## Abstract The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence‐order effects is an important and challenging problem. Ba
Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components
✍ Scribed by Hao Lin; Qian-Zhong Li
- Publisher
- John Wiley and Sons
- Year
- 2007
- Tongue
- English
- Weight
- 80 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0192-8651
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
The proteins structure can be mainly classified into four classes: all‐α, all**‐**β, α/β, and α + β protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007
📜 SIMILAR VOLUMES
## Abstract Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. However, how to optimally formulate the Pse