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 RNA inter-nucleotide 3D closeness prediction by deep residual neural networks

2020 ◽  
Author(s):  
Saisai Sun ◽  
Wenkai Wang ◽  
Zhenling Peng ◽  
Jianyi Yang
Keyword(s):  
Neural Networks ◽  
Secondary Structure ◽  
Rna Structure ◽  
Supplementary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Guide Rna ◽  
Multiple Sequence Alignments ◽  
Contact Distance ◽  
Distance Restraints

Abstract Motivation Recent years have witnessed that the inter-residue contact/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness. Results We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L/10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness. Availability and implementation The webserver and a standalone package are available at: http://yanglab.nankai.edu.cn/RNAcontact/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments

2015 ◽  
Vol 32 (6) ◽  
pp. 814-820 ◽  
Author(s):  
Gearóid Fox ◽  
Fabian Sievers ◽  
Desmond G. Higgins
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
De Novo ◽  
Biological Data ◽  
Supplementary Information ◽  
Test Case ◽  
Sequence Alignments ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

Abstract Motivation: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data. Results: We take advantage of recent developments in protein structure prediction methods to create a benchmark (ContTest) for protein MSAs containing many thousands of sequences in each test case and which is based on empirical biological data. We rank popular MSA methods using this benchmark and verify a recent result showing that chained guide trees increase the accuracy of progressive alignment packages on datasets with thousands of proteins. Availability and implementation: Benchmark data and scripts are available for download at http://www.bioinf.ucd.ie/download/ContTest.tar.gz. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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.

scholarly journals SAM/BAM format v1.5 extensions for de novo assemblies

2015 ◽  
Author(s):  
Peter J. A. Cock ◽  
James K Bonfield ◽  
Bastien Chevreux ◽  
Heng Li
Keyword(s):  
De Novo ◽  
Reference Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Plain Text ◽  
Multiple Sequence Alignments ◽  
Human Genomes ◽  
Reference Sequences ◽  
Unmapped Reads ◽  
Individual Human

Summary: The plain text Sequence Alignment/Map (SAM) file format and its companion binary form (BAM) are a generic alignment format for storing read alignments against reference sequences (and unmapped reads) together with structured meta-data (Li et al., 2009). Driven by the needs of the 1000 Genomes Project which sequenced many individual human genomes, early SAM/BAM usage focused on pairwise alignments of reads to a reference. However, through the CIGAR P operator multiple sequence alignments can also be preserved. Herein we describe clarifications and additions in version 1.5 of the specification to facilitate storing de novo sequence alignments: Padded reference sequences (with gap characters), annotation of reads or regions of the reference, and the option of embedding the reference sequence within the file. Availability: The latest public release of the specification is at http://samtools.sourceforge.net/SAM1.pdf, with in development drafts at https://github.com/samtools/hts-specs/ under version control.

scholarly journals The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing

2019 ◽  
Author(s):  
Mark Chonofsky ◽  
Saulo H P de Oliveira ◽  
Konrad Krawczyk ◽  
Charlotte M Deane
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Supplementary Information ◽  
Chemical Interactions ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Contact Prediction ◽  
Multiple Sequence Alignments ◽  
Physico Chemical

Abstract Motivation Over the last few years, the field of protein structure prediction has been transformed by increasingly-accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments. However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others. Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV, and DNCON2, as examples of direct coupling analysis, meta-prediction, and deep learning. Results We considered correctly-predicted contacts and compared their properties against the protein contacts that were not predicted. Predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important than contacts that were not predicted. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts. These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from multiple sequence alignments. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology. Availability We use publicly-available databases. Our code is available for download at http://opig.stats.ox.ac.uk/. Supplementary information Supplementary information is available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document