ArticleBMC bioinformatics2006
Classification of protein quaternary structure by functional domain composition.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Cellular and epigenetic perspective of protein stability and its implications in the biological system.Epigenomics · 2024Review
- QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition.BioMed research international · 2016Article
- A knowledge-based method to predict the cooperative relationship between transcription factors.Molecular diversity · 2010Article
- Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition.BMC bioinformatics · 2010Article
- Protein sequences classification by means of feature extraction with substitution matrices.BMC bioinformatics · 2010Article
- The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.BMC bioinformatics · 2008Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundThe number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences.
resultsTo explore this problem, we adopted an approach based on the functional domain composition of proteins. Every protein was represented by a vector calculated from the domains in the PFAM database. The nearest neighbor algorithm (NNA) was used for classifying the quaternary structure of proteins from this information. The jackknife cross-validation test was performed on the non-redundant protein dataset in which the sequence identity was less than 25%. The overall success rate obtained is 75.17%. Additionally, to demonstrate the effectiveness of this method, we predicted the proteins in an independent dataset and achieved an overall success rate of 84.11%
conclusionCompared with the amino acid composition method and Blast, the results indicate that the domain composition approach may be a more effective and promising high-throughput method in dealing with this complicated problem in bioinformatics.
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