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A conditional random fields method for RNA sequence-structure relationship modeling and conformation sampling.

Wang Z, Xu J - Bioinformatics (2011)

Bottom Line: In addition, neither of these methods makes use of sequence information in sampling conformations.Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling.Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE. zywang@ttic.edu; j3xu@ttic.edu Supplementary data are available at Bioinformatics online.

View Article: PubMed Central - PubMed

Affiliation: Toyota Technological Institute at Chicago, IL, USA. zywang@ttic.edu

ABSTRACT

Unlabelled: Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (CRFs), to model RNA sequence-structure relationship, which enables us to accurately estimate the probability of an RNA conformation from sequence. Coupled with a novel tree-guided sampling scheme, our CRF model is then applied to RNA conformation sampling. Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling. Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE.

Contact: zywang@ttic.edu; j3xu@ttic.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Show MeSH
Conformation of a nucleotide is represented by angles.
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Figure 1: Conformation of a nucleotide is represented by angles.

Mentions: We can represent an RNA 3D structure using a sequence of torsion angles, as shown in Figure 1. Every nucleotide has in total seven bonds that rotate freely. Six of them lie on the backbone: P–O5′, O5′−C5′, C5′−C4′, C4′−C3′, C3′−O3′ and O3′−P. The seventh bond connects a base to atom C1′. As shown in Figure 2 torsion χ around the seventh bond has a small variance, so we assume that it is independent of the other angles and has a normal distribution. The planar angles between two adjacent bonds on the backbone are almost constants, so are the lengths of the bonds.Fig. 1.


A conditional random fields method for RNA sequence-structure relationship modeling and conformation sampling.

Wang Z, Xu J - Bioinformatics (2011)

Conformation of a nucleotide is represented by angles.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117333&req=5

Figure 1: Conformation of a nucleotide is represented by angles.
Mentions: We can represent an RNA 3D structure using a sequence of torsion angles, as shown in Figure 1. Every nucleotide has in total seven bonds that rotate freely. Six of them lie on the backbone: P–O5′, O5′−C5′, C5′−C4′, C4′−C3′, C3′−O3′ and O3′−P. The seventh bond connects a base to atom C1′. As shown in Figure 2 torsion χ around the seventh bond has a small variance, so we assume that it is independent of the other angles and has a normal distribution. The planar angles between two adjacent bonds on the backbone are almost constants, so are the lengths of the bonds.Fig. 1.

Bottom Line: In addition, neither of these methods makes use of sequence information in sampling conformations.Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling.Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE. zywang@ttic.edu; j3xu@ttic.edu Supplementary data are available at Bioinformatics online.

View Article: PubMed Central - PubMed

Affiliation: Toyota Technological Institute at Chicago, IL, USA. zywang@ttic.edu

ABSTRACT

Unlabelled: Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (CRFs), to model RNA sequence-structure relationship, which enables us to accurately estimate the probability of an RNA conformation from sequence. Coupled with a novel tree-guided sampling scheme, our CRF model is then applied to RNA conformation sampling. Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling. Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE.

Contact: zywang@ttic.edu; j3xu@ttic.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Show MeSH