Prediction of Protein Secondary Structure Using the Weighted Combination of Homology Information of Protein Sequences

In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences). We use a hybrid system, NEUROSVM, consisting of neural networks and support vector machines for classification of secondary structures. We propose two schemes NSVMps and NSVM for protein secondary structure prediction. The NSVMps uses position-specific probability-based features and NEUROSVM classifier whereas NSVM uses the same classifier with position-independent probability-based features. The proposed method falls in the single-sequence category of methods because it does not use any sequence profile information such as position specific scoring matrices (PSSM) derived from PSI-BLAST. Two widely used datasets RS126 and CB513 are used in the experiments. The results obtained using the proposed features and NEUROSVM classifier are better than most of the existing single-sequence prediction methods. Most importantly, the results using NSVMps that are obtained using lower dimensional features, are comparable to those by other existing methods. The NSVMps and NSVM are finally tested on target proteins of the critical assessment of protein structure prediction experiment-9 (CASP9). A larger dataset is used to compare the performance of the proposed methods with that of two recent single-sequence prediction methods. We also investigate the impact of presence of different amino acid residues (in protein sequences) that are responsible for the formation of different secondary structures.

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Detect ‘protein word’ based on unsupervised word segmentation

10.7287/peerj.preprints.1433 ◽

2015 ◽

Author(s):

Liang Wang ◽

Kaiyong Zhao

Keyword(s):

Secondary Structure ◽

Protein Sequence ◽

Protein Secondary Structure ◽

Protein Sequences ◽

Word Segmentation ◽

Unsupervised Segmentation ◽

Noncoding Regions ◽

Segmentation Methods

Unsupervised word segmentation methods were applied to analyze the protein sequence. Protein sequences, such as ‘MTMDKSELVQKA …..’, were used as input to these methods. Segmented ‘protein word’ sequences, such as ‘MTM DKSE LVQKA’, were then obtained. We compare the ‘protein words’ produced by unsupervised segmentation and the protein secondary structure segmentation. An interesting finding is that the unsupervised word segmentation is more efficient than secondary structure segmentation in expressing information. Our experiment also suggests there may be some ‘protein ruins’ in current noncoding regions.

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Detect ‘protein word’ based on unsupervised word segmentation

10.7287/peerj.preprints.1433v1 ◽

2015 ◽

Author(s):

Liang Wang ◽

Kaiyong Zhao

Keyword(s):

Secondary Structure ◽

Protein Sequence ◽

Protein Secondary Structure ◽

Protein Sequences ◽

Word Segmentation ◽

Unsupervised Segmentation ◽

Noncoding Regions ◽

Segmentation Methods

Unsupervised word segmentation methods were applied to analyze the protein sequence. Protein sequences, such as ‘MTMDKSELVQKA …..’, were used as input to these methods. Segmented ‘protein word’ sequences, such as ‘MTM DKSE LVQKA’, were then obtained. We compare the ‘protein words’ produced by unsupervised segmentation and the protein secondary structure segmentation. An interesting finding is that the unsupervised word segmentation is more efficient than secondary structure segmentation in expressing information. Our experiment also suggests there may be some ‘protein ruins’ in current noncoding regions.

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Detecting “protein words” through unsupervised word segmentation

F1000Research ◽

10.12688/f1000research.7428.1 ◽

2015 ◽

Vol 4 ◽

pp. 1517

Author(s):

Wang Liang ◽

Zhao Kaiyong

Keyword(s):

Secondary Structure ◽

Protein Secondary Structure ◽

Protein Sequences ◽

Word Segmentation ◽

Unsupervised Segmentation ◽

Coding Regions ◽

Segmentation Methods

Unsupervised word segmentation methods were applied to analyze protein sequences. Protein sequences, such as “MTMDKSELVQKA…,” were used as input to these methods. Segmented protein word sequences, such as “MTM DKSE LVQKA,” were then obtained. We compared the protein words derived via unsupervised segmentation and protein secondary structure segmentation. An interesting finding is that unsupervised word segmentation is more efficient than secondary structure segmentation in expressing information. Our experiment also suggests the presence of several “protein ruins” in current non-coding regions.

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Faculty Opinions recommendation of Protein secondary structure prediction using deep convolutional neural fields.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726067888.793548469 ◽

2018 ◽

Author(s):

Patrice Koehl

Keyword(s):

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Neural Fields ◽

Protein Secondary Structure Prediction

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A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

Current Drug Discovery Technologies ◽

10.2174/1570163815666180227162157 ◽

2019 ◽

Vol 16 (2) ◽

pp. 159-172 ◽

Cited By ~ 3

Author(s):

Elaheh Kashani-Amin ◽

Ozra Tabatabaei-Malazy ◽

Amirhossein Sakhteman ◽

Bagher Larijani ◽

Azadeh Ebrahim-Habibi

Keyword(s):

Systematic Review ◽

Secondary Structure ◽

Structure Prediction ◽

Web Of Science ◽

Secondary Structure Prediction ◽

Structural Information ◽

Protein Secondary Structure ◽

Structural Features ◽

Prediction Tools ◽

Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

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