An improved profile-level domain linker propensity index for protein domain boundary prediction

Yanfeng Zhang, Bin Liu, Qiwen Dong, Victor X. Jin

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Protein domain boundary prediction is critical for understanding protein structure and function. In this study, we present a novel method, an order profile domain linker propensity index (OPI), which uses the evolutionary information extracted from the protein sequence frequency profiles calculated from the multiple sequence alignments. A protein sequence is first converted into smooth and normalized numeric order profiles by OPI, from which the domain linkers can be predicted. By discriminating the different frequencies of the amino acids in the protein sequence frequency profiles, OPI clearly shows better performance than our previous method, a binary profile domain linker propensity index (PDLI). We tested our new method on two different datasets, SCOP-1 dataset and SCOP-2 dataset, and we were able to achieve a precision of 0.82 and 0.91 respectively. OPI also outperforms other residue-level, profile-level indexes as well as other state-of-the-art methods.

Original languageEnglish (US)
Pages (from-to)7-16
Number of pages10
JournalProtein and Peptide Letters
Issue number1
StatePublished - Jan 1 2011
Externally publishedYes


  • Domain boundary
  • Domain linker
  • Multiple sequence alignments
  • Sequence-based prediction

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry


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