An Improved Profile-Level Domain Linker Propensity Index for Protein Domain Boundary Prediction.

Title: An Improved Profile-Level Domain Linker Propensity Index for Protein Domain Boundary Prediction.

Volume: 18 Issue: 1

Author(s): Yanfeng Zhang, Bin Liu, Qiwen Dong and Victor X. Jin

Affiliation:

Keywords: Domain boundary, domain linker, multiple sequence alignments, sequence-based prediction, computational modelling, pseudo amino acid, GPCR type, substrate-enzyme-product triads, mutagenesis, homology, multiple sequence alignment, DomSSEA, IGRN, CHOPnet, Nagarajan, SSEP-Domain, beta-turn types, entropy index, GHL, KDH, SCOP-1, ASTRAL, protein sequence frequency profiles, index OPI, PSI-BLAST, lower Z-score, OPI, NONE represents, SCOP-1 Dataset, SCOP-2 Dataset, PDLI, REI, sequence-level, protein-protein interactionDomain boundary, domain linker, multiple sequence alignments, sequence-based prediction, computational modelling, pseudo amino acid, GPCR type, substrate-enzyme-product triads, mutagenesis, homology, multiple sequence alignment, DomSSEA, IGRN, CHOPnet, Nagarajan, SSEP-Domain, beta-turn types, entropy index, GHL, KDH, SCOP-1, ASTRAL, protein sequence frequency profiles, index OPI, PSI-BLAST, lower Z-score, OPI, NONE represents, SCOP-1 Dataset, SCOP-2 Dataset, PDLI, REI, sequence-level, protein-protein interaction

Abstract: 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.

Cite this article as:

Zhang Yanfeng, Liu Bin, Dong Qiwen and X. Jin Victor, An Improved Profile-Level Domain Linker Propensity Index for Protein Domain Boundary Prediction., Protein & Peptide Letters 2011; 18 (1) . https://dx.doi.org/10.2174/092986611794328717