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Literature DB >> 12655018

Analysis of the conformational energy landscape of human snRNA with a metric based on tree representation of RNA structures.

Junji Kitagawa¹, Yasuhiro Futamura, Kenji Yamamoto.

Abstract

It is an outstanding problem to clarify how the RNA sequence is related to its structure and biological functions. We developed a simplified definition of a metric for tree representation of RNA secondary structures and analyzed the conformational energy landscapes of human spliceosomal snRNAs. We discuss the structural properties of the biological sequence by calculating the conformational energy landscapes based on the structural distance between each of the pairs in the set of suboptimal structures. The new index value is introduced for estimating the shapes of distribution patterns in conformational energy landscapes. We apply our method to the five human snRNAs and show that U1 snRNA has a multi-valley profile of the landscape, whereas the landscapes of the other four snRNAs have one steep valley. This result reflects different biological functions of these snRNAs in the pre-mRNA splicing process. The results of analyzing tRNAs and rRNAs show that the conformational energy landscapes of these sequences have multi-valley profiles.

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Year: 2003 PMID： 12655018 PMCID： PMC152804 DOI： 10.1093/nar/gkg288

Source DB: PubMed Journal: Nucleic Acids Res ISSN： 0305-1048 Impact factor: 16.971

24 in total

1. No evidence that mRNAs have lower folding free energies than random sequences with the same dinucleotide distribution.

Authors: C Workman; A Krogh
Journal: Nucleic Acids Res Date: 1999-12-15 Impact factor: 16.971

Analysis of the conformational energy landscape of human snRNA with a metric based on tree representation of RNA structures.

1. No evidence that mRNAs have lower folding free energies than random sequences with the same dinucleotide distribution.

2. Prediction and visualization of structural switches in RNA.

3. Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs.

4. A physical origin for functional domain structure in nucleic acids as evidenced by cross-linking entropy: I.

5. Statistical mechanics of secondary structures formed by random RNA sequences.

6. Structure and assembly of the spliceosomal snRNPs. Novartis Medal Lecture.

7. Mean free energy topology for nucleotide sequences of varying composition based on secondary structure calculations.

8. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information.

9. The nuclear 5S RNAs from chicken, rat and man. U5 RNAs are encoded by multiple genes.

10. Characterization of small nuclear RNA U1 gene candidates and pseudogenes from the human genome.

1. Abstract shapes of RNA.

2. In vitro RNA random pools are not structurally diverse: a computational analysis.

3. A comparison of RNA folding measures.

4. RDMAS: a web server for RNA deleterious mutation analysis.

5. Structural analysis of aligned RNAs.

6. Predicting candidate genomic sequences that correspond to synthetic functional RNA motifs.

7. Estimating the fraction of non-coding RNAs in mammalian transcriptomes.