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A Sequence-Based Model Accounts Largely for the Relationship of Intron Positions to Protein Structural Features
Author(s) -
Danny W De Kee,
Vivek Gopalan,
Arlin Stoltzfus
Publication year - 2007
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msm151
Subject(s) - intron , biology , gene , genetics , sequence (biology) , computational biology , evolutionary biology
Claims of intron-structure correlations have played a major role in debates surrounding split gene origins. In the formative (as opposed to disruptive or "insertional") model of split gene origins, introns represent the scars of chimaeric gene assembly. When analyzed retrospectively, formative introns should tend to fall between modular units, if such units exist, or at least to exhibit a preference for sites favorable to chimaera formation. However, there is another possible source of preferences: under a disruptive model of split gene origins, fortuitous intron-structure correlations may arise because the gain of introns is biased with respect to flanking nucleotide sequences. To investigate the extent to which a sequence-biased intron gain model may account for the present-day distribution of introns, data on over 10,000 introns in eukaryotic protein-coding genes were integrated with structural data from a set of 1,851 nonredundant protein chains. The positions of introns with respect to secondary structures, solvent accessibility, and so-called "modules" were evaluated relative to the expectations of a null model, a disruptive model based on amino acid frequencies at splice junctions, and a formative model defined relative to these. The null model can be excluded for most structural features and is highly improbable when intron sites are grouped by reading frame phase. Phase-dependent correlations with secondary structure and side-chain surface accessibility are particularly strong. However, these phase-dependent correlations are explained largely by the sequence-based disruptive model.

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