Protein Fold Recognition Using Hidden Markov Model

Based on the classification of SCOP,we chose 70 folding types.Each type consists of a subset of proteins(<25% sequence identity) which have more than 4 samples.These sequences were aligned by structure alignment tool combining with manual inspection,and the sequence alignment result was used to generate a profile HMM of each fold.In the single model identify test on 9 505 sequences of Astral-1.65,the sensitivity and specificity of the profile HMM reach to 91.93% and 99.95% respectively,and the Matthew correlation coefficient is 0.87.Compared with Pfam and SUPERFAMILY which construct HMM based on merely sequence alignment,the model number is significantly reduced,while keeping the sensitivity at the same level.The result show that,for those proteins with same fold type but low sequence identity,a unified HMM can be constructed by introducing structure alignment to implement fold identify with high accuracy.