ArticleBMC bioinformatics2010
Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- HH-suite3 for fast remote homology detection and deep protein annotation.BMC bioinformatics · 2019Article
- Estimating statistical significance of local protein profile-profile alignments.BMC bioinformatics · 2019Article
- IntFOLD: an integrated web resource for high performance protein structure and function prediction.Nucleic acids research · 2019Article
- Homology Inference Based on a Reconciliation Approach for the Comparative Genomics of Protozoa.Evolutionary bioinformatics online · 2018Article
- dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation.Scientific reports · 2016Article
- Using homology relations within a database markedly boosts protein sequence similarity search.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Evaluation and improvements of clustering algorithms for detecting remote homologous protein families.BMC bioinformatics · 2015Article
- From local structure to a global framework: recognition of protein folds.Journal of the Royal Society, Interface · 2014Review
- Computational design of glutamate dehydrogenase in Bacillus subtilis natto.Journal of molecular modeling · 2013Article
- A vitamin B₁₂ transporter in Mycobacterium tuberculosis.Open biology · 2013Article
- Insights into the mutation-induced HHH syndrome from modeling human mitochondrial ornithine transporter-1.PloS one · 2012Article
- Computational analysis of DNA replicases in double-stranded DNA viruses: relationship with the genome size.Nucleic acids research · 2011Article
- The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.Nucleic acids research · 2011Article
- Identification of new homologs of PD-(D/E)XK nucleases by support vector machines trained on data derived from profile-profile alignments.Nucleic acids research · 2011Article
- Essential roles for imuA'- and imuB-encoded accessory factors in DnaE2-dependent mutagenesis in Mycobacterium tuberculosis.Proceedings of the National Academy of Sciences of the United States of America · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundDetection of common evolutionary origin (homology) is a primary means of inferring protein structure and function. At present, comparison of protein families represented as sequence profiles is arguably the most effective homology detection strategy. However, finding the best way to represent evolutionary information of a protein sequence family in the profile, to compare profiles and to estimate the biological significance of such comparisons, remains an active area of research.
resultsHere, we present a new homology detection method based on sequence profile-profile comparison. The method has a number of new features including position-dependent gap penalties and a global score system. Position-dependent gap penalties provide a more biologically relevant way to represent and align protein families as sequence profiles. The global score system enables an analytical solution of the statistical parameters needed to estimate the statistical significance of profile-profile similarities. The new method, together with other state-of-the-art profile-based methods (HHsearch, COMPASS and PSI-BLAST), is benchmarked in all-against-all comparison of a challenging set of SCOP domains that share at most 20% sequence identity. For benchmarking, we use a reference ("gold standard") free model-based evaluation framework. Evaluation results show that at the level of protein domains our method compares favorably to all other tested methods. We also provide examples of the new method outperforming structure-based similarity detection and alignment. The implementation of the new method both as a standalone software package and as a web server is available at http://www.ibt.lt/bioinformatics/coma.
conclusionDue to a number of developments, the new profile-profile comparison method shows an improved ability to match distantly related protein domains. Therefore, the method should be useful for annotation and homology modeling of uncharacterized proteins.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.