Extending Protein Domain Boundary Predictors to Detect Discontinuous Domains

Abstract

A variety of protein domain predictors were developed to predict protein domain boundaries in recent years, but most of them cannot predict discontinuous domains. Considering nearly 40% of multidomain proteins contain one or more discontinuous domains, we have developed DomEx to enable domain boundary predictors to detect discontinuous domains by assembling the continuous domain segments. Discontinuous domains are predicted by matching the sequence profile of concatenated continuous domain segments with the profiles from a single-domain library derived from SCOP and CATH, and Pfam. Then the matches are filtered by similarity to library templates, a symmetric index score and a profile-profile alignment score. DomEx recalled 32.3% discontinuous domains with 86.5% precision when tested on 97 non-homologous protein chains containing 58 continuous and 99 discontinuous domains, in which the predicted domain segments are within ±20 residues of the boundary definitions in CATH 3.5. Compared with our recently developed predictor, ThreaDom, which is the state-of-the-art tool to detect discontinuous-domains, DomEx recalled 26.7% discontinuous domains with 72.7% precision in a benchmark with 29 discontinuous-domain chains, where ThreaDom failed to predict any discontinuous domains. Furthermore, combined with ThreaDom, the method ranked number one among 10 predictors. The source code and datasets are available at https://github.com/xuezhidong/DomEx.

ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly

“…Finally, a boundary-clustering based strategy is used to fine-tune the boundary positions, as well as to detect the DCDs from the templates. If no DCD is detected from the LOMETS templates, a segment assembly process guided by symmetric motif comparison, as proposed in DomEx ( 32 ), is employed for further detection of DCD structures.…”

“…The putative domain sequence is searched by PSI-BLAST through a non-redundant domain library collected from SCOP ( 47 ), CATH ( 44 ) and Pfam ( 48 ) databases. The template similarity score (TS-score) is calculated by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*}{\rm{TS - score}} = s \times h \times l\end{equation*}\end{document} where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$s$\end{document} is the sequence identity between the putative domain and template sequences; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$h = \min ( {10, - \lg E} )/10$\end{document} is the normalized E-value ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$E$\end{document} ) by PSI-BLAST; and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$l$\end{document} is a factor associated with the alignment coverage ( 32 ).…”

“…The putatively assembled domain sequence is predicted as a true DCD, if the scores calculated above satisfy the conditions of TS-score >TS-score 0 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${S_{PPA}} >S_{PPA}^0$\end{document} , and SI-score >SI-score 0 . The threshold parameters of TS-score 0 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$S_{PPA}^0$\end{document} and SI-score 0 have been determined by maximizing the Matthews correlation coefficient on an independent set of training proteins ( 32 ). This step of DCD detection and validation involves mainly the running of the PSI-BLAST search against a library of ∼500 millions single domain sequences and the profile-profile search through the Pfam database.…”

“…Most of the methods can only generate predictions for continuous domains (CDs) that consist of continuous residues along the query sequence, while a large number of proteins contain discontinuous domains (DCDs). For example, the current PDB library deposits about 18% proteins with at least one DCD which consists of two or more non-sequential segmental sequences ( 32 ). The detection and correct division of the DCDs represents a major challenging problem.…”

“…ThreaDom ( 30 ) can break up the size limit due to the adoption of threading-based template recognition technique, but the success relies on the existence of similar DCD structure in the threading template library. DomEx ( 32 ) was recently proposed to predict DCDs by the reassembly of DCD segments guided with homology and symmetry alignments, which is able to detect DCDs beyond the structures in the template library and therefore significantly increases the accuracy of the DCD predictions.…”