Domain boundary prediction based on profile domain linker propensity index

Authors:
Qiwen Dong;Xiaolong Wang;Lei Lin;Zhiming Xu
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, PR China
Venue:
Computational Biology and Chemistry
Year:
2006

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Abstract

Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multi-domain proteins but also for the experimental structure determination. In this work, a novel index at the profile level is presented, namely, the profile domain linker propensity index (PDLI), which uses the evolutionary information of profiles for domain linker prediction. The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. PDLI is then obtained by the frequencies of binary profiles in domain linkers as compared to those in domains. A smooth and normalized numeric profile is generated for any amino acid sequences from which the domain linkers can be predicted. Testing on the Structural Classification of Proteins (SCOP) database and CASP6 targets shows that PDLI outperforms other indexes at the amino acid level.