ArticleBioinformatics (Oxford, England)2012
POOL server: machine learning application for functional site prediction in proteins.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Conserved Active Site Loop Residues of E. coli DinB and Human DNA Polymerase Kappa Govern Activity.Environmental and molecular mutagenesis · 2026Article
- Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties.Chemical research in toxicology · 2023Article
- MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.Protein science : a publication of the Protein Society · 2023Article
- MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.bioRxiv : the preprint server for biology · 2023Article
- Identification and characterization of alternative sites and molecular probes for SARS-CoV-2 target proteins.Frontiers in chemistry · 2022Article
- Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development:: In Silico Smart Molecular Design.PET clinics · 2021Review
- Machine learning differentiates enzymatic and non-enzymatic metals in proteins.Nature communications · 2021Article
- Probing remote residues important for catalysis in Escherichia coli ornithine transcarbamoylase.PloS one · 2020Article
- Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites.Proteins · 2019Article
- Mechanistic and Evolutionary Insights from the Reciprocal Promiscuity of Two Pyridoxal Phosphate-dependent Enzymes.The Journal of biological chemistry · 2016Article
- Rsite2: an efficient computational method to predict the functional sites of noncoding RNAs.Scientific reports · 2016Article
- Prediction of distal residue participation in enzyme catalysis.Protein science : a publication of the Protein Society · 2015Article
- Quality control test for sequence-phenotype assignments.PloS one · 2015Article
- Structure- and context-based analysis of the GxGYxYP family reveals a new putative class of glycoside hydrolase.BMC bioinformatics · 2014Article
- Article
- EXIA2: web server of accurate and rapid protein catalytic residue prediction.BioMed research international · 2014Article
- Accurate prediction of protein catalytic residues by side chain orientation and residue contact density.PloS one · 2012Article
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2 authors.
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Abstract
summaryWe present an automated web server for partial order optimum likelihood (POOL), a machine learning application that combines computed electrostatic and geometric information for high-performance prediction of catalytic residues from 3D structures. Input features consist of THEMATICS electrostatics data and pocket information from ConCavity. THEMATICS measures deviation from typical, sigmoidal titration behavior to identify functionally important residues and ConCavity identifies binding pockets by analyzing the surface geometry of protein structures. Both THEMATICS and ConCavity (structure only) do not require the query protein to have any sequence or structure similarity to other proteins. Hence, POOL is applicable to proteins with novel folds and engineered proteins. As an additional option for cases where sequence homologues are available, users can include evolutionary information from INTREPID for enhanced accuracy in site prediction. AVAILABILITY: The web site is free and open to all users with no login requirements at http://www.pool.neu.edu. CONTACT: m.ondrechen@neu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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