scholarly journals Protein structural class prediction based on an improved statistical strategy

2008 ◽  
Vol 9 (Suppl 6) ◽  
pp. S5 ◽  
Author(s):  
Fei Gu ◽  
Hang Chen ◽  
Jun Ni
Keyword(s):  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Statistical Strategy

Protein Structural Class Prediction Based on Distance-related Statistical Features from Graphical Representation of Predicted Secondary Structure

2019 ◽  
Vol 16 (4) ◽  
pp. 317-324
Author(s):  
Liang Kong ◽  
Lichao Zhang ◽  
Xiaodong Han ◽  
Jinfeng Lv
Keyword(s):  
Feature Extraction ◽  
Secondary Structure ◽  
Protein Sequence ◽  
Function Analysis ◽  
Superior Performance ◽  
Support Vector ◽  
Chaos Game Representation ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class

Protein structural class prediction is beneficial to protein structure and function analysis. Exploring good feature representation is a key step for this prediction task. Prior works have demonstrated the effectiveness of the secondary structure based feature extraction methods especially for lowsimilarity protein sequences. However, the prediction accuracies still remain limited. To explore the potential of secondary structure information, a novel feature extraction method based on a generalized chaos game representation of predicted secondary structure is proposed. Each protein sequence is converted into a 20-dimensional distance-related statistical feature vector to characterize the distribution of secondary structure elements and segments. The feature vectors are then fed into a support vector machine classifier to predict the protein structural class. Our experiments on three widely used lowsimilarity benchmark datasets (25PDB, 1189 and 640) show that the proposed method achieves superior performance to the state-of-the-art methods. It is anticipated that our method could be extended to other graphical representations of protein sequence and be helpful in future protein research.

scholarly journals Protein structural class prediction using predicted secondary structure and hydropathy profile

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Secondary Structure ◽  
Classification Problem ◽  
Support Vector ◽  
Prediction Problem ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Vector Machines ◽  
Structural Classes ◽  
New Strategies

This thesis explores machine learning models based on various feature sets to solve the protein structural class prediction problem which is a significant classification problem in bioinformatics. Knowledge of protein structural classes contributes to an understanding of protein folding patterns, and this has made structural class prediction research a major topic of interest. In this thesis, features are extracted from predicted secondary structure and hydropathy sequence using new strategies to classify proteins into one of the four major structural classes: all-α, all-β, α/β, and α+β. The prediction accuracy using these features compares favourably with some existing successful methods. We use Support Vector Machines (SVM), since this learning method has well-known efficiency in solving this classification problem. On a standard dataset (25PDB), the proposed system has an overall accuracy of 89% with as few as 22 features, whereas the previous best performing method had an accuracy of 88% using 2510 features.

Prediction of Protein Structural Classes: Features Extraction to Classification Algorithm

2021 ◽  
Vol 18 ◽  
Author(s):  
Xiaoqing Liu ◽  
Zhenyu Yang ◽  
Yaoxin Wang ◽  
Qi Dai
Keyword(s):  
Tertiary Structure ◽  
Protein Sequencing ◽  
Conformational Space ◽  
Folding Rate ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Dna Binding Sites ◽  
Structural Classes ◽  
Protein Structure Data

: The fast growing of protein sequencing and protein structure data has promoted the development of the protein structural class prediction. Several prediction methods have been proposed to study protein folding rate, DNA binding sites, as well as reducing the search of conformational space and realizing the prediction of tertiary structure. This paper introduces the current approaches of protein structural class prediction and emphasize their steps from information extraction to classification algorithms.

SVM-BASED METHOD FOR PROTEIN STRUCTURAL CLASS PREDICTION USING SECONDARY STRUCTURAL CONTENT AND STRUCTURAL INFORMATION OF AMINO ACIDS

2011 ◽  
Vol 09 (04) ◽  
pp. 489-502 ◽  
Author(s):  
TABREZ ANWAR SHAMIM MOHAMMAD ◽  
HAMPAPATHALU ADIMURTHY NAGARAJARAM
Keyword(s):  
Amino Acids ◽  
Structural Information ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Classification Problem ◽  
Support Vector ◽  
Class Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Structural Content

The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.

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