AN OPTIMIZATION APPROACH TO PREDICTING PROTEIN STRUCTURAL CLASS FROM AMINO-ACID-COMPOSITION

被引:1
|
作者
ZHANG, CT [1 ]
CHOU, KC [1 ]
机构
[1] UPJOHN CO,LABS,COMPUTAT CHEM,KALAMAZOO,MI 49001
关键词
ALL-ALPHA; ALPHA+BETA; ALPHA-BETA; ALL-BETA; COMPONENT COEFFICIENTS; CONTRADICTORY EQUATIONS; 20-DIMENSIONAL SPACE; VECTOR DECOMPOSITION;
D O I
暂无
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Proteins are generally classified into four structural classes: all-alpha proteins, all-beta proteins, alpha + beta-proteins, and alpha/beta-proteins. In this article, a protein is expressed as a vector of 20-dimensional space, in which its 20 components are defined by the composition of its 20 amino acids. Based on this, a new method, the so-called maximum component coefficient method, is proposed for predicting the structural class of a protein according to its amino acid composition. In comparison with the existing methods, the new method yields a higher general accuracy of prediction. Especially for the all-alpha proteins, the rate of correct prediction obtained by the new method is much higher than that by any of the existing methods. For instance, for the 19 all-alpha proteins investigated previously by P.Y. Chou, the rate of correct prediction by means of his method was 84.2%, but the correct rate when predicted with the new method would be 100%! Furthermore, the new method is characterized by an explicable physical picture. This is reflected by the process in which the vector representing a protein to be predicted is decomposed into four component vectors, each of which corresponds to one of the norms of the four protein structural classes.
引用
收藏
页码:401 / 408
页数:8
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