ArticleBioinformatics (Oxford, England)2010
Active site prediction using evolutionary and structural information.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- M3Site: multiclass multimodal learning for protein active site identification and classification.Briefings in bioinformatics · 2025Article
- Language models can identify enzymatic binding sites in protein sequences.Computational and structural biotechnology journal · 2024Article
- Article
- ANN based prediction of ligand binding sites outside deep cavities to facilitate drug designing.Current research in structural biology · 2024Article
- Genome-wide identification of serine acetyltransferase (SAT) gene family in rice (Oryza sativa) and their expressions under salt stress.Molecular biology reports · 2021Article
- CATH functional families predict functional sites in proteins.Bioinformatics (Oxford, England) · 2021Article
- The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis.Bioinformatics (Oxford, England) · 2019Article
- Harnessing Marine Biocatalytic Reservoirs for Green Chemistry Applications through Metagenomic Technologies.Marine drugs · 2018Review
- Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database.BMC bioinformatics · 2018Article
- CSmetaPred: a consensus method for prediction of catalytic residues.BMC bioinformatics · 2017Article
- Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.International journal of molecular sciences · 2015Review
- Cutoff lensing: predicting catalytic sites in enzymes.Scientific reports · 2015Article
- Biochemical functional predictions for protein structures of unknown or uncertain function.Computational and structural biotechnology journal · 2015Review
- Article
- Structure and dynamics studies of sterol 24-C-methyltransferase with mechanism based inactivators for the disruption of ergosterol biosynthesis.Molecular biology reports · 2014Article
- Catalytic site identification--a web server to identify catalytic site structural matches throughout PDB.Nucleic acids research · 2013Article
- Article
- Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features.BMC bioinformatics · 2012Article
- Distribution and properties of the genes encoding the biosynthesis of the bacterial cofactor, pyrroloquinoline quinone.Biochemistry · 2012Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
motivationThe identification of catalytic residues is a key step in understanding the function of enzymes. While a variety of computational methods have been developed for this task, accuracies have remained fairly low. The best existing method exploits information from sequence and structure to achieve a precision (the fraction of predicted catalytic residues that are catalytic) of 18.5% at a corresponding recall (the fraction of catalytic residues identified) of 57% on a standard benchmark. Here we present a new method, Discern, which provides a significant improvement over the state-of-the-art through the use of statistical techniques to derive a model with a small set of features that are jointly predictive of enzyme active sites.
resultsIn cross-validation experiments on two benchmark datasets from the Catalytic Site Atlas and CATRES resources containing a total of 437 manually curated enzymes spanning 487 SCOP families, Discern increases catalytic site recall between 12% and 20% over methods that combine information from both sequence and structure, and by >or=50% over methods that make use of sequence conservation signal only. Controlled experiments show that Discern's improvement in catalytic residue prediction is derived from the combination of three ingredients: the use of the INTREPID phylogenomic method to extract conservation information; the use of 3D structure data, including features computed for residues that are proximal in the structure; and a statistical regularization procedure to prevent overfitting.
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.