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Structural genomics analysis of alternative splicing and application to isoform structure modeling
Peng Wang1, Bo Yan, Jun-Tao Guo
1Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30622, USA.
Summary
Alternative splicing creates protein diversity. This study shows protein structure prediction for alternatively spliced variants is viable using threading, aiding genome-scale analysis.
Area of Science:
- Molecular Biology
- Structural Biology
- Bioinformatics
Background:
- Alternative splicing regulates gene expression and protein functional diversity.
- Knowledge gaps exist regarding the protein tertiary structures of alternatively spliced variants, primarily studied via mRNA.
- Understanding these structures is crucial for comprehending protein function.
Purpose of the Study:
- To analyze alternatively spliced variants at both sequence and structure levels.
- To assess the viability of protein structure modeling for these variants.
- To establish a genome-scale method for predicting structures of alternatively spliced isoforms.
Main Methods:
- Large-scale analysis of known alternatively spliced variants.
- Protein structure modeling using the threading approach.
- Examination of splicing event characteristics at sequence and structural levels.
- Molecular dynamics simulations to assess fold stability.
Main Results:
- Threading is a generally viable approach for modeling alternatively spliced variant structures.
- Splicing event sizes follow a power law distribution; most isoforms have few alternations.
- Alternative splicing boundaries occur in coil regions and on exposed residues, primarily on protein surfaces.
- Threading-based structure prediction is feasible and fold stability can be addressed.
Conclusions:
- A viable method for predicting alternatively spliced isoform structures at the genome scale has been established.
- Threading is a useful tool for structure prediction of alternatively spliced isoforms.
- Insights into splicing event locations inform structure prediction strategies.