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Reduction of the secondary structure topological space through direct estimation of the contact energy formed by the secondary structures.

ABSTRACT: BACKGROUND: Electron cryomicroscopy is a fast developing technique aiming at the determination of the 3-dimensional structures of large protein complexes. Using this technique, protein density maps can be generated with 6 to 10 A resolution. At such resolutions, the secondary structure elements such as helices and beta-strands appear to be skeletons and can be computationally detected. However, it is not known which segment of the protein sequence corresponds to which of the skeletons. The topology in this paper refers to the linear order and the directionality of the secondary structures. For a protein with N helices and M strands, there are (N!2N)(M!2M) different topologies, each of which maps N helix segments and M strand segments on the protein sequence to N helix and M strand skeletons. Since the backbone position is not available in the skeleton, each topology of the skeletons corresponds to additional freedom to position the atoms in the skeletons. RESULTS: We have developed a method to construct the possible atomic structures for the helix skeletons by sampling the solution space of all the possible topologies of the skeletons. Our method also ranks the possible structures based on the contact energy formed by the secondary structures, rather than the entire chain. If we assume that the backbone atomic positions are known for the skeletons, then the native topology of the secondary structures can be found in the top 30% of the ranked list of all possible topologies for all the 30 proteins tested, and within the top 5% for most of the 30 proteins. Without assuming the backbone location of the skeletons, the possible atomic structures of the skeletons can be constructed using the axis of the skeleton and the sequence segments. The best constructed structure for the skeletons has RMSD to native between 4 and 5 A for the four tested alpha-proteins. These best constructed structures were ranked the 17th, 31st, 16th and 5th respectively for the four proteins out of 32066, 391833, 98755 and 192935 possible assignments in the pool. CONCLUSION: Our work suggested that the direct estimation of the contact energy formed by the secondary structures is quite effective in reducing the topological space to a small subset that includes a near native structure for the skeletons.

SUBMITTER: Sun W

PROVIDER: S-EPMC2648730 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Reduction of the secondary structure topological space through direct estimation of the contact energy formed by the secondary structures.

Sun Weitao W He Jing J

BMC bioinformatics 20090130

<h4>Background</h4>Electron cryomicroscopy is a fast developing technique aiming at the determination of the 3-dimensional structures of large protein complexes. Using this technique, protein density maps can be generated with 6 to 10 A resolution. At such resolutions, the secondary structure elements such as helices and beta-strands appear to be skeletons and can be computationally detected. However, it is not known which segment of the protein sequence corresponds to which of the skeletons. Th ...[more]

PMID: 19208142

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Project description:The prediction of secondary structure, i.e. the set of canonical base pairs between nucleotides, is a first step in developing an understanding of the function of an RNA sequence. The most accurate computational methods predict conserved structures for a set of homologous RNA sequences. These methods usually suffer from high computational complexity. In this paper, TurboFold, a novel and efficient method for secondary structure prediction for multiple RNA sequences, is presented.TurboFold takes, as input, a set of homologous RNA sequences and outputs estimates of the base pairing probabilities for each sequence. The base pairing probabilities for a sequence are estimated by combining intrinsic information, derived from the sequence itself via the nearest neighbor thermodynamic model, with extrinsic information, derived from the other sequences in the input set. For a given sequence, the extrinsic information is computed by using pairwise-sequence-alignment-based probabilities for co-incidence with each of the other sequences, along with estimated base pairing probabilities, from the previous iteration, for the other sequences. The extrinsic information is introduced as free energy modifications for base pairing in a partition function computation based on the nearest neighbor thermodynamic model. This process yields updated estimates of base pairing probability. The updated base pairing probabilities in turn are used to recompute extrinsic information, resulting in the overall iterative estimation procedure that defines TurboFold.TurboFold is benchmarked on a number of ncRNA datasets and compared against alternative secondary structure prediction methods. The iterative procedure in TurboFold is shown to improve estimates of base pairing probability with each iteration, though only small gains are obtained beyond three iterations. Secondary structures composed of base pairs with estimated probabilities higher than a significance threshold are shown to be more accurate for TurboFold than for alternative methods that estimate base pairing probabilities. TurboFold-MEA, which uses base pairing probabilities from TurboFold in a maximum expected accuracy algorithm for secondary structure prediction, has accuracy comparable to the best performing secondary structure prediction methods. The computational and memory requirements for TurboFold are modest and, in terms of sequence length and number of sequences, scale much more favorably than joint alignment and folding algorithms.TurboFold is an iterative probabilistic method for predicting secondary structures for multiple RNA sequences that efficiently and accurately combines the information from the comparative analysis between sequences with the thermodynamic folding model. Unlike most other multi-sequence structure prediction methods, TurboFold does not enforce strict commonality of structures and is therefore useful for predicting structures for homologous sequences that have diverged significantly. TurboFold can be downloaded as part of the RNAstructure package at http://rna.urmc.rochester.edu.

Dataset Information

Reduction of the secondary structure topological space through direct estimation of the contact energy formed by the secondary structures.

Publications

Reduction of the secondary structure topological space through direct estimation of the contact energy formed by the secondary structures.

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