Channel ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Current Method ◽  
Computational Method ◽  
Support Vector ◽  
Pseudo Amino Acid Composition ◽  
Feature Selection Technique ◽  
Drug Candidates

Conotoxins are a kind of neurotoxin which can specifically interact with potassium, sodium type, and calcium channels. They have become potential drug candidates to treat diseases such as chronic pain, epilepsy, and cardiovascular diseases. Thus, correctly identifying the types of ion channel-targeted conotoxins will provide important clue to understand their function and find potential drugs. Based on this consideration, we developed a new computational method to rapidly and accurately predict the types of ion-targeted conotoxins. Three kinds of new properties of residues were proposed to use in pseudo amino acid composition to formulate conotoxins samples. The support vector machine was utilized as classifier. A feature selection technique based onF-score was used to optimize features. Jackknife cross-validated results showed that the overall accuracy of 94.6% was achieved, which is higher than other published results, demonstrating that the proposed method is superior to published methods. Hence the current method may play a complementary role to other existing methods for recognizing the types of ion-target conotoxins.

scholarly journals Pseudo amino acid composition improves antifreeze protein prediction

2014 ◽  
Author(s):  
Sukanta Mondal ◽  
Priyadarshini P. Pai
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Support Vector ◽  
Order Information ◽  
Biotechnological Applications ◽  
Pseudo Amino Acid Composition ◽  
Cold Temperatures ◽  
Wide Range ◽  
Prediction Approach

Antifreeze proteins (AFP) in living organisms play a key role in their tolerance to extremely cold temperatures and have wide range of biotechnological applications. But on account of diversity, their identification has been challenging to biologists. Earlier work explored in this area did not cover introduction of sequence order information, known to represent important properties of various proteins and protein systems for prediction of their attributes. In this study, the effect of Chou's pseudo amino acid composition that presents sequence order of proteins was systematically explored using support vector machines for AFP prediction. Our findings suggest that introduction of sequence order information helps identify AFPs with an accuracy of 84.75% on independent test dataset, outperforming approaches such as AFP-Pred and iAFP. The relative performance calculated using Youden’s Index (Sensitivity + Specificity -1) was found to be 0.71 for our predictor (AFP-PseAAC), 0.48 for AFP-Pred and 0.05 for iAFP. We hope this novel prediction approach will aid in AFP based research for biotechnological applications.

scholarly journals Pseudo amino acid composition improves antifreeze protein prediction

2014 ◽  
Author(s):  
Sukanta Mondal ◽  
Priyadarshini P. Pai
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Support Vector ◽  
Order Information ◽  
Biotechnological Applications ◽  
Pseudo Amino Acid Composition ◽  
Cold Temperatures ◽  
Wide Range ◽  
Prediction Approach

Antifreeze proteins (AFP) in living organisms play a key role in their tolerance to extremely cold temperatures and have wide range of biotechnological applications. But on account of diversity, their identification has been challenging to biologists. Earlier work explored in this area did not cover introduction of sequence order information, known to represent important properties of various proteins and protein systems for prediction of their attributes. In this study, the effect of Chou's pseudo amino acid composition that presents sequence order of proteins was systematically explored using support vector machines for AFP prediction. Our findings suggest that introduction of sequence order information helps identify AFPs with an accuracy of 84.75% on independent test dataset, outperforming approaches such as AFP-Pred and iAFP. The relative performance calculated using Youden’s Index (Sensitivity + Specificity -1) was found to be 0.71 for our predictor (AFP-PseAAC), 0.48 for AFP-Pred and 0.05 for iAFP. We hope this novel prediction approach will aid in AFP based research for biotechnological applications.

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