Energy-Based RNA Consensus Secondary Structure Prediction in Multiple Sequence Alignments

2013 ◽  
pp. 125-141
Author(s):  
Stefan Washietl ◽  
Stephan H. Bernhart ◽  
Manolis Kellis
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Consensus Secondary Structure ◽  
Multiple Sequence Alignments

QuanTest2: benchmarking multiple sequence alignments using secondary structure prediction

2019 ◽  
Author(s):  
Fabian Sievers ◽  
Desmond G Higgins
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Reference Sequence ◽  
Supplementary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Reference Sequences ◽  
Selection Of

Abstract Motivation Secondary structure prediction accuracy (SSPA) in the QuanTest benchmark can be used to measure accuracy of a multiple sequence alignment. SSPA correlates well with the sum-of-pairs score, if the results are averaged over many alignments but not on an alignment-by-alignment basis. This is due to a sub-optimal selection of reference and non-reference sequences in QuanTest. Results We develop an improved strategy for selecting reference and non-reference sequences for a new benchmark, QuanTest2. In QuanTest2, SSPA and SP correlate better on an alignment-by-alignment basis than in QuanTest. Guide-trees for QuanTest2 are more balanced with respect to reference sequences than in QuanTest. QuanTest2 scores correlate well with other well-established benchmarks. Availability and implementation QuanTest2 is available at http://bioinf.ucd.ie/quantest2.tar, comprises of reference and non-reference sequence sets and a scoring script. Supplementary information Supplementary data are available at Bioinformatics online

scholarly journals Predicting Consensus Structures for RNA Alignments via Pseudo-Energy Minimization

2009 ◽  
Vol 3 ◽  
pp. BBI.S2578 ◽  
Author(s):  
Junilda Spirollari ◽  
Jason T.L. Wang ◽  
Kaizhong Zhang ◽  
Vivian Bellofatto ◽  
Yongkyu Park ◽  
...  
Keyword(s):  
Free Energy ◽  
Secondary Structure ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Energy Minimization ◽  
Secondary Structure Prediction ◽  
Sequence Alignments ◽  
Rna Sequences ◽  
Multiple Sequence ◽  
Consensus Secondary Structure

Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http://datalab.njit.edu/biology/RSpredict .

Structure and evolution of the spliceosomal peptidyl-prolylcis–transisomerase Cwc27

2014 ◽  
Vol 70 (12) ◽  
pp. 3110-3123 ◽  
Author(s):  
Alexander Ulrich ◽  
Markus C. Wahl
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Intramolecular Interactions ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Chaetomium Thermophilum ◽  
Ppiase Domain ◽  
Hydrogen Bond Networks

Cwc27 is a spliceosomal cyclophilin-type peptidyl-prolylcis–transisomerase (PPIase). Here, the crystal structure of a relatively protease-resistant N-terminal fragment of human Cwc27 containing the PPIase domain was determined at 2.0 Å resolution. The fragment exhibits a C-terminal appendix and resides in a reduced state compared with the previous oxidized structure of a similar fragment. By combining multiple sequence alignments spanning the eukaryotic tree of life and secondary-structure prediction, Cwc27 proteins across the entire eukaryotic kingdom were identified. This analysis revealed the specific loss of a crucial active-site residue in higher eukaryotic Cwc27 proteins, suggesting that the protein evolved from a prolyl isomerase to a pure proline binder. Noting a fungus-specific insertion in the PPIase domain, the 1.3 Å resolution crystal structure of the PPIase domain of Cwc27 fromChaetomium thermophilumwas also determined. Although structurally highly similar in the core domain, theC. thermophilumprotein displayed a higher thermal stability than its human counterpart, presumably owing to the combined effect of several amino-acid exchanges that reduce the number of long side chains with strained conformations and create new intramolecular interactions, in particular increased hydrogen-bond networks.

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