Abstract: One of the important tasks of the post genome project is protein structure prediction，the key step of which is protein secondary structure prediction.This paper makes use of a model of digital coding for amino acid，and uses cellular automata to generate image representation for protein sequences.A protein sequence can be represented by a unique image，and the image takes into account the interactional actions between amino acids.By the Gray Level Co-occurrence Matrix（GLCM） deriving from the CAI，a novel method has been developed that can be used to predict the secondary structure of the protein.The result thus obtained is quite promising.

摘要： 蛋白质结构预测是后基因组时代的一项重要任务，蛋白质二级结构预测是蛋白质结构预测的关键步骤。利用氨基酸数字编码模型生成蛋白质序列的元胞自动机图（Cellular Automata Image，CAI），提出了一种基于灰度共生矩阵（Gray Level Co-occurrence Matrix，GLCM）提取纹理图像特征的方法。用扩大的协方差算法进行预测，仿真结果显示有较好的分类效果，Jackknife检验的预测成功率达到94.61％。