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Prediction of protein local structures and folding fragments based on building‐block library
Author(s) -
Dong Qiwen,
Wang Xiaolong,
Lin Lei
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21931
Subject(s) - iterated function , protein structure prediction , block (permutation group theory) , folding (dsp implementation) , computer science , protein data bank (rcsb pdb) , sequence (biology) , structural alignment , local structure , protein structure , protein folding , algorithm , protein data bank , smith–waterman algorithm , sequence alignment , crystallography , mathematics , peptide sequence , biology , chemistry , geometry , engineering , genetics , mathematical analysis , biochemistry , gene , electrical engineering
In recent years, protein structure prediction using local structure information has made great progress. In this study, a novel and effective method is developed to predict the local structure and the folding fragments of proteins. First, the proteins with known structures are split into fragments. Second, these fragments, represented by dihedrals, are clustered to produce the building blocks (BBs). Third, an efficient machine learning method is used to predict the local structures of proteins from sequence profiles. Finally, a bi‐gram model, trained by an iterated algorithm, is introduced to simulate the interactions of these BBs. For test proteins, the building‐block lattice is constructed, which contains all the folding fragments of the proteins. The local structures and the optimal fragments are then obtained by the dynamic programming algorithm. The experiment is performed on a subset of the PDB database with sequence identity less than 25%. The results show that the performance of the method is better than the method that uses only sequence information. When multiple paths are returned, the average classification accuracy of local structures is 72.27% and the average prediction accuracy of local structures is 67.72%, which is a significant improvement in comparison with previous studies. The method can predict not only the local structures but also the folding fragments of proteins. This work is helpful for the ab initio protein structure prediction and especially, the understanding of the folding process of proteins. Proteins 2008. © 2008 Wiley‐Liss, Inc.