The multidimensional statistical technique of discriminant analysis is used to allocate amino acid sequences to one of four secondary structural classes: high a content, high / 3 content, mixed a and @, low content of ordered structure. Discrimination is based on four attributes: estimates of percen
The Prediction of the Structural Class of Protein: Application of the Measure of Diversity
β Scribed by QIAN-ZHONG LI; ZHI-QING LU
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 235 KB
- Volume
- 213
- Category
- Article
- ISSN
- 0022-5193
No coin nor oath required. For personal study only.
β¦ Synopsis
Based on the concept that the structural class of a protein is mainly determined by its secondary structure sequence, a new algorithm for prediction of the structural class of a protein is proposed. By use of the number of -helices, -strands, and fragments, the structural class of a protein can be predicted by an algorithm based on the increment of diversity (ID), in which the sole prediction parameter*the increment of diversity is used as the index of prediction of structural class of a protein. The results indicate that the high rates of correct prediction are obtained for complete set (standard set) from Brookhaven Protein Data Bank-CD ROM (PDB) published in October 1995 and the test set newly released from Brookhaven Protein Data Bank-CD ROM (PDB) before July 1998, respectively.
π SIMILAR VOLUMES
## Abstract An informationβtheoretical approach, which combines a sequence decomposition technique and a fuzzy clustering algorithm, is proposed for prediction of protein structural class. This approach could bypass the process of selecting and comparing sequence features as done previously. First,
## Abstract Supervised classifiers, such as artificial neural network, partition trees, and support vector machines, are often used for the prediction and analysis of biological data. However, choosing an appropriate classifier is not straightforward because each classifier has its own strengths an
Proteins of known structures are usually classified into four structural classes: all-β£, all-β€, β£Ψβ€, and β£/β€ type of proteins. A number of methods to predicting the structural class of a protein based on its amino acid composition have been developed during the past few years. Recently, a componentc
The support vector machines (SVMs) method was introduced for predicting the structural class of protein domains. The results obtained through the self-consistency test, jack-knife test, and independent dataset test have indicated that the current method and the elegant component-coupled algorithm de