Globin-like蛋白质折叠类型识别

蛋白质折叠类型识别是蛋白质结构研究的重要内容.以SCOP中的Globin-like折叠为研究对象,选择其中序列同一性小于25%的17个代表性蛋白质为训练集,采用机器和人工结合的办法进行结构比对,产生序列排比,经过训练得到了适合Globin-like折叠的概形隐马尔科夫模型(profile HMM)用于该折叠类型的识别.以Astrall.65中的68057个结构域样本进行检验,识别敏感度为99.64%,特异性100%.在折叠类型水平上,与Pfam和SUPERFAMILY单纯使用序列比对构建的HMM相比,所用模型由多于100个归为一个,仍然保持了很高的识别效果.结果表明:对序列相似度很低但具有相同折叠类型的蛋白质,可以通过引入结构比对的方法建立统一的HMM模型,实现高准确率的折叠类型识别.

Identification Proteins of Globin-like Fold

Identifying protein fold is an important issue in protein structure research. Based on the classification of SCOP1.65, 17 Globin-like proteins from four homology families ( < 25% sequence identity) are selected from Astral 1.65. The sequence alignment result, from structure alignment tool MUSTANG combined with manual inspection, has been used to generate a profile HMM of Globin-like fold. In a fold identify test on 68 057 sequences of Astral-1.65, the model identified 1 097 Globin-like proteins rightly, only 4 proteins of this fold are not correctly distinguished. The sensitivity and specificity of the profile HMM reach to 99.64% and 100%, respectively. Compared with Pfam and SUPERFAMILY which construct HMM based on merely sequence alignment, the model number is reduced from about 100 to 1, while keeping the sensitivity at the same level. The result shows that, for those proteins with same fold type but low sequence identity, a unified HMM could be constructed by introducing structure alignment to fold identify with high accuracy.