Evidence map›Paper›PMID 27610389›Full record

ArticleBioMed research international2016

QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition.

Chi-Hua Tung, Chi-Wei Chen, Ren-Chao Guo, Hui-Fuang Ng, Yen-Wei Chu

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Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chi-Hua TungDepartment of Bioinformatics, Chung-Hua University, Room S116, No. 707, Section 2, WuFu Road, Hsinchu 30012, Taiwan.
Chi-Wei ChenInstitute of Genomics and Bioinformatics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 402, Taiwan.
Ren-Chao GuoInstitute of Genomics and Bioinformatics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 402, Taiwan.
Hui-Fuang NgDepartment of Computer Science, Universiti Tunku Abdul Rahman, Jalan Universiti, 31900 Kampar, Malaysia.
Yen-Wei ChuInstitute of Genomics and Bioinformatics, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 402, Taiwan; Biotechnology Center, Agricultural Biotechnology Center, Institute of Molecular Biology, Graduate Institute of Biotechnology, National Chung Hsing University, 250 Kuo Kuang Road, Taichung 402, Taiwan.ORCID 0000-0002-5525-4011

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.

Indexed as

AlgorithmsModels, MolecularProtein Structure, QuaternaryAmino Acid SequenceComputer SimulationModels, ChemicalMolecular Sequence DataPattern Recognition, AutomatedProteinsSequence Analysis, ProteinSupport Vector MachineProteins

Identifiers

PMID27610389
PMCPMC5005774

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.