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.

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.

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.

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