A Protein Folding Degree Measure and Its Dependence on Crystal Packing, Protein Size, Secondary Structure, and Domain 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.

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Speed limit of protein folding evidenced in secondary structure dynamics

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1113649108 ◽

2011 ◽

Vol 108 (40) ◽

pp. 16622-16627 ◽

Cited By ~ 30

Author(s):

M. M. Lin ◽

O. F. Mohammed ◽

G. S. Jas ◽

A. H. Zewail

Keyword(s):

Protein Folding ◽

Secondary Structure ◽

Speed Limit ◽

Structure Dynamics

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Protein structural class prediction using predicted secondary structure and hydropathy profile

10.32920/ryerson.14657172 ◽

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.

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Importance of secondary structure specificity determinants in protein folding and stability

Peptides for the New Millennium - American Peptide Symposia ◽

10.1007/0-306-46881-6_111 ◽

2006 ◽

pp. 277-279

Author(s):

Stanley C. Kwok ◽

Colin T. Mant ◽

Robert S. Hodges

Keyword(s):

Protein Folding ◽

Secondary Structure ◽

Specificity Determinants

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Backbone dihedral angles prediction servers for protein early-stage structure prediction

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2019-0034 ◽

2019 ◽

Vol 15 (4) ◽

Author(s):

Tomasz Smolarczyk ◽

Katarzyna Stapor ◽

Irena Roterman-Konieczna

Keyword(s):

Protein Folding ◽

Secondary Structure ◽

Protein Function ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Early Stage ◽

Three Dimensional ◽

Stage Structure ◽

Dihedral Angles ◽

Starting Point

AbstractThree-dimensional protein structure prediction is an important task in science at the intersection of biology, chemistry, and informatics, and it is crucial for determining the protein function. In the two-stage protein folding model, based on an early- and late-stage intermediates, we propose to use state-of-the-art secondary structure prediction servers for backbone dihedral angles prediction and devise an early-stage structure. Early-stage structures are used as a starting point for protein folding simulations, and any errors in this stage affect the final predictions. We have shown that modern secondary structure prediction servers could increase the accuracy of early-stage predictions compared to previously reported models.

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