Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform

• Published in: Molecular bioSystems 2012-10, Vol.8 (12), p.3178-3184
• Main Authors: Sun, Xing-Yu, Shi, Shao-Ping, Qiu, Jian-Ding, Suo, Sheng-Bao, Huang, Shu-Yun, Liang, Ru-Ping
• Format: Article
• Language: English
• Subjects: Algorithms; Amino Acid Sequence; Amino Acids - chemistry; Computational Biology - methods; High-Throughput Screening Assays - methods; Protein Structure, Quaternary; Proteins - chemistry; Wavelet Analysis
• ISSN: 1742-206X; 1742-2051
• DOI: 10.1039/c2mb25280e

In vivo , some proteins exist as monomers and others as oligomers. Oligomers can be further classified into homo-oligomers (formed by identical subunits) and hetero-oligomers (formed by different subunits), and they form the structural components of various biological functions, including cooperative effects, allosteric mechanism and ion-channel gating. Therefore, with the avalanche of protein sequences generated in the post-genomic era, it is very important for both basic research and the pharmaceutical industry to acquire the possible knowledge about quaternary structural attributes of their proteins of interest. In view of this, a high throughput method (DWT_DT), a 2-layer approach by fusing discrete wavelet transform (DWT) and decision-tree algorithm (DT) with physicochemical features, has been developed to predict protein quaternary structures. The 1st layer is to assign a query protein to one of the 10 main quaternary structural attributes. The 2nd layer is to evaluate whether the protein in question is composed of homo- or hetero-oligomers. The overall accuracy by jackknife test for the 1st layer identification was 89.60%. The overall accuracy of the 2nd layer varies from 88.23 to 100%. The results suggest that this newly developed protocol (DWT_DT) is very promising in predicting quaternary structures with complicated composition. A high throughput method DWT_DT, a 2-layer approach by fusing discrete wavelet transform and decision-tree algorithm with physicochemical features, is designed to predict protein quaternary structures.