ArticleBMC structural biology2006
Identification of similar regions of protein structures using integrated sequence and structure analysis tools.
Article in BMC structural biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
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- Genomewide Analysis of Mode of Action of themSystems · 2019Article
- High temperatures affect the hypersensitive reaction, disease resistance and gene expression induced by a novel harpin HpaG-Xcm.Scientific reports · 2019Article
- The Structure of an As(III) S-Adenosylmethionine Methyltransferase with 3-Coordinately Bound As(III) Depicts the First Step in Catalysis.Biochemistry · 2018Article
- Arsenic methylation by a novel ArsM As(III) S-adenosylmethionine methyltransferase that requires only two conserved cysteine residues.Molecular microbiology · 2018Article
- High-throughput screening-compatible assays of As(III) S-adenosylmethionine methyltransferase activity.Analytical biochemistry · 2015Article
- A disulfide-bond cascade mechanism for arsenic(III) S-adenosylmethionine methyltransferase.Acta crystallographica. Section D, Biological crystallography · 2015Article
- Pathway of human AS3MT arsenic methylation.Chemical research in toxicology · 2014Article
- Identification and structural characterization of novel cyclotide with activity against an insect pest of sugar cane.The Journal of biological chemistry · 2012Article
- Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues.BMC structural biology · 2010Article
- Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble.Bioinformatics (Oxford, England) · 2009Article
- Clustering protein environments for function prediction: finding PROSITE motifs in 3D.BMC bioinformatics · 2007Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding protein function from its structure is a challenging problem. Sequence based approaches for finding homology have broad use for annotation of both structure and function. 3D structural information of protein domains and their interactions provide a complementary view to structure function relationships to sequence information. We have developed a web site http://www.sblest.org/ and an API of web services that enables users to submit protein structures and identify statistically significant neighbors and the underlying structural environments that make that match using a suite of sequence and structure analysis tools. To do this, we have integrated S-BLEST, PSI-BLAST and HMMer based superfamily predictions to give a unique integrated view to prediction of SCOP superfamilies, EC number, and GO term, as well as identification of the protein structural environments that are associated with that prediction. Additionally, we have extended UCSF Chimera and PyMOL to support our web services, so that users can characterize their own proteins of interest.
resultsUsers are able to submit their own queries or use a structure already in the PDB. Currently the databases that a user can query include the popular structural datasets ASTRAL 40 v1.69, ASTRAL 95 v1.69, CLUSTER50, CLUSTER70 and CLUSTER90 and PDBSELECT25. The results can be downloaded directly from the site and include function prediction, analysis of the most conserved environments and automated annotation of query proteins. These results reflect both the hits found with PSI-BLAST, HMMer and with S-BLEST. We have evaluated how well annotation transfer can be performed on SCOP ID's, Gene Ontology (GO) ID's and EC Numbers. The method is very efficient and totally automated, generally taking around fifteen minutes for a 400 residue protein.
conclusionWith structural genomics initiatives determining structures with little, if any, functional characterization, development of protein structure and function analysis tools are a necessary endeavor. We have developed a useful application towards a solution to this problem using common structural and sequence based analysis tools. These approaches are able to find statistically significant environments in a database of protein structure, and the method is able to quantify how closely associated each environment is to a predicted functional annotation.
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